Gender differences in the structural connectome of the teenage brain revealed by generalized q-sampling MRI

Sex-related patterns of functional brain networks in children and adolescents with autism spectrum disorder

Although numerous studies have emphasized the male predominance in autism spectrum disorder (ASD), how sex differences are related to the topological organization of functional networks remains unclear. This study utilized imaging data from 86 ASD (43 females, aged 7-18 years) and 86 typically developing controls (TCs) (43 females, aged 7-18 years) obtained from Autism Brain Imaging Data Exchange databases, constructed individual whole-brain functional networks, used a graph theory analysis to compute topological metrics, and assessed sex-related differences in topological metrics using a 2 × 2 factorial design. At the global level, females with ASD exhibited significantly higher cluster coefficient and local efficiency than female TCs, while no significant difference was observed between males with ASD and male TCs. Meanwhile, the neurotypical sex differences in cluster coefficient and local efficiency observed in TCs were not present in ASD. At the nodal level, ASD exhibited abnormal nodal centrality in the left middle temporal gyrus.

Utility of diffusion tensor imaging and generalized q-sampling imaging for predicting short-term clinical effect of deep brain stimulation in Parkinson's disease

PURPOSE

To assess whether diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) metrics could preoperatively predict the clinical outcome of deep brain stimulation (DBS) in patients with Parkinson's disease (PD).

METHODS

In this single-center retrospective study, from September 2021 to March 2023, preoperative DTI and GQI examinations of 44 patients who underwent DBS surgery, were analyzed. To evaluate motor functions, the Unified Parkinson's Disease Rating Scale (UPDRS) during on- and off-medication and Parkinson's Disease Questionnaire-39 (PDQ-39) scales were used before and three months after DBS surgery. The study population was divided into two groups according to the improvement rate of scales: ≥ 50% and < 50%. Five target regions, reported to be affected in PD, were investigated. The parameters having statistically significant difference were subjected to a receiver operating characteristic (ROC) analysis.

RESULTS

Quantitative anisotropy (qa) values from globus pallidus externus, globus pallidus internus (qa_Gpi), and substantia nigra exhibited significant distributional difference between groups in terms of the improvement rate of UPDRS-3 scale during on-medication (p = 0.003, p = 0.0003, and p = 0.0008, respectively). In ROC analysis, the best parameter in predicting DBS response included qa_Gpi with a cut-off value of 0.01370 achieved an area under the ROC curve, accuracy, sensitivity, and specificity of 0.810, 73%, 62.5%, and 85%, respectively. Optimal cut-off values of ≥ 0.01864 and ≤ 0.01162 yielded a sensitivity and specificity of 100%, respectively.

CONCLUSION

The imaging parameters acquired from GQI, particularly qa_Gpi, may have the ability to non-invasively predict the clinical outcome of DBS surgery.

MRI volumetry and diffusion tensor imaging for diagnosis and follow-up of late post-traumatic injuries

BACKGROUND

Traumatic Brain Injury (TBI) is a major cause of acquired disability and can cause devastating and progressive post-traumatic encephalopathy. TBI is a dynamic condition that continues to evolve over time. A better understanding of the pathophysiology of these late lesions is important for the development of new therapeutic strategies.

OBJECTIVES

The primary objective was to compare the ability of fluid-attenuated reversion recovery (FLAIR) and diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) markers to identify participants with a Glasgow outcome scale extended (GOS-E) score of 7-8, up to 10 years after their original TBI. The secondary objective was to study the brain regionalization of DTI markers. Finally, we analyzed the evolution of late-developing brain lesions using repeated MRI images, also taken up to 10 years after the TBI.

METHODS

In this retrospective study, participants were included from a cohort of people hospitalized following a severe TBI. Following their discharge, they were followed-up and clinically assessed, including a DTI-MRI scan, between 2012 and 2016. We performed a cross-sectional analysis on 97 participants at a median (IQR) of 5 years (3-6) post-TBI, and a further post-TBI longitudinal analysis over 10 years on a subpopulation (n = 17) of the cohort.

RESULTS

Although the area under the curve (AUC) of FLAIR, fractional anisotropy (FA), and mean diffusivity (MD) were not significantly different, only the AUC of FA was statistically greater than 0.5. In addition, only the FA was correlated with clinical outcomes as assessed by GOS-E score (P<10-4). On the cross-sectional analysis, DTI markers allowed study post-TBI white matter lesions by region. In the longitudinal subpopulation analysis, the observed number of brain lesions increased for the first 5 years post-TBI, before stabilizing over the next 5 years.

