Working Memory Dysfunction: An fMRI Analysis in Schizophrenia
Bibliographic record
Abstract
This thesis explores the relationship between brain activation and decreased performance in working memory in schizophrenia. This study analyzed differences in brain activation patterns during a working memory task-based fMRI comparing individuals with schizophrenia to healthy controls. Participants (n = 53) completed a working memory n-back paradigm while in an fMRI where they completed three levels that increased in working memory load; 0back, 1back, and 2back. Python analysis of fMRI data compared brain activation maps of individuals with schizophrenia (n = 22) to healthy controls (n = 31) during the different levels of the nback task. D prime of task scores accuracy was calculated and compared between groups. Through analysis of fMRI 2 group sample maps, individuals with schizophrenia showed greater overall activation specifically in areas of the default mode network (DMN) demonstrating that during working memory individuals with schizophrenia have an impaired ability to deactivate the DMN. Similarly, individuals with schizophrenia showed a significantly lower accuracy and decreased performance on the nback task. Understanding brain activation differences during working memory in schizophrenia is important in understanding the prevalent cognitive deficits and memory impairment symptoms. This can lead to development of specific risk markers identifiable before the onset of psychosis and effective treatment plans targeting memory symptoms. This study contributes to the understanding of the circuitry involved in working memory deficits and can be predictive of greater cognitive decline within individuals with schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".