The Media Keep Telling Us: There’s No Difference Between Male and Female Brains. What Dose Science Have To Say!

I Have Listed The Bedunked Below: O Bye The Way This Also debunked Gender Identity

Recent studies indicate that sex may have a substantial influence on human cognitive functions, including emotion, memory, perception, etc., (Cahill, 2006). Men and women appear to have different ways to encode memories, sense emotions, recognize faces, solve certain problems, and make decisions. Since the brain controls cognition and behaviors, these sex-related functional differences may be associated with the sex-specific structure of the brain (Cosgrove et al., 2007).

I don’t believe it. Many of you must be skeptical, too. Seventeen million people watched my old ABC show on sex differences, almost as many as watched “Game of Thrones.”

Nonetheless, people now fill auditoriums to hear neuroscientist Gina Rippon talk about her new book that claims “New Neuroscience Explodes the Myths of the Male and Female Minds.”

Rippon says it’s important to tell people that sex isn’t an important indicator in how brains work so we don’t fall prey to stereotypes. “You don’t want an idea that this (difference) is something that’s natural,” she says in my new video.

“It’s not natural,” I ask, “that in school, more boys want to play football and more girls want to do ballet? I want to run and bang into people.”

“Actually, girls might want to run and bang into people, but because there’s an image that girls don’t do that, they’re stopped from doing that,” she replies.

But in my reporting, I’ve covered research that shows innate differences.

In one experiment, students were blindfolded and then walked through tunnels running underneath a college campus. When the women were asked the direction of a college building, they weren’t so sure. One said: “How would I know? I’m blindfolded!”

Men, however, tend to have better spatial awareness and retained a sense of which direction they’d moved.

On the other hand, women have a better memory for detail.

In one test, students were told to wait in a cluttered room and later asked what was in that room.

Women often gave long answers like, “There were envelopes, university envelopes, a thing of Clearasil, a Bazooka Joe comic…”

Men were more likely to say, “I don’t know … some stuff.”

Of course, maybe they’d been molded by our sexist society — conditioned to do what’s expected of men and women.

But I reminded Rippon that even tests on infants find differences. Baby boys look longer at objects, such as tractor parts. Infant girls stare at faces.

“A third of the girls actually seem to respond more to the tractor parts,” said Rippon.

When I pointed out that meant two-thirds of the girls did not, Rippon said that the experiment should be redone “with a bigger set of newborns.”

Maybe. But scientists shouldn’t keep redoing experiments until they get results they like.

Some female scientists acknowledge that men’s and women’s brains are different.

You can tell, looking at brains, whether they belong to a male or a female 80% of the time,” says evolutionary psychologist Diana Fleischman.

Also: “Cultures around the world show very similar differences between men and women. Men are more likely to seek status; women are more likely to take care of children. Women are more likely to stay in the home; men are more likely to do dangerous, aggressive things like go to war.”

I suggest that perhaps the sexes are alike and all cultures have imposed similar biases.

Look at nonhuman animals, monkeys: They don’t have culture, yet there’s still very large differences between males and females,” she responds.

Among scientists, that’s common opinion. The Journal of Neuroscience Research says 70 studies found differences. Boys, for example, are more likely to be autistic, to be colorblind and to have speech problems.

Even Gina Rippon says, “I’m definitely not a brain difference denier.”

But her media coverage suggests she’s discovered that male and female brains are the same.

“It’s an incredibly alluring message,” says Fleishman with a laugh. “It’s really sad that it’s not right!”

Of course, science shouldn’t seek an alluring message. It should just be about the truth.

But the truth doesn’t stop politicians from demanding absolute equality in all things — even if men and women have different interests.

“Saying that men and women have different aptitudes isn’t sexism. It’s a statement about the true nature of the world,” says Fleischman. “If we keep saying that those differences … are because of sexism, nobody’s going to end up happy with what they’re doing, and we’re going to keep making laws to remedy what’s actually the result of freedom.” https://townhall.com/columnists/johnstossel/2019/10/16/different-sexes-n2554770

1.The cognitive differences between men and women

By Bruce Goldman
Illustration by Gérard DuBois

When Nirao Shah decided in 1998 to study sex-based differences in the brain using up-to-the-minute molecular tools, he didn’t have a ton of competition. But he did have a good reason.

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“I wanted to find and explore neural circuits that regulate specific behaviors,” says Shah, then a newly minted Caltech PhD who was beginning a postdoctoral fellowship at Columbia. So, he zeroed in on sex-associated behavioral differences in mating, parenting and aggression.

“These behaviors are essential for survival and propagation,” says Shah, MD, PhD, now a Stanford professor of psychiatry and behavioral sciences and of neurobiology. “They’re innate rather than learned — at least in animals — so the circuitry involved ought to be developmentally hard-wired into the brain. These circuits should differ depending on which sex you’re looking at.”

His plan was to learn what he could about the activity of genes tied to behaviors that differ between the sexes, then use that knowledge to help identify the neuronal circuits — clusters of nerve cells in close communication with one another — underlying those behaviors.

At the time, this was not a universally popular idea. The neuroscience community had largely considered any observed sex-associated differences in cognition and behavior in humans to be due to the effects of cultural influences. Animal researchers, for their part, seldom even bothered to use female rodents in their experiments, figuring that the cyclical variations in their reproductive hormones would introduce confounding variability into the search for fundamental neurological insights.

But over the past 15 years or so, there’s been a sea change as new technologies have generated a growing pile of evidence that there are inherent differences in how men’s and women’s brains are wired and how they work.

Not how well they work, mind you. Our differences don’t mean one sex or the other is better or smarter or more deserving. Some researchers have grappled with charges of “neuro­sexism”: falling prey to stereotypes or being too quick to interpret human sex differences as biological rather than cultural. They counter, however, that data from animal research, cross-​cultural surveys, natural experiments and brain-imaging studies demonstrate real, if not always earthshaking, brain differences, and that these differences may contribute to differences in behavior and cognition.

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Nirao Shah studies how some genes at work in the mouse brain determine sex-specific behaviors, like the female trait of protecting the nest from intruders. He says most of these genes have human analogues but their function is not fully understood.
Photograph by Lenny Gonzalez

Behavior differences

In 1991, just a few years before Shah launched his sex-differences research, Diane Halpern, PhD, past president of the American Psychological Association, began writing the first edition of her acclaimed academic text, Sex Differences in Cognitive Abilities. She found that the ​animal-​research literature had been steadily accreting reports of sex-associated neuroanatomical and behavioral differences, but those studies were mainly gathering dust in university libraries. Social psychologists and sociologists pooh-poohed the notion of any fundamental cognitive differences between male and female humans, notes Halpern, a professor emerita of psychology at Claremont McKenna College.

In her preface to the first edition, Halpern wrote: “At the time, it seemed clear to me that any between-sex differences in thinking abilities were due to socialization practices, artifacts and mistakes in the research, and bias and prejudice. … After reviewing a pile of journal articles that stood several feet high and numerous books and book chapters that dwarfed the stack of journal articles … I changed my mind.”

Why? There was too much data pointing to the biological basis of sex-based cognitive differences to ignore, Halpern says. For one thing, the animal-research findings resonated with sex-based differences ascribed to people. These findings continue to accrue. In a study of 34 rhesus monkeys, for example, males strongly preferred toys with wheels over plush toys, whereas females found plush toys likable. It would be tough to argue that the monkeys’ parents bought them sex-typed toys or that simian society encourages its male offspring to play more with trucks. A much more recent study established that boys and girls 9 to 17 months old — an age when children show few if any signs of recognizing either their own or other children’s sex — nonetheless show marked differences in their preference for stereotypically male versus stereotypically female toys.

Halpern and others have cataloged plenty of human behavioral differences. “These findings have all been replicated,” she says. Women excel in several measures of verbal ability — pretty much all of them, except for verbal analogies. Women’s reading comprehension and writing ability consistently exceed that of men, on average. They out­perform men in tests of fine-motor coordination and perceptual speed. They’re more adept at retrieving information from long-term memory.

Men, on average, can more easily juggle items in working memory. They have superior visuospatial skills: They’re better at visualizing what happens when a complicated two- or three-dimensional shape is rotated in space, at correctly determining angles from the horizontal, at tracking moving objects and at aiming projectiles.

Navigation studies in both humans and rats show that females of both species tend to rely on landmarks, while males more typically rely on “dead reckoning”: calculating one’s position by estimating the direction and distance traveled rather than using landmarks.New technologies have generated a growing pile of evidence that there are inherent differences in how men’s and women’s brains are wired and how they work.

Many of these cognitive differences appear quite early in life. “You see sex differences in spatial-visualization ability in 2- and 3-month-old infants,” Halpern says. Infant girls respond more readily to faces and begin talking earlier. Boys react earlier in infancy to experimentally induced perceptual discrepancies in their visual environment. In adulthood, women remain more oriented to faces, men to things.

All these measured differences are averages derived from pooling widely varying individual results. While statistically significant, the differences tend not to be gigantic. They are most noticeable at the extremes of a bell curve, rather than in the middle, where most people cluster. Some argue that we may safely ignore them.

But the long list of behavioral tendencies in which male-female ratios are unbalanced extends to cognitive and neuro­psychiatric disorders. Women are twice as likely as men to experience clinical depression in their lifetimes; likewise for post-traumatic stress disorder. Men are twice as likely to become alcoholic or drug-dependent, and 40 percent more likely to develop schizophrenia. Boys’ dyslexia rate is perhaps 10 times that of girls, and they’re four or five times as likely to get a diagnosis of autism spectrum disorder.

Could underlying biological differences — subtle though they may be for most of us — help explain these gaping
between-​sex imbalances in the prevalence of mental disorders and account for the cognitive and behavioral differences observed between men and women?

How our brains differ

The neuroscience literature shows that the human brain is a sex-typed organ with distinct anatomical differences in neural structures and accompanying physiological differences in function, says UC-Irvine professor of neurobiology and behavior Larry Cahill, PhD. Cahill edited the 70-article January/February 2017 issue of the Journal of Neuroscience Research — the first-ever issue of any neuroscience journal devoted entirely to the influence of sex differences on nervous-system function.

Brain-imaging studies indicate that these differences extend well beyond the strictly reproductive domain, Cahill says. Adjusted for total brain size (men’s are bigger), a woman’s hippo­campus, critical to learning and memorization, is larger than a man’s and works differently. Conversely, a man’s amygdala, associated with the experiencing of emotions and the recollection of such experiences, is bigger than a woman’s. It, too, works differently, as Cahill’s research has demonstrated.

In 2000, Cahill scanned the brains of men and women viewing either highly aversive films or emotionally neutral ones. The aversive films were expected to trip off strong negative emotions and concomitant imprinting in the amygdala, an almond-shaped structure found in each brain hemisphere. Activity in the amygdala during the viewing experience, as expected, predicted subjects’ later ability to recall the viewed clips. But in women, this relationship was observed only in the left amygdala. In men, it was only in the right amygdala. Cahill and others have since confirmed these results.

Discoveries like this one should ring researchers’ alarm buzzers. Women, it’s known, retain stronger, more vivid memories of emotional events than men do. They recall emotional memories more quickly, and the ones they recall are richer and more intense. If, as is likely, the amygdala figures into depression or anxiety, any failure to separately analyze men’s and women’s brains to understand their different susceptibilities to either syndrome would be as self-defeating as not knowing left from right.

The two hemispheres of a woman’s brain talk to each other more than a man’s do. In a 2014 study, University of Pennsylvania researchers imaged the brains of 428 male and 521 female youths — an uncharacteristically huge sample — and found that the females’ brains consistently showed more strongly coordinated activity between hemispheres, while the males’ brain activity was more tightly coordinated within local brain regions. This finding, a confirmation of results in smaller studies published earlier, tracks closely with others’ observations that the corpus callosum-— the white-matter cable that crosses and connects the hemispheres — is bigger in women than in men and that women’s brains tend to be more bilaterally symmetrical than men’s.
Many of these cognitive differences appear quite early in life. ‘You see sex differences in spatial-visualization ability in 2- and 3-month-old infants.’

“To some appreciable degree, these brain differences have to translate to behavioral differences,” says Cahill. Numerous studies show that they do, sometimes with medically meaningful implications.

A 2017 study in JAMA Psychiatry imaged the brains of 98 individuals ages 8 to 22 with autism spectrum disorder and 98 control subjects. Both groups contained roughly equal numbers of male and female subjects. The study confirmed earlier research showing that the pattern of variation in the thickness of the brain’s cortex differed between males and females. But the great majority of female subjects with ASD, the researchers found, had cortical-thickness variation profiles similar to those of typical non-ASD males.

In other words, having a typical male brain structure, whether you’re a boy or a girl, is a substantial risk factor for ASD. By definition, more boys’ than girls’ brains have this profile, possibly helping explain ASD’s four- to fivefold preponderance among boys compared with girls.

Why our brains differ

But why are men’s and women’s brains different? One big reason is that, for much of their lifetimes, women and men have different fuel additives running through their tanks: the sex-steroid hormones. In female mammals, the primary additives are a few members of the set of molecules called estrogens, along with another molecule called progesterone; and in males, testosterone and a few look-alikes collectively deemed androgens. Importantly, males developing normally in utero get hit with a big mid-gestation surge of testosterone, permanently shaping not only their body parts and proportions but also their brains. (Genetic defects disrupting testosterone’s influence on a developing male human’s cells induce a shift to a feminine body plan, our “default” condition.)

In general, brain regions that differ in size between men and women (such as the amygdala and the hippocampus) tend to contain especially high concentrations of receptors for sex hormones.

Another key variable in the composition of men versus women stems from the sex chromosomes, which form one of the 23 pairs of human chromosomes in each cell. Generally, females have two X chromosomes in their pair, while males have one X and one Y chromosome. A gene on the Y chromosome is responsible for the cascade of developmental events that cause bodies and brains to take on male characteristics. Some other genes on the Y chromosome may be involved in brain physiology and cognition.

Scientists routinely acknowledge that the presence or absence of a single DNA base pair can make a medically important difference. What about an entire chromosome? While the genes hosted on the X chromosome and the Y chromosome (about 1,500 on the X, 27 on the Y) may once have had counterparts on the other, that’s now the case for only a few of them. Every cell in a man’s body (including his brain) has a slightly different set of functioning ​sex-​chromosome genes from those operating in a woman’s.

Sex-based differences in brain structure and physiology reflect the alchemy of these hormone/receptor interactions, their effects within the cells, and the intermediating influence of genetic variables — particularly the possession of an XX versus an XY genotype, says Cahill.

Zeroing in on neural circuits

Shah’s experiments in animals employ technologies enabling scientists to boost or suppress the activity of individual nerve cells — or even of single genes within those nerve cells — in a conscious, active animal’s brain. These experiments have pinpointed genes whose activity levels differ strongly at specific sites in male versus female mice’s brains.

What would happen, Shah’s team wondered, if you knocked out of commission one or another of these genes whose activity level differed between male and female brains? They tried it with one of their candidate genes, turning off one that was normally more active in females.

Doing this, they found, totally shredded mouse moms’ willingness to defend their nests from intruders and to retrieve pups who had wandered away — maternal mandates that normal female mice unfailingly observe — yet had no observable effect on their sexual behavior. Torpedoing a different gene radically reduced a female mouse’s mating mood, but males in which the gene has been trashed appear completely normal.

All this points to a picture of at least parts of the brain as consisting of modules. Each module consists of a neural or genetic pathway in charge of one piece of a complicated behavior, and responds to genetic and hormonal signals. These modules — or at least some of them — are masculinized or feminized, respectively, by the early testosterone rush or its absence. The mammalian brain features myriad modules of this sort, giving rise to complex combinations of behavioral traits.

Which is not to say every man’s or woman’s brain looks the same. Our multitudinous genetic variations interact with some of our genes’ differential responsiveness to estrogens versus androgens. This complicated pinball game affects goings-on in at least some of the brain’s neural circuits and in whatever little piece of behavior each of these neural circuits manages.

“We think gender-specific behavior is a composite of all these modules, which, added up, give you your overall degree of maleness and femaleness,” says Shah.

Consider the genes Shah has isolated whose activity levels differ significantly in the brains of male and female mice. “Almost all of these genes have human analogues,” he says. “We still don’t completely understand their function in human social behavior. But when we looked at publicly available databases to find out what we do know about them, we found a surprising number that in humans have been linked with autism, alcoholism and other conditions.”

