Local structural-functional coupling with counterfactual explanations for epilepsy prediction

NeuroImage, Journal Year: 2025, Volume and Issue: 306, P. 120978 - 120978

Published: Jan. 2, 2025

The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural (SC) and corresponding functional activation or (FC). It has been widely used to identify disorders. However, existing research on SC-FC focuses global regional scales, few studies have investigated impact of disorders this from perspective multi-brain region cooperation (i.e., local scale). Here, we propose pattern for prediction. Compared with previous methods, proposed patterns quantify SC FC in terms subgraphs rather than whole single regions. Specifically, first construct using diffusion tensor imaging (DTI) resting-state magnetic resonance (rs-fMRI) data, subsequently organizing them into a multimodal network. Then, extract these networks select based their frequencies generate patterns. Finally, employ while refining abnormal counterfactual explanations. Results real epilepsy dataset suggest that method not only outperforms methods accuracy but also provides insights changes Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.

Language: Английский

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Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: 373, P. 219 - 226

Published: Jan. 5, 2025

Language: Английский

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Hemispheric asymmetries and network dysfunctions in adolescent depression: A neuroimaging study using resting-state functional magnetic resonance imaging

World Journal of Psychiatry, Journal Year: 2025, Volume and Issue: 15(2)

Published: Jan. 14, 2025

BACKGROUND Currently, adolescent depression is one of the most significant public health concerns, markedly influencing emotional, cognitive, and social maturation. Despite advancements in distinguish neurobiological substrates underlying depression, intricate patterns disrupted brain network connectivity adolescents warrant further exploration. AIM To elucidate neural correlates by examining using resting-state functional magnetic resonance imaging (rs-fMRI). METHODS The study cohort comprised 74 depressed 59 healthy controls aged 12 to 17 years. Participants underwent rs-fMRI evaluate within across critical networks, including visual, default mode (DMN), dorsal attention, salience, somatomotor, frontoparietal control networks. RESULTS Analyses revealed pronounced disparities key circuits among with depression. results demonstrated existence hemispheric asymmetries characterized enhanced activity left visual network, which contrasted diminished right hemisphere. DMN facilitated increased prefrontal cortex reduced engagement hemisphere, implicating self-referential emotional processing mechanisms. Additionally, an overactive attention a hypoactive salience were identified, underscoring abnormalities attentional regulation CONCLUSION findings from this underscore distinct disruptions role specific markers for precise early diagnosis observed network-specific deviations complex architecture supporting development targeted therapeutic strategies.

Language: Английский

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Research Progress of Functional Magnetic Resonance of the Brain in Reward Network of Adolescent Major Depressive Disorder

Advances in Clinical Medicine, Journal Year: 2025, Volume and Issue: 15(02), P. 1152 - 1160

Published: Jan. 1, 2025

Language: Английский

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Detection of structural-functional coupling abnormalities using multimodal brain networks in Alzheimer’s disease: A comparison of three computational models

NeuroImage Clinical, Journal Year: 2025, Volume and Issue: 46, P. 103764 - 103764

Published: Jan. 1, 2025

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the disconnection of white matter fibers and disrupted functional connectivity gray matter; however, pathological mechanisms linking structural changes remain unclear. This study aimed to explore interaction between brain network in AD using advanced structural-functional coupling (S-F coupling) models assess whether these correlate with cognitive function, Aβ deposition levels, gene expression. In this study, we utilized multimodal magnetic resonance imaging data from 41 individuals AD, 112 mild impairment, 102 healthy controls mechanisms. We applied different computational examine S-F associated AD. Our results showed that communication graph harmonic demonstrated greater heterogeneity were more sensitive than statistical detecting AD-related changes. addition, increases progression at global, subnetwork, regional node especially medial prefrontal anterior cingulate cortices. The regions also partially mediated decline deposition. Furthermore, enrichment analysis revealed strongly regulation cellular catabolic processes. advances our understanding highlights importance elucidating neural underlying

Language: Английский

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Abnormal Structure–Function Coupling in Major Depressive Disorder Patients With and Without Anhedonia