CONCLUSIONS

This study has shown for the first time that post-TBI lesions can present in a two-phase evolution. These results must be confirmed in larger studies. French Data Protection Agency (Commission nationale de l'informatique et des libertés; CNIL) study registration no: 1934708v0.

Impaired segregation of the attention deficit hyperactivity disorder related pattern in children

BACKGROUND

Inattention is a key characteristic of attention deficit hyperactivity disorder (ADHD). Specific brain abnormalities associated with this symptom form a discernible pattern related with ADHD in children (i.e., ADHD related pattern) in our earlier research. The developmental processes of segregation and integration may be crucial to ADHD. However, how brains reconfigure these processes of the ADHD related pattern in different subtypes of ADHD and across sexes remain unclear.

METHODS

Nested-spectral partition method was applied to identify effects of subtype and sex on segregation and integration of the ADHD related pattern, using 145 ADHD patients and 135 typically developing controls (TDC) aged 7-14. Relationships between the measures and inattention symptoms were also investigated.

RESULTS

Children with ADHD exhibited lower segregation of the ADHD related pattern (p = 1.17 × 10-8) than TDCs. Only the main effect of subtype was significant (p = 1.14 × 10-5). Both ADHD-C (p = 2.16 × 10-6) and ADHD-I (p = 2.87 × 10-6) patients had lower segregation components relative to the TDC. Moreover, segregation components were negatively correlated with inattention scores.

CONCLUSIONS

This study identified impaired segregation in the ADHD related pattern of children with ADHD and found shared neural bases among different subtypes and sexes.

Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review

BACKGROUND

Suicide is a global public health issue causing around 700,000 deaths worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can help lower the suicide-related mortality rate. This review aimed to investigate the feasibility of suicidality identification by applying supervised Machine Learning (ML) methods to Magnetic Resonance Imaging (MRI) data.

METHODS

We conducted a systematic search on PubMed, Scopus, and Web of Science to identify studies examining suicidality by applying ML methods to MRI features. Also, the Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed for the quality assessment.

RESULTS

23 studies met the inclusion criteria. Of these, 20 developed prediction models without external validation and 3 developed prediction models with external validation. The performance of ML models varied among the reviewed studies, with the highest reported values of accuracies and Area Under the Curve (AUC) ranging from 51.7 % to 100 % and 0.52 to 1, respectively. Over half of the studies that reported accuracy (12/21) or AUC (13/16) achieved values of ≥0.8. Our comparative analysis indicated that deep learning exhibited the highest predictive performance compared to other ML models. The most commonly identified discriminative imaging features were resting-state functional connectivity and grey matter volume within prefrontal-limbic structures.

LIMITATIONS

Small sample sizes, lack of external validation, heterogeneous study designs, and ML model development.

CONCLUSIONS

Most of the studies developed ML models capable of ML-based suicide identification, although ML models' predictive performance varied across the reviewed studies. Thus, further well-designed is necessary to uncover the true potential of different ML models in this field.

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

BACKGROUND

Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.

PURPOSE

To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.

STUDY TYPE

Prospective.

SUBJECTS

A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging.

ASSESSMENT

Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.

STATISTICAL TESTS

The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).

DATA CONCLUSION

The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 2.

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network

PURPOSE

The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers.

METHODS

Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability.

RESULTS

The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware.

CONCLUSIONS

The results show low variability of connectivity topological metrics across sites running a harmonised protocol.

Comparative survey of multigraph integration methods for holistic brain connectivity mapping

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.

Diffusion tensor imaging reveals sex differences in pain sensitivity of rats

Studies on differences in brain structure and function according to sex are reported to contribute to differences in behavior and cognition. However, few studies have investigated brain structures or used tractography to investigate gender differences in pain sensitivity. The identification of tracts involved in sex-based structural differences that show pain sensitivity has remained elusive to date. Here, we attempted to demonstrate the sex differences in pain sensitivity and to clarify its relationship with brain structural connectivity. In this study, pain behavior test and brain diffusion tensor imaging (DTI) were performed in male and female rats and tractography was performed on the whole brain using fiber tracking software. We selected eight brain regions related to pain and performed a tractography analysis of these regions. Fractional anisotropy (FA) measurements using automated tractography revealed sex differences in the anterior cingulate cortex (ACC)-, prefrontal cortex (PFC)-, and ventral posterior thalamus-related brain connections. In addition, the results of the correlation analysis of pain sensitivity and DTI tractography showed differences in mean, axial, and radial diffusivities, as well as FA. This study revealed the potential of DTI for exploring circuits involved in pain sensitivity. The behavioral and functional relevance's of measures derived from DTI tractography is demonstrated by their relationship with pain sensitivity.