Bigger imaging studies and imaginative animal research now in the works promise to reveal much more about humanity’s inherent — although by no means uniform, and often not substantial — sex-associated cognitive differences and vulnerability to diseases.

Trying to assign exact percentages to the relative contributions of “culture” versus “biology” to the behavior of free-living human individuals in a complex social environment is tough at best. Halpern offers a succinct assessment: “The role of culture is not zero. The role of biology is not zero.” https://stanmed.stanford.edu/2017spring/how-mens-and-womens-brains-are-different.html

2.Brain Differences Between Men and Women: Evidence From Deep Learning

Do men and women have different brains? Previous neuroimage studies sought to answer this question based on morphological difference between specific brain regions, reporting unfortunately conflicting results. In the present study, we aim to use a deep learning technique to address this challenge based on a large open-access, diffusion MRI database recorded from 1,065 young healthy subjects, including 490 men and 575 women healthy subjects. Different from commonly used 2D Convolutional Neural Network (CNN), we proposed a 3D CNN method with a newly designed structure including three hidden layers in cascade with a linear layer and a terminal Softmax layer. The proposed 3D CNN was applied to the maps of factional anisotropy (FA) in the whole-brain as well as specific brain regions. The entropy measure was applied to the lowest-level image features extracted from the first hidden layer to examine the difference of brain structure complexity between men and women. The obtained results compared with the results from using the Support Vector Machine (SVM) and Tract-Based Spatial Statistics (TBSS). The proposed 3D CNN yielded a better classification result (93.3%) than the SVM (78.2%) on the whole-brain FA images, indicating gender-related differences likely exist in the whole-brain range. Moreover, high classification accuracies are also shown in several specific brain regions including the left precuneus, the left postcentral gyrus, the left cingulate gyrus, the right orbital gyrus of frontal lobe, and the left occipital thalamus in the gray matter, and middle cerebellum peduncle, genu of corpus callosum, the right anterior corona radiata, the right superior corona radiata and the left anterior limb of internal capsule in the while matter. This study provides a new insight into the structure difference between men and women, which highlights the importance of considering sex as a biological variable in brain research.

Introduction

Recent studies indicate that gender may have a substantial influence on human cognitive functions, including emotion, memory, perception, etc., (Cahill, 2006). Men and women appear to have different ways to encode memories, sense emotions, recognize faces, solve certain problems, and make decisions. Since the brain controls cognition and behaviors, these gender-related functional differences may be associated with the gender-specific structure of the brain (Cosgrove et al., 2007).

Diffusion tensor imaging (DTI) is an effective tool for characterizing nerve fibers architecture. By computing fractional anisotropy (FA) parameters in DTI, the anisotropy of nerve fibers can be quantitatively evaluated (Lasi et al., 2014). Differences in FA values are thought to associate with developmental processes of axon caliber, myelination, and/or fiber organization of nerve fibers pathways. By computing FA, researchers has revealed subtle changes related to normal brain development (Westlye et al., 2009), learning (Golestani et al., 2006), and healthy aging (Kochunov et al., 2007). Nevertheless, existing studies are yet to provide consistent results on exploring the difference of brain structure between men and women. Ingalhalikar et al. (2014) argued that the men have greater intra-hemispheric connection via the corpus callosum while women have greater interhemispheric connectivity. However, other studies reported no significant gender difference in brain structure (Raz et al., 2001Salat et al., 2005). A recent critical opinion article suggested that more research is needed to investigate whether men and women really have different brain structures (Joel and Tarrasch, 2014).

Most existing DTI studies used the group-level statistical methods such as Tract-Based Spatial Statistics (TBSS) (Thatcher et al., 2010Mueller et al., 2011Shiino et al., 2017). However, recent studies indicated that machine learning techniques may provide us with a more powerful tool for analyzing brain images (Shen et al., 2010Lu et al., 2017Tang et al., 2018). Especially, deep learning can extract non-linear network structure, realize approximation of complex function, characterize distributed representation of input data, and demonstrate the powerful ability to learn the essential features of datasets based on a small size of samples (Zeng et al., 20162018aTian et al., 2018Wen et al., 2018). In particular, the deep convolutional neural network (CNN) uses the convolution kernels to extract the features of image and can find the characteristic spatial difference in brain images, which may promise a better result than using other conventional machine learning and statistical methods (Cole et al., 2017).

In this study, we performed CNN-based analyses on the FA images and extracts the features of the hidden layers to investigate the difference between man and woman brains. Different from commonly used 2D CNN model, we innovatively proposed a 3D CNN model with a new structure including 3 hidden layers, a linear layer and a softmax layer. Each hidden layer is comprised of a convolutional layer, a batch normalization layer, an activation layer and followed by a pooling layer. This novel CNN model allows using the whole 3D brain image (i.e., DTI) as the input to the model. The linear layer between the hidden layers and the softmax layer reduces the number of parameters and therefore avoids over-fitting problems.

Materials and Methods

MRI Data Acquisition and Preprocessing

The database used in this work is from the Human Connectome Project (HCP) (Van Essen et al., 2013). This open-access database contains data from 1,065 subjects, including 490 men and 575 women. The ages range is from 22 to 36. This database represents a relatively large sample size compared to most neuroimaging studies. Using this open-access dataset allows replication and extension of this work by other researchers.

We performed DTI data preprocessing includes format conversion, b0 image extraction, brain extraction, eddy current correction, and tensor FA calculation. The first four steps were processed with the HCP diffusion pipeline, including diffusion weighting (bvals), direction (bvecs), time series, brain mask, a file (grad_dev.nii.gz) for gradient non-linearities during model fitting, and log files of EDDY processing. In the final step we use dtifit to calculate the tensors to get the FA, as well as mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values.

The original data were too large to train the model and it would cause RESOURCE EXAUSTED problem while training due to the insufficient of GPU memory. The GPU we used in the experiment is NVIDIAN TITAN_XP with 12G memory each. To solve the problem, we scaled the size of FA image to [58 × 70 × 58]. This procedure may lead to a better classification result, since a smaller size of the input image can provide a larger receptive field to the CNN model. In order to perform the image scaling, “dipy” (http://nipy.org/dipy/) was used to read the .nii data of FA. Then “ndimage” in the SciPy (http://www.scipy.org) was used to reduce the size of the data. Scaled data was written into the TFRecord files (http://www.tensorflow.org) with the corresponding labels. TFRecord file format is a simple record oriented binary format that is widely used in Tensorflow application for the training data to get a high performance of input efficiency. The labels were processed into the format of one-hot. We implemented a pipeline to read data asynchronously from TFRecord according to the interface specification provided by Tensorflow (Abadi et al., 2016). The pipeline included the reading of TFRecord files, data decoding, data type conversion, and reshape of data.

CNN Model

We did the experiments on a GPU work station, which has four NVIDIA TITAN Xp GPUs. The operation system of the GPU work station was Ubutnu16.04. We used FSL to preprocess the data. The CNN model was designed using the open source machine learning framework Tensorflow (Abadi et al., 2016).

Model Design

The commonly used CNN structures are based on 2D images. When using a 2D CNN to process 3D MRI images, it needs to map the original image from different directions to get 2D images, which will lose the spatial structure information of the image. In this study, we designed a 3D CNN with 3D convolutional kernels, which allowed us to extract 3D structural features from FA images. Besides, traditional CNN model usually uses several fully connected layers to connect the hidden layers and the output layer. The fully connected layer may be prone to the over-fitting problem in binary classification when the number of samples is limited (like our data). To address this problem, we used a linear layer to replace the fully connected layer. The linear layer integrates the outputs of hidden layers (i.e., a 3D matrix comprised of multiple featuremaps) into the inputs (i.e., a 1D vector) of the output layer which is a softmax classifier. Moreover, we performed a Batch Normalization (Ioffe and Szegedy, 2015) after each convolution operation. The Batch Normalization is used to avoid internal covariate shift problem in training the CNN model. Therefore, our designed model is a 3D “pure” CNN (3D PCNN). The architecture of the 3D PCNN model is shown in Figure 1. The 3D PCNN consists of three hidden layers, a linear layer and a softmax layer. Each of the hidden layer contains a convolutional layer, a Batch Normalization layer, an activation layer, a pooling layer with several feature maps as the outputs.FIGURE 1

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Figure 1. 3D PCNN architecture.

Convolutional layer

The process of convolutional layer is to convolve the input vector I with the convolution kernel K, represented by IK. The shape of the input vector in our 3D PCNN model was [n, d, w, h, c], where d, w, h, c represent the depth, width, height and channel numbers (which is 1 for a grayscale image) of the input vector, respectively, and n is the batch size which is a hyperparameter that was set to 45 (an empirical value) in this paper. In the first layer, the input size was 58 × 70 × 58 × 1, which was the 3D size (58 × 70 × 58) of the input image plus a single channel (grayscale image). The shape of the convolution kernel was [dkwkhkcincout], where dkwkhk represents the depth, width, and height of the convolution kernel, respectively. In all three hidden layers, the kernel size was set to3 × 3 × 3, which means that dk = wk = hk = 3. The cin is the number of input channels which is equal to the channel number of the input vector. The cout is the number of output channels. As each kernel has an output channel, cout is equal to the number of convolution kernels, and is also the same as the number of input channels for the next hidden layer. In all convolution layers, the moving stride of the kernel was set to 1 and padding mode was to “SAME.”

Batch normalization layer

Batch normalization was performed after the convolutional layer. Batch normalization is a kind of training trick which normalizes the data of each mini-batch (with zero mean and variance of one) in the hidden layers of the network. To alleviate the gradient internal covariate shift phenomenon and speed up the CNN training, an Adam Gradient Decent method was used to train the model (Kingma and Ba, 2015).

Activation layer

After the batch normalization operation, an activation function was used to non-linearize the convolution result. The activation function we used in the model was the Rectified linear unit, ReLU (Nair and Hinton, 2010).

Pooling layer

Pooling layer was added after the activation layer. Pooling layers in the CNN summarize the outputs of neighboring groups of neurons in the same kernel map (Krizhevsky et al., 2012). Max-pooling method was used in this layer.

The outputs of each hidden layer were feature maps, which were the features extracted from the input images to the hidden layer. The outputs from the previous hidden layer were the inputs to the next layer. In our model, the first hidden layer generated 32 feature maps, the second hidden layer produced 64 feature maps, and the third hidden layer yielded 128 feature maps. Finally, we integrated the last 128 feature maps into the input of the softmax layer through a linear layer, and then got the final classification results from the softmax layer.

In our model, the input X ∈ {x(1)x(2), …, x(n)}, x(i) was the ith subject’s FA value. Y ∈ {y(1)y(2), …, y(n)}, y(i) was the ith subject’s label that were processed to one-hot vector where [1 0] represents man and [0 1] woman. We used h(θ, x) to represent the proposed 3D PCNN model. Then we had:ŷ=h(θ,x)    (1)ŷ=h(θ,x)    (1)

where ŷ represents the predicted value obtained using the 3D PCNN on a sample x.

Parameters Optimization

The initial values of the weights of the convolution kernels were random values selected from a truncated normal distribution with standard deviation of 0.1. We defined a cost function to adjust these weights based on the softmax cross entropy (Dunne and Campbell, 1997):J(θ,x)=−∑i=1nŷ(i)logP(ŷ(i)=y(i) ∣∣ x=x(i) )    (2)J(θ,x)=-∑i=1nŷ(i)logP(ŷ(i)=y(i) | x=x(i) )    (2)

As such, the task of adjusting the weight value became an optimization problem with J(θ, x) as the optimization goal, where a small penalty was given if the classification result was correct, and vice versa. We used the Adam Gradient Descent (Kingma and Ba, 2015) optimization algorithm to achieve this goal in the model training. All parameters in the Adam algorithm were set to the empirical values recommended by Kingma and Ba (2015), i.e., learning rate was α = 0.001, exponential decay rates for the moment estimates were β1 = 0.9, β1 = 0.999, ε = 10−8.

Cross-Validation

To ensure the independent training and testing in the cross-validation. The process of cross-validation is shown in Figure 2. We implemented a two-loop nested cross-validation scheme (Varoquaux et al., 2017). We divided the data set into three parts, i.e., 80% of the data as the training set for model training, 10% as the verification set for parameter selection, and 10% as the testing set for evaluating the generalization ability of the model. To eliminate the random error of model training, we run 10 fold cross validation and then took the average of classification accuracies as the final result.FIGURE 2

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Figure 2. Model training and nested cross validation. (A) General overview. (B) 10 fold cross validation.

Features in First Hidden Layer

CNN has an advantage that it can extract key features by itself (Zeng et al., 2018c). However, these features may be difficult to interpret since they are highly abstract features. Thus, in this study, we only analyzed the features obtained in the first hidden layer, since they are the direct outputs from the convolution on the grayscale FA images. In this case, the convolution operation of the first layer is equivalent to applying a convolution kernel based spatial filter on the FA images. The obtained features are less abstractive than those from the second and three hidden layers. There are totally 32 features in the first hidden layer. These features are the lowest-level features which may represent the structural features of FA images. We firstly computed the mean of voxel values across all subjects in each group (man vs. woman) for each feature and then evaluated their group-level difference using a two-sample t-test. Besides, we also computed the entropy on each feature for each individual:H=−∑i=0255pilogpi    (3)H=-∑i=0255pilogpi    (3)

where pi indicates the frequency of pixel with value i appears in the image. The entropy of each feature likely indicates the complexity of brain structural encoded in that feature. We also performed a two-sample t-test on entropy results to explore the differences between men and women. A strict Bonferroni correction was applied for multiple comparisons with the threshold of 0.05/32 = 1.56 × 10−3 to remove spurious significance.

Discriminative Power of Brain Regions

In order to determine which brain regions may play important role in gender-related brain structural differences, we repeated the same 3D PCNN-based classification on each specific brain region. We segmented each FA image into 246 gray matter regions of interests (ROIs) according to the Human Brainnetome Atlas (Fan et al., 2016) and 48 white matter ROIs according to the ICBM-DTI-81 White-Matter Labels Atlas (Mori et al., 2005). The classification accuracy was then obtained for each ROIs. A higher accuracy indicates a more important role of that ROI in gender-related difference. A map was then obtained based on the classification accuracies of different ROIs to show their distribution in the brain.

Comparisons With Tract Based Spatial Statistics and Support Vector Machine

To justify the effectiveness of our method, the Tract Based Spatial Statistics (TBSS) and Support Vector Machine (SVM) were applied to our dataset as comparisons, since these are two popular methods for data analysis in neuroimaging studies (Bach et al., 2014Zeng et al., 2018b). We compared the results in following two conditions: (1) We used the SVM as the classifier while keeping the same preprocessing procedure in order to compare its results with our 3D PCNN method. We flatten each sample from the 3D FA matrix into a vector, and then fed the SVM with the vector. (2) We used the TBSS to identify the brain regions where are shown the statistically significant gender-related difference.

Results

Classification Results on the Whole-Brain FA Images

Using our 3D PCNN methods on the whole-brain FA images, we can well-distinguish men and women with the classification accuracy of 93.3%. This result is much better than using the SVM, whose classification accuracy is only 78.2%.

As comparisons, we also used MD, AD, and RD to repeat the same analysis. The classification accuracy of MD is 65.8%, AD is 69.9%, and RD is 67.8%. All of them are lower than the classification accuracy obtained by using FA.

Feature Analysis in the First Hidden Layer of 3D PCNN

The result of two-sample t-test of 32 features of men and women shows that there are 25 features had significant gender differences including 13 features that women have larger values and 12 features that men have larger values (see Figure 3). Interestingly, men have significantly higher entropy than women for all features (see Figure 4).FIGURE 3

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Figure 3. Between-group differences of 32 features in voxel values. The mean (bar height) and standard deviation (error bars) of voxel values across all subjects in each group were evaluated for each feature. Their group-level difference was examined using a two-sample t-test. Bonferroni correction was applied for multiple comparisons with the threshold equal to 0.05/32 = 1.56 × 10−3 to remove spurious significance. The features with significantly larger mean voxel values for men are marked out with*, while features with significantly larger mean voxel values for women are indicated by +.FIGURE 4

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Figure 4. Between-group differences of 32 features in entropy values. The mean (bar height) and standard deviation (error bars) of entropy value were computed across all subjects in each group for each feature. Their group-level difference was evaulated using a two-sample t-test. Bonferroni correction was applied for multiple comparisons with the threshold equal to 0.05/32 = 1.56 × 10−3 to remove spurious significance. The entropy values are significantly larger in men than in women for features.

