Information Processing and Magnetic Resonance Imaging Indices of Brain Pathology in Multiple Sclerosis
Bibliographic record
Abstract
Accumulation of central nervous system (CNS) pathology affects cognitive processing speed and efficiency and is thought to underlie attentional and executive deficits in multiple sclerosis (MS). Most clinical neuropsychological tests are multifactorial and are limited in their sensitivity to specific cognitive processes. This may, in part, account for the low to moderate correlations between clinical test results and magnetic resonance imaging (MRI) indices of brain pathology. We compared the ability of a clinical and an experimental test of cognitive processing speed to differentiate domain-specific cognitive changes in MS, and examined relations between test performance and MRI measures of brain pathology. Twelve MS patients and 12 controls completed the Paced Auditory Serial Addition Test (PASAT) and the Attention Networks Test-Interactions (ANT-I), a computerized response latency task. Subjects also had MRI scans that included T1, T2, and fluid-attenuated inversion recovery (FLAIR) sequences that provided global and localized volumetric measures. Patients made more errors on the PASAT and were slower on the ANT-I. The ANT-I also revealed specific deficits in response inhibition. In addition, ANT-I performance was associated with changes in a number of MRI measures, which was not the case for the PASAT. Reaction time paradigms that manipulate within-task demands on distinct cognitive functions may provide meaningful markers of brain disease burden in MS.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".