The Putative Impact of Metabolic Health on Default Mode Network Activity and Functional Connectivity in Neuropsychiatric Disorders
Bibliographic record
Abstract
The default mode network (DMN) describes a distributed network of brain regions that are predominantly activated and engaged during periods of spontaneous, stimulus independent thought (i.e., at rest) and remain quiescent during attention-demanding, goal-directed tasks. Replicated evidence in functional neuroimaging studies suggests that midline cortical and subcortical brain regions responsible for memory, self-relevant emotional and mental processes, as well as information integration comprise the DMN. The DMN is posited to represent self-referential mental activity via a dynamic interplay of cognitive and emotional processes by integrating information from the external environment with introspective thoughts to generate an autobiographical concept of the self. It has been amply documented that irregularities in the DMN and its functional connectivity are associated with various neuropsychiatric disorders. Moreover, accumulating evidence also suggests that individuals with select medical disorders (i.e., metabolic disorders) demonstrate alterations in DMN activity and functional connectivity. However, there is a paucity of data evaluating whether individuals with metabolically-based medical conditions, exhibiting altered DMN activity and functional connectivity, are at increased risk for developing neuropsychiatric disorders. Likewise, potential mechanisms (e.g., altered brain metabolism, insulin resistance) mediating these changes and their implications for novel treatment approaches have yet to be elucidated. Taken together, the overarching aim of this review is to provide a synthetic overview that suggests that this neural circuit may represent a common (or convergent) substrate affected in individuals with select neuropsychiatric and metabolic disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".