Classification on Each Specific ROI

TBSS could not detect any statistically significant gender-related difference in this dataset. However, using 3D PCNN, we did find gender-related differences in all ROIs in the both gray and white matters, as the classification accuracies (>75%) are much higher than the chance level (50%) for all ROIs. The maps of classification accuracies for different ROIs are shown in Figure 5. The detail classification results are provided in the supplement (see Table S1 for gray matter and Table S2 for white matter). In the gray matter, the top 5 regions with highest classification accuracies are the left precuneus (Broadman area, BA 31, 87.2%), the left postcentral gyrus (BA 1/2/3 trunk region, 87.2%), the left cingulate gyrus (BA 32 subgenual area, 87.2%), the right orbital gyrus of frontal lobe (BA 13, 87.1%) and the left occipital thalamus (86.9%). In the white matter, the top 5 regions with highest classification accuracies are middle cerebellum peduncle (89.7%), genu of corpus callosum (88.4%), the right anterior corona radiata (88.3%), the right superior corona radiata (86%), and the left anterior limb of internal capsule (85.4%).FIGURE 5

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Figure 5. Maps of classification accuracies for different ROIs in the gray and white matter of the brain. (A) Results in 246 gray matter regions of interests (ROIs) according to the Human Brainnetome Atlas (B) Results in 48 white matter ROIs according to the ICBM-DTI-81 White-Matter Labels Atlas.

Discussions

Classification on the Whole-Brain FA

The proposed 3D PCNN model achieved 93.3% classification accuracy in the whole-brain FA. The high classification accuracy rate indicates that the proposed model can accurately find the brain structure difference between men and women, which is the basis of subsequent feature analysis and subreginal analysis. Most existing classification, regression, and other machine learning methods are shallow learning algorithms, such as the SVM, Boosting, maximum entropy, and Logistic Regression. When complex functions need to be expressed, the models obtained by these algorithms will then have a limitation with small size of samples and limited computational resources. Thus, the generalization ability will be deteriorated as we demonstrated in the results from the SVM. The benefit of deep learning algorithms, using multiple layers in the artificial neural network, is that one can represent complex functions with few parameters. The CNN is one of the widely used deep learning algorithms. In compared to the method like SVM, which is just a classifier, 3D CNN is a method that can extract the 3D spatial structure features of the input image. Through constructing the 3D PCNN model, we extracted highly abstract features from FA images, which may, thusly, improve the classification accuracy. FA describes the partial anisotropy index, which indicates the difference between one direction and others (Feldman et al., 2010). It can reflect alterations in various tissue properties including axonal size, axonal packing density, and degree of myelination (Chung et al., 2016). In this study, we also run the same analysis using MD, AD, and RD images for comparisons. All their results are lower than that of FA, indicating that using FA is more effective to find the structure difference between men and women’s brain than using other images.

Feature Analysis in the First Hidden Layer of 3D PCNN

The degree of the macroscopic diffusion anisotropy is often quantified by the FA (Lasi et al., 2014). Previous studies found that wider skeleton of white matter in woman’s brain but wider region of gray matter in man’s brain (Witelson et al., 1995Zaidi, 2010Gong et al., 2011Menzler et al., 2011). These mean that men appear to have more gray matter, made up of active neurons, while women may have more white matter for the neuronal communication between different areas of the brain. Furthermore, a recent study found that men had higher FA values than women in middle aged to elderly (between 44 and 77 years old) people by using a statistical analysis (Ritchie et al., 2018). This study focuses on the young healthy individuals with the age range between 22 and 36 years old. The structural features extracted from 3D PCNN reflect the brain structure difference between men and women. In the first hidden layer of 3D PCNN model, we found 25 features that have significant difference between men and women in voxels value. Moreover, using entropy measure, we found that men’s brains likely have more complex features as reflected by significantly higher entropy. These results indicated that the gender-related differences likely exist in the whole-brain range including both white and gray matters.

Most Discriminative Brain Regions

Using FA images from each specific brain region as the input to the 3D PCNN, we found all tested brain regions may have gender-related difference, though the TBSS analysis cannot detect these differences. The brain regions with high classification accuracies include the left precuneus (Broadman area, BA 31, 87.2%), the left postcentral gyrus (BA 1/2/3 trunk region, 87.2%), the left cingulate gyrus (BA 32 subgenual area, 87.2%), the right orbital gyrus of frontal lobe (BA 13, 87.1%), and the left occipital thalamus (86.9%) in the gray matter, and middle cerebellum peduncle (89.7%), genu of corpus callosum (88.4%), the right anterior corona radiata (88.3%), the right superior corona radiata (86%), and the left anterior limb of internal capsule (85.4%).

The gender-related morphological difference at the corpus callosum has been previously reported, which may be associated with interhemispheric interaction (Sullivan et al., 2001Luders et al., 2003Prendergast et al., 2015). However, likely due to the limitation of applied methods, not all previous studies have reported this difference (Abe et al., 2002). Those likely results in the inconsistent findings were across different studies. Through 3D PCNN model, our results confirm that there is likely a morphological difference at the genu of corpus callosum between man and women.

The middle cerebellum peduncle is the brain area connected to the pons and receiving the inputs mainly from the pontine nuclei (Glickstein and Doron, 2008), which are the nuclei of the pons involved in motor activity (Wiesendanger et al., 1979). Raz et al. (2001) found larger volume in the cerebellum of men than women. The cerebellar cells release diffusible substances that promote the survival of thalamic neurons (Tracey et al., 1980Hisanaga and Sharp, 1990). Previous studies have reported gender-difference differences in the basic glucose metabolism in the thalamus of young subjects between the ages of 20 and 40 (Fujimoto et al., 2008). Beside the thalamus and cerebellum, the postcentral gyrus was also found in our results as the brain region with high classification accuracy. Thus, there is very likely a gender-related difference in the cerebellar-thalamic-cortical circuitry. This difference may also be related to the reported gender differences in neurological degenerative diseases such as Parkinson’s Disease (Lyons et al., 1998Dluzen and Mcdermott, 2000Miller and Cronin-Golomb, 2010), where the pathological changes are usually found in the cerebellar-thalamic-cortical circuitry.

The findings of the current study also indicated the gender-related difference in the limbic-thalamo-cortical circuitry. Anterior corona radiata is part of the limbic-thalamo-cortical circuitry and includes thalamic projections from the internal capsule to the prefrontal cortex. White matter changes in the anterior corona radiata could result in many of the cognitive and emotion regulation disturbances (Drevets, 2001). The orbital gyrus of frontal cortex gray matter areas and cingulate gyrus have also been reported to be associated with the emotion regulation system (Fan et al., 2005). Thus, the gender-related difference in the limbic-thalamo-cortical circuitry may explain the gender differences in thalamic activation during the processing of emotional stimuli or unpleasant linguistic information concerning interpersonal difficulties as demonstrated by previous fMRI (Lee and Kondziolka, 2005Shirao et al., 2005).

In summary, by using the designed 3D PCNN algorithm, we confirmed that the gender-related differences exist in the whole-brain FA images as well as in each specific brain regions. These gender-related brain structural differences might be related to gender differences in cognition, emotional control as well as neurological disorders.

Data Availability

Publicly available datasets were analyzed in this study. This data can be found here: https://www.humanconnectome.org/.

Author Contributions

JX, YT, and YY contributed to the conception and design of the study. YT, JX, and YZ performed data analysis. YT and JX drafted manuscript. YT and YY participated in editing the manuscript.

Funding

JX and YZ are supported by 111 Project (No. B18059). YT is supported by grant 2016JJ4090 from the Natural Science Foundation of Hunan Province and grants 2017T100613 and 2016M592452 from the China Postdoctoral Science Foundation, China. YY is supported by the Dixon Translational Research Grants Initiative (PI: YY) from the Northwestern Memorial Foundation (NMF) and the Northwestern University Clinical and Translational Sciences (NUCATS) Institute.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2019.00185/full#supplementary-material

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Keywords: gender difference, deep learning, neural network, diffusion MRI, entropy

Citation: Xin J, Zhang Y, Tang Y and Yang Y (2019) Brain Differences Between Men and Women: Evidence From Deep Learning. Front. Neurosci. 13:185. doi: 10.3389/fnins.2019.00185

Received: 13 December 2018; Accepted: 15 February 2019;
Published: 08 March 2019.

Edited by:Nianyin Zeng, Xiamen University, China

Reviewed by:Gan Huang, UCLouvain, Belgium
Xia-an Bi, Hunan Normal University, ChinaCo*Correspondence: Yan Tang, tangyan@csu.edu.cn
Yuan Yang, yuan.yang@northwestern.edu https://www.frontiersin.org/articles/10.3389/fnins.2019.00185/full

3.Are Male and Female Brains Different?

Of course, there’s isn’t a simple answer to that question.

While some brain features are more common in one sex than the other, and some are typically found in both, most people have a unique mix.

Research has found some key differences that could explain why we expect males and females to think and behave in characteristic ways.

But even if the physical brain doesn’t change, how it works can.

Most Brains Are Both

A 2015 study at Tel Aviv University used an interesting and very thorough approach to compare the structure of male and female brains. Researchers looked at MRI scans of more than 1,400 people.

First, they measured the amount and location of gray matter (sometimes called “thinking matter”) in 116 parts of the brain to find out which areas had the biggest sex differences. Next, the team scored these areas on each scan as either falling into the “female-end” zone, the “male-end” zone, or somewhere in the middle.

It turned out that maybe 6 in every 100 of the brains they studied were consistently a single sex. Many others had a patchwork quilt of masculine and feminine features that varied widely from person to person.

To check their findings, the team used similar methods to analyze more than 5,500 people’s personality traits and behavior. While some activities were more common in women (including scrapbooking, chatting on the phone, and keeping in touch with mom) and others in men (such as golfing, playing video games, and gambling), 98% of those studied didn’t fit a clear-cut gender profile.

Overall, the findings suggest that “human brains do not belong to one of two distinct categories.”

Brain Road Maps’ Reveal Differences

While the MRI research mainly focused on brain structures, another scientist has been exploring the nerve pathways that link them, like a highway system for the brain’s traffic.

We know that hormones influence brain development in the womb, yet before age 13, boys’ and girls’ mental circuitry appears similar. During puberty, hormones may again have a powerful effect and contribute to rewriting the teen brain. https://www.webmd.com/brain/features/how-male-female-brains-differ#1

4. Male and Female Brains Are they wired differently

In a previous article, we talked about how men and women have distinct perspectives and often approach and interpret events differently. Not that one is better than the other, they’re just different. This is certainly no earth-shattering revelation.

Just to continue on this theme, we decided to take a look at the research as to what might be underlying these differences. There’s a debate going on in the scientific community as to whether or not the unique characteristics of men and women have a physiological basis.

The male brain is about 10% larger, but size doesn’t matter here. After all, elephants have brains that are three times larger and have more neurons than humans, but we don’t see them doing brain surgery, and it’s not just because they don’t have fingers.

Some researchers argue that the brains of men and women are wired differently. The male brain is wired from front to back, with few connections across the two hemispheres. Women, on the other hand, have more wiring from left to right, so the two hemispheres are more inter-connected.

Without getting into the neurological details, researchers propose that these wiring differences result in men and women having different strengths. So, while we mentioned that one sex is not better than the other overall, each is better, on average, in certain respects. Here are some of the findings:

Men are better at performing single tasks; women are better at multi-tasking.

·       Women are better at attention, word memory and social cognition, and verbal abilities.

·       Men are better at spatial processing and sensorimotor speed

·       Women are better at fine-motor coordination and retrieving information from long-term memory

·       Women are more oriented toward and have better memories of faces, men of things.


·       Men are better at visualizing a two- or three-dimensional shape rotated in space, at correctly determining angles from the horizontal, at tracking moving objects, and at aiming projectiles.

·       In finding their way, men rely more on dead reckoning – that is, they determine their position from the direction and distance traveled. Women tend to rely more on landmarks.

Unfortunately, there are also gender-specific tendencies that are not so good. Women are more prone to experience depression and post-traumatic stress disorder. Men are more likely to suffer from schizophreniadyslexia, and autism, and to become alcoholic or drug-dependent. These are proposed to result from their distinctive wiring patterns.

We know that men and women react differently from an emotional perspective, and that, researchers argue, may also have to do with brain issues. The female brain has greater blood flow in the cingulate gyrus, the part of the brain that’s involved in processing emotions, resulting in more intense emotional reactions and stronger emotional memories.

There are also some sex-specific behaviors that seem to be innate, not learned. Since behaviors are initiated by our brains, this suggests there some hard-wiring going on. Female mice, for example, have a trait not found in males of protecting their nests from invaders. In monkeys, males prefer toys with wheels while females prefer plush toys. Moving up the ladder, human toddlers show a preference for sex-specific toys, before they know they’re a gender, and show some of the perceptual differences found in adults that are mentioned above.

The female brain also has more wiring in the areas that play a role in social cognition and verbal communication. That may be why they’re better at empathizing with others, have a better sense of what is happening around them, and are richer in their verbal descriptions.

Because there’s less connectivity in the male brain between their verbal centers, and their emotions and memories, they’re not as effective as communicators, and that may be why they also tend to have less interest in conversations.

During activities, the male brain uses much more gray matter while the female brain uses more white matter. This difference is believed to account for the greater ability of males to focus on a specific task to the exclusion of what’s happening around them, while women are better at switching between tasks.

This sounds pretty convincing, right? Well, not to everyone. There are researchers who argue the other side—even if the adult male and female brains are wired differently, it’s a huge leap to say that these differences are programmed in at birth. There are socio-cultural factors at work.

Brain connections change as a result of experience and learning. When the same signals are processed over and over, those neural networks get stronger, just as muscles or skills develop with usage and practice. Male and female brains may start out similar but become different over time as boys and girls are treated differently, and for whom there are different expectations. How we’re brought up plays a major role in how we act, think, and believe and our brains may adapt accordingly.

A reasonable conclusion is that it’s both—there may be neurological differences, but there are also cultural influences. The percent of differences that are neurological vs. societal/cultural (i.e. nature vs. nurture) is anybody’s guess at this point in time.

This debate is likely to continue for quite some time. At least that gives researchers something to do. https://www.psychologytoday.com/us/blog/so-happy-together/201904/male-and-female-brains

5. Do men and women have different brains?

Scientists have known for a while now that men and women have slightly different brains, but they thought the changes were limited to the hypothalamus, the part of the brain that controls sex drive and food intake. A few scientists may have admitted that men’s brains were indeed bigger, but they would have tried to qualify this finding by telling you that it was because men were bigger. Because brain size has been linked with intelligence, it’s very tricky to go around saying that men have bigger brains. Yet men do seem to have women beat here; even when accounting for height and weight differences, men have slightly bigger brains. Does this mean they’re smarter? Let’s keep going.

In 2001, researchers from Harvard found that certain parts of the brain were differently sized in males and females, which may help balance out the overall size difference. The study found that parts of the frontal lobe, responsible for problem-solving and decision-making, and the limbic cortex, responsible for regulating emotions, were larger in women [source: Hoag]. In men, the parietal cortex, which is involved in space perception, and the amygdala, which regulates sexual and social behavior, were larger [source: Hoag].

Men also have approximately 6.5 times more gray matter in the brain than women, but before the heads of all the men out there start to swell, listen to this: Women have about 10 times more white matter than men do [source: Carey]. This difference may account for differences in how men and women think. Men seem to think with their gray matter, which is full of active neurons. Women think with the white matter, which consists more of connections between the neurons. In this way, a woman’s brain is a bit more complicated in setup, but those connections may allow a woman’s brain to work faster than a man’s [source: Hotz].

