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Neuroimaging studies have reported functional connectome aberrancies in autism spectrum disorder (ASD). However, the time-varying patterns of connectome topology in individuals with ASD and the connection between these patterns and gene expression profiles remain unknown.To investigate case-control differences in dynamic connectome topology, we conducted mega- and meta-analyses of resting-state functional magnetic resonance imaging data of 939 participants (440 patients with ASD and 499 healthy control subjects, all males) from 18 independent sites, selected from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Functional data were preprocessed and analyzed using harmonized protocols, and brain module dynamics was assessed using a multilayer network model. We further leveraged postmortem brain-wide gene expression data to identify transcriptomic signatures associated with ASD-related alterations in brain dynamics.Compared with healthy control participants, individuals with ASD exhibited a higher global mean and lower standard deviation of whole-brain module dynamics, indicating an unstable and less regionally differentiated pattern. More specifically, individuals with ASD showed higher module switching, primarily in the medial prefrontal cortex, posterior cingulate gyrus, and angular gyrus, and lower switching in the visual regions. These alterations in brain dynamics were predictive of social impairments in individuals with ASD and were linked with expression profiles of genes primarily involved in the regulation of neurotransmitter transport and secretion as well as with previously identified autism-related genes.This study is the first to identify consistent alterations in brain network dynamics in ASD and the transcriptomic signatures related to those alterations, furthering insights into the biological basis behind this disorder.

The human cerebral cortex is connected by intricate inter-areal wiring at the macroscale. The cortical hierarchy from primary sensorimotor to higher-order association areas is a unifying organizational principle across various neurobiological properties; however, previous studies have not clarified whether the connections between cortical regions exhibit a similar hierarchical pattern. Here, we identify a connectional hierarchy indexed by inter-individual variability of functional connectivity edges, which continuously progresses along a hierarchical gradient from within-network connections to between-network edges connecting sensorimotor and association networks. We found that this connectional hierarchy of variability aligns with both hemodynamic and electromagnetic connectivity strength and is constrained by structural connectivity strength. Moreover, the patterning of connectional hierarchy is related to inter-regional similarity in transcriptional and neurotransmitter receptor profiles. Using the Neurosynth cognitive atlas and cortical vulnerability maps in 13 brain disorders, we found that the connectional hierarchy of variability is associated with similarity networks of cognitive relevance and that of disorder vulnerability. Finally, we found that the prominence of this hierarchical gradient of connectivity variability declines during youth. Together, our results reveal a novel hierarchal organizational principle at the connectional level that links multimodal and multiscale human connectomes to individual variability in functional connectivity.

Individualized cortical network topography (ICNT) varies between people and exhibits great variability in the association networks in the human brain. However, these findings were mainly discovered in Western populations. It remains unclear whether and how ICNT is shaped by the non-Western populations. Here, we leveraged a multisession hierarchical Bayesian model to define individualized functional networks in White American and Han Chinese populations with data from both US and Chinese Human Connectome Projects. We found that both the size and spatial topography of individualized functional networks differed between White American and Han Chinese groups, especially in the heteromodal association cortex (including the ventral attention, control, language, dorsal attention, and default mode networks). Employing a support vector machine, we then demonstrated that ethnicity-related ICNT diversity can be used to identify an individual’s ethnicity with high accuracy (74%, pperm

The current study explored the feasibility of a newly developed self-guided digital intervention program TEA (Training for Emotional Adaptation) in alleviating depressive and anxiety symptoms, corresponding to the urgent call for remote mental health service. It is one of a few studies which adapted from theoretical models with effective intervention techniques. The first part involved 11 professional mental health practitioners giving feedback on the feasibility; while the second part involved a single-arm study with 32 participants recruited online, who went through the seven intervention sessions. The questionnaires were collected before, after, 14-days after and 30-days after the intervention. Moreover, 10 participants were invited to semi-structured interviews. Practitioners thought the TEA showed high professionalism (8.91/10) and is suitable for treating emotional symptoms (8.09/10). The Generalized estimating equation (GEE) model showed that the TEA significantly reduced their psychological symptoms, while the effects of the intervention were retained for 30-day post intervention (Cohen’s d >1). Thematic analysis revealed three main themes about future improvement, including content improvement, interaction improvement and bug-fixing. Taken together, current study supported the effectiveness of TEA, with the potential to address the urgent need for remote mental health service. Future randomized controlled trials to confirm the effectiveness are required.

