BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping

NeuroImage, Год журнала: 2024, Номер 292, С. 120594 - 120594

Опубликована: Апрель 1, 2024

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum (ASD), are not unitary diseases, but rather heterogeneous syndromes involve diverse, co-occurring symptoms divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis treatment effectiveness in disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging famed prototype learning. addition, introduce novel generation process subgraph discover essential edges distinct prototypes employ total correlation (TC) ensure independence patterns. BPI-GNN can effectively discriminate patients healthy controls (HC), identify biological meaningful subtypes We evaluate performance against 11 popular brain classification methods on three datasets observe our always achieves highest accuracy. More importantly, examine differences symptom profiles gene expression among identified brain-based have relevance. It also discovers subtype biomarkers align with current neuro-scientific knowledge.

Язык: Английский

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Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition

Chaos Solitons & Fractals, Год журнала: 2024, Номер 188, С. 115566 - 115566

Опубликована: Окт. 1, 2024

Язык: Английский

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Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach

Chaos An Interdisciplinary Journal of Nonlinear Science, Год журнала: 2023, Номер 33(9)

Опубликована: Сен. 1, 2023

We present a novel method for analyzing brain functional networks using magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to standard group-based patients diagnosed with major depressive disorder (MDD) and healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the network uncovers distinct characteristics in measures degree distributions when considering connection strengths. as strengths increase, observe transition topology from combination scale-free random topologies small-world topology. Conversely, MDD group exhibits uncertainty weak connections, while strong connections display properties. contrast, does not exhibit significant differences behavior between two groups. However, it indicate scale-free-like structure topologies. The use also holds immense potential classification patients, unveils substantial distinctions

Язык: Английский

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PTSD Classification Based on Surface-Based Morphometry: Integrating SHAP Analysis for Key Feature Identification

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

Background Post-Traumatic Stress Disorder (PTSD) is associated with neurobiological alterations, which can be examined using surface-based morphology (SBM). While machine learning (ML) approaches have shown potential in classifying PTSD based on SBM features, further exploration needed to improve interpretability and clinical relevance. Objectives This study seeks integrate ML-based classification of SHAP analysis identify important features their associations symptomatology, providing insights into the structural changes underlying PTSD. Methods High-resolution T1-weighted MRI data from 101 participants (62 PTSD, 39 healthy controls) were analyzed FreeSurfer’s pipeline, extracting cortical thickness, surface area, curvature aparc.a2009s atlas. Several ML models, including Random Forest, SVM, XGBoost, trained evaluated ten-fold cross-validation. was applied determine feature importance, correlation analyses conducted examine relationships between key symptom severity. Results Sixteen regions identified significant differences reduced thickness left lingual gyrus increased bilateral central sulcus. The Forest model achieved highest accuracy (91%) classification. highlighted parahippocampal as features. Correlation suggested links these specific clusters. Conclusion integration interpretable methods provides a promising approach for investigating brain validation needed, findings contribute better understanding neurobiology may support future research diagnostic therapeutic strategies.

Язык: Английский

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Features of Functional Brain Connectivity in Children during Solving Cognitive Tasks

Bulletin of the Russian Academy of Sciences Physics, Год журнала: 2025, Номер 89(3), С. 482 - 485

Опубликована: Март 1, 2025

Язык: Английский

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Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis

Neuroscience & Biobehavioral Reviews, Год журнала: 2025, Номер unknown, С. 106201 - 106201

Опубликована: Май 1, 2025

Язык: Английский

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Acoustic-based machine learning approaches for depression detection in Chinese university students

Frontiers in Public Health, Год журнала: 2025, Номер 13

Опубликована: Май 15, 2025

Depression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predominantly depend on subjective self-reported methods. There an urgent necessity to develop objective means identifying depression. Acoustic features, which convey emotional information, have potential enhance objectivity assessments. This study aimed investigate feasibility utilizing acoustic features for automated identification characterization Chinese A cross-sectional was undertaken involving 103 students with controls matched age, gender, education. Participants' voices were recorded using a smartphone as they read neutral texts. analysis feature extraction performed OpenSMILE toolkit, yielding 523 encompassing spectral, glottal, prosodic characteristics. These extracted utilized discriminant between control groups. Pearson correlation analyses conducted evaluate relationship Patient Health Questionnaire-9 (PHQ-9) scores. Five machine learning algorithms including Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Classification, Naive Bayes, Random Forest used perform classification. For training testing, ten-fold cross-validation employed. Model performance assessed receiver operating characteristic (ROC) curve, area under curve (AUC), precision, accuracy, recall, F1 score. Shapley Additive exPlanations (SHAP) method model interpretation. In group, 32 (25 spectral 5 2 glottal features) showed significant alterations compared controls. Further, 27 (10 3 1 significantly correlated severity. Among five algorithms, LDA demonstrated highest classification performance, AUC 0.771. SHAP suggested that Mel-frequency cepstral coefficients (MFCC) contributed most model's efficacy. The integration demonstrates high accuracy in distinguishing students, suggesting its utility rapid large-scale screening. MFCC may serve valid campuses.

Язык: Английский

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Review on the use of AI-based methods and tools for treating mental conditions and mental rehabilitation

The European Physical Journal Special Topics, Год журнала: 2024, Номер unknown

Опубликована: Авг. 12, 2024

Язык: Английский

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Recent achievements in nonlinear dynamics, synchronization, and networks

Chaos An Interdisciplinary Journal of Nonlinear Science, Год журнала: 2024, Номер 34(10)

Опубликована: Окт. 1, 2024

This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior dynamical networks. It targets current progress on issues such as time series analysis data-driven modeling from real data climate, brain, social dynamics. Predicting detecting early warning signals extreme climate conditions, epileptic seizures, or other catastrophic conditions are primary tasks experimental data. Exploring machine-based learning for purpose prediction is an emerging area. Application evolutionary game theory biological systems (eco-evolutionary theory) a developing direction future research understanding interactions between species. Recent bifurcations, analysis, control, time-delay also discussed.

Язык: Английский

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Characteristics of brain functional networks specific for different types of tactile perception

The European Physical Journal Special Topics, Год журнала: 2023, Номер 233(3), С. 499 - 504

Опубликована: Дек. 1, 2023

Язык: Английский

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