Minimum detectable change in motor and prefrontal cortex activity over repeated sessions using 3T functional MRI and a block design
Bibliographic record
Abstract
PURPOSE: To determine the minimum detectable change (MDC) in functional magnetic resonance imaging (fMRI) measurements of brain activity over repeated sessions with 95% confidence using a block design of tasks. MATERIALS AND METHODS: Fourteen individuals participated in three sessions on different days during which four scans each of a motor task and working memory task were performed. Using a region-of-interest analysis of variance, we calculated the MDC in the volume of activated primary sensorimotor cortex (for motor) and dorsolateral prefrontal cortex (for working memory), as well as the percent increase in MR signal and the center and location of maxima of the activated voxels. RESULTS: The MDC of activated volume was 5.0-8.8 cm3, and the MDC of percent increase in signal from baseline during tasks was 0.3%-0.6%. The MDC of the center of mass of activity was 3-4 mm, compared to 6-10 mm for the location of maxima. CONCLUSION: fMRI measurements that quantify the strength of activity in response to tasks and centers of mass offer sensitive measurements of change over repeated imaging sessions. fMRI can be used for serial investigations of individual participants using simple motor and cognitive tasks using a simple block design.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".