IMPORTANCEAccurate prediction of suicide or non-suicidal self-injury (NSSI) among children within a uniform time frame is an essential but challenging task. Furthermore, few studies have comprehensively considered clinical, behavioural, and neurobiological factors to produce multi-dimensional prediction models.OBJECTIVETo examine predictive effects of general psychopathology, behavior inhibition system, and brain signature on children’s suicidality or NSSI transition.DESIGN, SETTING, AND PARTICIPANTSWe adopted a retrospective and longitudinal methodology by utilising the data from the Adolescent Brain Cognitive Development (ABCD) cohort. In total, 9332 individuals aged 9-10 years without any suicidality or non-suicidal self-injury (NSSI) history at baseline were included in our analyses. Then, four subgroups were generated based on whether they had developed suicide ideation (Healthy control [HC]-SI), NSSI (HC-NSSI) or suicide attempt (HC-SA) in a year, while the remaining group was considered a control group (HC-HC).MAIN OUTCOMES AND MEASURESParticipants suicidal behaviors and non-suicidal self-injury behaviors were assessed with the Kiddle Schedule for Affective Disorders and Schizophrenia. Meanwhile, general psychopathology (i.e.,p-factor) was calculated based on scores of Child Behavior Checklist, behavioral inhibition system (BIS) was assessed though BIS/BAS scale, and the brain morphometrics were also collected though sMRI. Multinomial logistic regression models were used for assessing the predictive effects of general psychopathology, behavioral inhibition system, and whole-brain cortical area on children’s STB and NSSI transition.RESULTSAs a result, we found higher general psychopathology in baseline predicted higher NSSI (1.52 [1.28-1.80]), SI (OR=1.34 [95%CI 1.17-1.53]) and SA (2.05 [1.34-3.14]) risk in a year. From a behavioural perspective, higher BIS sensitivity predicted higher SI (2.05 [1.61, 2.61], and NSSI (1.68 [1.24, 2.28]) in a year. From a neurobiological perspective, abnormalities in the cortical area of the superior insula, inferior frontal area, superior temporal area, and superior precentral area were all shown to be associated with children’s NSSI, SI and SA in the future.CONCLUSIONS AND RELEVANCEThis study is the first to look at the predictive factors for the different transitions of NSSI and suicidal behaviour from the biopsychosocial framework. Our findings offered empirical evidence on the predictive effect of baseline general psychopathology, BIS sensitivity and biological marker on children’s suicidality or NSSI in a year, providing early biomarkers for all types of transition. In this case, the early identification of those factors may facilitate the development of early prevention or intervention that could potentially alleviate more relevant public health issues.Key PointsQuestionCould general psychopathology, behavior inhibition system, and brain signature predict suicidality or NSSI transition in children?FindingsIn a longitudinal observational study (9332 children), higher general psychopathology at baseline predict higher risk of suicidality and NSSI transition in a year. Meanwhile, higher BIS sensitivity also predict higher risk of suicidality and NSSI transition. To note, abnormalities in the cortical area of the superior insula, inferior frontal area, superior temporal area, and superior precentral area were all shown to be associated with children’s suicidality and NSSI transition.MeaningThe early identification of biopsychosocial factors associated with suicidality or NSSI transition in children could facilitate early prevention.

