Establishing a link between sex-related differences in the structural connectome and behaviour

Sex and gender differences in cognitive resilience to aging and Alzheimer's disease

Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders

BACKGROUND

Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics.

METHODS

We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV).

RESULTS

Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction.

CONCLUSIONS

Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Residual graph transformer for autism spectrum disorder prediction

Brain functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been in vogue to predict Autism Spectrum Disorder (ASD), which is a neuropsychiatric disease up the plight of locating latent biomarkers for clinical diagnosis. Albeit massive endeavors have been made, most studies are fed up with several chronic issues, such as the intractability of harnessing the interaction flourishing within brain regions, the astriction of representation due to vanishing gradient within deeper network architecture, and the poor interpretability leading to unpersuasive diagnosis. To ameliorate these issues, a FC-learned Residual Graph Transformer Network, namely RGTNet, is proposed. Specifically, we design a Graph Encoder to extract temporal-related features with long-range dependencies, from which interpretable FC matrices would be modeled. Besides, the residual trick is introduced to deepen the GCN architecture, thereby learning the higher-level information. Moreover, a novel Graph Sparse Fitting followed by weighted aggregation is proposed to ease dimensionality explosion. Empirically, the results on two types of ABIDE data sets demonstrate the meliority of RGTNet. Notably, the achieved ACC metric reaches 73.4%, overwhelming most competitors with merely 70.9% on the AAL atlas using a five-fold cross-validation policy. Moreover, the investigated biomarkers concord closely with the authoritative medical knowledge, paving a viable way for ASD-clinical diagnosis. Our code is available at https://github.com/CodeGoat24/RGTNet.

Sex and Gender in Population Neuroscience

To understand psychiatric and neurological disorders and the structural and functional properties of the human brain, it is essential to consider the roles of sex and gender. In this chapter, I first define sex and gender and describe studies of sex differences in non-human animals. In humans, I describe the sex differences in behavioral and clinical phenotypes and neuroimaging-derived phenotypes, including whole-brain measures, regional subcortical and cortical measures, and structural and functional connectivity. Although structural whole-brain sex differences are large, regional effects (adjusting for whole-brain volumes) are typically much smaller and often fail to replicate. Nevertheless, while an individual neuroimaging feature may have a small effect size, aggregating them in a "maleness/femaleness" score or machine learning multivariate paradigm may prove to be predictive and informative of sex- and gender-related traits. Finally, I conclude by summarizing emerging investigations of gender norms and gender identity and provide methodological recommendations to incorporate sex and gender in population neuroscience research.

Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates

Introduction

Although the growth of digital tools for cognitive health assessment, there's a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify clinical risk factors associated with these measures.

Methods

The sample included 932 cognitively intact participants from the Framingham Heart Study, who completed at least one DANA task. Participants were stratified into subgroups based on sex and three age groups. Reference values were established for digital cognitive assessments within each age group, divided by sex, at the 2.5th, 25th, 50th, 75th, and 97.5th percentile thresholds. To validate these values, 57 cognitively intact participants from Boston University Alzheimer's Disease Research Center were included. Associations between 19 clinical risk factors and these digital neuropsychological measures were examined by a backward elimination strategy.

Results

Age- and sex-specific reference values were generated for three DANA tasks. Participants below 60 had median response times for the Go-No-Go task of 796 ms (men) and 823 ms (women), with age-related increases in both sexes. Validation cohort results mostly aligned with these references. Different tasks showed unique clinical correlations. For instance, response time in the Code Substitution task correlated positively with total cholesterol and diabetes, but negatively with high-density lipoprotein and low-density lipoprotein cholesterol levels, and triglycerides.

Discussion

This study established and validated reference values for digital neuropsychological measures of DANA in cognitively intact white participants, potentially improving their use in future clinical studies and practice.

Sex-dependent differences in connectivity patterns are related to episodic memory recall

Previous studies have shown that females typically outperform males on episodic memory tasks. In this study, we investigated if (1) there are differences between males and females in their connectome characteristics, (2) if these connectivity patterns are associated with memory performance, and (3) if these brain connectome characteristics contribute to the differences in episodic memory performance between sexes. In a sample of 655 healthy young subjects (n = 391 females; n = 264 males), we derived brain network characteristics from diffusion-weighted imaging (DWI) data using models of crossing fibers within each voxel of the brain and probabilistic tractography (graph strength, shortest path length, global efficiency, and weighted transitivity). Group differences were analysed with linear models and mediation analyses were used to explore how connectivity patterns might relate to sex-dependent differences in memory performance. Our results show significant sex-dependent differences in weighted transitivity (d = 0.42), with males showing higher values. Further, we observed a negative association between weighted transitivity and memory performance (r = -0.12). Finally, these distinct connectome characteristics partially mediated the observed differences in memory performance (effect size of the indirect effect r = 0.02). Our findings indicate a higher interconnectedness in females compared to males. Additionally, we demonstrate that the sex-dependent differences in episodic memory performance can be partially explained by the differences in this connectome measure. These results further underscore the importance of sex-dependent differences in brain connectivity and their impact on cognitive function.

PLSNet: Position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting

Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of modeling the sophisticated brain neuronal connectivity; (2) the mismatch of identically graph node setup to the variations of different brain regions; (3) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (4) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a position-aware graph-convolution-network-based model, namely PLSNet, with superior accuracy and compelling built-in interpretability for ASD diagnosis. Specifically, a time-series encoder is designed for context-rich feature extraction, followed by a function connectivity generator to model the correlation with long range dependencies. In addition, to discriminate the brain nodes with different locations, the position embedding technique is adopted, giving a unique identity to each graph region. We then embed a rarefying method to sift the salient nodes during message diffusion, which would also benefit the reduction of the dimensionality complexity. Extensive experiments conducted on Autism Brain Imaging Data Exchange demonstrate that our PLSNet achieves state-of-the-art performance. Notably, on CC200 atlas, PLSNet reaches an accuracy of 76.4% and a specificity of 78.6%, overwhelming the previous state-of-the-art with 2.5% and 6.5% under five-fold cross-validation policy. Moreover, the most salient brain regions predicted by PLSNet are closely consistent with the theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Our code is available at https://github.com/CodeGoat24/PLSNet.