If you’re a lady still concerned about the size issues brought up in the first paragraph, let’s address that now. In women’s brains, the neurons are packed in tightly, so that they’re closer together. This proximity, in conjunction with speedy connections facilitated by the white matter, is another reason why women’s brains work faster. Some women even have as many as 12 percent more neurons than men do [source: Hotz]. In studying women’s brains, psychologist Sandra Witelson found that those neurons were most densely crowded on certain layers of the cortex, namely the ones responsible for signals coming in and out of the brain. This, Witelson believed, may be one reason why women tend to score higher on tests that involve language and communication, and she came to believe that these differences were present from birth [source: Hotz].

But the density of women’s neurons, much like the size of a guy’s brain, isn’t any sort of magic bullet for predicting intelligence. Scientists know this because they’ve conducted imaging studies on how men and women think. As we’ve said, men use gray matter, and women use white, but they’re also accessing different sections of the brain for the same task. In one study, men and women were asked to sound out different words. Men relied on just one small area on the left side of the brain to complete the task, while the majority of women used areas in both sides of the brain [source: Kolata]. However, both men and women sounded out the words equally well, indicating that there is more than one way for the brain to arrive at the same result. For example, while women get stuck with a bad reputation for reading maps, it may just be that they orient to landmarks differently. And as for intelligence, average IQ scores are the same for both men and women [source: Crenson].

But do we get to these IQ scores through nature or nurture? On the next page, we’ll examine whether these different brain structures are set at birth, or whether they’re shaped by the environment. https://science.howstuffworks.com/life/inside-the-mind/human-brain/men-women-different-brains1.htm

6.Sex differences in the adolescent brain

Abstract

Adolescence is a time of increased divergence between males and females in physical characteristics, behavior, and risk for psychopathology. Here we will review data regarding sex differences in brain structure and function during this period of the lifespan. The most consistent sex difference in brain morphometry is the 9-12% larger brain size that has been reported in males. Individual brain regions that have most consistently been reported as different in males and females include the basal ganglia, hippocampus, and amygdala. Diffusion tensor imaging and magnetization transfer imaging studies have also shown sex differences in white matter development during adolescence. Functional imaging studies have shown different patterns of activation without differences in performance, suggesting male and female brains may use slightly different strategies for achieving similar cognitive abilities. Longitudinal studies have shown sex differences in the trajectory of brain development, with females reaching peak values of brain volumes earlier than males. Although compelling, these sex differences are present as group averages and should not be taken as indicative of relative capacities of males or females.

Across species that reproduce by combining genetic material, it is frequently adaptive for one of the pair to contribute DNA from a larger, stationary gamete, and the other member of the pair to contribute DNA from a gamete that is smaller and more mobile. The individual contributing the larger gamete is conventionally designated as the female of the species. Evolutionary forces often favor different characteristics in some domains for males and females, including differences in anatomy. Although the extent of these differences varies widely between species, it is the case that within a given species, the sex of an organism is usually the single greatest determinate of size and shape.

In humans, differing evolutionary forces have led to group average differences in brain and behavior between men and women. Recognition of the importance of the adolescent period in the pathogenesis of common psychiatric disorders such as schizophrenia and depression has gained attention (Kessler et al., 2005) and has stimulated increased interest in how brain development differs between males and females and how this may be contributing to their relative risks for specific disorders.

In this review, we will discuss sex differences in brain structure and function during adolescence, a time of increased divergence between males and females in physical characteristics, behavior, and risk for psychopathology (Häfner, 2003Kessler et al., 2005). We refer to male/female differences in physiology or behavior as “sex differences” as opposed to “gender differences”, considering gender to refer to the social role adapted by the person.Go to:

Overview of the process of sexual differentiation

Sexual differentiation is a cascade of events beginning with the process of sex determination and continuing through different stages of development to establish male or female phenotypes. Following a schema proposed by Phoenix and colleagues in 1959, sex-specific events are often categorized as early occurring organizational effects or later occurring activational effects (McCarthy, Schwarz, Wright, & Dean, 2008Phoenix, Goy, Gerall, & Young, 1959). Although there are many variations on this basic categorization, including the method of sex determination, timing and number of organizational events, magnitude of differences between the sexes, and which steroid hormones are operative, it continues to serve as a valuable template (McCarthy & Ball, 2008). An important modification to the original hypothesis is that sex steroid exposures during puberty are also associated with organizational effects, and that brain structural modifications in response to changing hormonal levels continue throughout adult life (Sisk & Zehr, 2005).

Sex differences in embryos can be detected as early as the 2nd day after conception, with male embryos in both humans (Ray, Conaghan, Winston, & Handyside, 1995) and mice(Burgoyne et al., 1995) being observed to have more cells and a higher metabolic rate (Wilson & Davies, 2007). Around the 6th week post-conception, the SRY gene on the Y chromosome interacts with products of genes on the X chromosome and autosomes to stimulate the primordial fetal gonad to develop into testes (Koopman, 1999). Testicular hormones, including testosterone and Mullerian inhibiting hormone, are produced beginning between 12-16 weeks gestation, which then triggers the separate processes that will masculinize and defeminize the developing organism. In the absence of these factors, the fetus develops into a female.

The occurrence of sex differences prior to the introduction of hormones implies that genes on the X or Y sex chromosomes have direct effects on sexual differentiation. Sex chromosome genes may directly produce products that contribute to sexual dimorphism or regulate transcription on autosomal genes (Malone & Oliver, 2008Ober, Loisel, & Gilad, 2008). One method of exploring this hypothesis has been the development of a transgenic mouse model in which the sex chromosome complement is dissociated from the gonadal phenotype. The four potential combinations include XX and XY mice without the SRY genes, who develop ovaries, and XX and XY mice with the SRY gene, who develop testes (Arnold, 2009). Studies of this mouse model have revealed dosages of sex chromosome genes affecting a variety of brain structural and behavioral features, including gene expression in multiple tissues (Arnold, 2009Reisert & Pilgrim, 1991), regional densities of tyrosine hydroxylase and vasopressin neurons, and behaviors such as habit formation (Quinn, Hitchcott, Umeda, Arnold, & Taylor, 2007) and responses to intruders (Arnold, 2009).

There are several potential mechanisms by which sex chromosome genes can affect phenotype directly (Davies & Wilkinson, 2006). One is through male-specific genes present only on the Y chromosome. A second is through dosage differences of sex chromosome genes between XX and XY organisms. As the X chromosome contains many more genes than the Y chromosome, one avenue is through dosage differences between XX and XY organisms of X and Y chromosome genes. Parity is achieved for a majority of these genes by a process of inactivation of most genes on one of the X chromosomes combined with upregulation of the remaining X chromosome in both sexes (Payer & Lee, 2008). Although most of the genes that escape inactivation of the second X chromosome have functional homologues on the Y chromosome, several do not, raising the possibility of dosage effects. A third means by which X and Y chromosome genes may contribute directly to sex differences is through imprinting. Females receive X chromosomes imprinted by both mother and father, while males only receive a single maternally imprinted X chromosome.

The effects of hormones such as estrogen, testosterone, and their multiple active metabolites proceed by several distinct mechanisms. Testosterone is metabolized to the much more potent dihydrotestosterone (DHT) and then to estradiol. Binding of DHT and estradiol to specific receptors in the cell nucleus affects transcription of a broad variety of genes. Estrogen also promotes neurogenesis and synaptic growth directly or via stimulation of GABAergic neurons (McCarthy, Schwarz, Wright, & Dean, 2008). Early in development, GABA acts as an activating neurotransmitter and induces synaptogenesis. In addition to slower transcriptome-mediated effects, estrogen can act quickly through a membrane bound receptor and through second messenger systems (Balthazart & Ball, 2006Milner et al., 2001). In addition to effects that are related to GABA transmission, the rate of conversion of testosterone to estradiol by aromatase changes in response to fluctuating glutamate levels. Modulation of local estrogen dosages by the rapid conversion of testosterone to estradiol allows effectiveness of estrogen as a neurotransmitter. Although both males and females have endogenous testosterone, which could serve as a substrate for aromatase, it is not known how the much lower endogenous levels of testosterone in females may affect this pathway.

The organizational processes occuring in utero and during early development set the stage for adrenarche, a rise in adrenal steroids beginning between the ages of 6-8 years that is associated with the development of axillary and pubic hair (Patton & Viner, 2007). Gonadarche is initiated in a separate process by the increasing activation of specialized hypothalamic neurons that secrete gonadotropin releasing hormone (GnRH), typically occurring between ages 8-13 in females and between 9-14 in males (Euling et al., 2008Senzaki et al., 1993). The rise in GnRH stimulates increased secretion of lutenizing hormone (LH) and follicle-stimulating hormone (FSH) from the pituitary, triggering the rise of output of the gonadal steroids estradiol and testosterone, which in turn brings about the somatic events associated with sexual maturation. Studies of twins indicate that 60-80% of the 4-5 year variation in onset of gonadarche may be due to genetic factors (Silventoinen et al., 2007van den Berg & Boomsma, 2007). Physiologic and environmental signals appear to have more of a permissive role that prevents pubertal onset in the presence of adverse conditions such as inadequate nutrition (Roseweir & Millar, 2009), and may accelerate pubertal onset in the presence of some kinds of social stressors (Tither & Ellis, 2008). Kisspeptin is a protein triggering the pubertal upsurge in GnRH production, in part through acting as a mediator by which environmental signals are transferred. However, what signals the up regulation of Kisspeptin is not yet known (Gottsch, Clifton, & Steiner, 2009Navarro, Castellano, García-Galiano, & Tena-Sempere, 2007).Go to:

Sex differences in the brains of adolescents and adults

Total brain volume

Postmortem data (Dekaban, 1977; H. Pakkenberg & Voight, 1964Witelson, Beresh, & Kigar, 2006), in vivo imaging studies of adults (Allen, Damasio, Grabowski, Bruss, & Zhang, 2003Andreasen et al., 1993Goldstein et al., 2001Good et al., 2001Nopoulos, Flaum, O’Leary, & Andreasen, 2000), and in vivo imaging studies of children (Giedd, Castellanos, Rajapakse, Vaituzis, & Rapoport, 1997Reiss, Abrams, Singer, Ross, & Denckla, 1996) (De Bellis et al., 2001) all consistently find a 9-12% greater brain size in males. This difference is not accounted for by body size either in adults, (Ankney, 1992Ho, Roessmann, Straumfjord, & Monroe, 1980O’brien et al., 2006Witelson et al., 2006) or in children, where larger brain volumes in males are observed despite the minor sex differences in height and weight characteristic of prepubertal development.

Allometry, the relationship between size and shape, is highly relevant to studies of sexual dimorphism. At least some changes in proportions of different brain regions occur solely as a factor of increasing brain volume (Finlay & Darlington, 1995). For example, it has been reported that the ratio of gray matter (GM) to white matter (WM) is larger in females (Allen et al., 2003Gur, Gunning-Dixon, Bilker, & Gur, 2002). However, studies within and between species have found that WM volume increases more quickly than GM following a 4/3 power law (Zhang & Sejnowski, 2000). Studies directly comparing male-female differences to effects of overall brain size have found that sex differences in the GM/WM ratio are minimal once overall brain size differences have been accounted for, meaning that males and females with equal brain volumes will also have equal gray/white matter ratios (Leonard et al., 2008).

The complex relationship of cortical morphometry and brain size is highlighted by a recent report in healthy adults relating brain volume to cortical features including thickness, surface area and gyrification (Im et al., 2007a). Consistent with previous reports from postmortem data (Pakkenberg & Gundersen, 1997), they found that increases in gray matter volume were driven primarily by increased surface area rather than cortical thickening. They also found that increased brain size was associated with a marked increase in folding of the cortical surface. There were no sex differences after accounting for differences in total brain size. This is inconsistent with some previous studies which found relatively thicker cortex and greater cortical complexity in females if differences in total brain volume were taken into account (Luders et al., 2004Luders et al., 2006Sowell et al., 2006), although the authors speculated that this may have been due to the use of a linear scaling method in the previous studies, which did not completely account for brain size differences. If different brain regions do not scale linearly, it is possible that contrasts in some areas may be ascribed to sexual dimorphism when they are related instead to overall differences in brain volume (Brun et al., 2009).

With respect for the complexities of interpretation introduced by allometric issues, we will next discuss reports of sexual dimorphism in measures adjusted in some way for differences in total brain volume (TBV).

Sexual dimorphism of brain regions

Besides overall differences in brain volume, there are specific areas of the brain which show differences in male and female adolescents and adults. It would seem logical that these may be areas that contain significant populations of sex steroid receptors. Estrogen, androgen, and progesterone receptors are all found in the hypothalamus, consistent with its central role in the control of sexual and reproductive function (Cameron, 2001). Many of the areas with strong connections to the hypothalamus also contain dense numbers of one or more of the sex steroid receptors. Prominent among these regions are the amygdala, bed nucleus of the stria terminalis, and parts of the nucleus of the solitary tract and parabrachial nucleus. Other regions observed to contain sex steroid receptors include the basal ganglia, hippocampus, and cerebellum (Goldstein et al., 2001Simerly, Chang, Muramatsu, & Swanson, 1990).

Sex steroids impact on cortical function through both direct and indirect pathways. Dopaminergic neurons in the midbrain and elsewhere have been shown to be sensitive to sex steroid activity(Creutz & Kritzer, 2004Kritzer & Creutz, 2008Stewart & Rajabi, 1994). Serotonergic neurons in areas such as the dorsal raphe nucleus have also been identified as containing sex steroid receptors.(Bethea, Lu, Gundlah, & Streicher, 2002Vanderhorst, Gustafsson, & Ulfhake, 2005). Both dopaminergic and serotonergic nuclei project diffusely to wide areas of the cortex. In addition to effects mediated through these systems, sex steroid receptors have within many cortical regions, (Montague et al., 2008), adding the potential for direct effects of sex steroids on cortical development. Among the cortical areas with high densities of steroid receptors are areas of the frontal cortex, motor and somatosensory cortex, posterior parietal cortex, agranular insular cortex and parahippocampal regions (Goldstein et al., 2001).

Our picture of the role of sex steroids in different parts of the brain is still incomplete. More recent studies using assays such as immunohistochemistry methods and in situ hybridization techniques have extended the range of brain regions, cell types, and cellular locations in which sex steroids have been found (Guerriero, 2009Sarkey, Azcoitia, Garcia-Segura, Garcia-Ovejero, & DonCarlos, 2008). Besides questions relating to the “non-classic” functions of receptors outside of the nucleus, we are just beginning to explore how splice variations in steroid receptor transcription may impact both function and the ability of existing assays to register their presence (Weickert et al., 2008Weiser, Foradori, & Handa, 2008). There is also limited data regarding developmental changes in sex steroid receptor expression, particularly during puberty (Sugiyama et al., 2009).

Nevertheless, there does appear to be some consistency between regions of the brain with structural sexual dimorphism and those found to have high numbers of sex steroid receptors. After covarying for TBV, orbitofrontal and caudate volumes have been reported to be larger in adult females (Filipek, Richelme, Kennedy, & Caviness Jr., 1994). Goldstein et al (2001) found several frontal and medial paralimbic brain regions to be relatively larger in women, whereas the frontomedial cortex, hypothalamus, amygdala, and angular gyrus were proportionately larger in men. The differences with the greatest effect sizes were also areas that are richly endowed with sex steroid receptors during development as determined by animal models (Goldstein et al., 2001).

In three independent pediatric cohorts, the caudate nucleus has been found to be relatively larger in females (Giedd et al., 1997Sowell, Trauner, Gamst, & Jernigan, 2002Wilke, Krägeloh-Mann, & Holland, 2007). Findings of sexual dimorphism in other regions have been less consistent. These include reports of relatively larger regions in the temporal lobes, thalamus, and basomesial diencephalons in females (Sowell et al., 2002), inferior frontal gyrus gray matter (Wilke et al., 2007) and relatively larger measures of total white matter (Wilke et al., 2007) and globus pallidus white matter (Giedd et al., 1997) in males.

