Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study

Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

Sex differences in human brain networks in normal and psychiatric populations from the perspective of small-world properties

Females and males are known to be different in the prevalences of multiple psychiatric disorders, while the underlying neural mechanisms are unclear. Based on non-invasive neuroimaging techniques and graph theory, many researchers have tried to use a small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks in both normal and psychiatric populations from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed: randomization, regularization, stronger small-worldization, and weaker small-worldization, we found that current results point to a significant trend toward more regularization in normal females and more randomization in normal males in functional brain networks. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks in normal populations. Nevertheless, we noticed that the sample sizes in many published studies are small, and future studies with larger samples are warranted to obtain more reliable results. Moreover, the number of related studies conducted in psychiatric populations is still limited and more investigations might be needed. We anticipate that these conclusions will contribute to a deeper understanding of the sex differences in the brain, which may be also valuable for developing new methods in the treatment of psychiatric disorders.

A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research

Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.

3DCNN predicting brain age using diffusion tensor imaging

Neuroimaging-based brain age prediction using deep learning is gaining popularity. However, few studies have attempted to leverage diffusion tensor imaging (DTI) to predict brain age. In this study, we proposed a 3D convolutional neural network model (3DCNN) and trained it on fractional anisotropy (FA) data from six publicly available datasets (n = 2406, age = 17-60) to estimate brain age. Implementing a two-loop nested cross-validation scheme with a tenfold cross-validation procedure, we achieved a robust prediction performance of a mean absolute error (MAE) of 2.785 and a correlation coefficient of 0.932. We also employed Grad-Cam++ to visualize the salient features of the proposed model. We identified a few highly salient fiber tracts, including the genu of corpus callosum and the left cerebellar peduncle, among others that play a pivotal role in our model. In sum, our model reliably predicted brain age and provided novel insight into age-related changes in brains' axonal structure.

Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants

There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.

Neuroanatomical asymmetry in the canine brain

The brains of humans and non-human primates exhibit left/right asymmetries in grey matter morphology, white matter connections, and functional responses. These asymmetries have been implicated in specialized behavioral adaptations such as language, tool use, and handedness. Left/right asymmetries are also observed in behavioral tendencies across the animal kingdom, suggesting a deep evolutionary origin for the neural mechanisms underlying lateralized behavior. However, it is still unclear to what extent brain asymmetries supporting lateralized behaviors are present in other large-brained animals outside the primate order. Canids and other carnivorans evolved large, complex brains independently and convergently with primates, and exhibit lateralized behaviors. Therefore, domestic dogs offer an opportunity to address this question. We examined T2-weighted MRI images of 62 dogs from 33 breeds, opportunistically collected from a veterinary MRI scanner from dogs who were referred for neurological examination but were not found to show any neuropathology. Volumetrically asymmetric regions of gray matter included portions of the temporal and frontal cortex, in addition to portions of the cerebellum, brainstem, and other subcortical regions. These results are consistent with the perspective that asymmetry may be a common feature underlying the evolution of complex brains and behavior across clades, and provide neuro-organizational information that is likely relevant to the growing field of canine behavioral neuroscience.

Sex/gender differences in cognitive abilities

Sex/gender differences in cognitive sciences are riddled by conflicting perspectives. At the center of debates are clinical, social, and political perspectives. Front and center, evolutionary and biological perspectives have often focused on 'nature' arguments, while feminist and constructivist views have often focused on 'nurture arguments regarding cognitive sex differences. In the current narrative review, we provide a comprehensive overview regarding the origins and historical advancement of these debates while providing a summary of the results in the field of sexually polymorphic cognition. In so doing, we attempt to highlight the importance of using transdisciplinary perspectives which help bridge disciplines together to provide a refined understanding the specific factors that drive sex differences a gender diversity in cognitive abilities. To summarize, biological sex (e.g., birth-assigned sex, sex hormones), socio-cultural gender (gender identity, gender roles), and sexual orientation each uniquely shape the cognitive abilities reviewed. To date, however, few studies integrate these sex and gender factors together to better understand individual differences in cognitive functioning. This has potential benefits if a broader understanding of sex and gender factors are systematically measured when researching and treating numerous conditions where cognition is altered.

Toward reframing brain-social dynamics: current assumptions and future challenges

Evolutionary analyses suggest that the human social brain and sociality appeared together. The two fundamental tools that accelerated the concurrent emergence of the social brain and sociality include learning and plasticity. The prevailing core idea is that the primate brain and the cortex in particular became reorganised over the course of evolution to facilitate dynamic adaptation to ongoing changes in physical and social environments. Encouraged by computational or survival demands or even by instinctual drives for living in social groups, the brain eventually learned how to learn from social experience via its massive plastic capacity. A fundamental framework for modeling these orchestrated dynamic responses is that social plasticity relies upon neuroplasticity. In the present article, we first provide a glimpse into the concepts of plasticity, experience, with emphasis on social experience. We then acknowledge and integrate the current theoretical concepts to highlight five key intertwined assumptions within social neuroscience that underlie empirical approaches for explaining the brain-social dynamics. We suggest that this epistemological view provides key insights into the ontology of current conceptual frameworks driving future research to successfully deal with new challenges and possible caveats in favour of the formulation of novel assumptions. In the light of contemporary societal challenges, such as global pandemics, natural disasters, violent conflict, and other human tragedies, discovering the mechanisms of social brain plasticity will provide new approaches to support adaptive brain plasticity and social resilience.