Brain architecture-based vulnerability to traumatic injury

The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups’ dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.

Assessment of disrupted brain functional connectome in tuberous sclerosis complex using resting-state fMRI

ABSTRACT

Tuberous sclerosis complex (TSC) is a rare genetic disorder with multisystem involvement. TSC is characterized by benign hamartomas in multiple organs, including the brain, and its clinical phenotypes may be associated with abnormal functional connections. We aimed to use resting-state functional connectivity to provide findings of disrupted functional brain networks in TSC patients using graph theoretical analysis (GTA) and network-based statistic (NBS) analysis.Forty TSC patients (age = 24.11+/-11.44 years old) and 18 age-matched (25.13+/- 10.01 years old) healthy controls were recruited; they underwent resting-state functional magnetic resonance imaging using a 3T magnetic resonance imaging scanner. After image preprocessing and removing physiological noises, GTA was used to calculate the topological parameters of the brain network. NBS analysis was then used to determine the differences in cerebrum functional connectivity between the 2 groups.In GTA, several topological parameters, including the clustering coefficient, local efficiency, transitivity, and modularity, were better in controls than in TSC patients (P < .05). In NBS analysis, the edges of the brain networks between the groups were compared. One subnetwork showed more edges in controls than in TSC patients (P < .05), including the connections from the frontal lobe to the temporal and parietal lobe.The study results provide the findings on disrupted functional connectivity and organization in TSC patients compared with controls. The findings may help better understand the underlying physiological mechanisms of brain connection in TSC.

Obsessive-compulsive symptoms and resting-state functional characteristics in pre-adolescent children from the general population

While functional brain characteristics of obsessive-compulsive disorder have been extensively studied, literature on network topology and subnetwork connectivity related to obsessive-compulsive symptoms (OCS) is sparse. Here we investigated the functional brain characteristics of OCS in children from the general population using a multiscale approach. Since we previously observed OCS-related differences in thalamus morphology, we also focused on the network participation of thalamic subregions. The study included 1701 participants (9-12 years) from the population-based Generation R study. OCS were measured using the Short Obsessive-Compulsive Disorder Screener. We studied the brain network at multiple scales: global network topology, subnetwork connectivity and network participation of thalamic nodes (pre-registration: https://osf.io/azr9c ). Modularity, small-worldness and average participation coefficient were calculated on the global scale. We used a data-driven consensus community approach to extract a partition of five subnetworks involving thalamic subregions and calculate the within- and between-subnetwork functional connectivity and topology. Multiple linear regression models were fitted to model the relationship between OCS and functional brain measures. No significant associations were found when using our preregistered definition of probable OCS. However, post-hoc analyses showed that children endorsing at least one OCS (compared with controls) had higher modularity, lower connectivity between frontoparietal, limbic and visual networks as well as altered participation of the lateral prefrontal thalamus node. Our results suggest that network characteristics of OCS in children from the general population are partly symptom-specific and severity-dependent. Thorough assessment of symptom dimensions can deepen our understanding of OCS-related brain networks.

A cup of black coffee with GI, please! Evidence of geographical indication influence on a coffee tasting experiment

Geographical Indication (GI) certifications enable producers to set production standards and create competitive advantage based on product's origin. In a coffee tasting experiment, brain responses to origin information of 40 participants, grouped equally by gender and involvement level, were collected by electroencephalography to verify: the impact of the GI cue in four brain waves (alpha, beta, delta and theta) and two brain lobes (frontal and temporal); preference; gender and involvement moderations. Results show that women presented power differences in both hemispheres, more channels/waves, which indicates greater sensitivity to the origin cue. Men presented power differences in fewer channels/waves. It is observed that involvement has a tenuous moderation effect when compared to gender. As for preference, the analysis of delta and theta waves indicated that men preferred coffee with GI; while women preferred coffee without GI, even though most of them indicated the opposite when verbally asked at the end of the tasting section.

