Investigating full neuraxis correlates of hand dexterity
Poster No:
2085
Submission Type:
Abstract Submission
Authors:
Valeria Oliva1, Sandrine Bédard1, Christine Law1, Dario Pfyffer1, Merve Kaptan1, Brett Chy2, Susanna Aufrichtig2, Nazrawit Berhe2, John Ratliff3, Serena Hu4, Zachary Smith5, Andrew Smith6, Scott Delp7, Akshay Chaudhari8, Trevor Hastie9, Gary Glover1, Sean Mackey1, Kenneth Weber10
Institutions:
1Stanford University, Palo Alto, CA, 2Systems Neuroscience and Pain Lab, Division of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, 3Department of Neurosurgery, Stanford University School of Medicine, Stanford, Palo Alto, CA, 4Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, Palo Alto, CA, 5Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 6Department of Physical Medicine and Rehabilitation, University of Colorado School of Medicine, Aurora, CO, 7Department of Bioengineering and Mechanical Engineering, Stanford University, Palo Alto, CA, 8Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, 9Department of Statistics, Stanford University, Palo Alto, CA, 10Stanford University, PALO ALTO, CA
First Author:
Co-Author(s):
Brett Chy
Systems Neuroscience and Pain Lab, Division of Pain Medicine, Stanford University School of Medicine
Palo Alto, CA
Systems Neuroscience and Pain Lab, Division of Pain Medicine, Stanford University School of Medicine
Palo Alto, CA
Susanna Aufrichtig
Systems Neuroscience and Pain Lab, Division of Pain Medicine, Stanford University School of Medicine
Palo Alto, CA
Systems Neuroscience and Pain Lab, Division of Pain Medicine, Stanford University School of Medicine
Palo Alto, CA
Nazrawit Berhe
Systems Neuroscience and Pain Lab, Division of Pain Medicine, Stanford University School of Medicine
Palo Alto, CA
Systems Neuroscience and Pain Lab, Division of Pain Medicine, Stanford University School of Medicine
Palo Alto, CA
John Ratliff
Department of Neurosurgery, Stanford University School of Medicine, Stanford
Palo Alto, CA
Department of Neurosurgery, Stanford University School of Medicine, Stanford
Palo Alto, CA
Serena Hu
Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford
Palo Alto, CA
Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford
Palo Alto, CA
Zachary Smith
Department of Neurosurgery, University of Oklahoma Health Sciences Center
Oklahoma City, OK
Department of Neurosurgery, University of Oklahoma Health Sciences Center
Oklahoma City, OK
Andrew Smith
Department of Physical Medicine and Rehabilitation, University of Colorado School of Medicine
Aurora, CO
Department of Physical Medicine and Rehabilitation, University of Colorado School of Medicine
Aurora, CO
Scott Delp
Department of Bioengineering and Mechanical Engineering, Stanford University
Palo Alto, CA
Department of Bioengineering and Mechanical Engineering, Stanford University
Palo Alto, CA
Introduction:
The execution of fine voluntary hand movement relies on contralateral cortical inputs to the ipsilateral ventral horn of the spinal cord. These motor functions can be disrupted in conditions related to cervical spinal cord injury. Brain functional MRI (fMRI) has been extensively used to reliably measure BOLD signal changes during a variety of motor tasks, resolving activations in regions including the primary and supplementary motor areas related to hand function 1. However, due to the technical challenges of spinal cord fMRI, only a few studies have been able to identify BOLD signal changes in the spinal cord during motor activity 2,3. Here, we aim to take advantage of the recent advancements in simultaneous brain and spinal cord fMRI 4 to measure neural activity related to hand dexterity in healthy volunteers.
Methods:
Ten right-handed healthy volunteers were enrolled in this study (Age = 40.3 ± 15.4, 6 females, 4 males) and completed 30 sequences of 15s right hand finger taps with an MRI-compatible 5-button response pad. Visual cues indicating when and which button to press were presented on a projector screen for three different dexterity levels: single-finger response 2nd digit only (low), single-finger response all digits sequential order (medium), single-finger response all digits random order (high) 5.
Functional images were collected with a combined brain-spinal cord EPI pulse sequence 4 with dynamic per slice shimming (3T GE SIGNA Premier scanner; 21-channel Head-Neck coil; TR=2.5s, TE=30ms, GRAPPA=2), including 30 brain slices (3.43x3.43x5.00mm) and 15 cervical spinal cord slices (1.25x1.25x5.00mm) centered at C5-C6 intervertebral disc. Axial and sagittal field maps were collected to calculate shimming parameters. Anatomical scans were acquired for the brain (T1-weighted 1.00x1.00x1.00mm) and spinal cord (T2-weighted 0.70x0.50x0.50mm). Brain and spinal cord functional data were preprocessed with similar pipelines using FSL 6 and the Spinal Cord Toolbox 7, respectively: motion correction, high pass filtering, physiological noise correction, white matter and cerebrospinal fluid regression, spatial normalization, spatial smoothing. Subject-level task activity maps were obtained using a general linear model and entered into a group level single-sample t-tests.