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain’s functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSMStudy. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortex’s sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

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

SummaryBackgroundAdolescents’ suicidality and peer bullying are rising as a serious public health concern globally. However, the neural correlates of perpetrators and the impact of different types of bullying perpetration/peer victimisation on preadolescents needs elucidation. Besides, there has been a lack of research studying the cumulative risk pattern of bullying perpetration/peer victimisation on preadolescents with suicidality.MethodsWe adopted a retrospective and longitudinal methodology by utilising the data from the Adolescent Brain Cognitive Development (ABCD) cohort. Firstly, participants were assigned into two groups (i.e., perpetrators vs non-perpetrators) based on their bullying perpetration experiences. Next, different types of bullying perpetration/peer victimisation (i.e., overt, relational, and reputational) were extracted to evaluate their associations with suicidal ideation (SI), non-suicidal self-injury (NSSI), and suicide attempt (SA) separately. Then, participants were further assigned into four groups (i.e., bully-uninvolved preadolescents without suicidality/NSSI history (HC group), bully-uninvolved preadolescents with SA (SA group), preadolescents with both SA and bullying perpetration (SA+P group), and preadolescents with both SA and peer victimisation (SA+V group)). We used multinomial logistic regression models, analysis of variance (ANOVA), and brain network analysis for assessing potential associations in study’s objectives.FindingsIn total, 9992 individuals aged 11-12 years were included in our analysis. Of these individuals, 1111 (11.1%) were classified as perpetrators, and had significantly lower bilateral cortical volume in the superior frontal sulcus (SFS), lower functional connectivity within default mode network (DMN), and lower anti-correlation between DMN and dorsal attention network (DAN) than non-perpetrators. Additionally, preadolescents’ overt, relational, and reputational perpetration/ victimisation experiences were all shown to be associated with suicidality/NSSI, despite varying strengths, while overt perpetration showed the strongest association with SA (OR 3.6 [95%CI 2.4-5.4]), followed by overt victimisation (2.4 [1.6-3.5]). Besides, the SA+V group was characterised by the highest psychopathology among four groups, while the SA+P group was characterised by the highest aggression among four groups. Lastly, lower bilateral cortical volume in the precentral gyrus (PCG) while higher cortical volume in both the lateral occipitotemporal cortex (LOTC) and posterior cingulate cortex (PCC) were found in the SA+P group when compared to SA and HC group.InterpretationFindings from the present study offered empirical evidence on the impact of bullying experience, suicidality and their cumulative risk on preadolescents’ behavior and brain development, contributing to the growing literature on discerning the impact of different types of bullying perpetration/peer victimisation on preadolescents.

A novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

SummaryBackgroundParent-child transmission of suicidal behaviors has been widely elucidated, while the three-generation family suicide risk paradigm remains to be explored. This longitudinal study aimed to examine the influences of family history of suicidality (FHoS) among two prior generations on offspring’s neurodevelopment.MethodsWe conducted a retrospective, longitudinal study using the Adolescent Brain Cognitive Development (ABCD) study data collected from 2016 to 2021. Participants were allocated into four groups according to their parents’ (Generation 1 [G1]) and grandparents’ (Generation 2 [G2]) history of suicidality (G1−G2−; G1+G2−; G1−/G2+; G1+/G2+). We estimated adjusted associations between FHoS and offspring’s suicide ideation (SI), psychopathology, impulsivity and brain cortical volumes while controlling for age, sex, parental education, household income and marital status.FindingsA total of 11,875 children aged 9-10 years were observed from baseline to 3-year follow-up. Compared to G1-G2-, higher odds of SI were observed for G1-G2+ (OR=1·99, 95% CI [1·54-2·56]) and G1+G2+ (2·25 [1·46-3·47]) by child-report. Higher odds of SI were also observed for G1+G2- (1·54 [1·12-2·12]), G1-G2+ (2·57 [1·89-3·48]) and G1+G2+ (2·70 [1·60-4·56]) by caregiver-report. Higher odds for psychopathology were also observed (1·47 [1·11-1·96]; 3·33 [2·57-4·33]; 5·44 [3·42-8·66]), while higher family suicide risk was associated with high impulsivity (B=1·32 [0·48-2·17]; 2·24 [1·32-3·15]; 2·26 [0·47-4·05]). Offspring in G1+G2-had higher cortical volumes in 12 brain regions, including the bilateral insula, temporal regions and occipital regions, which were also significantly associated with their lifetime SI.DiscussionA cumulative risk pattern of FHoS in two prior generations was found for offspring’s neurodevelopmental outcomes. Earlier preventive interventions are warranted to weaken the familial transmission of suicidal risk.