Brain plasticity of structural connectivity networks and topological properties in baseball players with different levels of expertise

Brain plasticity in structural connectivity networks along the development of expertise has remained largely unknown. To address this, we recruited individuals with three different levels of baseball-playing experience: skilled batters (SB), intermediate batters (IB), and healthy controls (HC). We constructed their structural connectivity networks using diffusion tractography and compared their region-to-region structural connections and the topological characteristics of the constructed networks using graph-theoretical analysis. The group differences were detected in 35 connections predominantly involving sensorimotor and visual systems; the intergroup changes could be depicted either in a stepwise (HC < / = IB < / = SB) or a U-/inverted U-shaped manner as experience increased (IB < SB and/or HC, or opposite). All groups showed small-world topology in their constructed networks, but SB had increased global and local network efficiency than IB and/or HC. Furthermore, although the number and cortical regions identified as hubs of the networks in the three groups were highly similar, SB exhibited higher nodal global efficiency in both the dorsolateral and medial parts of the bilateral superior frontal gyri than IB. Our findings add new evidence of topological reorganization in brain networks associated with sensorimotor experience in sports. Interestingly, these changes do not necessarily increase as a function of experience as previously suggested in literature.

Prediction of the Topography of the Corticospinal Tract on T1-Weighted MR Images Using Deep-Learning-Based Segmentation

INTRODUCTION

Tractography is an invaluable tool in the planning of tumor surgery in the vicinity of functionally eloquent areas of the brain as well as in the research of normal development or of various diseases. The aim of our study was to compare the performance of a deep-learning-based image segmentation for the prediction of the topography of white matter tracts on T1-weighted MR images to the performance of a manual segmentation.

METHODS

T1-weighted MR images of 190 healthy subjects from 6 different datasets were utilized in this study. Using deterministic diffusion tensor imaging, we first reconstructed the corticospinal tract on both sides. After training a segmentation model on 90 subjects of the PIOP2 dataset using the nnU-Net in a cloud-based environment with graphical processing unit (Google Colab), we evaluated its performance using 100 subjects from 6 different datasets.

RESULTS

Our algorithm created a segmentation model that predicted the topography of the corticospinal pathway on T1-weighted images in healthy subjects. The average dice score was 0.5479 (0.3513-0.7184) on the validation dataset.

CONCLUSIONS

Deep-learning-based segmentation could be applicable in the future to predict the location of white matter pathways in T1-weighted scans.

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants

Recent developments in image analysis have enabled an individual’s brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Gender effects on the controllability of hemispheric white matter networks

Male and female adults exhibited significant group differences in brain white matter (WM) asymmetry and WM network controllability. However, gender differences in controllability of hemispheric WM networks between males and females remain to be determined. Based on 1 principal atlas and 1 replication atlas, this work characterized the average controllability (AC) and modal controllability (MC) of hemispheric WM network based on 1 principal dataset and 2 replication datasets. All results showed that males had higher AC of left hemispheric networks than females. And significant hemispheric asymmetry was revealed in regional AC and MC. Furthermore, significant gender differences in the AC asymmetry were mainly found in regions lie in the frontoparietal network, and the MC asymmetry was found in regions involving auditory and emotion process. Finally, we found significant associations between regional controllability and cognitive features. Taken together, this work could provide a novel perspective for understanding gender differences in hemispheric WM asymmetry and cognitive function between males and females.

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.

Sex differences in fronto-limbic white matter tracts in youth with mood disorders

AIMS

Patients with depression and bipolar disorder have previously been shown to have impaired white matter (WM) integrity compared with healthy controls. This study aimed to investigate potential sex differences that may provide further insight into the pathophysiology of these highly debilitating mood disorders.

METHODS

Participants aged 17 to 30 years (168 with depression [60% females], 107 with bipolar disorder [74% females], and 61 controls [64% females]) completed clinical assessment, self-report measures, and a neuropsychological assessment battery. Participants also underwent magnetic resonance imaging from which diffusion tensor imaging data were collected among five fronto-limbic WM tracts: cingulum bundle (cingulate gyrus and hippocampus subsections), fornix, stria terminalis, and the uncinate fasciculus. Mean fractional anisotropy (FA) scores were compared between groups using analyses of variance with sex and diagnosis as fixed factors.

RESULTS

Among the nine WM tracts analyzed, one revealed a significant interaction between sex and diagnosis, controlling for age. Male patients with bipolar disorder had significantly lower FA scores in the fornix compared with the other groups. Furthermore, partial correlations revealed a significant positive association between FA scores for the fornix and psychomotor speed.

CONCLUSIONS

Our findings suggest that males with bipolar disorder may be at increased risk of disruptions in WM integrity, especially in the fornix, which is thought to be responsible for a range of cognitive functions. More broadly, our findings suggest that sex differences may exist in WM integrity and thereby alter our understanding of the pathophysiology of mood disorders.

