Sex differences in human behavior show adaptive complementarity: Males have better motor and spatial abilities, whereas females have superior memory and social cognition skills. Studies also show sex differences in human brains but do not explain this complementarity. In this work, we modeled the structural connectome using diffusion tensor imaging in a sample of 949 youths (aged 8-22 y, 428 males and 521 females) and discovered unique sex differences in brain connectivity during the course of development. Connection-wise statistical analysis, as well as analysis of regional and global network measures, presented a comprehensive description of network characteristics. In all supratentorial regions, males had greater within-hemispheric connectivity, as well as enhanced modularity and transitivity, whereas between-hemispheric connectivity and cross-module participation predominated in females. However, this effect was reversed in the cerebellar connections. Analysis of these changes developmentally demonstrated differences in trajectory between males and females mainly in adolescence and in adulthood. Overall, the results suggest that male brains are structured to facilitate connectivity between perception and coordinated action, whereas female brains are designed to facilitate communication between analytical and intuitive processing modes. diffusion imaging | gender differences S ex differences are of enduring scientific and societal interest because of their prominence in the behavior of humans and nonhuman species (1). Behavioral differences may stem from complementary roles in procreation and social structure; examples include enhanced motor and spatial skills and greater proclivity for physical aggression in males and enhanced verbally mediated memory and social cognition in females (2, 3). With the advent of neuroimaging, multiple studies have found sex differences in the brain (4) that could underlie the behavioral differences. Males have larger crania, proportionate to their larger body size, and a higher percentage of white matter (WM), which contains myelinated axonal fibers, and cerebrospinal fluid (5), whereas women demonstrate a higher percentage of gray matter after correcting for intracranial volume effect (6). Sex differences in the relative size and shape of specific brain structures have also been reported (7), including the hippocampus, amygdala (8, 9), and corpus callosum (CC) (10). Furthermore, developmental differences in tissue growth suggest that there is an anatomical sex difference during maturation (11,12), although links to observed behavioral differences have not been established.Recent studies have used diffusion tensor imaging (DTI) to characterize WM architecture and underlying fiber tracts by exploiting the anisotropic water diffusion in WM (13-15). Examination of DTI-based scalar measures (16) of fractional anisotropy (FA) and mean diffusivity (MD) has demonstrated diverse outcomes that include increased FA and decreased MD in males in major WM regions (17-19), higher CC-specific...

This paper presents a paradigm for generating a quantifiable marker of pathology that supports diagnosis and provides a potential biomarker of neuropsychiatric disorders, such as autism spectrum disorder (ASD). This is achieved by creating high-dimensional nonlinear pattern classifiers using Support Vector Machines (SVM), that learn the underlying pattern of pathology using numerous atlas-based regional features extracted from Diffusion Tensor Imaging (DTI) data. These classifiers, in addition to providing insight into the group separation between patients and controls, are applicable on a single subject basis and have the potential to aid in diagnosis by assigning a probabilistic abnormality score to each subject that quantifies the degree of pathology and can be used in combination with other clinical scores to aid in diagnostic decision. They also produce a ranking of regions that contribute most to the group classification and separation, thereby providing a neurobiological insight into the pathology. As an illustrative application of the general framework for creating diffusion based abnormality classifiers we create classifiers for a dataset consisting of 45 children with autism spectrum disorder (ASD) (mean age 10.5 ± 2.5 yrs) as compared to 30 typically developing (TD) controls ( mean age 10.3 ± 2.5 yrs). Based on the abnormality scores, a distinction between the ASD population and TD controls was achieved with 80% leave one out (LOO) cross-validation accuracy with high significance of p < 0.001, ~84% specificity and ~74% sensitivity. Regions that contributed to this abnormality score involved fractional anisotropy (FA) differences mainly in right occipital regions as well as in left superior longitudinal fasciculus, external and internal capsule while mean diffusivity (MD) discriminates were observed primarily in right occipital gyrus and right temporal white matter.

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Current techniques employ estimation of contrast ratios of the SNc, visualized on NMS-MRI, to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique that uses convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. Our technique not only performs with a superior testing accuracy (80%) as compared to contrast ratio-based classification (56.5% testing accuracy) and radiomics classifier (60.3% testing accuracy), but also supports discriminating PD from atypical parkinsonian syndromes (85.7% test accuracy). Moreover, it has the capability to locate the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc.

Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced network efficiency was associated with worse performance in executive function and verbal learning. The shortest path length measure was also correlated with family-reported FrSBe scores. These findings support the notion that the diffuse form of neuropathology caused by TBI results in alterations in structural connectivity that contribute to cognitive and real-world behavioral impairment.

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