Mapping Hand Function with Simultaneous Brain-Spinal Cord Functional MRI
Bibliographic record
Abstract
INTRODUCTION: Hand motor control depends on intricate brain-spinal cord interactions that regulate muscle activity. Hand function can be disrupted by injury to the brain, spinal cord, and peripheral nerves leading to weakness and impaired coordination. Functional MRI (fMRI) can map motor-related neural activity and potentially characterize the mechanisms underlying hand weakness and diminished coordination. Although brain motor control has been extensively studied, spinal cord mechanisms remain less explored. Here we use simultaneous brain-spinal cord fMRI to map neural activity related to hand strength and dexterity across the central nervous system using force matching and finger tapping tasks. This study pioneers the use of simultaneous brain-spinal cord fMRI to comprehensively map hand function, offering novel insights into coordinated motor processing across the central nervous system. METHODS: We performed simultaneous brain-spinal cord fMRI in 28 right-handed healthy volunteers (age: 40.0 ± 13.8 years, 14 females, 14 males) using a 3T GE SIGNA Premier scanner equipped with a 21-channel head-neck coil. Participants performed a force-matching task at 10%, 20%, and 30% of maximum voluntary contraction using a hand dynamometer. For the finger tapping task, participants completed button-presses at 1 Hz with a 5-button response pad for three task levels: single-digit response with the second digit only (low), single-digit response with all digits in a sequential order (medium), single-digit response with all digits in a random order (high). Visual cues and feedback were provided during the tasks.Brain and spinal cord images were processed separately using FSL and the Spinal Cord Toolbox, with motion correction, physiological noise filtering, and spatial normalization to standard templates. Subject level activity maps were generated and entered into group level analyses to explore both activations and deactivations. For the brain, we used a mixed effect design with a voxelwise threshold of Z score > 3.10 and cluster threshold of p < 0.05. For the spinal cord, we used a fixed effect design with a voxelwise threshold of Z score > 1.64 and cluster threshold of p < 0.05. Region of interest (ROI) analyses were conducted to examine localized changes in activation across task levels. RESULTS: Both tasks elicited activation in motor and sensory regions of the brain and spinal cord, with graded responses in the left primary motor (M1), left primary sensory (S1) cortex, and right spinal cord gray matter across task levels. Deactivation of the right M1 and S1 was also present for both tasks. Deactivation of the left spinal cord gray matter was present in the high task level of the force matching task. The ROI analysis findings complemented the group level activity maps. DISCUSSION: Our study provides a detailed map of brain-spinal cord interactions in hand function, revealing graded neural activation and inhibition patterns across motor and sensory regions. Interhemispheric inhibition, reflected in right M1 deactivation, likely restricts extraneous motor output during unilateral tasks. For force matching, the deactivation of the left ventral and dorsal horns of the spinal cord, provides the first evidence that the inhibition of motor areas during a unilateral motor task extends to the spinal cord. Whether this inhibition results from direct descending modulation from the brain or interneuronal inhibition in the cord remains to be interrogated. These findings expand our understanding of central motor control mechanisms and could inform rehabilitation strategies for individuals with motor impairments. CONCLUSIONS: Our simultaneous brain-spinal cord fMRI approach provides novel insights into the neural coordination of hand function, enhancing our understanding of motor control and its modulation. This approach may offer a foundation for studying motor dysfunction in conditions such as stroke, spinal cord injury, and neurodegenerative diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".