From Default Mode Network to the Basal Configuration: Sex Differences in the Resting-State Brain Connectivity as a Function of Age and Their Clinical Correlates

Sex differences in development of functional connections in the face processing network

BACKGROUND AND PURPOSE

Understanding sex differences in typical development of the face processing network is important for elucidating disruptions during atypical development in sex-linked developmental disorders like autism spectrum disorder. Based on prior sex difference studies in other cognitive domains, this study examined whether females show increased integration of core and extended face regions with age for face viewing, while males would show increased segregation.

METHODS

This study used a cross-sectional design with typically developing children and adults (n = 133) and a functional MRI face localizer task. Psychophysiological interaction (PPI) analysis examined functional connectivity between canonical and extended face processing network regions with age, with greater segregation indexed by decreased core-extended region connectivity with age and greater integration indexed by increased core-extended region connectivity with age.

RESULTS

PPI analysis confirmed increased segregation for males-right fusiform face area (FFA) coupling to right inferior frontal gyrus (IFG) opercular when viewing faces and left amygdala when viewing objects decreased with age. Females showed increased integration with age (increased coupling of the right FFA to right IFG opercular region and right occipital face area [OFA] to right IFG orbital when viewing faces and objects, respectively) and increased segregation (decreased coupling with age of the right OFA with IFG opercular region when viewing faces).

CONCLUSIONS

Development of core and extended face processing network connectivity follows sexually dimorphic paths. These differential changes mostly occur across childhood and adolescence, with males experiencing segregation and females both segregation and integration changes in connectivity.

Sex matters: acute functional connectivity changes as markers of remission in late-life depression differ by sex

The efficacy of antidepressant treatment in late-life is modest, a problem magnified by an aging population and increased prevalence of depression. Understanding the neurobiological mechanisms of treatment response in late-life depression (LLD) is imperative. Despite established sex differences in depression and neural circuits, sex differences associated with fMRI markers of antidepressant treatment response are underexplored. In this analysis, we assess the role of sex on the relationship of acute functional connectivity changes with treatment response in LLD. Resting state fMRI scans were collected at baseline and day one of SSRI/SNRI treatment for 80 LLD participants. One-day changes in functional connectivity (differential connectivity) were related to remission status after 12 weeks. Sex differences in differential connectivity profiles that distinguished remitters from non-remitters were assessed. A random forest classifier was used to predict the remission status with models containing various combinations of demographic, clinical, symptomatological, and connectivity measures. Model performance was assessed with area under the curve, and variable importance was assessed with permutation importance. The differential connectivity profile associated with remission status differed significantly by sex. We observed evidence for a difference in one-day connectivity changes between remitters and non-remitters in males but not females. Additionally, prediction of remission was significantly improved in male-only and female-only models over pooled models. Predictions of treatment outcome based on early changes in functional connectivity show marked differences between sexes and should be considered in future MR-based treatment decision-making algorithms.

Signed graph representation learning for functional-to-structural brain network mapping

MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.

Controllability of Structural Brain Networks and the Waxing and Waning of Negative Affect in Daily Life

Background

The waxing and waning of negative affect in daily life is normative, reflecting an adaptive capacity to respond flexibly to changing circumstances. However, understanding of the brain structure correlates of affective variability in naturalistic settings has been limited. Using network control theory, we examine facets of brain structure that may enable negative affect variability in daily life.

Methods

We used diffusion-weighted imaging data from 95 young adults (age [in years]: mean = 20.19, SD = 1.80; 56 women) to construct structural connectivity networks that map white matter fiber connections between 200 cortical and 14 subcortical regions. We applied network control theory to these structural networks to estimate the degree to which each brain region's pattern of structural connectivity facilitates the spread of activity to other brain systems. We examined how the average controllability of functional brain systems relates to negative affect variability, computed by taking the standard deviation of negative affect self-reports collected via smartphone-based experience sampling twice per day over 28 days as participants went about their daily lives.

Results

We found that high average controllability of the cingulo-insular system is associated with increased negative affect variability. We also found that greater negative affect variability is related to the presence of more depressive symptoms, yet average controllability of the cingulo-insular system was not associated with depressive symptoms.

Conclusions

Our results highlight the role that brain structure plays in affective dynamics as observed in the context of daily life, suggesting that average controllability of the cingulo-insular system promotes normative negative affect variability.

Sex differences in the functional topography of association networks in youth

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Sex and gender differences in pain

Chronic pain affects 20% of adults and is one of the leading causes of disability worldwide. Women and girls are disproportionally affected by chronic pain. About half of chronic pain conditions are more common in women, with only 20% having a higher prevalence in men. There are also sex and gender differences in acute pain sensitivity. Pain is a subjective experience made up of sensory, cognitive, and emotional components. Consequently, there are multiple dimensions through which sex and gender can influence the pain experience. Historically, most preclinical pain research was conducted exclusively in male animals. However, recent studies that included females have revealed significant sex differences in the physiological mechanisms underlying pain, including sex specific involvement of different genes and proteins as well as distinct interactions between hormones and the immune system that influence the transmission of pain signals. Human neuroimaging has revealed sex and gender differences in the neural circuitry associated with pain, including sex specific brain alterations in chronic pain conditions. Clinical pain research suggests that gender can affect how an individual contextualizes and copes with pain. Gender may also influence the susceptibility to develop chronic pain. Sex and gender biases can impact how pain is perceived and treated clinically. Furthermore, the efficacy and side effects associated with different pain treatments can vary according to sex and gender. Therefore, preclinical and clinical research must include sex and gender analyses to understand basic mechanisms of pain and its relief, and to develop personalized pain treatment.