Functional images were collected with a combined brain-spinal cord EPI pulse sequence 4 with dynamic per slice shimming (3T GE SIGNA Premier scanner; 21-channel Head-Neck coil; TR=2.5s, TE=30ms, GRAPPA=2), including 30 brain slices (3.43x3.43x5.00mm) and 15 cervical spinal cord slices (1.25x1.25x5.00mm) centered at C5-C6 intervertebral disc. Axial and sagittal field maps were collected to calculate shimming parameters. Anatomical scans were acquired for the brain (T1-weighted 1.00x1.00x1.00mm) and spinal cord (T2-weighted 0.70x0.50x0.50mm). Brain and spinal cord functional data were preprocessed with similar pipelines using FSL 6 and the Spinal Cord Toolbox 7, respectively: motion correction, high pass filtering, physiological noise correction, white matter and cerebrospinal fluid regression, spatial normalization, spatial smoothing. Subject-level task activity maps were obtained using a general linear model and entered into a group level single-sample t-tests.
Results:
Brain activity was identified in primary and supplementary motor cortex, primary somatosensory cortex, primary visual cortex, superior parietal lobule, thalamus, and cerebellum, and BOLD signal changes in these regions increased linearly with task dexterity levels. Interestingly, the activity became more bilateral with increasing dexterity (Z > 3.1, cluster corrected p < 0.05, mixed effects analysis, Figure 1).
Spinal cord activity was identified in the ipsilateral side of the C6 spinal cord segment. Increase in dexterity levels was linearly associated with higher ipsilateral and medial spinal cord activity. Interestingly, higher dexterity levels were associated with larger clusters that expanded beyond the ventral horn (Z > 1.6, uncorrected, fixed effects analysis, Figure 2).
Spinal cord activity was identified in the ipsilateral side of the C6 spinal cord segment. Increase in dexterity levels was linearly associated with higher ipsilateral and medial spinal cord activity. Interestingly, higher dexterity levels were associated with larger clusters that expanded beyond the ventral horn (Z > 1.6, uncorrected, fixed effects analysis, Figure 2).
Conclusions:
We identified that activity in cortical and subcortical brain regions related to motor functions increases linearly with higher dexterity levels. The increase in task difficulty was also associated with activity in cortical areas related to higher cognitive-attentional processes. We also show significant ipsilateral spinal cord activity during finger tapping, that increases linearly with the task dexterity.
The protocol developed in the present study may be used for the characterization of the full neuraxis in patients with upper limb motor dysfunction due to spinal cord or nerve root injury.
The protocol developed in the present study may be used for the characterization of the full neuraxis in patients with upper limb motor dysfunction due to spinal cord or nerve root injury.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Methods Development
Motor Behavior:
Visuo-Motor Functions 1
Motor Behavior Other
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
ADULTS
Cortex
FUNCTIONAL MRI
Modeling
Motor
Open-Source Code
Spatial Normalization
Spinal Cord
Sub-Cortical
Other - Dexterity
1|2Indicates the priority used for review
Provide references using author date format
2. Weber KA 2nd, Chen Y, Wang X, Kahnt T, Parrish TB. Lateralization of cervical spinal cord activity during an isometric upper extremity motor task with functional magnetic resonance imaging. Neuroimage. 2016 Jan 15;125:233-243.
3. Vahdat S, Lungu O, Cohen-Adad J, Marchand-Pauvert V, Benali H, Doyon J. Simultaneous Brain-Cervical Cord fMRI Reveals Intrinsic Spinal Cord Plasticity during Motor Sequence Learning. PLoS Biol. 2015 Jun 30;13(6):e1002186.
4. Islam H, Law CSW, Weber KA, Mackey SC, Glover GH. (2019) Dynamic per slice shimming for simultaneous brain and spinal cord fMRI. Magn Reson Med. 81(2):825-838.
5. Olman CA, Pickett KA, Schallmo MP, Kimberley TJ. Selective BOLD responses to individual finger movement measured with fMRI at 3T. Hum Brain Mapp. 2012 Jul;33(7):1594-606.
6. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782–790 (2012).
7. De Leener, B. et al. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Neuroimage 145, 24–43 (2017).