Detecting functional connectivity change points for single-subject fMRI data
Why this work is in the frame
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Bibliographic record
Abstract
Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.
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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 | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it