Order-Dependent Functional Brain Connectivity in a Cue-Separation Grasp Task
Bibliographic record
Abstract
Prehension involves location-dependent reach transport and orientation-dependent grasp components. To understand how the brain integrates object location and orientation for grasp, we studied how the order of transport / grasp cues influences whole brain functional connectivity. We collected BOLD signal data from 12 participants in an Event-related fMRI Experiment. Participants were instructed to reach and grasp a cube illuminated to the left or right of midline (Location Cue: L) and a verbal instruction to orient the hand for vertical or horizontal grasp (Orientation Cue: O). The order of these cues (LO vs. OL) varied randomly. fMRI data were analyzed separately based on three predictors: Delay 1 (between the two cues), Delay 2 (between the 2nd cue and go signal), and an Action Phase. Graph Theory Analysis was performed based on 200 regions of interest (nodes) at each phase. Preliminary analysis based on 3 participants: During Delay 1, nodes coalesced into three modules: 1) a central parietofrontal strip approximating primary somatomotor cortex, 2) two more anterior-posterior premotor / visuomotor parietofrontal regions, and 3) a ring of cortex skirting 1+2 but with no occipital/temporal involvement. Occipital involvement increased in Delay 2. Parietofrontal Modules 1 + 2 joined (reducing to two modules) after Delay 2 for LO and the action phase for OL, i.e., always after the location cue. The Global Clustering Coefficient is always reduced in the action phase. We conclude the order of L-O cues influences modularity, such that location information produces more parietofrontal ‘binding’, presumably in preparation for transport.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".