Abstract
How similar are the representations of executed and observed hand movements in the human brain? We used functional magnetic resonance imaging (fMRI) and multivariate pattern classification analysis to compare spatial distributions of cortical activity in response to several observed and executed movements. Subjects played the rock-paper-scissors game against a videotaped opponent, freely choosing their movement on each trial and observing the opponent's hand movement after a short delay. The identities of executed movements were correctly classified from fMRI responses in several areas of motor cortex, observed movements were classified from responses in visual cortex, and both observed and executed movements were classified from responses in either left or right anterior intraparietal sulcus (aIPS). We interpret above chance classification as evidence for reproducible, distributed patterns of cortical activity that were unique for execution and/or observation of each movement. Responses in aIPS enabled accurate classification of movement identity within each modality (visual or motor), but did not enable accurate classification across modalities (i.e., decoding observed movements from a classifier trained on executed movements and vice versa). These results support theories regarding the central role of aIPS in the perception and execution of movements. However, the spatial pattern of activity for a particular observed movement was distinctly different from that for the same movement when executed, suggesting that observed and executed movements are mostly represented by distinctly different subpopulations of neurons in aIPS.
Original language | English |
---|---|
Pages (from-to) | 11231-11239 |
Number of pages | 9 |
Journal | Journal of Neuroscience |
Volume | 28 |
Issue number | 44 |
DOIs | |
State | Published - 29 Oct 2008 |
Externally published | Yes |
Keywords
- Classification
- Mirror neurons
- Mirror system
- Motor control
- Movement perception
- Movement selectivity
- fMRI
ASJC Scopus subject areas
- General Neuroscience