Concurrent Multimodal Data Acquisition During Brain Scanning is within Reach
Bibliographic record
Abstract
Abstract Background Previous brain-scanning research exploring the neural mechanisms underpinning visuomotor planning and control has mostly been done without simultaneous motion-tracking and eye-tracking. Employing concurrent methodologies would enhance understanding of the brain mechanisms underlying visuomotor integration of cognitive, visual, ocular, and motor aspects of reaching and grasping behaviours. Therefore, this work presents the methods and validation for a high-speed, multimodal and synchronized system to holistically examine neural processes that are involved in visually-guided movement. Methods The multimodal methods included high speed 3D motion tracking (Qualisys), 2D eye-tracking (SR Research), and magnetoencephalography (MEG; Elekta) that were synchronized to millisecond precision. Previous MRIs were taken to provide improved spatial localization. The methods section describes the system layout and acquisition parameters to achieve multimodal synchronization. Pilot results presented here are preliminary data from a larger study including 29 participants. Using a pincer grip, five people (3 male, 2 female, ages 30-32) reached for and grasped a translucent dowel 50 times, after it was pseudorandomly illuminated. The object illumination was the Go cue. Seven discrete time points (events) throughout the task were chosen for investigation of simultaneous brain, hand and eye activity associated with specific visual (Go cue), oculomotor (1 st saccade after Go), motor (Reaction Time; RT, Maximum Velocity: MV, Maximum Grip Width; MGW) or cognitive (Ready, End) mechanisms. Time-frequency analyses were performed on the MEG data sourced from the left precentral gyrus to explore task-related changes time-locked to these chosen events. Pilot results Basic kinematic parameters including RT, MV, MGW, Movement Time, and Total Time were similar to previous, seminal research by Castiello, Paulignan and Jeannerod, (1991), using a similar task. Although no gaze instructions were given, eye-tracking results indicated volunteers mostly gazed at or near the target object when Ready (72%), and then hardly looked away throughout the rest of the task at the important events sampled here (92% - 98%). At the End event, when lifting the dowel, on average, participants gazed at or near the target object 100% of the time. Although saccades > 100 ms after Go, but prior to RT were made on average in about one fourth ( M = 13, SD = 6) of trials, a mixed model (REML) indicated their latency in timing after the Go was significantly ( F = 13.376, p = .001) associated with RT scores on those trials ( AIC = 724, R m 2 = 0.407, R c 2 = 0.420). Neural activity relative to baseline in the beta band was desynchronized for the visually guided reach periods, beginning prior to Go, and remaining sustained until beyond End, after the grasp and lift were executed. Conclusion This study presents the layout, acquisition parameters and validation for a multimodal, synchronized system designed to record data from the hand, eye and brain simultaneously, with millisecond precision during an ecologically-valid prehension task with physical, 3D objects. The pilot results align with previous research made with single or bimodal data recordings. This multimodal method enables full-brain modelling that can holistically map the precise location and timing of neural activity involved in the visual, oculomotor, motor and cognitive aspects of reach-to-grasp planning and control.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".