Cognitive modes underlying attentional control impairments in schizophrenia: an fMRI study
Bibliographic record
Abstract
Among the cognitive impairments characteristic of schizophrenia, deficits in cognitive control are particularly prominent. In the current functional magnetic resonance imaging (fMRI) study, we examined the spatial patterns and task-induced blood-oxygenation-level-dependent (BOLD) changes associated with attentional control deficits in participants with schizophrenia relative to neurotypical participants. We employed a cognitive paradigm that required participants to alternated between three judgment tasks (i.e., identifying colours, parity, or letter case), while being presented with stimuli comprised of one (univalent), two (bivalent), or three (trivalent) dimension(s). Three cognitive modes were detected: (1) Multiple Demand (MD), (2) Language (LAN), and (3) Default mode B (DM-B). Participants with schizophrenia showed impaired task performance, muted but sustained activations of the MD mode, hypo-activations of the LAN mode, and hyper-deactivations of the DM-B mode relative to neurotypical participants. Thus, attentional control impairments in individuals with schizophrenia are likely due to disrupted activity of the MD, LAN, and DM-B modes. Additionally, in participants with schizophrenia, the sustained activation of the MD mode in the Trivalent condition was associated with higher scores on positive symptoms (hallucinations and delusions), while the reduced activation of the LAN mode in the Trivalent condition and the pronounced deactivation of the DM-B mode in the Univalent condition were associated with higher scores on negative symptoms (i.e., poverty of speech and flattened affect). Our findings highlight the contributions of fMRI to individualized medicine due to symptom-specific effects, and identifying potential targets for neuromodulation techniques that aim to restore BOLD activity and improve cognitive function in schizophrenia.
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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) | 0.001 | 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".