Pregnancy leads to changes in the brain functional network: a connectome analysis

Pregnancy leads to long-lasting changes in human brain structure; however, little is known regarding alterations in the topological organization of functional networks. In this study, we investigated the effect of pregnancy on human brain function networks. Resting-state fMRI data was collected from eighteen primiparous mothers and twenty-four nulliparous control women of similar age, education level and body mass index (BMI). The functional brain network and topological properties were calculated by using GRETNA toolbox. The demographic data differences between two groups were computed by the independent two sample t-test. We tested group differences in network metrics' area under curve (AUC) using non-parametric permutation test of 1,000 permutations and corrected for false discovery rate (FDR). Differences in regional networks between groups were evaluated using non-parametric permutation tests by network-based statistical analysis (NBS). Compared with the nulliparous control women, a hub node changed from left inferior temporal gyrus to right precentral gyrus in primiparous mothers, while primiparous mothers showed enhanced network global efficiency (p = 0.247), enhanced local efficiency (p = 0.410), larger clustering coefficient (p = 0.410), but shorter characteristic path length (p = 0.247), smaller normalized clustering coefficient (p = 0.111), and shorter normalized characteristic path length (p = 0.705). Although both groups of functional networks have small-world property (σ > 1), the σ values of primiparous mothers were decreased significantly. NBS evaluation showed the majority of altered connected sub-network in the primiparous mothers occurred in the bilateral frontal lobe gyrus (p < 0.05). Altered functional network metrics and an abnormal sub-network were found in primiparous mothers, suggested that pregnancy may lead to changes in the brain functional network.

Estimation of gender-specific connectional brain templates using joint multi-view cortical morphological network integration

The estimation of a connectional brain template (CBT) integrating a population of brain networks while capturing shared and differential connectional patterns across individuals remains unexplored in gender fingerprinting. This paper presents the first study to estimate gender-specific CBTs using multi-view cortical morphological networks (CMNs) estimated from conventional T1-weighted magnetic resonance imaging (MRI). Specifically, each CMN view is derived from a specific cortical attribute (e.g. thickness), encoded in a network quantifying the dissimilarity in morphology between pairs of cortical brain regions. To this aim, we propose Multi-View Clustering and Fusion Network (MVCF-Net), a novel multi-view network fusion method, which can jointly identify consistent and differential clusters of multi-view datasets in order to capture simultaneously similar and distinct connectional traits of samples. Our MVCF-Net method estimates a representative and well-centered CBTs for male and female populations, independently, to eventually identify their fingerprinting regions of interest (ROIs) in four main steps. First, we perform multi-view network clustering model based on manifold optimization which groups CMNs into shared and differential clusters while preserving their alignment across views. Second, for each view, we linearly fuse CMNs belonging to each cluster, producing local CBTs. Third, for each cluster, we non-linearly integrate the local CBTs across views, producing a cluster-specific CBT. Finally, by linearly fusing the cluster-specific centers we estimate a final CBT of the input population. MVCF-Net produced the most centered and representative CBTs for male and female populations and identified the most discriminative ROIs marking gender differences. The most two gender-discriminative ROIs involved the lateral occipital cortex and pars opercularis in the left hemisphere and the middle temporal gyrus and lingual gyrus in the right hemisphere.

Prevalence of the interthalamic adhesion in the human brain: a review of literature

The interthalamic adhesion (IA) is a midline structure connecting the two thalami. Though it has been studied for centuries its exact function remains elusive. Early studies had noted its peculiar absence even among some healthy individuals. Population studies have investigated the differences in prevalence of IA in pathologic conditions and healthy controls. However, there is a general lack of consensus on IA prevalence in the healthy population. Understanding the true prevalence is critical in providing context for future studies, as well as uncovering further clues regarding IA's function. We systematically reviewed the existing literature to evaluate the prevalence of IA. The average prevalence among reviewed studies was higher than previously reported, at 87.3%. Studies utilizing magnetic resonance imaging rather than cadaveric specimens reported higher rates of IA prevalence. A higher prevalence among females was noted throughout the literature that persisted regardless of acquisition modality utilized.