Neufang and colleagues used voxel based morphometry to explore sex differences, including the influences of sex steroid levels and pubertal stage to GM and WM densities in a sample of 46 males and 46 females aged 8-15 years (Neufang et al., 2009). In girls, the hippocampus was larger bilaterally, as was the right striatum. In boys, a region of the amygdala was larger in males. With the exception of higher levels of serum testosterone in older males, there were no differences in sex steroid levels in their sample. They first examined the relationship of steroid levels and pubertal stages to brain structure in regions that had already been demonstrated to be sexually dimorphic; they found that GM intensity in the amygdala was predicted by testosterone levels in both males and females. Testosterone levels also predicted hippocampal size in females, but with younger females having larger hippocampi. In a whole brain regression analysis, testosterone was positively associated with increased GM density in right sided diencephalic structures in males, and negatively correlated with parietal GM volume in males. Estradiol levels were positively correlated with greater GM density in the uncus and parahippocampal gyri in girls only. Although this study did not look at effects of sex steroids and pubertal stages separately from effects of chronologic age, the sex-specific effects of testosterone and estrogen are consistent with findings that sex steroids continue to have organizational effects on brain structure during puberty. For more discussion of the effects of pubertal timing on behavior in adolescents, please see the paper by Dahl and Forbes, this issue.

Reports regarding sex dimorphism and pubertal effects on brain development are beginning to appear from a large cohort of typically developing twins currently being followed longitudinally by researchers in the Netherlands (Peper et al., 2009Peper et al., 2008). Lutenizing hormone (LH) levels were measured as an indication of onset of puberty in 57 male twins (age 9.20 +- 0.10) and 47 female twins (age 9.21 +-0.12), and voxel based morphometry was used to relate LH levels to regional gray and WM densities (Peper et al., 2008). LH levels were found to predict WM volumes when both sexes were looked at together, although not when they were looked at separately, potentially due to the loss in power from the smaller sample sizes. A second analysis in an overlapping cohort of 10-15 year olds, also using voxel based morphometry, compared brain gray and WM density and their relationships to estradiol and testosterone levels (Peper et al., 2009). Males (n=37; age 11.6 +- 1.0 yrs) had overall larger brain volumes than females (n=41, age=12.2 +-1.2yrs) after correction for age differences, although no difference was seen in the ratio of gray to WM between the sexes. The largest regional sex differences were in the putamen, insula, and amygdala, all larger in males. Total GM volumes correlated negatively with estradiol levels in females and positively with testosterone levels in males. A limitation of this study was that the female twin pairs were significantly older than the males. While a factor to account for age was included in all analyses, the authors note that it is possible that males were on the upward portion of their developmental trajectory in brain volume while females were on the downward trajectory, making exact comparisons difficult.

The relationship between WM development and testosterone was examined in a cross sectional sample of 408 healthy adolescents (204 males; age range 12-18)(Perrin et al., 2008). They measured serum testosterone levels and genotyped the androgen receptor, which contains a CAG repeat that affects testosterone activity. They further sought to separate out developmental changes in myelination by measuring changes in the magnetization transfer ratio (MTR), a measure sensitive to macromolecular structure and composition of tissue. In the absence of overt pathology such as edema, the predominant factor affecting MTR in WM is the amount of myelin, and thus developmental changes in MTR are thought to indicate changes in myelination. Similarly to previous studies, they found a much more rapid increase in WM volume in males than females. Although levels of bioavailable testosterone did not add significantly to chronologic age in explaining variation in WM volume in the overall group, it appeared to have a stronger effect in the subset of males with the AR genotype having fewer CAG repeats, a variation associated with higher transcriptional activity of the AR gene and suggesting an interaction of testosterone level with genotype (see Figure 1 in the paper by Paus, this issue). Despite the increasing WM volume, the MTR ratio decreased with age, with age explaining a greater proportion of the variance in the MTR ratio in males (8%) than in females (1%). This pattern suggests that the rapid increase in WM volume in males may be related to other structural elements such as axonal volume rather than myelination.Open in a separate windowFigure 1

Mean volume by age in years for males (N = 475 scans) and females (N = 354 scans). Middle lines in each set of three lines represent mean values, and upper and lower lines represent upper and lower 95% confidence intervals. All curves differed significantly in height and shape. Figure adapted from Lenroot et al., 2007.

A follow-up study in the same group of adolescents found that males had significantly greater apparent GM density (aGMD) in the putative cortico-spinal tract, a region containing fibers emanating from Brodman’s area 4 and thought likely to be involved in motor control (Herve et al., 2009). Males showed a significant increase in aGMD with age, while females did not, such that the sex differences in volume were only present in older adolescents. Rising levels of bioavailable testosterone in males contributed to the variance in the aGMD even after accounting for age effects; females did not show a similar rise in testosterone levels or a similar relationship to aGMD.

Diffusion tensor imaging

In recent years, more studies have begun to appear using imaging methods that aim to quantify brain tissue characteristics directly rather than by measuring volumes of gray and white matter in anatomic structures. One of these is magnetization transfer imaging, discussed above. Another is diffusion tensor imaging (DTI), which provides information about the properties of diffusion of water through different regions of the brain(Mori & Zhang, 2006). If unconstrained, water molecules will randomly diffuse in all directions, whereas water molecules interacting with tissue components such as cell membranes or large molecules will be more likely to diffuse in particular directions. In DTI, a tensor can be calculated for each voxel which represents the overall distribution of the diffusion of the water molecules in that area (Le Bihan et al., 2001). Although many potential measures of water diffusion can be obtained from diffusion imaging data, the most frequently reported metrics are general diffusivity, a measure of how quickly water diffuses in any direction, and fractional anisotropy (FA), which refers to the proportion of water molecules within a certain brain region that are diffusing in the same direction. Factors that increase FA include organization of tissue into tightly packed unidirectional structures and increased myelination; thus structures such as the corpus callosum and interior capsule tend to have high fractional anisotropy values. Mean diffusivity tends to be decreased in the presence of these same factors. Tissues such as the cortex will have lower FA levels despite their highly organized states because tissue elements are going in different directions, a phenomenon termed fiber crossing (see paper by Schmithorst, this issue).

In general, neurodevelopmental studies of diffusion tensor imaging parameters have found FA to be increasing and mean diffusivity to be decreasing in key WM tracts throughout adolescence (Barnea-Goraly et al., 2005Mukherjee et al., 2002Schmithorst & Holland, 2007Schmithorst, Holland, & Dardzinski, 2008Snook, Paulson, Roy, Phillips, & Beaulieu, 2005). This pattern has been most often interpreted in relation to the increases in myelination associated with maturation previously observed in postmortem studies, although the magnetization transfer findings described above suggest that it may also be related to changes in other structural components.

In the largest study in adolescents thus far, Schmithorst et al measured anisotropy and diffusivity in a group of typically developing subjects (104, 52 male, mean age 12.3 +- 3.5 years)(Schmithorst et al., 2008). They found higher FA and lower mean diffusivity in females in the splenium of the corpus callosum, while in males, FA was higher and mean diffusivity was lower in bilateral frontal WM regions, the right arcuate fasciculus, and left parietal and parieto-occipital WM. Correlations of FA with age also differed between brain regions in males and females; for example, left frontal lobe FA was positively correlated with age in boys, but negatively correlated with age in girls.

Adjusting for presumed earlier maturity in females by comparing females with boys who were approximately two years older resulted in even more pronounced effects with the exception of the splenium, where the effects size of the difference decreased. As increases in FA and decreases in mean diffusivity with development had been reported in a similar population by this group as well as others, the finding of greater FA values in males, despite their later maturation, suggested that sexually dimorphic processes are at work beyond differences in maturational rates. The authors speculated that this may be related to more extensive fiber crossing in females. A study in 21 adolescents (9 male, mean age 12.3 +- 2.9yrs) also found that males overall had higher FA values in left frontal WM regions than females (Silveri et al., 2006). These results are different than those found in a study in adults, which reported greater anisotropy in frontal regions in females (Szeszko et al., 2003). It is possible that relative anisotropy values in males and females may change as maturation completes, a question awaiting the availability of longitudinal data. Further information on changes in diffusion tensor imaging parameters during adolescence can be found elsewhere in this issue.

Cortical morphometry

Postmortem studies in adults have consistently found sex differences in the cortical cytoarchitecture, including higher neuronal densities in granular cortical layers in females (Witelson, Glezer, & Kigar, 1995), higher overall neuronal densities and numbers in males (Rabinowicz, Dean, Mcdonald-Comber Petetot, & De Courten-Myers, 1999), and more neuropil in females(Rabinowicz et al., 2002) without overall differences in cortical thickness (Mayhew, Mwamengele, & Dantzer, 1996). A greater number of neurons in the brain and a thicker cortex has also been reported in males regardless of overall body size (Pakkenberg & Gundersen, 1997) as well as higher synaptic density in males throughout the cortex(Alonso-Nanclares, Gonzalez-Soriano, Rodriguez, & DeFelipe, 2008). Regional differences include a larger visual cortex in males (Amunts et al., 2007). Language-related areas such as the superior temporal cortex and Broca’s region have been reported larger in females (Harasty, Double, Halliday, Kril, & McRitchie, 1997), and one study found the cortex to be thicker in females (Henery & Mayhew, 1989). Cortical complexity has been reported as similar in males and females (Zilles et al., 1998). There are currently no equivalent postmortem studies in pediatric subjects.

Results from neuroimaging studies have been mixed. Some neuroimaging studies have not found sex differences in cortical thickness (Nopoulos et al., 2000O’Donnell, Noseworthy, Levine, & Dennis, 2005Salat, 2004) after covarying for total brain volume, or they have found trends towards greater thickness in males (Salat, 2004). Others have found thicker cortex in females after taking differences in overall brain volume into account. Luders and colleagues measured cortical thickness in sixty healthy right handed adults (females aged 24.32 +- 4.35 years; males 25.45 +- 4.72 years), using a method in which spatially homologous regions of the cortex were aligned between individuals by matching patterns of cortical landmarks (Luders et al., 2006). They found that when total brain volume was covaried, the cortex was thicker in females across nearly the entire lateral surface of the brain. If unadjusted values were used, a similar but less widespread pattern of greater thickness in females was found, which was most pronounced in the left inferior and superior frontal gyri, and then to a lesser extent in the superior pre- and post-central regions and occipital lobe. In contrast, males had an area of increased thickness in the left posterior temporal lobe. Surface area was significantly larger in females when scaled data were used and was larger in males using the original unscaled data. Gyrification, measured through determining the degree of curvature at thousands of points across the brain, was higher in females in several regions in the frontal, parietal, and temporal lobes. The areas of greatest differences were in the anterior regions of the frontal lobe. No regions had greater gyrification in men(Luders et al., 2004Luders et al., 2006).

Using the same cortical-pattern matching method of comparing cortical thickness measurement as Luder et al, a cross-sectional study in 176 subjects who ranged from 7 to 87 years of age reported females to have thicker cortices in the right inferior parietal and posterior temporal regions (Sowell et al., 2007). In this study, the authors accounted for scaling issues related to differences in total brain size by creating a subsample of 36 adult subjects (18 males and 18 females) who had been individually matched on total brain volume and age. They found that even when overall brain volume and GM volumes were identical, females had thicker cortex in the right lateral frontal, temporal, and parietal cortices. These were in similar regions as found when using the entire sample, except even more statistically robust, despite the smaller sample size. While there were also significant changes in cortical thickness with age, these were not in the same regions as where sex differences were identified, leading the authors to suggest that these sex differences may exist prior to the age of the youngest participants in their study, which was age 7.

Sex Differences in Developmental Trajectories

An emerging theme from longitudinal studies is that in neuroimaging, as in life, the journey is often as important as the destination. This is exemplified in MRI studies where developmental trajectories of morphometry (i.e. size by age curves) show discriminating features not found with static measures for predicting cognitive parameters (Shaw et al., 2006), separating clinical groups (Shaw et al., 2007), and predicting good and bad outcomes (Mackie et al., 2007). Understanding the sexual dimorphism of developmental brain trajectories may also clarify some of the allometric issues previously discussed.

In 1989, the Child Psychiatry branch at the NIMH initiated a large scale longitudinal study of typical brain development, which to date has acquired data regarding brain development and function from over 1000 typically developing children (including twins and siblings) scanned from 1-7 times at approximately two year intervals. A study of a subset of these data, which included 829 scans from 387 unrelated individuals (age range 3-27, 209 males), demonstrated that neurodevelopmental trajectories were significantly different between males and females (Lenroot et al., 2007). Total brain size followed an inverted U trajectory in both sexes, with peak total brain size occurring at approximately 10.5 years in females and 14.5 years in males. Regional GM volumes also followed an inverted U shaped maturational curve and peaked earlier in females {SEE FIGURE 1}.

WM volumes continued to increase in both males and females throughout the age range of the study. Consistent with a prior report from an independent cohort of 188 children and adolescents (De Bellis et al., 2001), WM in males grew more rapidly, resulting in increasingly larger volumes relative to females with age. After covarying for total brain volume, many of the regional size differences disappeared. Differences were still present in the frontal lobe, in which GM volume was proportionately larger in females, and which had sex differences in rates of growth for GM and WM. The lateral ventricles were larger in males, while the corpus callosum was relatively larger in females (Lenroot et al., 2007).

In an earlier study from the NIMH sample, males had a more rapid increase in amygdala size, while the hippocampus grew more quickly in females (Giedd et al., 1997). Suzuki and colleagues also found sexual dimorphism in the growth of the hippocampus, but in this case, they reported more pronounced growth in males. They measured age and sex effects on volumes of the hippocampus and parahippocampus in a group of 23 adolescents (13-14 yrs age, 10 male) and 30 young adults (18-21 years, 15 males)(Suzuki et al., 2005). Hippocampal volumes were significantly larger in the adult males than in the adolescents, but there were no differences in females. The authors speculated that the discrepancy could be related to the older age of participants in this study compared to previous reports, suggesting that hippocampal growth may occur earlier in girls than boys, consistent with the pattern seen in other brain regions (Lenroot et al., 2007).Go to:

Sex differences in brain physiology

Studies have reported sex differences in brain activation levels in the presence of equal cognitive ability (Bell, Willson, Wilman, Dave, & Silverstone, 2006). Some have suggested that, at least in adulthood, females tend to have more bilateral activity during tasks, and males more regional activation (Shaywitz et al., 1995). One study found that females had less relative activation for a given level of task performance than males, possibly indicating greater efficiency (Christova, Lewis, Tagaris, Uğurbil, & Georgopoulos, 2008). Although there has been very little investigation yet into sex differences in functional brain development during adolescence, one study looking at an interaction of sex and age during adolescence with regards to responses to viewing different types of faces found that females and males had similar responses to angry faces during childhood. However, females showed greater responses to angry faces after puberty, while males did not change. This pattern is possibly related to sex-related changes in HPA axis functioning, which is discussed in more detail below (Mcclure, 2004).

A hint towards the potential relevance of sex steroid differences to brain activity can be suggested by studies looking at brain activation across the different phases of the menstrual cycle. Brain activation levels themselves change as a function of menstrual phase (Goldstein, 2006). The relation to cognitive function also may change. For example, several studies have found that performance and brain activation fluctuate across the menstrual cycle on tasks including spatial ability (Hausmann & Gunturkun, 2000Schöning et al., 2007) and semantic performance (Konrad et al., 2008). Tests of learning and memory also show fluctuations across the menstrual cycle, suggesting that temporary changes in sex steroid exposure can affect neuronal plasticity (Farage, Osborn, & Maclean, 2008Sherwin, 2003). A recent study examining interhemispheric inhibition found that the influence of left hemispheric regions is much stronger during the menses, while lateralization decreases during the follicular phase as estradiol levels rise (Weis et al., 2008). Although brain imaging studies in general rarely explicitly account for influences such as menstrual phase, the opportunity to study the interaction of changing levels of steroid hormones with neuronal function may provide an invaluable window onto the processes underlying neuronal plasticity and how it may change in response to the hormonal changes that characterize adolescence.Go to:

Sex differences in brain development during adolescence: impact on function

Sex differences in cognitive ability are modest (McCarthy & Konkle, 2005). Patterns of social interaction typically show much stronger contrast, extending in varying form and degrees of magnitude across species. In general, species having the greatest differences in roles in procreation tend also to have the most marked behavioral differences (McCarthy, 2008). In humans and most other mammalian species, females have generally been characterized as being more sensitive to social cues and stresses, such as perception of rejection. Evolutionarily this has been tied to adaptation of social roles to facilitate bearing offspring and having primary responsibility for care of the very young, including the capacity for attunement needed to foster cognitive and social development of the neonate (Cyranowski, Frank, Young, & Shear, 2000). The relationship of the sex differences in brain development described by the neuroimaging studies above to these functional differences in social behavior is as yet largely unexplored. It is intriguing to speculate that a better understanding of the neurodevelopmental processes underlying sex differences in social cognition may also provide a key to another functional aspect of sex differences during adolescence: the disparity in rates of onset, course, and symptomatology of the common psychiatric disorders whose incidence begins to rise during this time.