Brain network topology and personality traits: A source level magnetoencephalographic study

Personality neuroscience is focusing on the correlation between individual differences and the efficiency of large-scale networks from the perspective of the brain as an interconnected network. A suitable technique to explore this relationship is the magnetoencephalography (MEG), but not many MEG studies are aimed at investigating topological properties correlated to personality traits. By using MEG, the present study aims to evaluate how individual differences described in Cloninger's psychobiological model are correlated with specific cerebral structures. Fifty healthy individuals (20 males, 30 females, mean age: 27.4 ± 4.8 years) underwent Temperament and Character Inventory examination and MEG recording during a resting state condition. High harm avoidance scores were associated with a reduced centrality of the left caudate nucleus and this negative correlation was maintained in females when we analyzed gender differences. Our data suggest that the caudate nucleus plays a key role in adaptive behavior and could be a critical node in insular salience network. The clear difference between males and females allows us to suggest that topological organization correlated to personality is highly dependent on gender. Our findings provide new insights to evaluate the mutual influences of topological and functional connectivity in neural communication efficiency and disruption as biomarkers of psychopathological traits.

Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study

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Structural disconnectomes can be modelled without diffusion using tractography atlases.

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Atlas-based and DTI-derived disconnectome topological metrics correlate strongly.

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MS patient disconnectomes relate to clinical scores.

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision.

Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Regional metabolic and network changes in Meige syndrome

To contribute to the understanding of the aetiology and pathogenesis of Meige syndrome, the metabolic networks of patients with Meige syndrome were investigated using 18F-fluoro-D-glucose positron emission tomography (18F-FDG-PET) imaging of cerebral glucose metabolism. Fifty right-handed and unmedicated primary Meige syndrome patients enrolled between September 2017 and September 2020 at the Department of Neurosurgery, Peking University People’s Hospital, and 50 age- and sex-matched healthy control subjects participated in the study. Metabolic connectivity and graph theory analysis were used to investigate metabolic network differences based on 18F-FDG-PET images. Glucose hypometabolism was detected in the left internal globus pallidus and parietal lobe, right frontal lobe and postcentral gyrus, and bilateral thalamus and cerebellum of patients with Meige syndrome. Clustering coefficients (Cps) (density threshold: 16–28%; P < 0.05) and shortest path lengths (Lps) (density threshold: 10–15%; P < 0.05) were higher in Meige syndrome patients than in healthy controls. Small-worldness was lower in Meige syndrome patients than in healthy controls, and centrality was significantly lower in the right superior occipital gyrus and pallidum and higher in the right thalamus. Hypometabolism in the globus pallidus and thalamus may indicate basal ganglia-thalamocortical motor circuit abnormalities as a pathogenic mechanism of Meige syndrome, providing a possible explanation for the efficacy of deep brain stimulation (DBS) in improving symptoms. Meige syndrome patients had abnormal small-world properties. Centrality changes in the right pallidus and thalamus verified the important roles of these regions in the pathogenesis of Meige syndrome.

Brain network analyses of diffusion tensor imaging for brain aging

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

Rich-Club Analysis in Adults With ADHD Connectomes Reveals an Abnormal Structural Core Network

Objective: Whether the abnormal connectome of brain's rich-club structure in adults with attention-deficit hyperactivity disorder (ADHD) remains unclear. Method: The current study used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) to compare the performance of 42 adults with ADHD and 59 typical development (TD) adults. Results: A reduced density of rich-clubs among structural hub nodes, including the bilateral precuneus, the insula, the caudate nucleus, the left putamen, and the right calcarine, was found in adults with ADHD. Moreover, lower global efficiency was found in adults with ADHD than in TD, which might be caused by a reduced density of rich-club connections in ADHD patients. Conclusion: Given that adults with ADHD have greater coupling strength between structural and functional connectivity than TD adults, connectome abnormalities with a reduced rich-club connectivity density might be accompanied by altered functional brain dynamics in ADHD patients.

Preliminary Comparison of Subcortical Structures in Elderly Subclinical Depression: Structural Analysis with 3T MRI

Depression in the elderly population has shown increased likelihood of neurological disorders due to structural changes in the subcortical area. However, further investigation into depression related subcortical changes is needed due to mismatches in structural analysis results between studies as well as scarcities in research regarding subcortical connectivity patterns of subclinical depression populations. This study aims to investigate structural differences in subcortical regions of aged participants with subclinical depression using 3Tesla MRI. In structural analysis, volumes of each subcortical region were measured to observe the volumetric difference and asymmetry between groups, but no significant difference was found. In addition, fractional anisotropy (FA) and apparent diffusion coefficient (ADC) did not show any significant differences between groups. Structural analysis using probabilistic tractography indicated that the connection strength between left nucleus accumbens-right hippocampus, and right thalamus-right caudate was higher in the control group than the subclinical depression group. The differences in subcortical connection strength of subclinical depression groups, have shown to correlate with emotional and cognitive disorders, such as anxiety and memory impairment. We believe that the analysis of structural differences and cross-regional network measures in subcortical structures can help identify neurophysiological changes occurring in subclinical depression.