A Differential Threshold of Breakfast, Caffeine and Food Groups May Be Impacting Mental Well-Being in Young Adults: The Mediation Effect of Exercise

Diet and exercise are known to influence mental health. However, the interaction between diet, dietary practices, and exercise and its impact on the mood of young adults (YA) is poorly understood. YA are inherently at risk for mental distress. They tend to consume a low-quality diet and are generally active. The purpose of the study was to assess these relationships through validating causal loop diagrams (CLD) that describe these connections by using a system dynamic (SD) modeling methodology. Adults 18-29 years were invited to complete the Food-Mood questionnaire. The anonymous questionnaire link was distributed to several institutional listservs and via several social media platforms targeting young adults. A multi-level analysis, including machine learning techniques, was used to assess these relationships. The key findings were then built into gender based CLD, which suggest that a differential repertoire may be needed to optimize diet quality, exercise, and mental well-being. Additionally, a potential net threshold for dietary factors and exercise may be needed to achieve mental well-being in young adults. Moreover, our findings suggest that exercise may boost the enhancing effect of food groups on mental well-being and may lessen the negative impact of dietary impediments of mental well-being.

Molecular mechanisms of sex differences in epilepsy and seizure susceptibility in chemical, genetic and acquired epileptogenesis

This article provides a succinct overview of sex differences in epilepsy and putative molecular mechanisms underlying sex differences in seizure susceptibility in chemical, genetic, and acquired epileptogenesis. The susceptibility to excitability episodes and occurrence of epileptic seizures are generally higher in men than women. The precise molecular mechanisms remain unclear, but differences in regional morphology and neural circuits in men and women may explain differential vulnerability to seizures and epileptogenic cascades. Changes in seizure sensitivity can be attributed to steroid hormones, including fluctuations in neurosteroids as well as neuroplasticity in their receptor signaling systems. Other potential neurobiological bases for sex differences in epilepsies include differences in brain development, neurogenesis, neuronal chloride homeostasis, and neurotrophic and glial responses. In catamenial epilepsy, a gender-specific neuroendocrine condition, epileptic seizures are most often clustered around a specific menstrual period in adult women. A deeper understanding of the molecular and neural network basis of sex differences in seizures and response to antiepileptic drugs is highly warranted for designing effective, sex-specific therapies for epilepsy, epileptogenesis, and seizure disorders.

Beyond the binary: Rethinking sex and the brain

The paper reviews the relations between sex and brain in light of the binary conceptualization of these relations and the challenges posed to it by the 'mosaic' hypothesis. Recent formulations of the binary framework range from arguing that the typical male brain is different from the typical female brain to claiming that brains are typically male or female because brain structure can be used to predict the sex category (female/male) of the brain's owner. These formulations are challenged by evidence that sex effects on the brain may be opposite under different conditions, that human brains are comprised of mosaics of female-typical and male-typical features, and that sex category explains only a small part of the variability in human brain structure. These findings led to a new, non-binary, framework, according to which mosaic brains reside in a multi-dimensional space that cannot meaningfully be reduced to a male-female continuum or to a binary variable. This framework may also apply to sex-related variables and has implications for research.

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

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

The association between prenatal endocrine-disrupting chemical exposure and altered resting-state brain fMRI in teenagers

Many studies have reported that prenatal exposure to endocrine-disrupting chemicals (EDCs) can cause adverse behavioral effects or cognitive dysfunction in children. This study aimed to investigate a relationship of the concentration of prenatal EDCs and brain function in teenagers. We recruited 59 mother-child pairs during the third trimester of pregnancy, and collected and examined the concentration of EDCs, such as heavy metals, phthalates and perfluoroalkyl substances (PFASs), in maternal urine and serum. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected in teenagers 13-16 years of age, and fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) were performed to find the association between maternal EDC concentrations and the functional development of the teenage brain. We found a correlation between MBP concentration and activity in the superior frontal gyrus, middle frontal gyrus, middle temporal gyrus and inferior temporal gyrus in the combined group of boys and girls. We also observed a correlation between MBzP concentration and activity in the anterior cingulum gyrus and insula in girls. We found a correlation between lead concentration and activity in the cuneus in the combined group. We also observed a correlation between MeHg concentration and activity in the superior temporal gyrus, caudate nucleus and putamen in the combined group. The PFOS results revealed a negative relationship between activity in the right putamen in boys, girls and the combined group after phthalate or heavy metals were applied as covariates. The PFNA results showed a negative correlation between activity in the left/right putamen and left caudate nucleus in boys, girls and the combined group after phthalate, heavy metals or PFOS were applied as covariates. We examined the correlations between maternal EDC concentrations and brain development and found that the associations with resting-state teenage brains in some circumstances are sex-related.

