Individualized Functional Connectome Identified Replicable Biomarkers for Dysphoric Symptoms in First-Episode Medication-Naïve Patients With Major Depressive Disorder
Bibliographic record
Abstract
Background: Major depressive disorder (MDD) is a heterogeneous syndrome and can be conceptualized as a mixture of dimensional abnormalities across several specific brain circuits. The neural underpinnings of different symptom dimensions in MDD remain elusive. Here, we aimed to identify robust, generalizable brain-based biomarkers for different symptom dimensions in MDD, using individualized functional connectomes. Methods: Patterns of functional connectivity (FC) associated with symptom severity were identified using a novel individualized functional network parcellation analysis in conjunction with hierarchical clustering. Dimension-specific prediction models were trained to estimate symptom severity in first-episode medication-naïve patients (Discovery dataset, N=95) and replicated in an independent Validation dataset (N=94). In a cohort of subjects who underwent antidepressant treatment (N=55), correlation between FC changes and symptom changes was further explored. Results: Two distinct symptom clusters previously identified in MDD patients, namely dysphoric and anxiosomatic symptoms, were robustly replicated in our data. A connectivity biomarker associated with dysphoric symptoms was identified, which mainly involved the default, dorsal attention, and limbic networks. Critically, this brain-symptom association was confirmed in the Validation dataset. Moreover, the marker also tracked with dysphoric symptom improvement following a two-week antidepressant treatment. For comparison, we repeated our analyses using a non-individualized approach and failed to identify replicable brain-symptom biomarkers. Further quantitative analysis indicated that generalizability of connectivity-symptom association was hampered when functional regions were not localized in individuals. Conclusions: This work reveals robust, replicable FC biomarkers for dysphoric symptoms in MDD, demonstrates the advantage of individual-oriented approach, and emphasizes the importance of generalizability in psychiatric neuroimaging analysis. Funding: This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 81820108018, 8202780056, and 82001795), Program for Changjiang Scholars and Innovative Research Team in University of China (PCSIRT, Grant No. IRT16R52), Functional and Molecular Imaging Key Laboratory of Sichuan Province (FMIKLSP, Grant: 2019JDS0044), and China Postdoctoral Science Foundation (2020M673245). L.D. is supported by a Canadian Institutes of Health Research postdoctoral fellowship, FRN: MFE-171291. Declaration of Interest: None to declare Ethical Approval: This study was approved by the ethics committee of Sichuan University, China.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".