Multimodal data revealed different neurobiological correlates of intelligence between males and females

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

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 effects on brain structure in de novo Parkinson's disease: a multimodal neuroimaging study

Parkinson's disease varies in severity and age of onset. One source of this variability is sex. Males are twice as likely as females to develop Parkinson's disease, and tend to have more severe symptoms and greater speed of progression. However, to date, there is little information in large cohorts on sex differences in the patterns of neurodegeneration. Here we used MRI and clinical information from the Parkinson Progression Markers Initiative to measure structural brain differences between sexes in Parkinson's disease after regressing out the expected effect of age and sex. We derived atrophy maps from deformation-based morphometry of T1-weighted MRI and connectivity from diffusion-weighted MRI in de novo Parkinson's disease patients (149 males: 83 females) with comparable clinical severity, and healthy control participants (78 males: 39 females). Overall, even though the two patient groups were matched for disease duration and severity, males demonstrated generally greater brain atrophy and disrupted connectivity. Males with Parkinson's disease had significantly greater tissue loss than females in 11 cortical regions including bilateral frontal and left insular lobe, right postcentral gyrus, left inferior temporal and cingulate gyrus and left thalamus, while females had greater atrophy in six cortical regions, including regions in the left frontal lobe, right parietal lobe, left insular gyrus and right occipital cortex. Local efficiency of white matter connectivity showed greater disruption in males in multiple regions such as basal ganglia, hippocampus, amygdala and thalamus. These findings support the idea that development of Parkinson's disease may involve different pathological mechanisms and yield distinct prognosis in males and females, which may have implications for research into neuroprotection, and stratification for clinical trials.

Sex Differences in Anatomical Rich-Club and Structural-Functional Coupling in the Human Brain Network

Structural and functional differences between the brains of female and male adults have been well documented. However, potential sex differences in the patterns of rich-club organization and the coupling between their structural connectivity (SC) and functional connectivity (FC) remain to be determined. In this study, functional magnetic resonance imaging and diffusion tensor imaging techniques were combined to examine sex differences in rich-club organization. Females had a stronger SC-FC coupling than males. Moreover, stronger SC-FC coupling in the females was primarily located in feeder connections and non-rich-club nodes of the left inferior frontal gyrus and inferior parietal lobe and the right superior frontal gyrus and superior parietal gyrus, whereas higher coupling strength in males was primarily located in rich-club connections and rich-club node of the right insula, and non-rich-club nodes of the left hippocampus and the right parahippocampal gyrus. Sex-specific patterns in correlations were also shown between SC-FC coupling and cognitive function, including working memory and reasoning ability. The topological changes in rich-club organization provide novel insight into sex-specific effects on white matter connections that underlie a potential network mechanism of sex-based differences in cognitive function.

Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample

The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.

Preeclampsia may influence offspring neuroanatomy and cognitive function: a role for placental growth factor†

Preeclampsia (PE) is a common pregnancy complication affecting 3-5% of women. Preeclampsia is diagnosed clinically as new-onset hypertension with associated end organ damage after 20 weeks of gestation. Despite being diagnosed as a maternal syndrome, fetal experience of PE is a developmental insult with lifelong cognitive consequences. These cognitive alterations are associated with distorted neuroanatomy and cerebrovasculature, including a higher risk of stroke. The pathophysiology of a PE pregnancy is complex, with many factors potentially able to affect fetal development. Deficient pro-angiogenic factor expression is one aspect that may impair fetal vascularization, alter brain structure, and affect future cognition. Of the pro-angiogenic growth factors, placental growth factor (PGF) is strongly linked to PE. Concentrations of PGF are inappropriately low in maternal blood both before and during a PE gestation. Fetal concentrations of PGF appear to mirror maternal circulating concentrations. Using Pgf-/- mice that may model effects of PE on offspring, we demonstrated altered central nervous system vascularization, neuroanatomy, and behavior. Overall, we propose that development of the fetal brain is impaired in PE, making the offspring of preeclamptic pregnancies a unique cohort with greater risk of altered cognition and cerebrovasculature. These individuals may benefit from early interventions, either pharmacological or environmental. The early neonatal period may be a promising window for intervention while the developing brain retains plasticity.

Dynamic Reconfiguration of Functional Topology in Human Brain Networks: From Resting to Task States

Task demands evoke an intrinsic functional network and flexibly engage multiple distributed networks. However, it is unclear how functional topologies dynamically reconfigure during task performance. Here, we selected the resting- and task-state (emotion and working-memory) functional connectivity data of 81 health subjects from the high-quality HCP data. We used the network-based statistic (NBS) toolbox and the Brain Connectivity Toolbox (BCT) to compute the topological features of functional networks for the resting and task states. Graph-theoretic analysis indicated that under high threshold, a small number of long-distance connections dominated functional networks of emotion and working memory that exhibit distinct long connectivity patterns. Correspondently, task-relevant functional nodes shifted their roles from within-module to between-module: the number of connector hubs (mainly in emotional networks) and kinless hubs (mainly in working-memory networks) increased while provincial hubs disappeared. Moreover, the global properties of assortativity, global efficiency, and transitivity decreased, suggesting that task demands break the intrinsic balance between local and global couplings among brain regions and cause functional networks which tend to be more separated than the resting state. These results characterize dynamic reconfiguration of large-scale distributed networks from resting state to task state and provide evidence for the understanding of the organization principle behind the functional architecture of task-state networks.

Gender-based functional connectivity differences in brain networks in childhood

BACKGROUND AND OBJECTIVE

Understanding the effect of gender differences on the brain can provide important information to characterize normal changes throughout life and to increase the likelihood of sex-specific approaches for neurological and psychiatric diseases. In this study, Functional Connectivity (FC), Amplitude of Low-Frequency Fluctuations (ALFF) and fractional ALFF (fALFF) analyzes will be compared between female and male brains between the ages of 7 and 18 years using resting state-functional magnetic resonance imaging (rs-fMRI).

