Multi-voxel pattern analysis for characterizing functional connectivity and neurocognitive function in major depression: A CAN-BIND-1 report
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) affects not only mood but also neurocognitive function. In this study, we used whole-brain functional connectivity multi-voxel pattern analysis (fc-MVPA) to examine the relationship between resting-state functional connectivity (rsFC) and neurocognitive function in individuals with MDD compared to healthy controls (HC). METHODS: Baseline functional magnetic resonance imaging (fMRI) scans from the CAN-BIND-1 dataset were analyzed using a data-driven whole-brain fc-MVPA approach in 147 individuals with MDD and 98 HC. All participants completed the Computerized Neurocognitive Assessment Vital Signs (CNS-VS) battery outside the scanner, and correlations between rsFC differences and CNS-VS domain scores were explored. RESULTS: The fc-MVPA reduced the dimensionality of fMRI data at both individual and group levels, identifying six clusters with altered rsFC in MDD relative to HC: left cerebellar crus I, right precuneus, left superior lateral occipital cortex, right ventral caudate, left superior parietal lobule, and left dorsal anterior cingulate cortex. Using these clusters as seeds, post-hoc analyses identified 24 patterns of altered rsFC in MDD involving the default mode, central executive, visual recognition, salience, and sensorimotor networks. Five of these patterns showed significant correlations with CNS-VS domain scores for composite memory, neurocognition index, processing speed, executive function, and simple attention in HC, but these associations were absent in individuals with MDD. CONCLUSIONS: Our findings highlight that MDD is associated with disrupted rsFC across networks relevant to neurocognitive function. The data-driven nature of the fc-MVPA identified the left cerebellar crus I as the most significant region of aberrant rsFC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".