Task-Related Controllability of Functional Connectome During a Working Memory Task in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder
Bibliographic record
Abstract
Abstract Working memory (WM) deficit is a prominent and common cognitive impairment in major psychiatric disorders (MPDs). Altered control of brain states transitions may underlie the neural basis of WM deficit. Brain controllability derived from Network Control Theory provides a mathematical framework to study how external signals may affect neural network dynamics and influence the transition to desired states. We investigate if shared and illness-specific alterations in controllability underlie WM deficits in MPDs. We examined fMRI data during a n-back WM task from 105 patients with schizophrenia (SZ), 67 with bipolar disorder (BD), 51 with major depressive disorder (MDD), and 80 healthy controls (HCs). A region’s capacity to steer transitions to connectomic states with less input (average controllability) and difficult-to-reach states with high input (modal controllability) were compared across groups. The effect of altered controllability on clinical and cognitive characteristics, and their likely genetic and neurotransmitter basis were investigated. Compared to HCs, all MPDs had lower modal controllability of frontoparietal network. SZ and MDD shared modal controllability in default mode network and salience network nodes compared to BD and HCs. Only SZ had lower modal controllability of sensorimotor, auditory, and visual network nodes than HCs, indicating the need for higher sensory inputs to facilitate a state transition in SZ. Expression of genes that determine synaptic biology and chemoarchitecture involving glutamate/GABA and monoamine (dopamine and 5HT) receptor systems were more likely in the affected brain regions. A graded, transdiagnostic reduction in the influence of the triple network system and sensory networks in implementing state transitions underlies working memory deficits in MPDs. This deficit, especially pronounced in SZ, has its likely basis in synaptic biology and in glutamate/GABA and monoamine (dopamine and 5HT) systems.
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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 | 0.000 | 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) | 0.000 | 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".