Sex/Gender Differences in Brain Lateralisation and Connectivity

There is now a significant body of literature concerning sex/gender differences in the human brain. This chapter will critically review and synthesise key findings from several studies that have investigated sex/gender differences in structural and functional lateralisation and connectivity. We argue that while small, relative sex/gender differences reliably exist in lateralisation and connectivity, there is considerable overlap between the sexes. Some inconsistencies exist, however, and this is likely due to considerable variability in the methodologies, tasks, measures, and sample compositions between studies. Moreover, research to date is limited in its consideration of sex/gender-related factors, such as sex hormones and gender roles, that can explain inter-and inter-individual differences in brain and behaviour better than sex/gender alone. We conclude that conceptualising the brain as 'sexually dimorphic' is incorrect, and the terms 'male brain' and 'female brain' should be avoided in the neuroscientific literature. However, this does not necessarily mean that sex/gender differences in the brain are trivial. Future research involving sex/gender should adopt a biopsychosocial approach whenever possible, to ensure that non-binary psychological, biological, and environmental/social factors related to sex/gender, and their interactions, are routinely accounted for.

Breaking the binary: Gender versus sex analysis in human brain imaging

Despite decades of pursuit, human brain imaging has yet to uncover clear neural correlates of male-female behavioral differences. Given that such behavior does not always align with sex categories, we argue that neuroimaging research may find more success by partitioning subjects along nonbinary gender attributes in addition to sex. We review the handful of studies that have done this, several of which find as good or better association between brain measures and "gender" as they do with "sex." Recent advances in operationalizing "gender" as a multidimensional variable should facilitate such studies, along with discovery-based approaches that mine brain imaging data for gender-associated attributes, independent of sex.

Gyral and sulcal connectivity in the human cerebral cortex

The rapid evolution of image acquisition and data analytic methods has established in vivo whole-brain tractography as a routine technology over the last 20 years. Imaging-based methods provide an additional approach to classic neuroanatomical studies focusing on biomechanical principles of anatomical organization and can in turn overcome the complexity of inter-individual variability associated with histological and tractography studies. In this work we propose a novel, reliable framework for determining brain tracts resolving the anatomical variance of brain regions. We distinguished 4 region types based on anatomical considerations: (i) gyral regions at borders between cortical communities; (ii) gyral regions within communities; (iii) sulcal regions at invariant locations across subjects; and (iv) other sulcal regions. Region types showed strikingly different anatomical and connection properties. Results allowed complementing the current understanding of the brain’s communication structure with a model of its anatomical underpinnings.

Transwoman Elite Athletes: Their Extra Percentage Relative to Female Physiology

There is increasing debate as to whether transwoman athletes should be included in the elite female competition. Most elite sports are divided into male and female divisions because of the greater athletic performance displayed by males. Without the sex division, females would have little chance of winning because males are faster, stronger, and have greater endurance capacity. Male physiology underpins their better athletic performance including increased muscle mass and strength, stronger bones, different skeletal structure, better adapted cardiorespiratory systems, and early developmental effects on brain networks that wires males to be inherently more competitive and aggressive. Testosterone secreted before birth, postnatally, and then after puberty is the major factor that drives these physiological sex differences, and as adults, testosterone levels are ten to fifteen times higher in males than females. The non-overlapping ranges of testosterone between the sexes has led sports regulators, such as the International Olympic Committee, to use 10 nmol/L testosterone as a sole physiological parameter to divide the male and female sporting divisions. Using testosterone levels as a basis for separating female and male elite athletes is arguably flawed. Male physiology cannot be reformatted by estrogen therapy in transwoman athletes because testosterone has driven permanent effects through early life exposure. This descriptive critical review discusses the inherent male physiological advantages that lead to superior athletic performance and then addresses how estrogen therapy fails to create a female-like physiology in the male. Ultimately, the former male physiology of transwoman athletes provides them with a physiological advantage over the cis-female athlete.

Laterality and Sex Differences of Human Lateral Habenula Afferent and Efferent Fiber Tracts

Introduction

The lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway.

Methods

Multi-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target.

Results

We were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04).

Conclusion

We developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.

Neurocognitive and psychiatric post-coronavirus disease 2019 conditions: pathogenic insights of brain dysfunction following severe acute respiratory syndrome coronavirus 2 infection

PURPOSE OF REVIEW

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronavirus disease 2019 (COVID-19), can trigger a myriad of neuropsychiatric manifestations. As a 2-year-old disease (at the writing of this manuscript), its long-term cognitive and neuropsychiatric implications, known as post-COVID-19 conditions, are incompletely recognized and mechanistically obscure.

RECENT FINDINGS

Fatigue, anxiety, depression, posttraumatic stress disorder, and cognitive dysfunction are reported more frequently in COVID-19 survivors than in matching, non-COVID-19 population. Risk factors are unclear, including comorbidities, age at COVID-19 onset, or disease severity; women, however, have been reported to be at increased risk than men. Although the frequency of these symptoms decreases over time, at least one in five will have persistent cognitive and neuropsychiatric manifestations one year after recovering from COVID-19.

SUMMARY

Neurocognitive and psychiatric post-COVID-19 long-term conditions are frequent and complex multifactorial sequelae. Several acute and chronic factors such as hypoxemia, cerebral thrombotic and inflammatory endothelial damage, and disruption of the blood-brain barrier (leading to parenchymal translocation of pro-inflammatory molecules, cytokines, and cytotoxic T lymphocytes) are involved, leading to microglial activation and astrogliosis. As an evolving topic, evidence derived from prospective studies will expand our understanding of post-COVID-19 these long-term outcomes.

Resemblance Reporting on Children: Sisters Are More Proactive than Brothers

The asymmetric grandparental investment in humans may ultimately be explained by the paternity uncertainty hypothesis. The proximate mechanisms leading to grandparental bias in investment in grandchildren are, however, unclear. In a study of 233 males and females with an opposite sexed sibling, we examined whether comments on resemblance regarding one’s own child, or one’s sibling’s child, changed in frequency after both siblings became parents. We found that comments among siblings on resemblance of children occurred more frequently after both became parents, compared to when only one of the siblings had children, suggesting that resemblance descriptions may become more important after both siblings have children. Furthermore, and in line with the suggestion that mothers may mentally exploit the alloparenting environment by holding a stronger belief about resemblance, brothers reported that their sisters commented on resemblance concerning their own child more often and more intensely. Additionally, sisters corroborated this finding by self-reporting that they were the most proactive during resemblance descriptions of their brothers’ child. Thus, sisters might, through more frequent voicing of stronger opinions on parent–child resemblance than their brothers, influence alloparents’ perception of resemblance to their children and thus influence alloparental investments.

