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Tryptophan and Brain Disorders · Journal article
Cns Neuroscience & Therapeutics · September 1, 2026
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An unsupervised machine learning analysis of resting-state fMRI in 261 emotional disorder patients identified five functional network patterns that partition patients into five clinically distinct subtypes with different symptom profiles. This is an exploratory, hypothesis-generating study that proposes neurobiological stratification but lacks independent validation, prospective outcome data, or evidence of clinical utility for treatment selection.
Cross-sectional case-control observational study with unsupervised machine learning. 261 patients with emotional disorders (including anxiety disorders, major depression, and PTSD) and 201 healthy controls; specific eligibility criteria and recruitment setting not stated. n = 462.
Five representative functional networks (RFNs) identified from 261 ED patients and 201 healthy controls via nonnegative matrix factorization RFN1 (sensorimotor/dorsal attention connectivity) associated with ST1 subtype showing mixed anxiety/depression with somatic vigilance RFN2 (theory-of-mind connectivity) linked to ST2 mild-symptom resilient subtype
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If validated independently and shown to predict treatment response, these subtypes could guide personalized psychiatric interventions. At present, the findings are exploratory and cannot yet inform clinical decision-making without prospective confirmation and outcome linkage.
Cross-sectional neuroimaging study identifying putative biomarker-defined subtypes in emotional disorders; lacks prospective validation, treatment outcome data, or independent replication to support clinical utility.
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If validated independently and shown to predict treatment response, these subtypes could guide personalized psychiatric interventions. At present, the findings are exploratory and cannot yet inform clinical decision-making without prospective confirmation and outcome linkage.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
BACKGROUND: Emotional disorders (EDs), including anxiety disorders, major depression (MDD), and post-traumatic stress disorder (PTSD), show overlapping symptoms and neurobiological heterogeneity, limiting current diagnostic frameworks. Transdiagnostic biomarkers are critical for precision psychiatry. METHODS: Resting-state functional connectivity (FC) from 261 ED patients and 201 healthy controls (HCs) was decomposed via nonnegative matrix factorization (NMF). Differential FC features defined latent disease factors, classifying patients into subtypes with distinct network/structural profiles. RESULTS: The analysis identified five representative functional networks (RFNs) underlying the heterogeneity of emotional disorders. RFN1 reflected connectivity between sensorimotor and dorsal attention networks, while RFN2 was defined by theory-of-mind-related connectivity. RFN3, characterized by thalamocortical connections, was specifically linked to somatic sensation processing. RFN4 involved connectivity within the default mode network, and RFN5 captured multi-network-subcortical integration. These RFNs delineated five subtypes with distinct clinical profiles: ST1 (mixed anxiety/depression) exhibited RFN1-driven somatic vigilance and comorbid depressive symptoms; ST2 (mild symptoms) demonstrated resilience associated with RFN2; ST3 (somatic anxiety-dominant) showed RFN3-related elevations in somatic anxiety and panic symptoms; ST4 (anxiety-centric) was marked by RFN4-associated self-referential anxiety; and ST5 represented a depression-pure subgroup mapped to RFN5. CONCLUSION: This study proposes a neurobiologically informed stratification for EDs, revealing transdiagnostic subtypes with distinct network signatures and clinical implications. The findings bridge symptom heterogeneity to circuit-level dysfunction, offering potential biomarkers for personalized interventions and advocating for transdiagnostic approaches in psychiatric research.
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