Depression and Anxiety, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Background: As a core symptom of major depressive disorder (MDD), previous magnetic resonance studies have demonstrated that MDD with anhedonia may exhibit distinctive brain structural and functional alterations. Nevertheless, the impact on synchronized alterations in structure function regions remains uncertain. Methods: A total 92 individuals were enrolled study, including 29 patients anhedonia, 33 without 30 healthy controls (HCs). All subjects underwent resting‐state imaging (MRI) scans. The structure–function coupling cortical subcortical was constructed by using obtained data to quantify distributional similarity gray matter volume (GMV) amplitude low‐frequency fluctuations (ALFFs). Analysis covariance (ANCOVA) used compare differences among three groups. Partial correlation analyses conducted identify relationships between clinical features. Finally, receiver operating characteristic (ROC) curve support vector machine (SVM) analysis employed verify capacity distinguish HCs, HCs. Results: ANCOVA revealed significant structure‐function groups bilateral precentral gyrus (PrG), right insular (INS), cingulate (CG), thalamus (Tha), left superior temporal (STG), middle (MTG). Compared both showed reduced INS, PrG, while increased CG. Additionally, Tha, MTG, STG, compared other two Furthermore, ROC indicated CG, MTG exhibited greatest following groups: from anhedonia. combined metrics greater diagnostic value two‐by‐two comparisons. Conclusion: present findings highlight altered synchrony frontal, lobes, Tha be implicated development symptoms patients. Altered aforementioned serve as novel neuroimaging biomarker for

Language: Английский

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Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression

Molecular Psychiatry, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Abstract Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD’s complex and varied neuropathology. Identifying biomarkers for requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined advanced data-driven methods, can enhance our understanding the neurobiological processes influencing outcomes. To address this, we analyzed resting-state fMRI EEG connectivity data from 130 patients treated sertraline 135 placebo Establishing Moderators Biosignatures Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks integrate data-augmented cross-modality correlation, aiming predict individual symptom changes by revealing multimodal brain network signatures. The results showed that model demonstrated promising prediction accuracy, an R 2 value 0.24 0.20 placebo. It also exhibited potential transferring predictions only EEG. Key regions identified predicting included inferior temporal gyrus (fMRI) posterior cingulate cortex (EEG), while response, precuneus supplementary motor area (EEG) were critical. Additionally, both modalities superior as significant anterior postcentral common predictors arm. variations frontoparietal control, ventral attention, dorsal limbic notably associated MDD treatment. By integrating EEG, study established novel signatures responses MDD, providing interpretable circuit patterns may guide future targeted interventions. Trial Registration: Depression ClinicalTrials.gov Identifier: NCT#01407094.

Language: Английский

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Disruption of Multimodal Brain Networks and Structural-Functional Coupling in Adolescents with Major Depressive Disorder

Neuropsychiatric Disease and Treatment, Journal Year: 2025, Volume and Issue: Volume 21, P. 791 - 798

Published: April 1, 2025

Adolescent MDD has become a significant public health issue, yet its underlying mechanisms remain unclear. Multimodal brain imaging techniques offer powerful method for exploring complex mental disorders. However, evidence focusing on the multimodal networks and structural-functional coupling in adolescent depression is still limited. Participants with major depressive disorder (MDD) were Han Chinese individuals aged 13 to 18 who had been unmedicated at least two weeks. We conducted MRI acquisitions, including structural (sMRI), resting-state functional (rsfMRI), Diffusion Tensor Imaging (DTI). The cortex was parceled into 360 regions using HCP-MMP atlas. Functional connectivity deterministic matrices constructed, coefficients calculated. Differences between healthy controls (HCs) groups identified. A total of 25 adolescents (mean age: 15.68 years, standard deviation [SD]: 1.18; Female: 21 (84.00%)) 27 hCs 14.30 1.51; (48.15%)) included analysis. There 9 connections 122 that differed groups, involving multiple cortical regions. Additionally, we identified differences three areas, specifically posterior cingulate ventral visual cortex. involves disruptions networks, coupling. These differing indicators may serve as potential biomarkers MDD.

Language: Английский

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Graph theory reveals functional connectome disruptions in adolescent major depressive disorder with childhood trauma

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Childhood trauma (CT) is a major risk factor for adolescent depressive disorder (MDD), yet its neurobiological underpinnings and longitudinal treatment effects remain poorly characterized. Leveraging graph theory resting-state fMRI in 343 adolescents with MDD (211 CT history [MDD-CT], 106 without [MDD-NCT]) 149 healthy controls, we identified CT-associated functional connectome disruptions marked by increased network randomness topological deficits default mode (DMN) hubs (left parahippocampal gyrus, posterior cingulate temporal pole). Longitudinal neuroimaging revealed post-treatment normalization of these abnormalities, particularly the left precuneus amygdala, paralleling symptom improvement. Machine learning models using baseline connectomes predicted antidepressant response 82% accuracy. Our findings establish CT-driven disturbances MDD, map dynamic reorganization to therapeutic recovery, position connectivity as clinically actionable biomarker. This work bridges mechanisms trauma-related depression precision strategies, offering path toward biomarker-guided interventions.

Language: Английский

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Transcriptional characteristics of human brain alterations in major depressive disorder: a systematic review

Psychoneuroendocrinology, Journal Year: 2025, Volume and Issue: 177, P. 107472 - 107472

Published: April 18, 2025

Language: Английский

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