ArticleNeuroImage. Clinical2019
Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder.Human brain mapping · 2021Pooled it
- Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review.Frontiers in psychiatry · 2020Pooled it
- The efficacy of meditation on insula dysfunction in patients with chronic schizophrenia.Frontiers in psychiatry · 2025Article
- Enhanced computerized cognitive remediation therapy improved cognitive function, negative symptoms, and GDNF in male long-term inpatients with schizophrenia.Frontiers in psychiatry · 2024Article
- Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods.Diagnostics (Basel, Switzerland) · 2023Article
- Common and Distinct Functional Connectivity of the Orbitofrontal Cortex in Depression and Schizophrenia.Brain sciences · 2023Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders.Journal of personalized medicine · 2022Review
- Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal.Healthcare (Basel, Switzerland) · 2022Article
- Resting-state functional connectivity of salience network in schizophrenia and depression.Scientific reports · 2022Article
- Neurometabolic and functional changes of default-mode network relate to clinical recovery in first-episode psychosis patients: A longitudinalNeuroImage. Clinical · 2022Article
- Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Article
- Brain imaging features in schizophrenia with co-occurring auditory verbal hallucinations and depressive symptoms-Implication for novel therapeutic strategies to alleviate the reciprocal deterioration.Brain and behavior · 2021Review
- Altered Functional Connectivity in a Triple-Network Model in Autism With Co-occurring Attention Deficit Hyperactivity Disorder.Frontiers in psychiatry · 2021Article
- Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy.Frontiers in neuroscience · 2021Article
- White Matter Microstructure across the Psychosis Spectrum.Trends in neurosciences · 2020Review
- Functional connectivity of the anterior insula associated with intolerance of uncertainty in youth.Cognitive, affective & behavioral neuroscience · 2020Article
- The Effects of Music Intervention on Pallidum-DMN Circuit of Schizophrenia.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions.
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