The self in depression and anxiety as a transdiagnostic and differential-diagnostic neural marker: A systematic review
Bibliographic record
Abstract
Accurate and early diagnosis of Depression and Anxiety is met with the challenge of comorbid presentations and the neglect of the basic disturbances of self in current diagnostic criteria. Here, we review studies employing functional magnetic resonance imaging (fMRI) with self-based tasks in major depressive disorder (MDD) and anxiety disorders (AD) to determine the transdiagnostic and differential-diagnostic applicability of neural markers related to the self. This systematic review identified three main findings: (I) Large-scale brain-wide changes related to self-dysfunction overlap significantly between MDD and AD. (II) Regional changes are unspecific to tasks and stimuli confirming their specificity to the self as distinguished from other cognitive functions. (III) MDD affects regions related to emotional-cognitive processing like the anterior cingulate cortex, while AD involves prefrontal and insular regions associated with interoceptive and emotional-cognitive regulation. Our systematic review shows the utility of the self as a transdiagnostic marker that exhibits neural topographic similarities across the diagnostic boundaries of MDD and AD. More fine-grained regional differences between MDD and AD can be found within their underlying large scale neural similarities, allowing for their differential-diagnostic specification. In conclusion, we demonstrate the relevance of the self as both a transdiagnostic and differential diagnostic neural marker in MDD and AD. • Major depressive disorder (MDD) and anxiety disorder (AD) share significant brain-wide changes related to self-dysfunction. • These brain-wide changes persist across different task paradigms, supporting their specificity to self. • MDD and AD differ in their involvement with emotional processing and prefrontal/insular regions, respectively.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".