Brain Sex in Transgender Women Is Shifted towards Gender Identity

Transgender people report discomfort with their birth sex and a strong identification with the opposite sex. The current study was designed to shed further light on the question of whether the brains of transgender people resemble their birth sex or their gender identity. For this purpose, we analyzed a sample of 24 cisgender men, 24 cisgender women, and 24 transgender women before gender-affirming hormone therapy. We employed a recently developed multivariate classifier that yields a continuous probabilistic (rather than a binary) estimate for brains to be male or female. The brains of transgender women ranged between cisgender men and cisgender women (albeit still closer to cisgender men), and the differences to both cisgender men and to cisgender women were significant (p = 0.016 and p < 0.001, respectively). These findings add support to the notion that the underlying brain anatomy in transgender people is shifted away from their biological sex towards their gender identity.

Gender Differences in Hippocampal/Parahippocampal Functional Connectivity Network in Patients Diagnosed with Chronic Insomnia Disorder

BACKGROUND

Gender differences in hippocampal and parahippocampal gyrus (HIP/PHG) volumes have been reported in sleep disorders. Therefore, this study investigated the moderating effect of gender on the relationship between chronic insomnia disorder (CID) and the HIP/PHG functional connectivity (FC) network.

METHODS

For this study, 110 patients diagnosed with CID (43 men and 67 women) and 60 matched good sleep control (GSC) (22 men and 38 women) were recruited. These participants underwent resting-state functional magnetic resonance imaging scans, after which a 2 × 2 (diagnosis × gender) analysis of variance was used to detect the main and interactive effect of insomnia and gender on their HIP/PHG FC networks.

RESULTS

Although the main effect of insomnia on the HIP FC network was observed in the bilateral cerebellar tonsil, superior frontal gyrus, and the medial orbitofrontal cortex, effects on the PHG FC network were observed in the bilateral HIP and amygdala. In contrast, the main effect of gender on the HIP FC network was observed in the right cerebellum posterior lobe, the dorsolateral prefrontal cortex (DLPFC), and the supplemental motor area. Of note, the interactive effect of both insomnia and gender was observed in FCs between the right HIP and the dorsal anterior cingulate cortex, and then between the right PHG and DLPFC. Moreover, the FC between the right PHG and left DLPFC was positively associated with anxiety scores in the female patients with CID.

CONCLUSION

Our study identified that gender differences in brain connectivity existed between the HIP/PHG and executive control network in patients diagnosed with CID, these results will eventually extend our understanding of the important role that gender plays in the pathophysiology of CID.

Sex difference in neural substrates underlying the association between trait self-control and overeating in the COVID-19 pandemic

During the COVID-19 pandemic, people are at risk of developing disordered eating behaviors. The present study utilized resting-state functional magnetic resonance imaging (fMRI) to examine how trait self-control and its neural mechanisms predict overeating tendencies in young adults during the pandemic. Data on trait self-control, the amplitude of low-frequency fluctuation (ALFF), and resting-state functional connectivity (RSFC) were collected before COVID-19 (September 2019, T1), and data on overeating were collected during COVID-19 (February 2020, T2). Whole-brain regression analyses (N = 538) revealed that higher trait self-control was associated with higher ALFF in the right dorsolateral and ventrolateral prefrontal cortex (DLPFC, VLPFC) and the left anterior insula, and lower ALFF in the left fusiform gyrus and precuneus. With the DLPFC, fusiform gyrus and precuneus as seed regions, trait selfcontrol was associated with decreased connectivity of the orbitofrontal cortex, anterior cingulate cortex, temporal pole, and insula, and increased connectivity between the right VLPFC and anterior cerebellum. Longitudinal mediation models showed that trait self-control (T1) negatively predicted overeating (T2), and the mediating effects of the fusiform gyrus, DLPFC, and VLPFC were moderated by sex. The present study reveals that the brain networks for trait self-control are mainly involved in cognitive and executive control and incentive and emotional processing, demonstrating the longitudinal benefits of trait self-control in alleviating disordered eating behaviors during the pandemic. Sex differences in the neural substrates underlie this association. These finding may have implications of the interventions for behavioral maladjustment.

Are Sex Differences in Human Brain Structure Associated With Sex Differences in Behavior?

On average, men and women differ in brain structure and behavior, raising the possibility of a link between sex differences in brain and behavior. But women and men are also subject to different societal and cultural norms. We navigated this challenge by investigating variability of sex-differentiated brain structure within each sex. Using data from the Queensland Twin IMaging study (n = 1,040) and Human Connectome Project (n = 1,113), we obtained data-driven measures of individual differences along a male-female dimension for brain and behavior based on average sex differences in brain structure and behavior, respectively. We found a weak association between these brain and behavioral differences, driven by brain size. These brain and behavioral differences were moderately heritable. Our findings suggest that behavioral sex differences are, to some extent, related to sex differences in brain structure but that this is mainly driven by differences in brain size, and causality should be interpreted cautiously.

A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets, with application to adolescent brain development and osteoporosis

In recent years, a comprehensive study of complex disease with multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to explore complementary information of disease, analysis of multi-view data with complex interactions is challenging for a deeper and holistic understanding of biological systems. In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets (GKMAHCE). This generalized semi-parametric (a mixed-effect linear model) approach includes the marginal and joint Hadamard product of features from different views of data. The proposed kernel machine approach considers multi-view data as predictor variables to allow a more thorough and comprehensive modeling of a complex trait. We applied GKMAHCE approach to both synthesized datasets and real multi-view datasets from adolescent brain development and osteoporosis study. Our experiments demonstrate that the proposed method can effectively identify higher-order composite effects and suggest that corresponding features (genes, region of interests, and chemical taxonomies) function in a concerted effort. We show that the proposed method is more generalizable than existing ones. To promote reproducible research, the source code of the proposed method is available at.