The clearest example of this increased incidence is major depression. Prior to the onset of puberty, males and females have approximately equal rates of depression at 5%. With the onset of puberty, rates in females double, while males stay approximately the same (Angold, Costello, & Worthman, 1998). Many factors may play a role in this pattern, including different stresses associated with gender expectations, the higher incidence of exposure to trauma in young females, and differences in social cognitive function such as rejection sensitivity (Cyranowski et al., 2000Zahn-Waxler & Shirtcliff, 2006). One potential mechanism is sex differences in the development of the HPA axis. Studies indicate that the increase in incidence of depression is linked to pubertal maturation rather than increases in chronological age (Angold et al., 1998). In females, there is an increased response of the HPA axis to stress with advancing puberty, while in males, the response is decreased, possibly associated with increased testosterone levels (Mccormick & Mathews, 2007).

Schizophrenia is another disorder whose incidence rises markedly during adolescence and whose presentation shows significant sex differences (Castle, Sham, & Murray, 1998Grossman, Harrow, Rosen, Faull, & Strauss, 2008McGlashan & Bardenstein, 1990). Schizophrenia is slightly more common in males (McGrath, Saha, Chant, & Welham, 2008). Males also have a distinct peak age of onset during late adolescence and young adulthood, while the peak in females is later and more gradual, and there is a second rise in incidence around the time of menopause (Angermeyer & Kuhn, 1988). If schizophrenia is linked to abnormalities in adolescent brain development, this would seem counter-intuitive based on the behavioral and brain imaging data that females mature earlier than males. It has been suggested that the pubertal surge in estrogen levels seen in females but not males has a neuroprotective effect, possibly serving to delay onset of the disorder and ameliorate some of its effects (Kulkarni et al., 2008). Women with schizophrenia also tend to continue to have better outcomes, linked at least in part to better social functioning than males (Castle & Murray, 1991Grossman et al., 2008Thorup et al., 2007). It has also been observed that males are more likely to have a history of lower functioning prior to their first psychotic break, suggesting that males may be more at risk for earlier abnormal neurodevelopmental processes that could interact with other processes during adolescence to facilitate the onset of schizophrenia (Castle & Murray, 1991Thorup et al., 2007).Go to:

Conclusion

In summary, male adolescents, as a group, have larger brain volumes than females. The longitudinal data show that adolescent males reach their peak volumes later than females, such that volumes become increasingly divergent as males and females reach adulthood, particularly for WM. Assessing regional differences is complicated by the observation that scaling with increased volume is not necessarily linear, leading to regional differences that could be attributable to variations in brain size alone (Im et al., 2007bLeonard et al., 2008). The complexities of comparing brain measurements in cross-sectional data across development are highlighted by findings in the hippocampus and the corpus callosum, in which studies done at different ages have found different patterns of sex differences (Giedd et al., 1997Lenroot et al., 2007Suzuki et al., 2005). If brain regions are growing at different rates, the size or even direction of the difference between them could depend on the age at which measurements are made.

The regions most frequently reported by imaging studies as showing morphological sex differences include the basal ganglia and limbic structures. The caudate has been reported as proportionately larger in females by several studies across different ages and using different methodologies (Filipek, Richelme, Kennedy, & Caviness, 1994Giedd et al., 1997Sowell et al., 2002), which is intriguing given the involvement of the basal ganglia in disorders with pronounced sex differences in incidence such as Attention Deficit/Hyperactivity Disorder and Tourette’s syndrome. The other areas most frequently reported as being different even after accounting for overall differences in brain size are the hippocampus and amygdala (Giedd et al., 1997Goldstein et al., 2001Suzuki et al., 2005Wilke et al., 2007), with larger size or more rapid growth of the hippocampus typically reported in females, and of the amygdala in males. These findings appear consistent with observations of greater densities of androgen receptors in the amygdala versus higher levels of estrogen receptors in the hippocampus, as well as with the preliminary data combining steroid levels and brain volumes, which seem to indicate more sensitivity of the amygdala to testosterone levels and of the hippocampus to estrogen. These areas have also been associated with disorders such as depression and anxiety, disorders which show distinct differences between the sexes (Becker et al., 2007McEwen, 2001Romeo, Waters, & Mcewen, 2004).

DTI and magnetization transfer imaging have also shown sex dimorphism in measurements sensitive to the brain’s microstructural features such as myelination and tissue organization. Studies of brain activity have shown different patterns of activation in the presence of equal cognitive performance, suggesting that male and female brains may follow slightly different paths to achieve similar levels of function.

Do sex differences in brain structure or function during adolescence explain sex differences in functional capacity and behavior? Answering this question is one of the next pressing tasks for better understanding how the processes of sexual differentiation affect behavior or risk for psychopathology. Separating out effects of development from those of sex is challenging, particularly in cross-sectional data, given the enormous variability within the normal range of both brain structural features and ability. For example, if brain volumes in the frontal lobe appear to peak two years earlier in females, suggesting more rapid development in females, is matching populations on chronological age the most appropriate method, or should developmentally equivalent groups be chosen? And if the latter, what measure of development should be used? Pubertal stage is one possibility, as seen in some of the studies here, but the timing of pubertal maturation relative to other aspects of adolescence varies between individuals. Moreover, different systems may not mature at the same rate even within the same person, and not all cognitive maturation is

Understanding how sex differences interact with other factors that lead to vulnerability or resilience for neuropsychiatric disorders could throw unexpected light on relevant pathways. A consequence of sex differences in brain development is the possibility that treatments may not have the same effects in males and females, which may be important to explore further for the goal of optimizing individual treatment strategies (Hodes, Yang, Van Kooy, Santollo, & Shors, 2009). Although a better understanding of how sex differences develop during childhood and adolescence may eventually help to guide interventions such as treatment and education, it should be remembered that all the findings discussed in this paper represent group averages with substantial overlap between groups. Causality has not yet been established between any normal variation of brain development and functional ability. Neuroimaging findings should be taken as clues pointing us towards different processes affecting male and female brain development rather than definitive statements about the capabilities of male or female individualsGo to:

Footnotes

Publisher’s Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Go to:

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7. Women Have More Frontal Lobe Neurons Than Men

Although each person looks different on the outside, we are quite similar on the inside — or are we? Canadian researcher Sandra Witelson presented evidence at the 2001 Society for Neuroscience meeting that shows women’s brains have more nerve cells in their frontal lobes than men’s brains.

Dr. Witelson should know. Not only did she examine Albert Einstein’s brain, she oversees a brain “bank” at McMaster University in Ontario, Canada. Since 1977, she has collected hundreds of brain specimens for study. What makes this brain collection special is that the people who donated their brains were neurologically normal — that is, they died of causes such as lung cancer that did not directly affect the brain. Also, the donors were interviewed to establish a profile of their cognitive abilities. This makes the anatomical information extremely valuable because it can be matched with a person’s cognitive profile.

By examining 20 brains (11 female, 9 male) from the “bank,” Witleson and her group found that women were able to pack approximately 18% more nerve cells into areas of the prefrontal lobes; the prefrontal lobes help regulate higher functions such as language, judgment, and planning future actions. Although women start out with more cells in these areas, by old age, men and women have similar number of cells in the frontal lobes. This is because women lose cells in these areas faster as they age than men do. This may contribute to the higher incidence of Alzheimer’s disease in women.

At birth, boys’ brains are bigger by weight than girls’ brains (upper chart). If you measured around a boy’s head, say as if to fit him for a hat, the boy’s head would be approximately 2% larger around than a girl’s head. In adults, men’s brains still weigh more than women’s — approximately 11% more (upper chart). Men’s heads are still 2% larger around than women’s heads. As adults, men generally weigh more than women and are taller. If you take body size into account, the brain size differences are small (see bottom chart, which shows brain weight as a percentage of body weight).

Perhaps the greater cell density in women’s prefrontal lobes compensates for the smaller brain size. Despite these differences, men and women perform equally at most tasks. So even though men may have bigger brains and women have more cells in some critical areas, neither can point to these facts as a measure of greater intelligence. Judging who is smarter is much more complex than finding out who has the largest brain. https://faculty.washington.edu/chudler/wome.html

8.Sex differences and structural brain maturation from childhood to early adulthood

Abstract

Recent advances in structural brain imaging have demonstrated that brain development continues through childhood and adolescence. In the present cross-sectional study, structural MRI data from 442 typically developing individuals (range 8–30) were analyzed to examine and replicate the relationship between age, sex, brain volumes, cortical thickness and surface area. Our findings show differential patterns for subcortical and cortical areas. Analysis of subcortical volumes showed that putamen volume decreased with age and thalamus volume increased with age. Independent of age, males demonstrated larger amygdala and thalamus volumes compared to females. Cerebral white matter increased linearly with age, at a faster pace for females than males. Gray matter showed nonlinear decreases with age. Sex-by-age interactions were primarily found in lobar surface area measurements, with males demonstrating a larger cortical surface up to age 15, while cortical surface in females remained relatively stable with increasing age. The current findings replicate some, but not all prior reports on structural brain development, which calls for more studies with large samples, replications, and specific tests for brain structural changes. In addition, the results point toward an important role for sex differences in brain development, specifically during the heterogeneous developmental phase of puberty.

Highlights

► A structural MRI study (N = 442): sex and age differences in typical development. ► Different age-related trajectories in subcortical brain volumes. ► Linear increase in white matter, non-linear decrease in gray matter volume with age. ► Sex-by-age interactions specifically in cortical surface measurements. ► Adolescence is characterized by large individual variance in brain maturation.

1. Introduction

Brain development is an organized and highly dynamic multistep process, which is genetically determined, epigenetically directed and environmentally influenced (Tau and Peterson, 2010). This process continues both through childhood and adolescence, the developmental period during which the body and brain emerge from an immature state to adulthood (Spear, 2000Steinberg and Morris, 2001). Although total brain size is approximately 90% of its adult size by age six, it is now well known that the gray and white matter subcomponents of the brain continue to undergo dynamic changes throughout adolescence (Giedd et al., 1999Paus, 2005).

1.1. Age differences in brain structures

There is increasing consensus on the overall pattern of gray matter development over the course of childhood and adolescence: in childhood a global increase of cortical gray matter volume takes place, peaking around the onset of puberty, which is then followed by a gradual decrease in adolescence and early adulthood (Giedd and Rapoport, 2010Gogtay and Thompson, 2010Raznahan et al., 2011Shaw et al., 2008Taki et al., 2012). Cortical thinning occurs throughout adolescence and extends well into adulthood, but patterns (e.g. linear, quadratic, cubic) differ across brain regions and are also dependent on the studied age range (Østby et al., 2009Raznahan et al., 2011Shaw et al., 2008Sowell et al., 2004Sowell et al., 2007Tamnes et al., 2009). In contrast, total white matter volume increases even until approximately the fifth decade of life and declines thereafter (Paus, 2010aPaus et al., 2001Walhovd et al., 2005). For subcortical regions, developmental patterns are less clear. For example, age-related volume increases for the hippocampus and amygdala (Østby et al., 2009Taki et al., 2012), but see (Gogtay et al., 2004), and age-related volume decreases in the caudate, putamen, pallidum and accumbens have been reported (Østby et al., 2009Sowell et al., 2002Sowell et al., 2004).

1.2. Sex differences in brain structures

Sex differences account in part for the aforementioned different developmental growth trajectories. Cerebral and gray matter volume in the frontal and parietal lobes peak earlier in girls than in boys (though the exact ages vary depending on the subregion), a pattern which may relate to sex differences in timing of puberty (Lenroot et al., 2007). Moreover, sex differences have been demonstrated in the hippocampus (larger in females; but see (Bramen et al., 2011)), amygdala (larger in males) (Neufang et al., 2009) and thalamus (larger in males) (Bramen et al., 2011), but see (Sowell et al., 2002). Some of these findings have also been replicated in a VBM (voxel-based morphometry) study, showing pronounced sexual dimorphism (males larger than females) in amygdala, thalamus, putamen and insula (Peper et al., 2009).

Sex differences have also been reported in cortical thickness, indicating thicker cortices in parietal and temporal regions in females compared to males (age-range 8–87) (Sowell et al., 2007), but the opposite has also been reported (Raznahan et al., 2011). In a sample with a narrow age range (10–14 years), no sex differences were present in cortical thickness (Bramen et al., 2012). However, significant sex differences were present when gonadal hormones, in this case testosterone, were used as a predictor of cortical thickness (Bramen et al., 2012Nguyen et al., 2012). Furthermore, maturational patterns for whole brain thickness show different trajectories between sexes (Raznahan et al., 2011).

These studies have provided important insights in the complex changes in brain development in late childhood and adolescence. However, there is still no general consensus on the developmental trajectories of all (sub)cortical brain structures in early and mid adolescence.

The aim of the current study was to perform a replication study focusing on cross-sectional age- and sex-related structural brain differences in a European sample (n = 442; 223 females, 219 males) in the age range of 8–30 years. Specifically, we used structural magnetic resonance imaging (MRI) to gain information on brain volumes, cortical thickness and surface area measurements. We had the following objectives: (1) examine age-related differences in (sub)cortical brain volumes, cortical thickness and surface area with possible sex-related differences. (2) The second goal was to examine the relationship between gray matter volume, cortical thickness and surface area with age and between sexes.

2. Materials and methods

2.1. Participants

We combined data sets from several different imaging studies performed at the Brain and Development Lab, Leiden University, between 2006 and 2010. The same scanner and scanner-protocols were utilized to create a large dataset of healthy participants. Four hundred forty-two (219 males; 223 females) unrelated typically developing children and young adults were included. The age range was between 8 and 30 with an about equal distribution across age cohorts (see Table 1 for subgroups).

Table 1. Age and sex distribution of the sample.

Age groupsSexTotal
Females (N)Males (N)
8–9162036
10–11233154
12–13303161
14–15312859
16–17273158
18–20463379
21–23343165
24–29161430
Total223219442

There were no differences in mean age between males (mean: 16.3 (SD = 4.74)) and females (mean: 17.0 (SD = 4.77; p = 0.08), and no differences in sex or age distribution and time of scan (all p‘s > 0.8). Participants had no self-reported history of neurological or psychiatric disorders, chronic illness, learning disabilities, or use of medicines known to affect nervous system functioning. They were required to be right handed and to have no MRI contraindications. Participants and primary caregivers (for minors) gave informed consent for the studies and received fixed payment for participation. All studies and procedures were approved by the Medical Ethics Committee of the Leiden University Medical Center.

2.2. Data acquisition

All participants were scanned with the same standard whole-head coil on the same 3-Tesla Philips Achieva MRI system (Best, The Netherlands). High-resolution T1-weighted anatomical scan were obtained: 3D-T1-weighted scan: TR = 9.717 ms; TE = 4.59 ms, flip angle = 8°, 140 slices, 0.875 mm × 0.875 mm × 1.2 mm, FOV = 224.000 × 168.000 × 177.333. All anatomical scans were reviewed and cleared by a radiologist.

2.3. (Sub)cortical volumes, thickness and area

Cortical reconstruction and volumetric segmentation was measured automatically using the software FreeSurfer version 5.0 (http://surfer.nmr.mgh.harvard.edu/) (Dale et al., 1999Fischl and Dale, 2000Fischl et al., 1999a). Until recently, manual tracing of brain regions by experts in neuroanatomy has been the accepted standard. However, as the size of the MRI datasets has increased, the time and cost required for the labor-intensive process of manual tracing has become prohibitive. It has been demonstrated that Freesurfer is sufficiently reliable and valid particularly in the context of larger sample sizes to detect associations with clinical or demographic variables (e.g. (Cherbuin et al., 2009Dewey et al., 2010Doring et al., 2011)).

