Impact of cognitive performance on the reproducibility of fMRI activation in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Longitudinal functional magnetic resonance imaging (fMRI) studies in patients with schizophrenia allow exploration of the course of the illness and brain activity after therapy. A crucial question, however, is whether fMRI findings are reliable, because they can be affected by performance deficits in patients with schizophrenia. Our aim was to evaluate the reproducibility of fMRI activations in highly integrated language areas in patients with schizophrenia, taking into account task performance. METHODS: Ten patients with schizophrenia and 10 matched healthy controls were scanned twice, 21 months apart, while performing a story comprehension task. The reproducibility of the activations in each participant was evaluated globally by the percentage of spatial overlap between the 2 sessions and locally by a voxel-wise computation of the between-session relative standard deviation. We performed between-group comparisons both with and without the inclusion of comprehension scores (measuring task performance) as a covariate. RESULTS: On average, patients with schizophrenia had significantly lower comprehension scores than controls (4.5/12 v. 7.8/12, p = 0.002). The mean spatial overlap between fMRI sessions was 30.6% in the patient group and 47.0% in the control group (p = 0.017). Locally, the lower reproducibility in patients was most prominent in the left posterior middle temporal gyrus, inferior frontal gyrus and medial prefrontal cortex (p < 0.001 uncorrected for multiple comparisons). Comprehension scores were positively correlated with both reproducibility measures in patients (overlap: r = 0.82, p = 0.004; relative standard deviation: several significant clusters at p < 0.001). When we included the comprehension scores as a covariate, most of the local between-group differences in reproducibility were removed, and the difference in overlap was not significant. LIMITATIONS: Owing to the small sample size, we could not investigate the impact of clinical subtypes and different types of medications on reproducibility. CONCLUSION: Our findings suggest that the greater variability in activation in patients with schizophrenia compared with controls concerns high-level areas and is mainly attributable to deficient task performance. Consequently, cognitive performance must be carefully controlled when longitudinal fMRI studies are undertaken.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".