Development of sex differences in the human brain

Sex differences in brain anatomy have been described from early childhood through late adulthood, but without any clear consensus among studies. Here, we applied a machine learning approach to estimate 'Brain Sex' using a continuous (rather than binary) classifier in 162 boys and 185 girls aged between 5 and 18 years. Changes in the estimated sex differences over time at different age groups were subsequently calculated using a sliding window approach. We hypothesized that males and females would differ in brain structure already during childhood, but that these differences will become even more pronounced with increasing age, particularly during adolescence. Overall, the classifier achieved a good performance, with an accuracy of 80.4% and an AUC of 0.897 across all age groups. Assessing changes in the estimated sex with age revealed a growing difference between the sexes with increasing age. That is, the very large effect size of d = 1.2 which was already evident during childhood increased even further from age 11 onward, and eventually reached an effect size of d = 1.6 at age 17. Altogether these findings suggest a systematic sex difference in brain structure already during childhood, and a subsequent increase of this difference during adolescence.

Differential Age Trajectories of White Matter Changes Between Sexes Correlate with Cognitive Performances

Background: Aging is accompanied by a gradual deterioration in multiple cognitive abilities and brain structures. Both cognitive function and white matter (WM) structure are found to be associated with neurodegeneration diseases and correlated with sex during aging. However, it is still unclear whether the brain structural change could be attributable to sex, and how sex would affect cognitive performances during aging. Materials and Methods: Diffusion magnetic resonance imaging (MRI) scans were performed on 1127 healthy participants (age range: 21-89) at a single site. The age trajectories of the WM tract microstructure were delineated to estimate the turning age and changing rate between sexes. The canonical correlation analysis and moderated mediation analysis were used to examine the relationship between sex-linked WM tracts and cognitive performances. Results: The axon intactness and demyelination of sex-linked tracts during aging were multifaceted. Sex-linked tracts in females peak around 5 years later than those in males but change significantly faster after the turning age. Projection and association tracts (e.g., corticospinal tracts and parahippocampal cingulum) contributed to a significant decrease in visuospatial functions (VS) and executive functions (E). We discovered that there is a stronger indirect effect of sex-linked tracts on cognitive functions in females than in males. Conclusion: Our findings suggest that the vulnerable projection and association tracts in females may induce negative impacts on integrating multiple functions, which results in a faster decrease in VS and E.

Considering Sex as a Biological Variable in Basic and Clinical Studies: An Endocrine Society Scientific Statement

In May 2014, the National Institutes of Health (NIH) stated its intent to "require applicants to consider sex as a biological variable (SABV) in the design and analysis of NIH-funded research involving animals and cells." Since then, proposed research plans that include animals routinely state that both sexes/genders will be used; however, in many instances, researchers and reviewers are at a loss about the issue of sex differences. Moreover, the terms sex and gender are used interchangeably by many researchers, further complicating the issue. In addition, the sex or gender of the researcher might influence study outcomes, especially those concerning behavioral studies, in both animals and humans. The act of observation may change the outcome (the "observer effect") and any experimental manipulation, no matter how well-controlled, is subject to it. This is nowhere more applicable than in physiology and behavior. The sex of established cultured cell lines is another issue, in addition to aneuploidy; chromosomal numbers can change as cells are passaged. Additionally, culture medium contains steroids, growth hormone, and insulin that might influence expression of various genes. These issues often are not taken into account, determined, or even considered. Issues pertaining to the "sex" of cultured cells are beyond the scope of this Statement. However, we will discuss the factors that influence sex and gender in both basic research (that using animal models) and clinical research (that involving human subjects), as well as in some areas of science where sex differences are routinely studied. Sex differences in baseline physiology and associated mechanisms form the foundation for understanding sex differences in diseases pathology, treatments, and outcomes. The purpose of this Statement is to highlight lessons learned, caveats, and what to consider when evaluating data pertaining to sex differences, using 3 areas of research as examples; it is not intended to serve as a guideline for research design.

Association of serum microcystin levels with neurobehavior of school-age children in rural area of Southwest China: A cross-sectional study

To investigate whether microcystin-LR (MC-LR) influences children's cognitive function and memory ability, we measured serum MC-LR and whole blood lead levels in 697 primary students, and collected their academic and neurobehavioral test scores. The median of serum MC-LR levels was 0.80 µg/L (the value below the limit of detection to 1.67 µg/L). The shapes of the associations of serum MC-LR levels (cut-point: 0.95 µg/L) with scores on academic achievements, digit symbol substitution test and long-term memory test were parabolic curves. Logistic regression analysis showed that MC-LR at concentrations of 0.80-0.95 µg/L was associated with the increased probability of higher achievements on academic achievements [odds ratio (OR) = 2.20, 95% confidence interval (CI): 1.28-3.79], and also with scores on digit symbol substitution test (OR = 1.73, 95% CI: 1.05-2.86), overall memory quotient (OR = 2.27, 95% CI: 1.21-4.26), long-term memory (OR = 1.85, 95% CI: 1.01-3.38) and short-term memory (OR = 2.13, 95% CI: 1.14-3.98) after adjustment for confounding factors. Antagonism of MC-LR and lead on long-term memory was observed (synergism index = 0.15, 95% CI: 0.03-0.74). In conclusion, serum MC-LR at concentrations of 0.80-0.95 µg/L was positively associated with higher scores on cognitive and neurobehavioral tests, and antagonism between MC-LR at concentrations of 0.80-1.67 µg/L and lead exposure was obviously observed on long-term memory in children. Concerning that MC-LR is a neurotoxin at high doses, our observation is interesting and need further investigation.

Dump the "dimorphism": Comprehensive synthesis of human brain studies reveals few male-female differences beyond size

With the explosion of neuroimaging, differences between male and female brains have been exhaustively analyzed. Here we synthesize three decades of human MRI and postmortem data, emphasizing meta-analyses and other large studies, which collectively reveal few reliable sex/gender differences and a history of unreplicated claims. Males' brains are larger than females' from birth, stabilizing around 11 % in adults. This size difference accounts for other reproducible findings: higher white/gray matter ratio, intra- versus interhemispheric connectivity, and regional cortical and subcortical volumes in males. But when structural and lateralization differences are present independent of size, sex/gender explains only about 1% of total variance. Connectome differences and multivariate sex/gender prediction are largely based on brain size, and perform poorly across diverse populations. Task-based fMRI has especially failed to find reproducible activation differences between men and women in verbal, spatial or emotion processing due to high rates of false discovery. Overall, male/female brain differences appear trivial and population-specific. The human brain is not "sexually dimorphic."