SUMMARYThe brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks dissociably mediated the emergence of executive function. These results delineate maturation of multi-scale brain networks, which varies according to a functional hierarchy and impacts cognitive development.

Adolescence is hypothesized to be a critical period for the development of association cortex. A reduction of the excitation:inhibition (E:I) ratio is a hallmark of critical period development; however it has been unclear how to assess the development of the E:I ratio using non-invasive neuroimaging techniques. Here, we used pharmacological fMRI with a GABAergic benzodiazepine challenge to empirically generate a model of E:I ratio based on multivariate patterns of functional connectivity. In an independent sample of 879 youth (ages 8-22 years), this model predicted reductions in the E:I ratio during adolescence, which were specific to association cortex and related to psychopathology. These findings support hypothesized shifts in E:I balance of association cortices during a neurodevelopmental critical period in adolescence.TeaserInhibitory maturation of the association cortex reflects an adolescent critical period.

Cultural differences and biological diversity play important roles in shaping human brain structure and function. To date, most large-scale multimodal neuroimaging datasets have been obtained primarily from people living in Western countries, omitting the crucial contrast with populations living in other regions. The Chinese Human Connectome Project (CHCP) aims to address these resource and knowledge gaps by acquiring imaging, genetic and behavioral data from a large sample of participants living in an Eastern culture. The CHCP collected multimodal neuroimaging data from healthy Chinese adults using a protocol comparable to that of the Human Connectome Project. Comparisons between the CHCP and Human Connectome Project revealed both commonalities and distinctions in brain structure, function and connectivity. The corresponding large-scale brain parcellations were highly reproducible across the two datasets, with the language processing task showing the largest differences. The CHCP dataset is publicly available in an effort to facilitate transcultural and cross-ethnic brain-mind studies.

Glioblastoma is characterized by diffuse infiltration into the surrounding tissue along white matter tracts. Identifying the invisible tumour invasion beyond focal lesion promises more effective treatment, which remains a significant challenge. It is increasingly accepted that glioblastoma could widely affect brain structure and function, and further lead to reorganization of neural connectivity. Quantifying neural connectivity in glioblastoma may provide a valuable tool for identifying tumour invasion. Here we propose an approach to systematically identify tumour invasion by quantifying the structural connectome in glioblastoma patients. We first recruit two independent prospective glioblastoma cohorts: the discovery cohort with 117 patients and validation cohort with 42 patients. Next, we use diffusion MRI of healthy subjects to construct tractography templates indicating white matter connection pathways between brain regions. Next, we construct fractional anisotropy skeletons from diffusion MRI using an improved voxel projection approach based on the tract-based spatial statistics, where the strengths of white matter connection and brain regions are estimated. To quantify the disrupted connectome, we calculate the deviation of the connectome strengths of patients from that of the age-matched healthy controls. We then categorize the disruption into regional disruptions on the basis of the relative location of connectome to focal lesions. We also characterize the topological properties of the patient connectome based on the graph theory. Finally, we investigate the clinical, cognitive and prognostic significance of connectome metrics using Pearson correlation test, mediation test and survival models. Our results show that the connectome disruptions in glioblastoma patients are widespread in the normal-appearing brain beyond focal lesions, associated with lower preoperative performance (P < 0.001), impaired cognitive function (P < 0.001) and worse survival (overall survival: hazard ratio = 1.46, P = 0.049; progression-free survival: hazard ratio = 1.49, P = 0.019). Additionally, these distant disruptions mediate the effect on topological alterations of the connectome (mediation effect: clustering coefficient −0.017, P < 0.001, characteristic path length 0.17, P = 0.008). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of connectivity reorganization, where the increased neural connectivity is associated with better overall survival (log-rank P = 0.005). In conclusion, our connectome approach could reveal and quantify the glioblastoma invasion distant from the focal lesion and invisible on the conventional MRI. The structural disruptions in the normal-appearing brain were associated with the topological alteration of the brain and could indicate treatment target. Our approach promises to aid more accurate patient stratification and more precise treatment planning.

