Salience and executive network connectivity analyses in schizophrenia during emotional memory tasks
Bibliographic record
Abstract
Schizophrenia affects about 1% of the population and is characterized by positive symptoms (e.g., hallucinations, delusions) and negative symptoms, particularly aboulia and reduced emotional expression (DSM-5). Cognitive deficits are also highly prevalent. The University of Montreal study aimed to investigate emotional memory and related activation patterns in schizophrenia and we used them for a connectivty analyses with currents tools. Seventy participants had an fMRI while completing a task with IAPS images in two phases, one emotional based and one memory based. In the emotion session, they passively viewed images of varying valence and arousal. In the memory session, they judged whether images had been presented previously. Symptom severity was assessed with the BPRS and PANSS. Controls group outperformed patients on memory accuracy. Connectivity analyses reveal a recurring pattern of dysconnectivity, with overconnectivity between sensory and salience networks and underconnectivity affecting frontal, limbic, and auditory regions. These alterations could explain difficulties in emotional processing, emotional blunting, and poor salience attribution. Clinically, they underscore the importance of targeting not only cognitive deficits but also neural dysfunctions, with a particular focus on non-invasive neuromodulation approaches such as tFUS. However, methodological limitations (sample size, absence of non-medicated patients, lack of temporal analyses) call for caution. This research reinforces the hypothesis of schizophrenia as a disorder of brain connectivity and paves the way for future longitudinal studies and more personalized treatments. Further studies adding precision to the protocol will be necessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".