Sex differences in health and disease: A review of biological sex differences relevant to cancer with a spotlight on glioma

The influence of biological sex differences on human health and disease, while being increasingly recognized, has long been underappreciated and underexplored. While humans of all sexes are more alike than different, there is evidence for sex differences in the most basic aspects of human biology and these differences have consequences for the etiology and pathophysiology of many diseases. In a disease like cancer, these consequences manifest in the sex biases in incidence and outcome of many cancer types. The ability to deliver precise, targeted therapies to complex cancer cases is limited by our current understanding of the underlying sex differences. Gaining a better understanding of the implications and interplay of sex differences in diseases like cancer will thus be informative for clinical practice and biological research. Here we review the evidence for a broad array of biological sex differences in humans and discuss how these differences may relate to observed sex differences in various diseases, including many cancers and specifically glioblastoma. We focus on areas of human biology that play vital roles in healthy and disease states, including metabolism, development, hormones, and the immune system, and emphasize that the intersection of sex differences in these areas should not go overlooked. We further propose that mathematical approaches can be useful for exploring the extent to which sex differences affect disease outcomes and accounting for those in the development of therapeutic strategies.

Estrogen-dependent hippocampal wiring as a risk factor for age-related dementia in women

Women are more prone than men to develop age-related dementia, such as Alzheimer's disease (AD). This has been linked to the marked decrease in circulating estrogens during menopause. This review proposes to change this perspective and consider women's vulnerability to developing AD as a consequence of sex differences in the neurobiology of memory, focusing on the hippocampus. The hippocampus of cognitively impaired subjects tends to shrink with age; however, in many cases, this can be prevented by exercise or cognitive training, suggesting that if you do not use the hippocampus you lose it. We will review the developmental trajectory of sex steroids-regulated differences on the hippocampus, proposing that the overall shaping action of sex-steroids results in a lower usage of the hippocampus in females, which in turn makes them more vulnerable to the effects of ageing, the "network fragility hypothesis". To explain why women rely less on hippocampus-dependent strategies, we propose a "computational hypothesis" that is based on experimental evidence suggesting that the direct effects of estrogens on hippocampal synaptic and structural plasticity during the estrous-cycle confers instability to the memory-dependent hippocampal network. Finally, we propose to counteract AD with training and/or treatments, such as orienteering, which specifically favour the use of the hippocampus.

Early learning in the common marmoset (Callithrix jacchus): Behavior in the family group is related to preadolescent cognitive performance

Early environment can have a major impact on development, with family life known to play an important role. Longitudinal studies can therefore help increase our understanding of variance in cognitive abilities in young animals, as well as over time. We followed 22 marmosets (11 male and 11 female) from infancy through to early adolescence. At 3 months old, the marmosets were trained to reliably touch a rewarded stimulus. At 5 months, behavior was observed within the natal group. At 9 months, the marmosets were given a visual discrimination task to assess learning ability. Mann-Whitney U tests found no sex or family size differences in number of errors at 3 or 9 months. While no significant relationships were found between behavior in the family and learning at 3 months, significant negative correlations were found between duration spent in locomotion and learning errors (p = .05), as well as between frequency of calm vocalizations and learning errors (p = .001) at 9 months. A U-shape curve was found between amount of social play and learning at 9 months. Positive family interactions, including moderate amounts of play, as well as calm individual behavior, may therefore be important in learning. This study sheds light on cognitive development in much younger marmosets than previously studied, and helps increase understanding of how individual differences in learning may arise.

Gender Differences Are Encoded Differently in the Structure and Function of the Human Brain Revealed by Multimodal MRI

Despite widely reported gender differences in both brain structure and brain function, very few studies have examined the relationship between the structural differences and the functional differences between genders. Here, different imaging measures including both structural [i.e., gray matter volume (GMV)] and functional [i.e., regional homogeneity (ReHo) and functional connectivity (FC)] measures were employed to detect the gender differences in the human brain based on univariate and multivariate approaches with a sample of 290 healthy adults (155 females). The univariate analyses revealed that gender differences were detected in both structural (i.e., GMV) and functional (ReHo or FC) imaging measures, mainly manifested as greater values in females than in males in regions of the frontal, parietal, occipital lobes and cerebellum. Importantly, there was little overlap between the differences detected in GMV and those detected in ReHo and FC, and their differences between genders were not correlated with each other. The multivariate pattern analyses revealed that each of these measures had discriminative power to successfully distinguish between genders (classification accuracy: 94.3%, 90.73%, and 83.89% for GMV, ReHo, and FC, respectively) and their combination further improved the classification performance (96.6%). Our results suggest that gender differences are encoded in both brain structure and brain function, but in different manners. The finding of different and complementary information contained in structural and functional differences between genders highlights the complex relationship between brain structure and function, which may underlie the complex nature of gender differences in behavior.

Connectomic consistency: a systematic stability analysis of structural and functional connectivity

OBJECTIVE

Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans.

APPROACH

In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes.

MAIN RESULTS

Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated.

SIGNIFICANCE

Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.

In vivo network models identify sex differences in the spread of tau pathology across the brain

Introduction

We examined networks of tau connectivity between brain regions based on correlations of their [¹⁸F]flortaucipir positron emission tomography (PET) uptake to evaluate sex‐specific differences in brain‐wide tau propagation.

Methods

PET data of clinically normal and mild cognitive impairment (MCI) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to examine differences in network architectures across the groups.

Results

The tau‐based network architecture resembled progression of tauopathy from Braak stage I to VI regions. Compared to men, women had higher network density and an increased number of direct regional connections in co‐occurrence with increased brain‐wide tau burden, particularly at MCI. Several regions, including superior parietal lobe and parahippocampus served as connecting bridges between communities at different Braak stages.

Discussion

Network characteristics in women may favor an accelerated brain‐wide tau spread leading to a higher tau burden in women than men with MCI with implications for the greater female preponderance in Alzheimer's disease diagnosis.

