ArticleFrontiers in neuroscience2018
Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Algebraic Connectivity Reveals Modulated High-Order Functional Networks in Alzheimer's Disease.ArXiv · 2026Article
- Effects of sleep quality on the default mode network and on anxiety-depression symptoms in premenstrual syndrome.BMC psychiatry · 2025Article
- Random Walk-Based Node Feature Learning for Major Depressive Disorder Identification Through Multi-Site rs-fMRI Data.Human brain mapping · 2025Article
- Disruptions in Cognitive-Affective Circuitry in Major Depression Disorder: Insights From REST-Meta-MDD and Its Implication for Predicting TMS Treatment Efficacy.CNS neuroscience & therapeutics · 2025Article
- Network Specificity in Predicting Childhood Trauma Characteristics Using Effective Connectivity.Alpha psychiatry · 2025Article
- Cerebral cortical functional hyperconnectivity in a mouse model of spinocerebellar ataxia type 8 (SCA8).Neurobiology of disease · 2025Article
- Predicting response speed and age from task-evoked effective connectivity.Network neuroscience (Cambridge, Mass.) · 2025Article
- Altered effective connectivity of the default mode network in juvenile myoclonic epilepsy.Cognitive neurodynamics · 2024Article
- Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.CNS neuroscience & therapeutics · 2024Article
- Cerebral cortical functional hyperconnectivity in a mouse model of spinocerebellar ataxia type 8 (SCA8).bioRxiv : the preprint server for biology · 2024Article
- Hyperthymic temperament predicts neural responsiveness for nonmonetary reward.PCN reports : psychiatry and clinical neurosciences · 2023Article
- Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.Human brain mapping · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity.Frontiers in psychiatry · 2023Article
- The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis.Frontiers in neuroscience · 2023Review
- Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study.Frontiers in aging neuroscience · 2023Article
- Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study.Frontiers in neuroscience · 2023Article
- Distinct resting-state effective connectivity of large-scale networks in first-episode and recurrent major depression disorder: evidence from the REST-meta-MDD consortium.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.