Larger tumor size and female gender suggest better tinnitus prognosis after surgical treatment in vestibular schwannoma patients with tinnitus

Background: Tinnitus is a subjective auditory phantom phenomenon which can be highly distressing. About 63%-75% vestibular schwannoma (VS) had a symptom of tinnitus.Objectives: To investigate the tinnitus maintenance mechanism from the view of tinnitus change after surgical treatment in VS patients.Material and methods: We conducted a retrospective study of VS patients with tinnitus from August 2008 to February 2019 and did follow-ups on their changes of tinnitus after surgery.Results: Among 298 VS cases, 201 of them had tinnitus symptom (67.4%). No statistical difference in the surgical approach was found between the tinnitus poor outcome and good outcome groups (p = .14), and statistical difference was found in gender (p = .04) and tumor size (p = .01) between the two groups. Binary logistic regression analysis revealed that gender (odds ratio [OR], 2.12; 95% CI, 1.10-4.08 [p = .03]) and tumor size (OR, 2.22; 95% CI, 1.16-4.24 [p = .02]) emerged as a significant and independent factor associated with the good outcome of tinnitus.Conclusions and significance: The results of this study confirmed that the cochlear nucleus of the brainstem and above part of the brainstem may play an important role in the maintenance of tinnitus.

How Creativity in STEAM Modules Intervenes with Self-Efficacy and Motivation

Many current curricula, in going beyond traditional goals, increasingly foster creativity in science classrooms, declaring creativity a core skill of the 21st century. For enhancing creativity in science classrooms, the subject Arts is considered to offer a potential way from STEM (Science, Technology, Engineering, Mathematics) to STEAM (STEM with Arts)). The Horizont-2020 project Creations prepared more than 100 creativity-enhancing STEAM modules based on the 5E instructional model. STEM subjects were mathematics, biology, physics, chemistry or technology, and often interdisciplinary for different school and class levels between the ages of nine and nineteen. All modules provided a social environment fostering creativity where students imagine, explore, experiment, test, manipulate, and speculate. Exemplarily, five modules including physics, math, and biology, were selected, for monitoring motivation and creativity. The first was measured on the level of career-motivation and self-efficacy, the latter focused on two sub-constructs: active cognition such as idea processing (Act), and a mental state of creative immersion (Flow). Subjects were a sample of 995 students (9–18 years). In summary, no gender impact or age effect appeared in any of the monitored variables. Participation intervened with Self-Efficacy and Act, while Career Motivation or Flow did not. Act as a cognitive variable associated with creativity might be more sensitive to changes, whereas Flow as a parameter measuring a state of mind related to emotion appears more stable. Path analysis supported the role of creativity for Career-Motivation by promoting Self-Efficacy. Conclusions for appropriate educational settings to foster STEAM environments are discussed.

A diffusion-weighted imaging tract-based spatial statistics study of autism spectrum disorder in preschool-aged children

BACKGROUND

The core symptoms of autism spectrum disorder (ASD) are widely theorized to result from altered brain connectivity. Diffusion-weighted magnetic resonance imaging (DWI) has been a versatile method for investigating underlying microstructural properties of white matter (WM) in ASD. Despite phenotypic and etiological heterogeneity, DWI studies in majority male samples of older children, adolescents, and adults with ASD have largely reported findings of decreased fractional anisotropy (FA) across several commissural, projection, and association fiber tracts. However, studies in preschool-aged children (i.e., < 30-40 months) suggest individuals with ASD have increased measures of WM FA earlier in development.

METHODS

We analyzed 127 individuals with ASD (85♂, 42♀) and 54 typically developing (TD) controls (42♂, 26♀), aged 25.1-49.6 months. Voxel-wise effects of ASD diagnosis, sex, age, and their interaction on DWI measures of FA, mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were investigated using tract-based spatial statistics (TBSS) while controlling mean absolute and relative motion.