Aberrant global and local efficiency of the executive subnetwork in essential tremor

This study aims to use probabilistic tractography to ascertain the global (GE) and local efficiency (LE) of the executive subnetwork in essential tremor (ET). Significantly lower GE of the whole executive subnetwork and lower LE of the left rostral middle frontal gyrus, frontal pole, inferior frontal gyrus, bilateral anterior cingulate cortex, and medial orbitofrontal cortex. These results imply ineffective and inadequate communication of the executive subnetwork, and may be causally associated with the executive dysfunction observed in ET.

The oblique effect: The relationship between profiles of visuospatial preference, cognition, and brain connectomics in older adults

The oblique effect (OE) describes the visuospatial advantage for identifying stimuli oriented horizontally or vertically rather than diagonally; little is known about brain aging and the OE. We investigated this relationship using the Judgment of Line Orientation (JLO) in 107 older adults (∼age = 67.8 ± 6.6; 51% female) together with neuropsychological tests of executive functioning (EF), attention/information processing (AIP), and neuroimaging. Only JLO lines falling between 36-54° or 126-144° were considered oblique. To quantify the oblique effect, we calculated z-scores for oblique errors (zOblique = #oblique errors/#oblique lines), and similarly, horizontal + vertical line errors (zHV), and a composite measure of oblique relative to HV errors (zOE). Composite z-scores of EF and AIP reflected domains associated with JLO performance. Graph theory analysis integrated T1-derived volumetry and diffusion MRI-derived white matter tractography into connectivity matrices analyzed for select network properties. Participants produced more zOblique than zHV errors (p < 0.001). Age was not associated with zOE adjusting for sex, education, and MMSE. Similarly adjusted linear regression models revealed that lower EF was associated with a larger oblique effect (p < 0.001). Modular analyses of neural connectivity revealed a differential patterns of network affiliation that varied by high versus low group status determined via median split of zOblique and zHV errors, separately. Older adults exhibit the oblique effect and it is associated with specific cognitive processes and regional brain networks that may facilitate future investigations of visuospatial preference in aging.

Functional Geometry of Human Connectomes

Mapping the brain imaging data to networks, where nodes represent anatomical brain regions and edges indicate the occurrence of fiber tracts between them, has enabled an objective graph-theoretic analysis of human connectomes. However, the latent structure on higher-order interactions remains unexplored, where many brain regions act in synergy to perform complex functions. Here we use the simplicial complexes description of human connectome, where the shared simplexes encode higher-order relationships between groups of nodes. We study consensus connectome of 100 female (F-connectome) and of 100 male (M-connectome) subjects that we generated from the Budapest Reference Connectome Server v3.0 based on data from the Human Connectome Project. Our analysis reveals that the functional geometry of the common F&M-connectome coincides with the M-connectome and is characterized by a complex architecture of simplexes to the 14th order, which is built in six anatomical communities, and linked by short cycles. The F-connectome has additional edges that involve different brain regions, thereby increasing the size of simplexes and introducing new cycles. Both connectomes contain characteristic subjacent graphs that make them 3/2-hyperbolic. These results shed new light on the functional architecture of the brain, suggesting that insightful differences among connectomes are hidden in their higher-order connectivity.

Beyond a Binary Classification of Sex: An Examination of Brain Sex Differentiation, Psychopathology, and Genotype

OBJECTIVE

Sex differences in the brain are traditionally treated as binary. We present new evidence that a continuous measure of sex differentiation of the brain can explain sex differences in psychopathology. The degree of sex-differentiated brain features (ie, features that are more common in one sex) may predispose individuals toward sex-biased psychopathology and may also be influenced by the genome. We hypothesized that individuals with a female-biased differentiation score would have greater female-biased psychopathology (internalizing symptoms, such as anxiety and depression), whereas individuals with a male-biased differentiation score would have greater male-biased psychopathology (externalizing symptoms, such as disruptive behaviors).

METHOD

Using the Philadelphia Neurodevelopmental Cohort database acquired from database of Genotypes and Phenotypes, we calculated the sex differentiation measure, a continuous data-driven calculation of each individual's degree of sex-differentiating features extracted from multimodal brain imaging data (magnetic resonance imaging [MRI] /diffusion MRI) from the imaged participants (n = 866, 407 female and 459 male).

RESULTS

In male individuals, higher differentiation scores were correlated with higher levels of externalizing symptoms (r = 0.119, p = .016). The differentiation measure reached genome-wide association study significance (p < 5∗10^-8) in male individuals with single nucleotide polymorphisms Chromsome5:rs111161632:RASGEF1C and Chromosome19:rs75918199:GEMIN7, and in female individuals with Chromosome2:rs78372132:PARD3B and Chromosome15:rs73442006:HCN4.

CONCLUSION

The sex differentiation measure provides an initial topography of quantifying male and female brain features. This demonstration that the sex of the human brain can be conceptualized on a continuum has implications for both the presentation of psychopathology and the relation of the brain with genetic variants that may be associated with brain differentiation.

Sex Differences in Cognitive Flexibility and Resting Brain Networks in Middle-Aged Marmosets

Sex differences in human cognitive performance are well characterized. However, the neural correlates of these differences remain elusive. This issue may be clarified using nonhuman primates, for which sociocultural influences are minimized. We used the marmoset (Callithrix jacchus) to investigate sex differences in two aspects of executive function: reversal learning and intradimensional/extradimensional (ID/ED) set shifting. Stress reactivity and motor function were also assessed. In agreement with human literature, females needed more trials than males to acquire the reversals. No sex differences in ED set shifting or motivational measures were observed. The findings suggest enhanced habit formation in females, perhaps due to striatal estrogenic effects. Both sexes showed increased urinary cortisol during social separation stressor, but females showed an earlier increase in cortisol and a greater increase in agitated locomotion, possibly indicating enhanced stress reactivity. Independent of sex, basal cortisol predicted cognitive performance. No sex differences were found in motor performance. Associations between brain networks and reversal learning performance were investigated using resting state fMRI. Resting state functional connectivity (rsFC) analyses revealed sex differences in cognitive networks, with differences in overall neural network metrics and specific regions, including the prefrontal cortex, caudate, putamen, and nucleus accumbens. Correlations between cognitive flexibility and neural connectivity indicate that sex differences in cognitive flexibility are related to sex-dependent patterns of resting brain networks. Overall, our findings reveal sex differences in reversal learning, brain networks, and their relationship in the marmoset, positioning this species as an excellent model to investigate the biological basis of cognitive sex differences.

