ArticleCNS neuroscience & therapeutics2024
Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.
Article in CNS neuroscience & therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Weight matters: Higher BMI is associated with changes in the brain connectome in health and depression.Neuroscience applied · 2026Article
- Anhedonia in Depressive Disorder and Schizophrenia: An Overview of Causes, Diagnosis, and Treatment.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.CNS neuroscience & therapeutics · 2024Article
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Authors and funding
9 authors.
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Abstract
main problemAnhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia.
methodsA predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients.
resultsCPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_
conclusionsThis study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.
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