RESULTS

Compared to TD controls, males and females with ASD had significantly increased measures of FA in eight clusters (threshold-free cluster enhancement p < 0.05) that incorporated several WM tracts including regions of the genu, body, and splenium of the corpus callosum, inferior frontal-occipital fasciculi, inferior and superior longitudinal fasciculi, middle and superior cerebellar peduncles, and corticospinal tract. A diagnosis by sex interaction was observed in measures of AD across six significant clusters incorporating areas of the body, genu, and splenium of the corpus collosum. In these tracts, females with ASD showed increased AD compared to TD females, while males with ASD showed decreased AD compared to TD males.

CONCLUSIONS

The current findings support growing evidence that preschool-aged children with ASD have atypical measures of WM microstructure that appear to differ in directionality from alterations observed in older individuals with the condition. To our knowledge, this study represents the largest sample of preschool-aged females with ASD to be evaluated using DWI. Microstructural differences associated with ASD largely overlapped between sexes. However, differential relationships of AD measures indicate that sex likely modulates ASD neuroanatomical phenotypes. Further longitudinal study is needed to confirm and quantify the developmental relationship of WM structure in ASD.

Sex differences in limbic network and risk-taking propensity in healthy individuals

Little is known about the structural neural substrates that may contribute to sex differences in risk-taking propensity (RTP). A close association between risk-seeking behavior and the emotional-regulation network led us to hypothesize that the sex differences in RTP would be associated with sex differences in brain morphometry of the limbic network (LN). We collected RTP scores using the bubble sheet version of the evaluation of risk (EVAR) scale and neuroanatomical data from 57 healthy individuals (29 males). The EVAR scale included sub-scales measuring recklessness/impulsivity, self-confidence, and need for control (NFC). We observed significant sex differences in NFC showing greater desire for control and dominance in males than females (multivariate analysis of covariance, MANCOVAN: p = .01). Morphometry analysis showed that it was only the right LN, which showed significant sex differences in normalized surface area, normalized cortical volume, and adjusted mean curvature index (females > males) at p < .01 (MANCOVAN, corrected for multiple comparisons). Correlation analysis showed that greater curvature of the right LN was significantly associated with lower desire for control in high-risk events (r = -.31, p = .02 at 95% CI of [-0.53, -0.05]). Our findings suggest that the normalized cortical measures could indicate specific sex differences in brain morphometry, particularly within the LN. The curvature index was the only differentiating factor for greater/lower propensity for risk-taking behavior in overall sample. Therefore, the LN and the curvature measures could be key biomarkers, which play an important role in predicting risk-taking behavior.

Neurobiological Sex Differences in Developmental Dyslexia

Understanding sex differences at the neurobiological level has become increasingly crucial in both basic and applied research. In the study of developmental dyslexia, early neuroimaging investigations were dominated by male-only or male-dominated samples, due at least in part to males being diagnosed more frequently. While recent studies more consistently balance the inclusion of both sexes, there has been little movement toward directly characterizing potential sex differences of the disorder. However, a string of recent work suggests that the brain basis of dyslexia may indeed be different in males and females. This potential sex difference has implications for existing models of dyslexia, and would inform approaches to the remediation of reading difficulties. This article reviews recent evidence for sex differences in dyslexia, discusses the impact these studies have on the understanding of the brain basis of dyslexia, and provides a framework for how these differential neuroanatomical profiles may develop.

Analysis of Human Brain Structure Reveals that the Brain "Types" Typical of Males Are Also Typical of Females, and Vice Versa

Findings of average differences between females and males in the structure of specific brain regions are often interpreted as indicating that the typical male brain is different from the typical female brain. An alternative interpretation is that the brain types typical of females are also typical of males, and sex differences exist only in the frequency of rare brain types. Here we contrasted the two hypotheses by analyzing the structure of 2176 human brains using three analytical approaches. An anomaly detection analysis showed that brains from females are almost as likely to be classified as “normal male brains,” as brains from males are, and vice versa. Unsupervised clustering algorithms revealed that common brain “types” are similarly common in females and in males and that a male and a female are almost as likely to have the same brain “type” as two females or two males are. Large sex differences were found only in the frequency of some rare brain “types.” Last, supervised clustering algorithms revealed that the brain “type(s)” typical of one sex category in one sample could be typical of the other sex category in another sample. The present findings demonstrate that even when similarity and difference are defined mathematically, ignoring biological or functional relevance, sex category (i.e., whether one is female or male), is not a major predictor of the variability of human brain structure. Rather, the brain types typical of females are also typical of males, and vice versa, and large sex differences are found only in the prevalence of some rare brain types. We discuss the implications of these findings to studies of the structure and function of the human brain.