System-level matching of structural and functional connectomes in the human brain

The brain can be considered as an information processing network, where complex behavior manifests as a result of communication between large-scale functional systems such as visual and default mode networks. As the communication between brain regions occurs through underlying anatomical pathways, it is important to define a "traffic pattern" that properly describes how the regions exchange information. Empirically, the choice of the traffic pattern can be made based on how well the functional connectivity between regions matches the structural pathways equipped with that traffic pattern. In this paper, we present a multimodal connectomics paradigm utilizing graph matching to measure similarity between structural and functional connectomes (derived from dMRI and fMRI data) at node, system, and connectome level. Through an investigation of the brain's structure-function relationship over a large cohort of 641 healthy developmental participants aged 8-22 years, we demonstrate that communicability as the traffic pattern describes the functional connectivity of the brain best, with large-scale systems having significant agreement between their structural and functional connectivity patterns. Notably, matching between structural and functional connectivity for the functionally specialized modular systems such as visual and motor networks are higher as compared to other more integrated systems. Additionally, we show that the negative functional connectivity between the default mode network (DMN) and motor, frontoparietal, attention, and visual networks is significantly associated with its underlying structural connectivity, highlighting the counterbalance between functional activation patterns of DMN and other systems. Finally, we investigated sex difference and developmental changes in brain and observed that similarity between structure and function changes with development.

Male-Specific cAMP Signaling in the Hippocampus Controls Spatial Memory Deficits in a Mouse Model of Autism and Intellectual Disability

BACKGROUND

The prevalence of neurodevelopmental disorders is biased toward male individuals, with male-to-female ratios of 2:1 in intellectual disability and 4:1 in autism spectrum disorder. However, the molecular mechanisms of such bias remain unknown. While characterizing a mouse model for loss of the signaling scaffold coiled-coil and C2 domain-containing protein 1A (CC2D1A), which is mutated in intellectual disability and autism spectrum disorder, we identified biochemical and behavioral differences between male and female mice, and explored whether CC2D1A controls male-specific intracellular signaling.

METHODS

CC2D1A is known to regulate phosphodiesterase 4D (PDE4D), which regulates cyclic adenosine monophosphate (cAMP) signaling. We tested for activation of PDE4D and downstream signaling molecules in the hippocampus of Cc2d1a-deficient mice. We then performed behavioral studies in female mice to analyze learning and memory, and then targeted PDE4D activation with a PDE4D inhibitor to define how changes in cAMP levels affect behavior in male and female mice.

RESULTS

We found that in Cc2d1a-deficient male mice PDE4D is hyperactive, leading to a reduction in cAMP response element binding protein signaling, but this molecular deficit is not present in female mice. Cc2d1a-deficient male mice show a deficit in spatial memory, which is not present in Cc2d1a-deficient female mice. Restoring PDE4D activity using an inhibitor rescues cognitive deficits in male mice but has no effect on female mice.

CONCLUSIONS

Our findings show that CC2D1A regulates cAMP intracellular signaling in a male-specific manner in the hippocampus, leading to male-specific cognitive deficits. We propose that male-specific signaling mechanisms are involved in establishing sex bias in neurodevelopmental disorders.

Sex differences and the neurobiology of affective disorders

Observations of the disproportionate incidence of depression in women compared with men have long preceded the recent explosion of interest in sex differences. Nonetheless, the source and implications of this epidemiologic sex difference remain unclear, as does the practical significance of the multitude of sex differences that have been reported in brain structure and function. In this article, we attempt to provide a framework for thinking about how sex and reproductive hormones (particularly estradiol as an example) might contribute to affective illness. After briefly reviewing some observed sex differences in depression, we discuss how sex might alter brain function through hormonal effects (both organizational (programmed) and activational (acute)), sex chromosome effects, and the interaction of sex with the environment. We next review sex differences in the brain at the structural, cellular, and network levels. We then focus on how sex and reproductive hormones regulate systems implicated in the pathophysiology of depression, including neuroplasticity, genetic and neural networks, the stress axis, and immune function. Finally, we suggest several models that might explain a sex-dependent differential regulation of affect and susceptibility to affective illness. As a disclaimer, the studies cited in this review are not intended to be comprehensive but rather serve as examples of the multitude of levels at which sex and reproductive hormones regulate brain structure and function. As such and despite our current ignorance regarding both the ontogeny of affective illness and the impact of sex on that ontogeny, sex differences may provide a lens through which we may better view the mechanisms underlying affective regulation and dysfunction.

Adiponectin and sporadic Alzheimer's disease: Clinical and molecular links

Obesity has been consistently associated with Alzheimer's disease (AD) though the exact mechanisms by which it influences cognition are still elusive and subject of current research. Adiponectin, the most abundant adipokine in circulation, is inversely correlated with adipose tissue dysfunction and seems to be a central player in this association. In fact, different signalling pathways are shared by adiponectin and proteins involved in AD pathophysiology and considerable amount of evidence supports its direct and indirect influence on β-amyloid and tau aggregates formation. In this paper we present a critical review of cellular, animal and clinical studies which have contributed to a more thorough understanding of the extent to which adiponectin influences the risk of developing AD as well as its progression. Finally, the effect of acetylcholinesterase inhibitors on circulating adiponectin levels, possible therapeutic applications and future research strategies are also discussed.

Effects of Estrogens on Central Nervous System Neurotransmission: Implications for Sex Differences in Mental Disorders

Nearly one of every five US individuals aged 12 years old or older lives with certain types of mental disorders. Men are more likely to use various types of substances, while women tend to be more susceptible to mood disorders, addiction, and eating disorders, all of which are risks associated with suicidal attempts. Fundamental sex differences exist in multiple aspects of the functions and activities of neurotransmitter-mediated neural circuits in the central nervous system (CNS). Dysregulation of these neural circuits leads to various types of mental disorders. The potential mechanisms of sex differences in the CNS neural circuitry regulating mood, reward, and motivation are only beginning to be understood, although they have been largely attributed to the effects of sex hormones on CNS neurotransmission pathways. Understanding this topic is important for developing prevention and treatment of mental disorders that should be tailored differently for men and women. Studies using animal models have provided important insights into pathogenesis, mechanisms, and new therapeutic approaches of human diseases, but some concerns remain to be addressed. The purpose of this chapter is to integrate human and animal studies involving the effects of the sex hormones, estrogens, on CNS neurotransmission, reward processing, and associated mental disorders. We provide an overview of existing evidence for the physiological, behavioral, cellular, and molecular actions of estrogens in the context of controlling neurotransmission in the CNS circuits regulating mood, reward, and motivation and discuss related pathology that leads to mental disorders.