Details of the surface-based cortical reconstruction and subcortical volumetric segmentation procedures have been documented in detail previously (Dale et al., 1999Fischl and Dale, 2000Fischl et al., 1999aFischl et al., 1999bFischl et al., 2001Fischl et al., 2002Han et al., 2006Segonne et al., 2004). In short, for each subject the T1 MRI scan was used to construct a three-dimensional model of the cortical surface that included: (1) segmentation of the white matter; (2) tessellation of the gray/white matter boundary; (3) inflation of the folded, tessellated surface; and (4) correction of topological defects (Dale et al., 1999Fischl et al., 1999a). Measures of cortical thickness are obtained from this surface reconstruction by estimating and then refining the gray/white boundary, deforming the surface outward to the pial surface, and measuring the distances from each point on the white matter surface to the pial surface (Fischl and Dale, 2000).

Volumetric subcortical segmentation and measurement was performed using automated procedures that have been validated as comparable in accuracy to much slower, labor-intensive manual tracing and labeling methods (Fischl et al., 2002Fischl et al., 2004). This procedure automatically classifies brain tissue into multiple distinct structures such as cerebral and cerebellar gray and white matter, cerebrospinal fluid, basal ganglia, and other subcortical structures. Using probabilistic information derived from a manually labeled training data set, this approach automatically assigns a neuroanatomical label to each voxel in the MRI volume. First, data are rigid-body registered and morphed nonlinearly into a standard stereotactic space. Then, previously manually segmented images are used to calculate statistics about how likely a particular label is at any given location throughout the brain, and these data are used as Bayesian priors for estimating voxel identity in a given subject’s brain. Three kinds of information are used by the segmentation to help disambiguate anatomical labels: (1) the prior probability of a given tissue class occurring at a specific location in the atlas space; (2) the image intensity likelihood given that tissue class; and (3) the probability of the local spatial configuration of the labels given the tissue class. Segmentations produced by this procedure can be visually inspected for accuracy and edited prior to inclusion in research analyses. For the purposes of the current study, automated image surfaces and segmentations were inspected and screened for quality control but were not manually edited, in order to maintain the objectivity of results. Intracranial volume was determined by a validated automated method known to be equivalent to manual intracranial volume estimation (Buckner et al., 2004).

Once the cortical models are complete the cerebral cortex was parcellated into units based on gyral and sulcal structure (Desikan et al., 2006). This parcellation method based on major sulci has been shown to be both valid and reliable, with high intraclass correlation coefficient between the manual and automated procedures for both cortical volume estimates and parcel boundaries. The parcellation produces 34 gyral regions subdivided into eleven frontal regions, nine temporal regions, five parietal regions, four occipital regions and four parts of the cingulate cortex (Desikan et al., 2006). In the present study, the subregions per lobe were combined to form one structure for comparison. The four parts of the anterior cingulate cortex were included in the frontal lobe segment (rostral and caudal part) and the temporal lobe segment (posterior and isthmus part), as suggested by the lobe mapping procedure of FreeSurfer (results were similar if these parts were excluded). Gray matter volume, cortical thickness and pial surface area (further referred to as surface area in this paper) were derived for each lobe. Procedures for the measurement of cortical thickness have been validated against histological analysis (Rosas et al., 2002) and manual measurements (Kuperberg et al., 2003Salat et al., 2004). Freesurfer morphometric procedures have good test–retest reliability across scanner manufacturers and across field strengths (Han et al., 2006Reuter et al., 2012).

2.4. Statistics

The volume of each subcortical structure, cerebral and cerebellar gray and white matter, was calculated by FreeSurfer as described in the previous section. The volumes of all structures described were averaged across hemisphere within subject. Variations with sex and (the interaction with) age or age-squared (i.e. non-linear trajectories) were estimated using a general linear model in those structures. Brain volumes, lobar gray matter and cortical thickness and surface area measures were corrected for intracranial volume (IC) as an estimate of head size, because the head size in males is in general about 10% larger than in females. Furthermore, in a review by Paus (2010b), it was demonstrated that without IC volume (overall brain size) correction sex differences were found in absolute volumes, but after correction only few differences remained. There is an ongoing discussion whether to correct for IC volume in cortical thickness and surface area measurements. However, to be stringent, the findings reported here include IC volume correction. In addition, information regarding Bonferroni correction for multiple comparisons is noted in Table 2Table 3.

Table 2. Volumes of cortical and subcortical brain structures.

StructureRaw volumes in ml (SD)Intracranial volume corrected
AllFemalesMalesSexAgeSex × ageAge2Sex × age2
N = 442N = 223N = 219p-Valuesp-Valuesp-Valuesp-Valuesp-Values
Accumbens1.35 (0.21)1.28 (0.19)1.41 (0.21)nsnsnsnsns
Amygdala3.43 (0.38)3.25 (0.32)3.61 (0.35)0.023nsnsnsns
Caudate8.09 (1.06)7.71 (1.01)8.48 (0.97)nsnsnsnsns
Hippocampus9.2 (0.91)8.78 (0.78)9.62 (0.82)nsnsnsnsns
Pallidum3.65 (0.40)3.45 (0.33)3.85 (0.36)nsnsnsnsns
Putamen11.89 (1.34)11.26 (1.21)12.53 (1.14)ns<0.001ans<0.001ans
Thalamus14.95 (1.44)14.25 (1.26)15.66 (1.25)0.045<0.001ans<0.001ans
Lateral Ventricle11.95 (6.66)11.11 (6.26)12.81 (6.94)ns<0.001ans<0.001ans
3rd ventricle0.85 (0.21)0.8 (0.16)0.9 (0.23)ns<0.001a0.028<0.001a0.016
4th ventricle1.94 (0.58)1.82 (0.58)2.06 (0.56)nsnsnsnsns
Cerebellum GM89.55 (11.81)83.88 (10.24)95.33 (10.43)0.008<0.001ans<0.001ans
Cerebellum WM31.29 (4.32)30.11 (4.06)32.5 (4.24)ns<0.001ans<0.001ans
Cerebral GM741.93 (72.95)700.96 (59.26)783.64 (61.03)<0.001a<0.001a<0.001a<0.001a<0.001a
Cerebral WM489.95 (55.61)461.16 (44.96)519.26 (49.86)0.002<0.001a0.013<0.001a0.022
Total brain1231.88 (117.81)1162.12 (92.02)1302.9 (97.00)<0.001a0.002ns<0.001ans
Intracranium1573.02 (184.05)1464.54 (154.30)1683.48 (141.33)<0.001ans0.0010.045<0.001a
Frontal GM187.78 (21.01)177.27 (17.88)198.49 (18.43)<0.001a<0.001a0.001<0.001a0.001
Parietal GM136.5 (16.75)128.96 (14.42)144.18 (15.44)<0.001a<0.001a0.001<0.001a<0.001a
Temporal GM133.6 (15.31)125.88 (12.91)141.46 (13.47)0.002<0.001a0.018<0.001a0.016
Occipital GM50.84 (6.72)47.98 (5.88)53.75 (6.27)0.002<0.001a0.014<0.001a0.014
ACC GM24.56 (3.24)23.27 (2.85)25.88 (3.07)0.034<0.001ans<0.001ans

Abbreviations: GM, gray matter; WM, white matter; ACC, anterior cingulate cortex; ns, not significant.

Bonferroni p-Value = 0.002.a

Survives Bonferroni correction.

Table 3. Thickness and area measurements of the four lobes and cingulate cortex.

Raw mean (SD)Intracranial volume corrected
AllFemalesMalesSexAgeSex × ageAge2Sex × age2
N = 442N = 223N = 219p-Valuesp-Valuesp-Valuesp-Valuesp-Values
Thickness (in mm)
Frontal lobe2.85 (0.13)2.84 (0.13)2.85 (0.13)ns<0.001ans<0.001ans
Parietal lobe2.58 (0.13)2.57 (0.13)2.59 (0.13)ns<0.001ans<0.001ans
Temporal lobe2.94 (0.13)2.94 (0.13)2.95 (0.12)ns<0.001ans<0.001ans
Occipital lobe2.13 (0.12)2.12 (0.12)2.14 (0.12)ns<0.001ans<0.001ans
ACC2.97 (0.15)2.98 (0.15)2.95 (0.14)0.023<0.001ans<0.001ans
Surface area (in mm2)
Frontal lobe56.7 (6.01)53.6 (4.83)59.9 (5.36)<0.001a<0.001a<0.001a<0.001a0.001a
Parietal lobe46.5 (4.90)44.0 (3.96)49.1 (4.40)<0.001a<0.001a<0.001a<0.001a<0.001a
Temporal lobe38.6 (4.13)36.4 (3.31)40.8 (3.66)0.001a<0.001a0.001a<0.001a0.001a
Occipital lobe22.4 (2.52)21.3 (2.20)23.5 (2.35)0.0090.005ns0.015ns
ACC7.42 (0.95)7.0 (0.84)7.85 (0.88)0.01ns0.018<0.001a0.036

Abbreviations: ACC, anterior cingulate cortex; ns, not significant.

Bonferroni p-Value = 0.005.a

Survives Bonferroni correction.

Model fits were calculated to examine developmental trajectories of brain volumes, cortical thickness and surface area measures. In case of significant sex or sex-by-age(-squared) interactions, model fits were calculated separately for both sexes as well as for the whole sample. F-tests were used to determine whether a linear or quadratic model significantly best fit the data. We used the extra sum-of-squares approach to obtain an F-ratio from the relative increase in the sum-of-squares and the relative increase in the degrees of freedom reflecting the number of parameters used for a linear or quadratic model (this information is available in the ANOVA table for each regression fit) (Motulsky and Christopoulos, 2004Thomas et al., 2009).

3. Results

Raw brain volumes for the whole sample, and for males and females separately are reported in Table 2 along with the statistical results of comparisons between sex, age (/-squared) and their interactions. Table 3 lists gray matter volumes, thickness and area measurements for frontal, temporal, parietal and occipital lobes and the anterior cingulate cortex. All volume, thickness and area measurements were corrected for intracranial volume.

3.1. Age differences in brain volumes

Analyses of subcortical volumes demonstrated that the only subcortical brain area showing a negative age association was the putamen and the only subcortical brain area showing a positive age association was the thalamus (Table 2). Positive associations with age were further found for the lateral and third ventricle volumes, and for cerebral and cerebellar white matter. Negative associations with age were found for gray matter volumes of the four lobes, ACC, cerebrum and cerebellum and total brain. Again, we calculated total gray matter-white matter (GM/WM) ratios. The total GM/WM-ratio decreased with age (p < 0.001) but there was no sex-by-age interaction.

3.2. Sex differences in brain volumes

As expected, significant sex differences were found in intracranial, total brain, cerebral gray and white matter, and cerebellar gray matter, with males showing larger volumes compared to females (Table 2). Analyses of subcortical volume differences showed that males had significantly larger amygdala and thalamus volumes than females. Second, gray matter volume within all four lobes as well as in ACC was larger in males than in females. Finally, we calculated total gray matter-white matter (GM/WM) ratios. This analysis revealed a larger total GM/WM-ratio in females than in males (p = 0.006).

3.3. Sex-by-age interaction and brain volumes

No sex-by-age interactions were found for any of the subcortical regions.

In contrast, significant sex-by-age interactions were found for lobar and cerebral gray matter volumes, with both groups showing gray matter volume loss with increasing age, but males demonstrated a general pattern of larger volume decreases with increasing age compared to females (see Fig. 1A and B). Males also showed a marginally larger third ventricle volume increases than females. Conversely, cerebral white matter volume increases were larger in females compared to males. Fig. 1A and B shows the significant relationships between age and brain volumes corrected for IC. Separate curves were fitted for males and females if the slopes were significantly different from each other (see Table 4). These slopes demonstrated that the quadratic fit was better than the linear fit for all structures. However, given that quadratic fits in cross-sectional studies may be affected by the age at which sampling started (or ended) (Fjell et al., 2010), we also displayed the linear fits to demonstrate the difference between both models.

Table 4. Model-fits of brain volumes, cortical thickness and surface area measures with age.

Brain structureModel fit
LinearQuadratica
R-squareR-square
AllMalesFemalesAllMalesFemales
Amygdala0.0003
Caudate nucleus0.001
Nucleus accumbens0.007
Hippocampus0.001
Pallidum0.001
Putamen0.06***
Thalamus0.028***
Lateral ventricles0.123***
3rd ventricle0.053***0.066***0.046**
4th ventricle0.005
Cerebellum GM0.088***
Cerebellum WM0.073***
Total GMb0.283***0.43***0.15***0.324***
Total WMb0.193***0.13***0.278***
Intracranium0.01*0.050.026**
Total brain0.029***
ACC surface area0.00020.0150.011
ACC GMb0.114***0.123***
acc thickness0.215***
Frontal surface areab0.04***0.144***0.00010.055***0.161***
Frontal GMb0.383***0.49***0.274***0.405***0.518***
Frontal thickness0.288***
Temporal surface areab0.027**0.103***0.00010.06***0.125***
Temporal GMb0.166***0.255***0.089***0.198***0.285***0.114**
Temporal thickness0.169***
Parietal surface areab0.08***0.211***0.0070.142***0.273***
Parietal GMb0.401***0.506***0.296***0.471***0.574***0.354***
Parietal thickness0.404***0.416***
Occipital surface areab0.011*0.055***
Occipital GMb0.192***0.261***0.123***0.256***0.318***0.185***
Occipital thickness0.389***0.411***

Abbreviations: GM, gray matter; WM, white matter. A non-significant R-square indicates that the regression line is not different from zero.a

All quadratic fits were significantly better than linear fits based on F-ratio derived from “extra sum-of-squares method”. Obtained p-values of the F-ratio indicate if the simpler linear model is really correct, and the chance that randomly obtained data would show a better fit to a more complicated (quadratic) model. Low p-values indicate that the quadratic model is significantly better than the linear model Motulsky and Christopoulos (2004, p. 141).b

Trajectories differed significantly between sexes.*

p < 0.05.**

p < 0.01.***

p < 0.001.

3.4. Relationship between sex, age, cortical thickness and surface area

Besides volume differences, we also examined differences in cortical thickness and surface areas (Table 3 and Fig. 2A–C).

As can be seen in Fig. 2A–C, gray matter volume and cortical thickness followed the same trajectory with increasing age, while there were only small age differences in surface area measurements. These associations are also expressed in terms of partial correlations between gray matter volume, cortical thickness and surface area in Table 5. Thus, age differences were most pronounced for gray matter volume and cortical thickness. Next, we tested whether these age differences were different for the sexes.

Table 5. Partial correlations between lobar gray matter volumes, cortical thickness and surface area.

Partial correlationsa
GM volume–cortical thicknessGM volume–surface areaCortical thickness–surface area
Frontal cortex0.350.690.40
Parietal cortex0.470.720.23
Temporal cortex0.500.720.20
Occipital cortex0.470.83ns
ACC0.200.860.29

Abbreviations: GM, gray matter; ACC, anterior cingulate cortex; IC, intracranial volume; ns, not significant.a

Partial correlations were controlled for IC, sex and age.

All bold findings were significant: p < 0.0001.

Only surface area measurements showed significant sex-by-interactions on a lobar level, except for the occipital lobe and the ACC. Males demonstrated relatively large surface area contractions with increasing age, primarily in frontal, parietal and temporal cortex, while females showed only marginal or no surface contractions in these regions.

In contrast to the surface analyses, the lobar thickness measurement analyses were not sensitive to sex differences. That is, all lobar thickness measurements showed negative associations with increasing age, but no sex effects were found in cortical thickness of the four lobes (Table 3).

4. Discussion

This study aimed to replicate the effects of age, sex, and age by sex interactions on brain volumes, cortical thickness and surface area in a European sample of typically developing children, adolescents and young adults. Specific age and sex differences, and intriguing sex-by-age interactions were observed; these will be described in more detail below.

4.1. Age-effects and brain volumes

Total brain volume decreased with increasing age, and this was accompanied by strong cerebral and cerebellar gray matter volume decreases. In addition, cerebral and cerebellar white matter volume increased with age, replicating earlier findings on gray and white matter during adolescence (Brain Development Cooperative, 2012Giedd et al., 1999). The four lobes and the ACC showed similar gray matter volume decreases with age. The steepness of the slopes differed between lobes. The larger temporal, parietal and frontal gray matter volume loss with increasing age may be associated with more protracted brain maturation; while the moderate occipital volume loss with age could indicate a more mature pattern of development (Casey et al., 2005).

