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Decoding intransitive actions in primary motor cortex using fMRI: toward a componential theory of 'action primitives'
Elizabeth A Shay1, Quanjing Chen1, Frank E Garcea1,2,3
1a Department of Brain & Cognitive Sciences , University of Rochester , Rochester , NY , USA.
Cognitive Neuroscience
|March 17, 2018
Summary
Multivoxel pattern analysis (MVPA) can differentiate brain activity for simple hand movements within the primary motor cortex. This technique successfully distinguished between distinct actions, even when overall brain signal amplitude was similar.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Brain Imaging
Background:
- Multivoxel pattern analysis (MVPA) of functional MRI data can detect subtle differences in neural representations.
- Distinguishing between simple, intransitive actions in the brain remains a challenge, particularly within primary motor areas.
Purpose of the Study:
- To investigate if MVPA can differentiate between simple intransitive actions (rotation and flexion) of the hand in the primary motor cortex.
- To determine if neural activity patterns differ for distinct movements of the same body part.
Main Methods:
- Functional MRI (fMRI) was used to scan participants performing hand and foot movements.
- Primary motor cortex regions of interest (ROIs) for the hand/wrist were functionally defined for each subject.
- MVPA, specifically linear correlation, was applied to fMRI data within subject-specific ROIs.
Main Results:
- The BOLD contrast amplitude was higher for contralateral hand movements compared to ipsilateral hand or foot movements.
- No significant difference in BOLD contrast amplitude was found between the two distinct hand movements (rotation vs. flexion).
- MVPA successfully distinguished between the two different contralateral hand movements.
Conclusions:
- Simple intransitive actions can be differentiated in primary motor cortex using MVPA.
- MVPA reveals distinct neural representations for movements that do not differ in overall signal amplitude.
- This study highlights the potential of MVPA for decoding fine-grained motor intentions.