Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Дек. 4, 2024

Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging two disorders. However, significant heterogeneity most presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common factor (cb factor) characterize general structure these symptoms, analogous p factor. We identified cb from 12 in depression-anxiety network. Moreover, this could be predicted using edge-centric connectomes with robust generalizability, was characterized by connectome patterns attention frontoparietal networks. In an independent twin cohort, found that were moderately heritable, their genetic connectome-transcriptional markers associated neurobiological enrichment vasculature cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed its architectures, which enriched neurogenetic understanding comorbidity. Phenotyping depression anxiety prominently heterogeneous. Authors (cb) model identify homogeneous signatures

Язык: Английский

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Identifying autism spectrum disorder using edge-centric functional connectivity

Cerebral Cortex, Год журнала: 2023, Номер 33(13), С. 8122 - 8130

Опубликована: Март 28, 2023

Abstract Brain network analysis is an effective method to seek abnormalities in functional interactions for brain disorders such as autism spectrum disorder (ASD). Traditional studies of networks focus on the node-centric connectivity (nFC), ignoring edges miss much information that facilitates diagnostic decisions. In this study, we present a protocol based edge-centric (eFC) approach, which significantly improves classification performance by utilizing co-fluctuations between regions compared with nFC build mode ASD using multi-site dataset Autism Imaging Data Exchange I (ABIDE I). Our model results show even traditional machine-learning classifier support vector machine (SVM) challenging ABIDE dataset, relatively high achieved: 96.41% accuracy, 98.30% sensitivity, and 94.25% specificity. These promising suggest eFC can be used reliable framework diagnose mental promote identifications stable biomarkers. This study provides essential complementary perspective understanding neural mechanisms may facilitate future investigations early diagnosis neuropsychiatric disorders.

Язык: Английский

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Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification

iScience, Год журнала: 2023, Номер 26(5), С. 106575 - 106575

Опубликована: Апрель 6, 2023

The idea that brain networks have a hierarchical modular organization is pervasive. Increasing evidence suggests modules overlap. However, little known about the overlapping structure in brain. In this study, we developed framework to uncover structures based on nested-spectral partition algorithm and an edge-centric network model. Overlap degree between symmetrical across hemispheres, with highest overlap observed control salience/ventral attention networks. Furthermore, edges are clustered into two groups: intrasystem intersystem edges, form modules. At different levels, self-similar of Additionally, brain's contains more individual identifiable information than single-level structure, particularly Our results offer pathways for future studies aimed at relating cognitive behavior disorders.

Язык: Английский

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Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study

Cerebral Cortex, Год журнала: 2023, Номер 33(14), С. 8904 - 8912

Опубликована: Май 15, 2023

Abstract Despite node-centric studies revealing an association between resting-state functional connectivity and individual risk propensity, the prediction of future decisions remains undetermined. Herein, we applied a recently emerging edge-centric method, edge community similarity network (ECSN), to alternatively describe structure brain activity probe its contribution predicting propensity during gambling. Results demonstrated that inter-individual variability correlates with inter-subnetwork couplings spanning visual (VN) default mode (DMN), cingulo-opercular task control network, sensory/somatomotor hand (SSHN). Particularly, participants who have higher these subnetworks resting state tend choose riskier yielding bets. And in contrast low-risk participants, those behave high-risky show stronger VN SSHN/DMN. Eventually, based on ECSN properties, rate gambling is effectively predicted by multivariable linear regression model at level. These findings provide new insights into neural substrates neuroimaging metrics predict advance.

Язык: Английский

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Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

Brain Structure and Function, Год журнала: 2023, Номер 228(7), С. 1755 - 1769

Опубликована: Авг. 12, 2023

Язык: Английский

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