The subcortical brain structures showed less marked age-related trajectories. In fact, with the exception of the thalamus and putamen, all subcortical volumes remained stable with age. This was unexpected, because a subset of studies has found small increases in amygdala and hippocampal volumes during adolescence (Neufang et al., 2009Suzuki et al., 2005). Our findings are partly consistent with findings from Østby et al. (2009) who also reported an age-related decrease in the putamen, although they also reported small to moderate changes in several other subcortical regions. Notably, earlier reports from a longitudinal study including participants between ages 8–30 also showed that bilateral total hippocampal volume remained unchanged (Gogtay et al., 2006), but there were significant age differences in the development of hippocampal subregions (i.e. posterior vs. anterior portions). Despite differences in methodology between studies, these findings show that in this large sample only a subset of age effects could be replicated.

4.2. Sex differences and sex by age interaction effects

The second question concerned whether there were sex differences in brain volumes and whether these interacted with age differences in brain development. We replicated earlier findings on larger intracranial, total brain and total gray and white matter volume in males compared to females. On a subcortical level, we confirmed larger amygdala (Wilke et al., 2007) and thalamus (Sowell et al., 2002) volumes in males irrespective of age. Furthermore, males demonstrated larger gray matter volumes on all lobar levels, after covarying for intracranial volume to correct for differences in head size. The sex difference in hippocampal volumes and slopes could not be replicated (Bramen et al., 2011Giedd et al., 1996Neufang et al., 2009), but see other reports which also failed to replicate these findings (Giedd et al., 1997Gogtay et al., 2006Yurgelun-Todd et al., 2003). Thus, most of the predicted sex differences could be confirmed in the present study, although some inconsistencies remained.

Importantly, no sex-by-age interactions were observed for subcortical volumes. Yet, interesting sex-by-age interactions were found for cortical volumes, with males demonstrating larger total and lobar gray matter volume decreases with age than females. Concomitant white matter volume increase was larger in females. On these measures, all slopes were significantly different between sexes, indicating different developmental trajectories for gray and white matter development, although this should be confirmed in longitudinal research. The majority of these findings were predicted; however, white matter volume increase has been reported to be opposite to our current results, i.e. rapid white matter volume increases in males during adolescence (Giedd et al., 1999Lenroot et al., 2007), or similar growth (Wilke et al., 2007). Prior studies have reported an important role of gonadal hormones on white matter development, a question which should be followed up in future research (Ladouceur et al., 2012Perrin et al., 2008).

The sex-by-age interactions in cortical development were further supported by surface area analyses. Even though surface area measurements overall remained stable across development, there were sex-by-age interactions in the frontal, parietal and temporal lobe showing larger surface area in males between ages 8 and 15. This suggests that in boys there is still prolonged surface area expansion (but faster surface area loss with age), while in girls surface expansion seems to be completed (or slower). However, these findings should be interpreted with caution. Longitudinal research is necessary to determine how (change in) surface area expansion develops with increasing age.

Although volume is mathematically defined as area times height (in this case height can be seen as thickness), prior studies have suggested that cortical surface area and cortical thickness are (genetically) independent, both globally and regionally (Im et al., 2008Pakkenberg and Gundersen, 1997Panizzon et al., 2009Winkler et al., 2009). This is also reflected by the low correlation between surface area and cortical thickness reported here and consistent with the idea that surface area reduction and cortical thinning are independent processes, yet not necessarily biologically independent (Panizzon et al., 2009Winkler et al., 2009Winkler et al., 2010). Moreover, the high correlations between volume and surface area measurements compared to volume and cortical thickness resemble those of a longitudinal twin-study in 9- and 12-year olds (van Soelen et al., 2012) and adolescence (Raznahan et al., 2011). Further evidence for differences in cortical thinning and surface area comes from patient studies. For example, in ADHD and dyslexia a decrease in surface area with intact cortical thickness has been reported (Frye et al., 2010Wolosin et al., 2009). The exact relation between surface area and cortical thickness should be explored in more detail in future studies using longitudinal designs. The radial unit hypothesis of cortical development suggests that the cortical surface area is influenced by the number of columns, whereas cortical thickness is influenced by the number or the size of cells within a column (Rakic, 1988Rakic, 2000). This would suggest cell loss (or shrinkage) within columns (reduction of cortical thickness) while the number of columns remains relatively stable (surface area) during the transition into adulthood.

It must be noted that the exact mechanism behind cortical thinning in adolescence is not well understood. Cortical thinning during puberty and adolescence has been associated with the loss of unneeded connections (synaptic pruning; (Huttenlocher and Dabholkar, 1997)), but pruning cannot fully account for the observed thinning (Paus, 2005). Interestingly, a postmortem study by Petanjek et al. (2011) reported decreases of dendritic spine density and elimination of synaptic spines starting during puberty, continuing well into the third decade of life. The cortical gray matter loss during adolescence is thought to be the result from the encroachment of continued white matter growth which normally extends into the 4th decade (Benes et al., 1994Courchesne et al., 2000Gogtay et al., 2008Gogtay and Thompson, 2010Raz et al., 2005Westlye et al., 2010Yakovlev and Lecours, 1967).

It is evident that longitudinal (replication) studies are needed to further disentangle the relationship and mechanisms of cortical thinning and surface area expansion during adolescence, possibly with additional measures of development besides age (e.g. puberty). In addition, the field may also benefit from the use of (ultra) high field MRI (7 T) to examine these ongoing changes in more detail (Duyn, 2011).

4.3. Limitations and future directions

There are several limitations and questions for future research that remain to be answered. First, we were not able to replicate all of the previously reported age differences in subcortical brain development. One reason for this lack of replication is that the current methodology is not sensitive enough to detect subtle age-related volume differences. Furthermore, it is possible that there are shape changes with different developmental patterns, which cancel out overall volumetric patterns. Shape analysis allows statistical assessment of the subregional anatomy of morphologically changed areas with 3D modeling of these structures, so that contracted or expanded subregions of, for example, the hippocampus can be identified (Patenaude et al., 2011). Second, heterogeneous effects found in cross-sectional studies provide a general estimation of age-related trajectories, but longitudinal studies represent a more stable measurement of individual change over time. However, our cross-sectional findings on cortical brain development mimic those from a recent longitudinal study (Raznahan et al., 2011). Third, pubertal influences may account in part for the heterogeneous effects. We aimed to create a large sample of unique individuals and examine brain morphology from childhood to adulthood. Unfortunately, in these prior neuroimaging studies we did not consistently collect measures of pubertal development. Therefore, we could not analyze effects of pubertal development in this study. In prior studies, sex differences have been reported in the hippocampus, amygdala, and cortical gray matter in more sexually mature adolescents based on physical pubertal maturity and circulating testosterone (for reviews see: Peper and Dahl, 2013Peper et al., 2011). However, studies examining pubertal hormones are still underpowered in relation to sample size. The current study tried to overcome these issues by using a very large sample of children, (pre) adolescents and young adults. In future studies, it will be important to analyze effects of puberty in more detail, including longitudinal measurements of pubertal development.

Another limitation is that it is difficult to estimate the exact trajectories of change. The majority of brain regions showed linear relationships with age, but the large heterogeneity of this sample could mask curvilinear relationships. On the other hand, only large brain regions, i.e. lobar gray matter and total gray and white matter demonstrated significant curvilinear relationships with age. As mentioned earlier, caution must be taken when using quadratic model fits based on cross-sectional data, because the fit may be driven by sample characteristics at start (or end) of sampling age (Fjell et al., 2010). In addition, there may be biological reasons to reject a curvilinear fit. For example, in a normal population it is not expected that (lobar) gray matter volume increases after age 20.

5. Conclusion

In a large European sample, we described the role of developmental differences in a variety of structural brain indices from childhood to early adulthood. Consistent with prior research, we have shown that adolescence is characterized by large individual variance in brain volumes. Furthermore, sex differences were most prominent in cortical surface area measurements, and in gray and white matter, even after controlling for intracranial volume. The functional relevance of these structural findings in understanding typical brain development can be numerous. For example, thinning of frontal and parietal cortices has been linked to more mature brain activation patterns in children and adolescents (Lu et al., 2007Lu et al., 2009). The goal for the future is to combine these studies effectively with behavioral, functional and hormonal measurements to disentangle the structure–function relationship and its development during the transition into adulthood.

Funding

This research was supported by a VIDI grant (no. 91786368) from the Netherlands Organization for Scientific Research (NWO) awarded to the author E.A. Crone.

Conflict of interest

None declared.

Acknowledgement

The authors are thankful to all Brain and Development members and alumni for their help with data acquisition.

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9.Why is 18 the age of adulthood if the brain can take 30 years to mature?Neuroscience research suggests it might be time to rethink our ideas about when exactly a child becomes an adult.

Neuroscience research suggests it might be time to rethink our ideas about when exactly a child becomes an adult.

STEPHEN JOHNSON20 March, 2019

  • Research suggests that most human brains take about 25 years to develop, though these rates can vary among men and women, and among individuals.
  • Although the human brain matures in size during adolescence, important developments within the prefrontal cortex and other regions still take pace well into one’s 20s.
  • The findings raise complex ethical questions about the way our criminal justice systems punishes criminals in their late teens and early 20s.

At what age does someone become an adult? Many might say that the 18th birthday marks the transition from childhood to adulthood. After all, that’s the age at which people can typically join the military and become fully independent in the eyes of the law.

But in light of research showing our brains develop gradually over the course of several decades, and at different paces among individuals, should we start rethinking how we categorize children and adults?

“There isn’t a childhood and then an adulthood,” Peter Jones, who works as part of the epiCentre group at Cambridge University, told the BBC. “People are on a pathway, they’re on a trajectory.”

The prefrontal cortex, cerebellum and reward systems

One key part of that trajectory is the development of the prefrontal cortex, a significant part of the brain, in terms of social interactions, that affects how we regulate emotions, control impulsive behavior, assess risk and make long-term plans. Also important are the brain’s reward systems, which are especially excitable during adolescence. But these parts of the brain don’t stop growing at age 18. In fact, research shows that it can take more than 25 years for them to reach maturity.

The cerebellum also affects our cognitive maturity. But unlike the prefrontal cortex, the development of the cerebellum appears to depend largely on environment, as Dr. Jay Giedd, chair of child psychiatry at Rady Children’s Hospital-San Diego, told PBS:  TOP ARTICLES1/5READ MOREOuter space capitalism: The legal andtechnical challenges facing the private space industry

“Identical twins’ cerebellum are no more alike than non-identical twins. So we think this part of the brain is very susceptible to the environment. And interestingly, it’s a part of the brain that changes most during the teen years. This part of the brain has not finished growing well into the early 20s, even. The cerebellum used to be thought to be involved in the coordination of our muscles. So if your cerebellum is working well, you were graceful, a good dancer, a good athlete.

But we now know it’s also involved in coordination of our cognitive processes, our thinking processes. Just like one can be physically clumsy, one can be kind of mentally clumsy. And this ability to smooth out all the different intellectual processes to navigate the complicated social life of the teen and to get through these things smoothly and gracefully instead of lurching. . . seems to be a function of the cerebellum.”

The effects environment can bring upon the cerebellum even further complicate the question when does a child become an adult, considering the answer might depend on the kind of childhood an individual experienced.

Adulthood and the criminal justice system

These factors of cognitive develop raise many philosophical questions, but perhaps none are as important as those related to how we punish criminal, especially among young men, whose brains develop an average of two years later than women.

“The preponderance of young men engaging in these deadly, evil, and stupid acts of violence may be a result of brains that have yet to fully developed,” Howard Forman, an assistant professor of psychiatry at Albert Einstein College of Medicine, told Business Insider.

So, does that mean young criminals — say, 19- to 25-year-olds — should be receive the same punishment as a 35-year-old who commits the same crime? Both criminals would still be guilty, but each might not necessarily deserve the same punishment, as Laurence Steinberg, a professor of psychology at Temple University, told Newsweek.

“It’s not about guilt or innocence… The question is, ‘How culpable are they, and how do we punish them?'”

After all, most countries have separate juvenile justice systems to deal with children who commit crimes. These separate systems are predicated on the idea that there ought to be a spectrum of culpability that accounts for a criminal’s age. So, if we assume that the importance of age in the eyes of the justice system is based largely on cognitive differences between children and adults, then why shouldn’t that culpability spectrum be modified to better match the science, which clearly shows that 18 is not the age at which the brain is fully matured?

Whatever the answer, society clearly needs some definition of adulthood in order to be able to differentiate between children and adults in order to function smoothly, as Jones suggested to the BBC.

“I guess systems like the education system, the health system and the legal system make it convenient for themselves by having definitions.”

But that doesn’t mean these definitions make sense outside of a legal context.

“What we’re really saying is that to have a definition of when you move from childhood to adulthood looks increasingly absurd,” he said. “It’s a much more nuanced transition that takes place over three decades.” https://bigthink.com/mind-brain/adult-brain?rebelltitem=3#rebelltitem3

10. Men Mature After Women — 11 Years After, To Be Exact — A British Study Reveals

A new British study reveals that men have an 11 year lag behind women when it comes to maturing. According to the study, commissioned by Nickelodeon UK, the average man doesn’t reach full emotional maturity until age 43, while women mature by age 32.

The study was released Monday and conducted just in time for the results to launch with a new Nickelodeon UK comic series called Wendell & Vinnie, which features a 30-year-old bachelor who suddenly becomes the legal guardian of his mature 12-year-old nephew.

“As a man, especially one who works for a children’s channel, the question if men ever reach maturity is one I am well accustomed to,” Tim Patterson, Nickelodeon’s programming director, said.

The study confirmed the suspicions that men mature later than women. In fact, men were almost twice as likely to describe themselves as immature than women were, and one in four men believe they are actively immature. Three out of ten women ended a relationship because they lost patience with their man’s immaturity.   

The male and female perceptions of themselves and each other were alarming. Eight out of ten women believe that men will “never stop being childish.” Women defined the childish acts that bother them most as, passing gas, burping, eating fast food in the last hours of the night, and playing videogames.

Women were twice as likely to experience the feeling that they were the grownup one in their current relationship. Forty-six percent of the female participants studied have had a relationship in which they felt they had to mother their male counterpart. Women claimed they actually had to tell their man to, “act his age” on an average of 14 times a year, more than once a month.

Besides maturity, women yearned for communication. One quarter of women wished their partners would talk about themselves and what they felt more often. Women also felt they were the ones that made all the important decisions in their relationships.

Despite divorce rates, how do couples manage to stay together at all, considering the staggeringly distant ages of maturity? According to the study, 40 percent of people said they thought immaturity was an important component in keeping relationships fun and fresh, and 33 percent said the immaturity helped when bonding with kids.

“However as the characters show in our new program Wendell & Vinnie, a difference in maturity between two people makes for an amusing partnership,” Patterson pointed out.

The study also exposed ” Men’s Top 30 Maturity Failings — As Experienced By Women” The top ten are listed as the following:

  1. Finding their own passing of gas and burps amusing
  2. Eating fast food at 2:00 AM
  3. Playing videogames
  4. Driving too fast
  5. Finding rude words amusing
  6. Driving with loud music
  7. Playing practical jokes
  8. Trying to beat children at games and sports
  9. Staying silent during an argument
  10. Not being able to cook simple meals

These findings can be re-affirmed with previous studies conducted on the prefrontal cortex (PFC), the part of the brain just behind the forehead that is responsible for a lot of men’s shortcomings. The PFC has been referred to as the, “CEO of the brain” as well as the “mother.” This brain region controls cognitive analysis and abstract thought, as well as corrective behavior in social situations.

Magnetic resonance imaging (MRI) studies have made it possible for scientists to watch the rate at which the PFC matures, and have discovered the male brain doesn’t fully develop until age 25. Meanwhile, women experience a maturity rate of 21 years-old.

MRIs have revealed the brain has a developmental process that tends to occur from the back of the brain to the front, which explains why the prefrontal cortex develops last. With an immature PFC, even though the person can intellectualize dangerous situation or poor behavior, they may engage regardless. The slowness of a man’s brain maturation can explain the list of maturity failings, and their own recognition and admittance of those failings. https://www.medicaldaily.com/men-mature-after-women-11-years-after-be-exact-british-study-reveals-246716


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