Significance The human brain is organized into a hierarchy of functional systems that evolve in childhood and adolescence to support the dynamic control of attention and behavior. However, it remains unknown how developing white-matter architecture supports coordinated fluctuations in neural activity underlying cognition. We document marked remodeling of structure–function coupling in youth, which aligns with cortical hierarchies of functional specialization and evolutionary expansion. Further, we demonstrate that structure–function coupling in rostrolateral prefrontal cortex supports age-related improvements in executive ability. These findings have broad relevance for accounts of experience-dependent plasticity in healthy development and abnormal development associated with neuropsychiatric illness., The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure–function coupling using diffusion-weighted imaging and n-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure–function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure–function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n = 294). Moreover, structure–function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

BACKGROUNDNeuroimaging studies have reported functional connectome aberrancies in autism spectrum disorder (ASD). However, the time-varying patterns of connectome topology in ASD individuals and the connection between these patterns and gene expression profiles remain unknown.METHODSTo investigate case-control differences in dynamic connectome topology, we conducted mega- and meta-analyses of resting-state functional magnetic resonance imaging data of 939 participants (440 ASD patients and 499 healthy controls, all males) from 18 independent sites, selected from the ABIDE (Autism Brain Imaging Data Exchange) dataset. Functional data was preprocessed and analyzed using harmonized protocols, and brain module dynamics was assessed using a multilayer network model. We further leveraged postmortem brain-wide gene expression data to identify transcriptomic signatures associated with ASD-related alterations in brain dynamics.RESULTSCompared to healthy controls, ASD individuals exhibited a higher global mean and lower standard deviation of whole-brain module dynamics, indicating an unstable and less regionally differentiated pattern. More specifically, ASD individuals showed higher module switching, primarily in the medial prefrontal cortex, posterior cingulate gyrus, and angular gyrus, and lower switching in the visual regions. These alterations in brain dynamics were predictive of social impairments in ASD individuals and were linked with expression profiles of genes primarily involved in the regulation of neurotransmitter transport and secretion, as well as with previously identified autism-related genes.CONCLUSIONSThis study is the first to identify consistent alterations in brain network dynamics in ASD and the transcriptomic signatures related to those alterations, furthering insights into the biological basis behind this disorder.

A recent study by Witvliet et al. reconstructed the entire brain connectome for eight Caenorhabditis elegans spanning from birth to adulthood and described how synapse changes shape the connectome topology during development. Their data suggest some convergent developmental principles in connectome maturation between C. elegans and humans.