METHODS

The rs-fMRI data in this study has been provided by The New York University (NYU) Child Study Center of the publicly shared ADHD200 database. From the NYU dataset, 68 (34 females, 34 males) healthy subjects in the age range of 7-18 years were selected. The female group (mean age: 12.3271±3.1380) and male group (mean age: 11.8766±2.9697) consisted of right-handed, small head motion and similar IQ values. FC was obtained by seed voxel analysis and the effect of low-frequency fluctuations on gender was examined by ALFF and fALFF analyses. Two-sample t-test was used to compare female and male groups with the significance thresholds set to FDR-corrected p<0.05.

RESULTS

In the results of our study, both in the ALFF, fALFF analyses and the seed regions belonging to many network regions, higher FC rates were found in girls than boys. Our results show that the females' language functions, visual functions such as object detection and recognition, working memory, executive functions, and episodic memory are more developed than males in this age range. In addition, as another result of our study, the seed regions are statistically stronger where the higher activation of female participants than male participants has concentrated in the left hemisphere.

CONCLUSIONS

Gender differences in brain networks should be taken into consideration when examining childhood cognitive and neuropsychiatric disorders and the results should also be evaluated according to gender. Evaluation of gender differences in childhood can increase the likelihood of early and definitive diagnosis and correct treatment for neurological diseases and can help doctors and scientists find new diagnostic tools to discover brain differences.

How IQ depends on the running mode of brain network?

Increasing evidence has shown that intelligence quotient (IQ) depends not only on the aspect of phenomenology such as the size of brain and sexuality but also on the topology of the brain network. We here try to get a deeper understanding by asking how the running mode of the brain network influences IQ. We introduce a parameter α to represent the trade-off between wiring cost and processing efficiency and figure out the optimal value of α by the approach of network reconstruction. A negative correlation between optimal index α and IQ is revealed. To further find out the mechanism for the functional difference between males and females, we move to the local level of brain regions and study the relationship between regional optimal α and IQ. We find that the Pearson coefficients of males are significantly different from that of females, including both global and regional levels. These findings show that the functional differences between individuals, including the differences between males and females, are closely related to the different running modes of their brain networks.

Brain network similarity: methods and applications

Graph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a "network of networks" that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.

Alterations of functional and structural connectivity in patients with brain metastases

Metastases are the most prevalent tumors in the brain and are commonly associated with high morbidity and mortality. Previous studies have suggested that brain tumors can induce a loss of functional connectivity and alter the brain network architecture. Little is known about the effect of brain metastases on whole-brain functional and structural connectivity networks. In this study, 14 patients with brain metastases and 16 healthy controls underwent resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). We constructed functional connectivity network using rs-fMRI signal correlations and structural connectivity network using DTI tractography. Graph theoretical analysis was employed to calculate network properties. We further evaluated the performance of brain networks after metastases resection by a simulated method. Compared to healthy controls, patients with brain metastases showed an altered “small-world” architecture in both functional and structural connectivity networks, shifting to a more randomness organization. Besides, the coupling strength of functional-structural connectivity was decreased in patients. After removing nodes infiltrated by metastases, aggravated disruptions were found in both functional and structural connectivity networks, and the alterations of network properties correlated with the removed hubs number. Our findings suggest that brain metastases interfere with the optimal network organization and relationship of functional and structural connectivity networks, and tumor resection involving hubs could cause a worse performance of brain networks. This study provides neuroimaging guidance for neurosurgical planning and postoperative assessment of brain metastases from the aspect of brain networks.

Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders

BACKGROUND

Sleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality.

METHODS

Polysomnographic data from 44 healthy individuals (28 men and 16 women) recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realized by importing these connectivity matrices into EEGNET Toolbox.

RESULTS

In men and women, all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep. However, compared to men, women present greater small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage, which indicates the presence of a small-world network organization less marked during REM sleep as well as for all EEG bands during this sleep stage in women. In addition, in women, these small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage are positively correlated with the presence of subclinical symptoms of depression.

CONCLUSIONS

Thus, the highlighting of these sex differences in network organization during REM sleep indicates the presence of differences in the global and local processing of information during sleep between women and men. In addition, this small-world network organization less marked during REM sleep appears to be a marker of vulnerability to mood disorders specific to women, which opens up new perspectives in understanding sex differences in the occurrence of mood disorders.

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.

Altered brain white matter connectome in children and adolescents with prenatal alcohol exposure

Diffuson tensor imaging (DTI) has demonstrated widespread alterations of brain white matter structure in children with prenatal alcohol exposure (PAE), yet it remains unclear how these alterations affect the structural brain network as a whole. The present study aimed to examine changes in the DTI-based structural connectome in children and adolescents with PAE compared to unexposed controls. Participants were 121 children and adolescents with PAE (51 females) and 119 typically-developing controls (49 females) aged 5-18 years with DTI data collected at one of four research centers across Canada. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers via deterministic tractography. The PAE group had significantly decreased whole-brain global efficiency, degree centrality, and participation coefficients, as well as increased shortest path length and betweenness centrality compared to unexposed controls. Individuals with PAE had decreased connectivity between the attention, somatomotor, and default mode networks compared to controls. This study demonstrates decreased structural white matter connectivity in children and adolescents with PAE at a whole-brain level, suggesting widespread alterations in how networks are connected with each other. This decreased connectivity may underlie cognitive and behavioural difficulties in children with PAE.

Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores

Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions-particularly prefrontal-parietal and basal ganglia-whereas the network pattern differs between genders.

Negatively Linking Connector Networks in Cognitive Control of Affective Pictures

Cognitive control of emotions depends on intermodular long-distance communications. However, negative connections between connector hubs are removed by traditional hard-thresholding approach in graph-theoretical research. Using soft-thresholding approach to reserve negative links, we explore time-varying features of connector hubs in intermodular communications during cognitive control of affective pictures. We develop a novel approach to sparse functional networks and construct negatively linking connector networks for positive, negative, and neutral pictures. We find that consisting of flexible hubs, the frontoparietal system dynamically top–down inhibits neural activities through negative connections from the salience subnetwork and visual processing area. Moreover, the shared connectors form functional backbones that dynamically reconfigure according to differently-valenced pictures in order to coordinate both stability and flexibility of cognitive connector networks. These results reveal the necessity of conserving negative links between intermodular communications in chronnectome research and deepen the understanding of how connector networks dynamically evolute during cognitive control of affective processing.

The brain's functional connectome in young children with prenatal alcohol exposure

Prenatal alcohol exposure (PAE) can lead to altered brain function and structure, as well as lifelong cognitive, behavioral, and mental health difficulties. Previous research has shown reduced brain network efficiency in older children and adolescents with PAE, but no imaging studies have examined brain differences in young children with PAE, at an age when cognitive and behavioral problems often first become apparent. The present study aimed to investigate the brain's functional connectome in young children with PAE using passive viewing fMRI. We analyzed 34 datasets from 26 children with PAE aged 2-7 years and 215 datasets from 87 unexposed typically-developing children in the same age range. The whole brain functional connectome was constructed using functional connectivity analysis across 90 regions for each dataset. We examined intra- and inter-participant stability of the functional connectome, graph theoretical measurements, and their correlations with age. Children with PAE had similar inter- and intra-participant stability to controls. However, children with PAE, but not controls, showed increasing intra-participant stability with age, suggesting a lack of variability of intrinsic brain activity over time. Inter-participant stability increased with age in controls but not in children with PAE, indicating more variability of brain function across the PAE population. Global graph metrics were similar between children with PAE and controls, in line with previous studies in older children. This study characterizes the functional connectome in young children with PAE for the first time, suggesting that the increased brain variability seen in older children develops early in childhood, when participants with PAE fail to show the expected age-related increases in inter-individual stability.

Sex Differences in Functional Brain Networks for Language

Men and women process language differently, but how the brain functions to support this difference is poorly understood. A few studies reported sex influences on brain activation for language, whereas others failed to detect the difference at the functional level. Recent advances of brain network analysis have shown great promise in picking up brain connectivity differences between sexes, leading us to hypothesize that the functional connections among distinct brain regions for language may differ in males and females. To test this hypothesis, we scanned 58 participants’ brain activities (28 males and 30 females) in a semantic decision task using functional magnetic resonance imaging. We found marked sex differences in dynamic interactions among language regions, as well as in functional segregation and integration of brain networks during language processing. The brain network differences were further supported by a machine learning analysis that accurately discriminated males from females using the multivariate patterns of functional connectivity. The sex-specific functional brain connectivity may constitute an essential neural basis for the long-held notion that men and women process language in different ways. Our finding also provides important implications for sex differences in the prevalence of language disorders, such as dyslexia and stuttering.

A Critical Comparison of Pipelines for Structural Brain Network Analysis

The human brain could be understood as a vast network composed of interconnected regions forming hierarchical and overlapping subnetworks. Cortical thickness (CT) correlations, as measure of structural connectedness, can be transformed into structural networks. Those can be analyzed via cluster detection algorithms to investigate their organization. An analysis pipeline from CT to links, networks, clusters and beyond is composed of a lot of consecutive reasoned or just arbitrary steps. How much can different pipeline components alter the result? Which step is to be considered most influential? In order to give a sufficient answer, we critically compare 96 different pipelines. The results of this study are to some extent surprising as the choice of a specific CT correlation and correction procedure can lead to more diverse results than the decision between taking only absolute CT correlations or ignoring all negative ones. Even more crucial, exemplary cluster detectors were found to pair-wisely correlate from r = 0.98 to even r = -0.20 on the same data. Thus, a summary of multiple detector results with different but suited properties is highly advisable until a theory based neuroscientific recommendation for the best approach will be found.

Visual and Action-control Expressway Associated with Efficient Information Transmission in Elite Athletes

Effective information transmission for open skill performance requires fine-scale coordination of distributed networks of brain regions linked by white matter tracts. However, how patterns of connectivity in these anatomical pathways may improve global efficiency remains unclear. In this study, we hypothesized that the feeder edges in visual and motor systems have the potential to become "expressways" that increase the efficiency of information communication across brain networks of open skill experts. Thirty elite athletes and thirty novice subjects were recruited to participate in visual tracking and motor imagery tasks. We collected structural imaging data from these subjects, and then resolved structural neural networks using deterministic tractography to identify streamlines connecting cortical and subcortical brain regions of each participant. We observed that superior skill performance in elite athletes was associated with increased information transmission efficiency in feeder edges distributed between orbitofrontal and basal ganglia modules, as well as among temporal, occipital, and limbic system modules. These findings suggest that there is an expressway linking visual and action-control system of skill experts that enables more efficient interactions of peripheral and central information in support of effective performance of an open skill.

Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks

Structural and functional differences in brain hemispheric asymmetry have been well documented between female and male adults. However, potential differences in the connectivity patterns of the rich-club organization of hemispheric structural networks in females and males remain to be determined. In this study, diffusion tensor imaging was used to construct hemispheric structural networks in healthy subjects, and graph theoretical analysis approaches were applied to quantify hemisphere and gender differences in rich-club organization. The results showed that rich-club organization was consistently observed in both hemispheres of female and male adults. Moreover, a reduced level of connectivity was found in the left hemisphere. Notably, rightward asymmetries were mainly observed in feeder and local connections among one hub region and peripheral regions, many of which are implicated in visual processing and spatial attention functions. Additionally, significant gender differences were revealed in the rich-club, feeder, and local connections in rich-club organization. These gender-related hub and peripheral regions are involved in emotional, sensory, and cognitive control functions. The topological changes in rich-club organization provide novel insight into the hemisphere and gender effects on white matter connections and underlie a potential network mechanism of hemisphere- and gender-based differences in visual processing, spatial attention and cognitive control.

Motor skill learning induces brain network plasticity: A diffusion-tensor imaging study

Motor skills and the acquisition of brain plasticity are important topics in current research. The development of non-invasive white matter imaging technology, such as diffusion-tensor imaging and the introduction of graph theory make it possible to study the effects of learning skills on the connection patterns of brain networks. However, few studies have characterized the brain network topological features of motor skill learning, especially open skill. Given the need to interact with environmental changes in real time, we hypothesized that the brain network of high-level open-skilled athletes had higher transmission efficiency and stronger interaction in attention, visual and sensorimotor networks. We selected 21 high-level basketball players and 25 ordinary individuals as control subjects, collected their DTI data, built a network of brain structures, and used graph theory to analyze and compare the network properties of the two groups at global and regional levels. In addition, we conducted a correlation analysis on the training years of high-level athletes and brain network nodal parameters on the regional level to assess the relationship between brain network topological characteristics and skills learning. We found that on the global-level, the brain network of high-level basketball players had a shorter path length, small-worldness, and higher global efficiency. On the regional level, the brain nodes of the high-level athletes had nodal parameters that were significantly higher than those of control groups, and were mainly distributed in the visual network, the default mode network, and the attention network. The changes in brain node parameters were significantly related to the number of training years.

Hemispheric Module-Specific Influence of the X Chromosome on White Matter Connectivity: Evidence from Girls with Turner Syndrome

Turner syndrome (TS) is caused by the congenital absence of all or part of one of the X chromosomes in females, offering a valuable human “knockout model” to study the functioning patterns of the X chromosome in the human brain. Little is known about whether and how the loss of the X chromosome influences the brain structural wiring patterns in human. We acquired a multimodal MRI dataset and cognitive assessments from 22 girls with TS and 21 age-matched control girls to address these questions. Hemispheric white matter (WM) networks and modules were derived using refined diffusion MRI tractography. Statistical comparisons revealed a reduced topological efficiency of both hemispheric networks and bilateral parietal modules in TS girls. Specifically, the efficiency of right parietal module significantly mediated the effect of the X chromosome on working memory performance, indicating that X chromosome loss impairs working memory performance by disrupting this module. Additionally, TS girls showed structural and functional connectivity decoupling across specific within- and between-modular connections, predominantly in the right hemisphere. These findings provide novel insights into the functional pathways in the brain that are regulated by the X chromosome and highlight a module-specific genetic contribution to WM connectivity in the human brain.

Altered structural and functional brain network overall organization predict human intertemporal decision-making

Intertemporal decision-making is naturally ubiquitous to us: individuals always make a decision with different consequences occurring at different moments. These choices are invariably involved in life-changing outcomes regarding marriage, education, fertility, long-term well-being, and even public policy. Previous studies have clearly uncovered the neurobiological mechanism of the intertemporal decision in the schemes of regional location or sub-network. However, it still remains unclear how to characterize intertemporal behavior with multimodal whole-brain network metrics to date. Here, we combined diffusion tensor image and resting-state functional connectivity MRI technology, in conjunction with graph-theoretical analysis, to explore the link between topological properties of integrated structural and functional whole-brain networks and intertemporal decision-making. Graph-theoretical analysis illustrated that the participants with steep discounting rates exhibited the decreased global topological organizations including small-world and rich-club regimes in both functional and structural connectivity networks, and reflected the dreadful local topological dynamics in the modularity of functional connectome. Furthermore, in the cross-modalities configuration, the same relationship was predominantly observed for the coupling of structural-functional connectivity as well. Above topological metrics are commonly indicative of the communication pattern of simultaneous global and local parallel information processing, and it thus reshapes our accounts on intertemporal decision-making from functional regional/sub-network scheme to multimodal brain overall organization.