Genetic variants in the CNTNAP2 gene are associated with gender differences among dyslexic children in China

Background

It is well known that males have a higher prevalence of developmental dyslexia (DD) than females. Although the mechanism underlying this gender difference remains unknown, the contactin-associated protein-like 2 (CNTNAP2) gene, which shows sex-specific patterns in some neurodevelopmental disorders, has attracted extensive attention. This study aimed to explore whether CNTNAP2 shows a sex-specific association with DD in a Chinese population.

Methods

Using genomic DNA samples of 726 students [372 cases (282 male, 90 female), 354 controls (267 male, 87 female)], we genotyped five SNPs of CNTNAP2. Gender-stratified logistic regression models were used to determine the relationships between the CNTNAP2 variants and DD.

Findings

After adjustment for the false discovery rate (FDR), two SNPs (rs3779031, rs987456) of CNTNAP2 were associated with DD risk in females but not in males. Female participants carrying the rs3779031 G allele had a lower risk of DD than those with the A genotype [GG vs AA: OR (95%CI) = 0.281 (0.097–0.814)]. The rs987456 CC genotype was associated with a decreased risk of DD in females [CC vs AA+CA: OR (95%CI) = 0.222 (0.078–0.628)]. Furthermore, the interaction between CNTNAP2 (rs987456) and environmental factors (scheduled reading time) played a protective role in females [OR (95%CI) = 0.431 (0.188–0.987)].

Interpretation

We performed a genetic association study on CNTNAP2 variants and DD. The sex specificity of CNTNAP2 in DD, along with the gene-environment interaction may help us to understand gender differences in DD.

Network-based approaches to examining stress in the adolescent brain

Exposure to stress, particularly in periods of rapid brain maturation such as adolescence, can profoundly influence developmental processes that undergird the organization of structural and functional brain networks and that may mediate the association between stressful experiences and maladaptive outcomes. While studies in translational developmental neuroscience often focus on how specific brain regions or targeted connections are altered by stress and psychiatric disease, the emerging field of network science may be especially valuable for elucidating the impact of stress on the intricate connectomics of the adolescent brain. Here we review recent studies that use graph theory and other network science approaches to understand normative adolescent brain development, effects of childhood maltreatment on the brain, and disorders characterized by pathological responses to stress in adolescents. Overall, these studies demonstrate that graph theory can be useful in identifying and quantifying developmental processes related to segregation, integration, and localized hub influence that are affected by stress exposure and that may lead to psychopathology. Finally, we discuss limitations in the current application of graph theory in this area and suggest what we believe are important directions for future work.

Brain structural networks and connectomes: the brain-obesity interface and its impact on mental health

PURPOSE

Obesity is a complex and multifactorial disease identified as a global epidemic. Convergent evidence indicates that obesity differentially influences patients with neuropsychiatric disorders providing a basis for hypothesizing that obesity alters brain structure and function associated with the brain's propensity toward disturbances in mood and cognition. Herein, we characterize alterations in brain structures and networks among obese subjects (ie, body mass index [BMI] ≥30 kg/m²) when compared with non-obese controls.

PATIENTS AND METHODS

We obtained noninvasive diffusion tensor imaging and generalized q-sampling imaging scans of 20 obese subjects (BMI=37.9±5.2 SD) and 30 non-obese controls (BMI=22.6±3.4 SD). Graph theoretical analysis and network-based statistical analysis were performed to assess structural and functional differences between groups. We additionally assessed for correlations between diffusion indices, BMI, and anxiety and depressive symptom severity (ie, Hospital Anxiety and Depression Scale total score).

RESULTS

The diffusion indices of the posterior limb of the internal capsule, corona radiata, and superior longitudinal fasciculus were significantly lower among obese subjects when compared with controls. Moreover, obese subjects were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese subjects compared with non-obese controls. Topological measures of clustering coefficient (C), local efficiency (E_local), global efficiency (E_global), and transitivity were significantly lower among obese subjects. Similarly, three sub-networks were identified to have decreased structural connectivity among frontal-temporal regions in obese subjects compared with non-obese controls.

CONCLUSION

We extend knowledge further by delineating structural interconnectivity alterations within and across brain regions that are adversely affected in individuals who are obese.