BackgroundThis is the first study to investigate the effect of parental psychological abuse on potential psychopathological symptoms in gender minority youth subgroups, including transgender women, transgender men, and gender queer individuals.MethodsData was analysed from the Chinese National Transgender Survey in 2017; the survey was distributed through community-based organizations to transgender adolescents and adults residing in China, with representation from all 32 provinces and autonomous regions. A total of 1293 youth that self-identified as transgender or gender queer completed the study. Measures covered psychopathological symptoms including depression, anxiety, risk of suicideand self-harm. Parental psychological abuse was assessed in terms of neglect and avoidance, force to change, and verbal insults. Both the edges and centralities were computed via network analysis, and the network properties were then compared among the three gender minority subgroups. In addition, linear regression was adopted to test the predictive ability of node centrality for low self-esteem.ResultsDescriptive analysis revealed that among the three subgroups, transgender women had more severe psychopathological symptoms and reported the most psychological abuse. Network analysis revealed that the risk of suicide and self-harm was directly connected with one type of parental psychological abuse (“neglect and avoidance”). Node centrality was significantly associated with the predicting value of the nodes on low self-esteem (r2= 0.25, 0.17, 0.31) among all three gender minority subgroups.ConclusionsThe distinctive core psychopathological symptoms, within the networks of the gender minority subgroups, revealed specific symptoms across each group. The significant association between node centrality and low self-esteem indicated the extent of parental psychological abuse. Parental psychological abuse directed towards gender minority youth should be recognized as a form of family cold violence. It is recommended that schools and local communities should support early intervention to improve psychological well-being.

The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in association cortex that has been implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth. Capitalizing on a large sample with 27-minutes of high-quality functional MRI data (n=790, ages 8-23 years) and advances in machine learning, we examined associations between functional topography and four correlated dimensions of psychopathology (fear, psychosis, externalizing, anxious-misery) as well as an overall psychopathology factor. We found that functional topography significantly predicted individual differences in dimensions of psychopathology, driven mainly by robust associations between topography and overall psychopathology. Reduced cortical representations of association networks were among the most important features of the model. Our results emphasize the value of considering systematic differences in functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.

Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole-brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [18 F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region-of-interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo- and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE.

Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17-30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy-i.e., their "footprint distinctiveness". We found that statistical patterns of smartphone-based mobility features represented unique "footprints" that allow individual identification (p 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4-99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p 0.05) and circadian patterns (p 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology.

The inter-tract/region dependencies of white-matter in Parkinson’s disease are usually ignored by standard statistical tests. Moreover, it remains unclear whether the disruption of white-matter tracts/regions suffices to identify Parkinson’s disease patients from healthy controls. A machine learning approach was applied to capture the interdependencies between white-matter tracts/regions and to differentiate PD patients from healthy controls. First, the mean regional white-matter profiles, including white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were extracted as features in Parkinson’s disease patients (N = 78) and in healthy controls (N = 91). Then, the feature selection and classification were performed using t-test and linear support vector machine, respectively. Last, the relationships between clinical variables and regional magnetic resonance indices were estimated. Our results showed the combined features (white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) had the best performance with an accuracy of 75.15% and area under curve of 0.8171, respectively. The most discriminative white-matter features were centered on the association fibers, commissural fibers, projection fibers, and striatal fibers. The discriminative regions of right anterior limb of internal capsule had positive association trends with the Unified Parkinson Disease Rating Scale III score; while the genu of corpus callosum and right retrolenticular part of internal capsule had positively association trends with the Hamilton Depression Rating Scale score. Our finding showed the multivariate machine learning approach is a promising tool to detect abnormal white-matter tracts/regions in Parkinson’s disease, and provides us a multidimensional means for neuroimaging classification.

