Mapping Hand Function with Simultaneous Brain-Spinal Cord Functional MRI
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».