Evolutionary perspective on sex differences in the expression of neurological diseases

Sex-specific brain and cognitive deficits emerge with malnutrition, some infectious and neurodegenerative diseases, and often with prenatal or postnatal toxin exposure. These deficits are described in disparate literatures and are generally not linked to one another. Sexual selection may provide a unifying framework that integrates our understanding of these deficits and provides direction for future studies of sex-specific vulnerabilities. Sexually selected traits are those that have evolved to facilitate competition for reproductive resources or that influence mate choices, and are often larger and more complex than other traits. Critically, malnutrition, disease, chronic social stress, and exposure to man-made toxins compromise the development and expression of sexually selected traits more strongly than that of other traits. The fundamental mechanism underlying vulnerability might be the efficiency of mitochondrial energy capture and control of oxidative stress that in turn links these traits to current advances in neuroenergetics, stress endocrinology, and toxicology. The key idea is that the elaboration of these cognitive abilities, with more underlying gray matter or more extensive inter-modular white matter connections, makes them particularly sensitive to disruptions in mitochondrial functioning and oxidative stress. A framework of human sexually selected cognitive abilities and underlying brain systems is proposed and used to organize what is currently known about sex-specific vulnerabilities.

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).

Impact of X/Y genes and sex hormones on mouse neuroanatomy

Biological sex influences brain anatomy across many species. Sex differences in brain anatomy have classically been attributed to differences in sex chromosome complement (XX versus XY) and/or in levels of gonadal sex steroids released from ovaries and testes. Using the four core genotype (4CG) mouse model in which gonadal sex and sex chromosome complement are decoupled, we previously found that sex hormones and chromosomes influence the volume of distinct brain regions. However, recent studies suggest there may be more complex interactions between hormones and chromosomes, and that circulating steroids can compensate for and/or mask underlying chromosomal effects. Moreover, the impact of pre vs post-pubertal sex hormone exposure on this sex hormone/sex chromosome interplay is not well understood. Thus, we used whole brain high-resolution ex-vivo MRI of intact and pre-pubertally gonadectomized 4CG mice to investigate two questions: 1) Do circulating steroids mask sex differences in brain anatomy driven by sex chromosome complement? And 2) What is the contribution of pre- versus post-pubertal hormones to sex-hormone-dependent differences in brain anatomy? We found evidence of both cooperative and compensatory interactions between sex chromosomes and sex hormones in several brain regions, but the interaction effects were of low magnitude. Additionally, most brain regions affected by sex hormones were sensitive to both pre- and post-pubertal hormones. This data provides further insight into the biological origins of sex differences in brain anatomy.

Multimodal neuroimaging of male and female brain structure in health and disease across the life span

Sex differences in brain development and aging are important to identify, as they may help to understand risk factors and outcomes in brain disorders that are more prevalent in one sex compared with the other. Brain imaging techniques have advanced rapidly in recent years, yielding detailed structural and functional maps of the living brain. Even so, studies are often limited in sample size, and inconsistent findings emerge, one example being varying findings regarding sex differences in the size of the corpus callosum. More recently, large‐scale neuroimaging consortia such as the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium have formed, pooling together expertise, data, and resources from hundreds of institutions around the world to ensure adequate power and reproducibility. These initiatives are helping us to better understand how brain structure is affected by development, disease, and potential modulators of these effects, including sex. This review highlights some established and disputed sex differences in brain structure across the life span, as well as pitfalls related to interpreting sex differences in health and disease. We also describe sex‐related findings from the ENIGMA consortium, and ongoing efforts to better understand sex differences in brain circuitry. © 2016 The Authors. Journal of Neuroscience Research Published by Wiley Periodicals, Inc.

Complementarity of sex differences in brain and behavior: From laterality to multimodal neuroimaging

Although, overwhelmingly, behavior is similar in males and females, and, correspondingly, the brains are similar, sex differences permeate both brain and behavioral measures, and these differences have been the focus of increasing scrutiny by neuroscientists. This Review describes milestones from more than 3 decades of research in brain and behavior. This research was necessarily bound by available methodology, and we began with indirect behavioral indicators of brain function such as handedness. We proceeded to the use of neuropsychological batteries and then to structural and functional neuroimaging that provided the foundations of a cognitive neuroscience-based computerized neurocognitive battery. Sex differences were apparent and consistent in neurocognitive measures, with females performing better on memory and social cognition tasks and males on spatial processing and motor speed. Sex differences were also prominent in all major brain parameters, including higher rates of cerebral blood flow, higher percentage of gray matter tissue, and higher interhemispheric connectivity in females, compared with higher percentage of white matter and greater intrahemispheric connectivity as well as higher glucose metabolism in limbic regions in males. Many of these differences are present in childhood, but they become more prominent with adolescence, perhaps linked to puberty. Overall, they indicate complementarity between the sexes that would result in greater adaptive diversity. © 2016 Wiley Periodicals, Inc.

The effect of feature image on sensitivity of the statistical analysis in the pipeline of a tractography atlas-based analysis

Tractography atlas-based analysis (TABS) is a new diffusion tensor image (DTI) statistical analysis method for detecting and understanding voxel-wise white matter properties along a fiber tract. An important requisite for accurate and sensitive TABS is the availability of a deformation field that is able to register DTI in native space to standard space. Here, three different feature images including the fractional anisotropy (FA) image, T1 weighted image, and the maximum eigenvalue of the Hessian of the FA (hFA) image were used to calculate the deformation fields between individual space and population space. Our results showed that when the FA image was a feature image, the tensor template had the highest consistency with each subject for scalar and vector information. Additionally, to demonstrate the sensitivity and specificity of the TABS method with different feature images, we detected a gender difference along the corpus callosum. A significant difference between the male and female group in diffusion measurement appeared predominantly in the right corpus callosum only when FA was the feature image. Our results demonstrated that the FA image as a feature image was more accurate with respect to the underlying tensor information and had more accurate analysis results with the TABS method.