Structural white matter networks in myotonic dystrophy type 1

The myriad of neuropsychiatric manifestations reported in myotonic dystrophy type 1 may have its origin in alterations of complex brain network interactions at the structural level. In this study, we tested the hypothesis that altered white matter microstructural integrity and network organisation were present in a cohort of individuals with DM1 compared to unaffected controls, which was expected to be associated with CNS related disease manifestations of DM1. We performed a cross-sectional neuropsychological assessment and brain MRI in 25 myotonic dystrophy type 1 (DM1) patients and 26 age, sex and educational level matched unaffected controls. Patients were recruited from the Dutch cohort of the OPTIMISTIC study, a concluded trial which had included ambulant, genetically confirmed DM1 patients who were severely fatigued. We applied graph theoretical analysis on structural networks derived from diffusion tensor imaging (DTI) data and deterministic tractography to determine global and local network properties and performed group-wise comparisons. Furthermore, we analysed the following variables from structural MRI imaging: semi-quantitative white matter hyperintensity load andwhite matter tract integrity using tract-based spatial statistics (TBSS). Structural white matter networks in DM1 were characterised by reduced global efficiency, local efficiency and strength, while the network density was compatible to controls. Other findings included increased white matter hyperintensity load, and diffuse alterations of white matter microstructure in projection, association and commissural fibres. DTI and network measures were associated (partial correlations coefficients ranging from 0.46 to 0.55) with attention (d2 Test), motor skill (Purdue Pegboard test) and visual-constructional ability and memory (copy subtest of the Rey-Osterrieth Complex Figure Test). DTI and network measures were not associated with clinical measures of fatigue (checklist individual strength, fatigue subscale) or apathy (apathy evaluation scale - clinician version). In conclusion, our study supports the view of brain involvement in DM1 as a complex network disorder, characterised by white matter network alterations that may have relevant neuropsychological correlations. This work was supported by the European Community's Seventh Framework Programme (FP7/2007-2013; grant agreement n° 305,697) and the Marigold Foundation.

Alterations in structural rich-club connectivity of the precuneus are associated with depressive symptoms among individuals with subjective memory complaints

The association between subjective memory complaints (SMCs) and depressive symptoms has been widely reported and both have been regarded as risk factors for dementia, such as Alzheimer's disease (AD). Although SMCs arise as early as in middle age, the exact neural correlates of comorbid depressive symptoms among individuals who are middle-aged and with SMCs have not yet been well investigated. Because rich-club organization of the brain plays a key role in the pathophysiology of various neuropsychiatric disorders, the investigation of rich club organization may provide insight regarding the neurobiological mechanisms of depressive symptoms in SMCs. In the current study, we compared the rich-club organization in the structural brain connectivity between individuals who have SMCs along with depressive symptoms (SMCD) and individuals with SMCs but without depressive symptoms (SMCO). A total of 53 individuals with SMCD and 91 individuals with SMCO participated in the study. For all participants, high-resolution, T1-weighted images and diffusion tensor images were obtained, and the network analysis was performed. Individuals with SMCD had lower connectivity strength between the precuneus and other rich-club nodes than those with SMCO, which was significant after adjusting for potential confounders. Our findings suggest that disruptions of rich-club connectivity strength of the precuenus are associated with depressive symptoms in middle-aged individuals with SMCs. Given that the precuneus is one of the commonly affected regions in the early stages of AD, our findings may imply that the concomitant depressive symptoms in middle-aged individuals with SMCs could reflect structural alterations related to AD.

Normative pathways in the functional connectome

Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.

Reproducibility of structural brain connectivity and network metrics using probabilistic diffusion tractography

The structural connectivity network constructed using probabilistic diffusion tractography can be characterized by the network metrics. In this study, short-term test-retest reproducibility of structural networks and network metrics were evaluated on 30 subjects in terms of within- and between-subject coefficient of variance (CV_ws, CV_bs), and intra class coefficient (ICC) using various connectivity thresholds. The short-term reproducibility under various connectivity thresholds were also investigated when subject groups have same or different sparsity. In summary, connectivity threshold of 0.01 can exclude around 80% of the edges with CV_ws = 73.2 ± 37.7%, CV_bs = 119.3 ± 44.0% and ICC = 0.62 ± 0.19. The rest 20% edges have CV_ws < 45%, CV_bs < 90%, ICC = 0.75 ± 0.12. The presence of 1% difference in the sparsity can cause additional within-subject variations on network metrics. In conclusion, applying connectivity thresholds on structural network to exclude spurious connections for the network analysis should be considered as necessities. Our findings suggest that a connectivity threshold over 0.01 can be applied without significant effect on the short-term when network metrics are evaluated at the same sparsity in subject group. When the sparsity is not the same, the procedure of integration over various connectivity thresholds can provide reliable estimation of network metrics.

Precision medicine and drug development in Alzheimer's disease: the importance of sexual dimorphism and patient stratification

Neurodegenerative diseases (ND) are among the leading causes of disability and mortality. Considerable sex differences exist in the occurrence of the various manifestations leading to cognitive decline. Alzheimer's disease (AD) exhibits substantial sexual dimorphisms and disproportionately affects women. Women have a higher life expectancy compared to men and, consequently, have more lifespan to develop AD. The emerging precision medicine and pharmacology concepts - taking into account the individual genetic and biological variability relevant for disease risk, prevention, detection, diagnosis, and treatment - are expected to substantially enhance our knowledge and management of AD. Stratifying the affected individuals by sex and gender is an important basic step towards personalization of scientific research, drug development, and care. We hypothesize that sex and gender differences, extending from genetic to psychosocial domains, are highly relevant for the understanding of AD pathophysiology, and for the conceptualization of basic/translational research and for clinical therapy trial design.