Arterial spin labeling (ASL) magnetic resonance imaging has been widely applied to identify cerebral blood flow (CBF) abnormalities in a number of brain disorders. To evaluate its significance in detecting methamphetamine (MA) dependence, this study used a multivariate pattern classification algorithm, ie, a support vector machine (SVM), to construct classifiers for discriminating MA-dependent subjects from normal controls. Forty-five MA-dependent subjects, 45 normal controls, and 36 heroin-dependent subjects were enrolled. Classifiers trained with ASL-CBF data from the left or right cerebrum showed significant hemispheric asymmetry in their cross-validated prediction performance (P < 0.001 for accuracy, sensitivity, specificity, kappa, and area under the curve [AUC] of the receiver operating characteristics [ROC] curve). A classifier trained with ASL-CBF data from all cerebral regions (bilateral hemispheres and corpus callosum) was able to differentiate MA-dependent subjects from normal controls with a cross-validated prediction accuracy, sensitivity, specificity, kappa, and AUC of 89%, 94%, 84%, 0.78, and 0.95, respectively. The discrimination map extracted from this classifier covered multiple brain circuits that either constitute a network related to drug abuse and addiction or could be impaired in MA-dependence. The cerebral regions contribute most to classification include occipital lobe, insular cortex, postcentral gyrus, corpus callosum, and inferior frontal cortex. This classifier was also specific to MA-dependence rather than substance use disorders in general (ie, 55.56% accuracy for heroin dependence). These results support the future utilization of ASL with an SVM-based classifier for the diagnosis of MA-dependence and could help improve the understanding of MA-related neuropathology.

To support brain development during youth, the brain must balance energy delivery and consumption. Previous studies in adults have demonstrated high coupling between cerebral blood flow and brain function as measured using functional neuroimaging, but how this relationship evolves over adolescence is unknown. To address this gap, we studied a sample of 831 children and adolescents (478 females, ages 8-22) from the Philadelphia Neurodevelopmental Cohort who were scanned at 3T with both arterial spin labeled (ASL) MRI and resting-state functional MRI (fMRI). Local coupling between cerebral blood flow (CBF, from ASL) and the amplitude of low frequency fluctuations (ALFF, from fMRI) was first quantified using locally weighted regressions on the cortical surface. We then used generalized additive models to evaluate how CBF-ALFF coupling was associated with age, sex, and executive function. Enrichment of effects within canonical functional networks was evaluated using spin-based permutation tests. Our analyses revealed tight CBF-ALFF coupling across the brain. Whole-brain CBF-ALFF coupling decreased with age, largely driven by coupling decreases in the inferior frontal cortex, precuneus, visual cortex, and temporoparietal cortex (pfdr pfdr pfdr SIGNIFICANCEThe functions of the human brain are metabolically expensive and reliant on coupling between cerebral blood flow and neural activity. Previous neuroimaging studies in adults demonstrate tight physiology-function coupling, but how this coupling evolves over development is unknown. Here, we examine the relationship between blood flow as measured by arterial spin labeling and the amplitude of low frequency fluctuations from resting-state magnetic resonance imaging across a large sample of youth. We demonstrate regionally specific changes in coupling over age and show that variations in coupling are related to biological sex and executive function. Our results highlight the importance of CBF-ALFF coupling throughout development; we discuss its potential as a future target for the study of neuropsychiatric diseases.

BackgroundExcessive worry is a defining feature of generalized anxiety disorder and is present in a wide range of other psychiatric conditions. Therefore, individualized predictions of worry propensity could be highly relevant in clinical practice, with respect to the assessment of worry symptom severity at the individual level.MethodsWe applied a multivariate machine learning approach to predict dispositional worry based on microstructural integrity of white matter (WM) tracts.ResultsWe demonstrated that the machine learning model was able to decode individual dispositional worry scores from microstructural properties in widely distributed WM tracts (mean absolute error = 10.46, p < 0.001; root mean squared error = 12.82, p < 0.001; prediction R2 = 0.17, p < 0.001). WM tracts that contributed to worry prediction included the posterior limb of internal capsule, anterior corona radiate, and cerebral peduncle, as well as the corticolimbic pathways (e.g. uncinate fasciculus, cingulum, and fornix) already known to be critical for emotion processing and regulation.ConclusionsThe current work thus elucidates potential neuromarkers for clinical assessment of worry symptoms across a wide range of psychiatric disorders. In addition, the identification of widely distributed pathways underlying worry propensity serves to better improve the understanding of the neurobiological mechanisms associated with worry.