Examining age-dependent DNA methylation patterns and gene expression in the male and female mouse hippocampus

DNA methylation is a well-characterized epigenetic modification involved in numerous molecular and cellular functions. Methylation patterns have also been associated with aging mechanisms. However, how DNA methylation patterns change within key brain regions involved in memory formation in an age- and sex-specific manner remains unclear. Here, we performed reduced representation bisulfite sequencing (RRBS) from mouse dorsal hippocampus - which is necessary for the formation and consolidation of specific types of memories - in young and aging mice of both sexes. Overall, our findings demonstrate that methylation levels within the dorsal hippocampus are divergent between sexes during aging in genomic features correlating to mRNA functionality, transcription factor binding sites, and gene regulatory elements. These results define age-related changes in the methylome across genomic features and build a foundation for investigating potential target genes regulated by DNA methylation in an age- and sex-specific manner.

A whole-brain modeling approach to identify individual and group variations in functional connectivity

Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.

Use of random matrix theory in the discovery of resting state brain networks

Connectomics identifies brain networks in vivo in resting state functional MRI. However, the presence of noise produces spurious identification of brain networks, which have low test-retest reliability. A Network Based Statistics approach to network identification has been previously proposed that affords much better statistical power relative to Bonferroni method but nevertheless provides a sufficiently conservative, family-wise control for false positives. We propose the use of Random Matrix Theory (RMT) to discover brain networks and to associate those networks with demographic and clinical variables. We parcellated the brain into cortical and subcortical regions using either an anatomical or a functional brain atlas. We applied RMT to study functional connectivity across brain regions by first computing the correlation matrix for time courses in those brain regions and then identifying eigenvalues that deviate from the theoretical random distribution that RMT predicts, on the assumption that real brain networks would produce eigenvalues that differ significantly from the random distribution. We assessed the specificity and test-retest reliability of identified networks through application of this RMT-based approach to (1) synthetic data generated under the null-hypothesis, (2) resting state functional MRI data from 4 real-world cohorts of patients and healthy controls, and (3) synthetic data generated by the addition of increasing amounts of noise to real-world datasets. Our findings showed that RMT method was robust to the atlas used for parcellating the brain and did not discover a brain network in synthetic data when in fact a network was not present (i.e., specificity was high); RMT-identified networks in the real-world dataset had high test-retest reliability; and RMT-based method consistently discovered the same network in the presence of increasing noise in the real-world dataset.

Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis

The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. Simultaneously, the model learns the importance of graph edges within ST-GCN to gain insight into the functional connectivities contributing to the prediction. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N = 1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N = 773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. Furthermore, the brain regions and functional connections significantly contributing to the predictions of our model are important markers according to the neuroscience literature.

The neurobiological basis of sex differences in learned fear and its inhibition

Learning that certain cues or environments predict threat enhances survival by promoting appropriate fear and the resulting defensive responses. Adapting to changing stimulus contingencies by learning that such cues no longer predict threat, or distinguishing between these threat-related and other innocuous stimuli, also enhances survival by limiting fear responding in an appropriate manner to conserve resources. Importantly, a failure to inhibit fear in response to harmless stimuli is a feature of certain anxiety and trauma-related disorders, which are also associated with dysfunction of the neural circuitry underlying learned fear and its inhibition. Interestingly, these disorders are up to twice as common in women, compared to men. Despite this striking sex difference in disease prevalence, the neurobiological factors involved remain poorly understood. This is due in part to the majority of relevant preclinical studies having neglected to include female subjects alongside males, which has greatly hindered progress in this field. However, more recent studies have begun to redress this imbalance and emerging evidence indicates that there are significant sex differences in the inhibition of learned fear and associated neural circuit function. This paper provides a narrative review on sex differences in learned fear and its inhibition through extinction and discrimination, along with the key gonadal hormone and brain mechanisms involved. Understanding the endocrine and neural basis of sex differences in learned fear inhibition may lead to novel insights on the neurobiological mechanisms underlying the enhanced vulnerability to develop anxiety-related disorders that are observed in women.

A GENERALIZED FRAMEWORK OF PATHLENGTH ASSOCIATED COMMUNITY ESTIMATION FOR BRAIN STRUCTURAL NETWORK

Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.

Marijuana Use and Depressive Symptoms; Gender Differences in African American Adolescents

Introduction: This study aimed to examine gender differences in the bidirectional associations between marijuana use and depressive symptoms among African American adolescents. The study also tested gender differences in the effects of socioeconomic status, maternal support, and friends' drug use on adolescents' depressive symptoms and marijuana use. Methods: This is a secondary analysis of the Flint Adolescent Study (FAS). Six hundred and eighty one African American adolescents (335 males and 346 females) were followed for 3 years, from 1995 (mean age 16) to 1997 (mean age 19). Depressive symptoms (Brief Symptom Inventory) and marijuana use were measured annually during the follow up. We used multi-group latent growth curve modeling to explore the reciprocal associations between depressive symptoms and marijuana use over time based on gender. Results: Baseline marijuana use was predictive of an increase in depressive symptoms over time among male but not female African American adolescents. Baseline depressive symptoms were not predictive of an increase in marijuana use among male or female adolescents. Conclusion: Study findings suggest that male African American adolescents who use marijuana are at an increased risk of subsequent depressive symptoms. Interventions that combine screening and treatment for marijuana use and depression may be indicated for African American male adolescents.