Abstract
Recent advances in functional magnetic resonance imaging (fMRI) have been used to reconstruct cognitive states based on brain activity evoked by sensory or cognitive stimuli. To date, such decoding paradigms were mostly used for visual modalities. On the other hand, reconstructing functional brain activity in motor areas was primarily achieved through more invasive electrophysiological techniques. Here, we investigated whether non-invasive fMRI responses from human motor cortex can also be used to predict individual arm movements. To this end, we conducted fMRI studies in which participants moved their arm from a center position to one of eight target directions. Our results suggest that arm movement directions can be distinguished from the multivoxel patterns of fMRI responses in motor cortex. Furthermore, compared to multivoxel pattern analysis, encoding models were able to also reconstruct unknown movement directions from the predicted brain activity. We conclude for our study that non-invasive fMRI signal can be utilized to predict directional motor movements in human motor cortex.
Highlights
Recent functional magnetic resonance imaging (fMRI) studies have successfully discriminated visual object categories (Haxby et al, 2001; Cox and Savoy, 2003), hand gestures (Dinstein et al, 2008), and visual features such as orientation and motion direction (Kamitani and Tong, 2005, 2006) from patterns of activity across an array of voxels
We investigated whether human M1 voxels show directional sensitivity and whether the spatial patterns of the fMRI responses in M1 evoked by directional movements could be discriminable using multivoxel pattern analysis (MVPA) based on linear classifier (Norman et al, 2006)
We evaluated the classification performance of MVPA based on Support vector machine (SVM) classifier and the encoding model via a leave-one-out cross-validation scheme
Summary
Recent fMRI studies have successfully discriminated visual object categories (Haxby et al, 2001; Cox and Savoy, 2003), hand gestures (Dinstein et al, 2008), and visual features such as orientation and motion direction (Kamitani and Tong, 2005, 2006) from patterns of activity across an array of voxels. Invasive electrophysiological techniques have demonstrated that neuronal activities in human primary motor cortex (M1) can be used to control an artificial devices (Hochberg et al, 2006, 2012; Truccolo et al, 2008; Collinger et al, 2013). Such invasive techniques have been found to be more precise and intuitive when used to control an external effector using neuronal signals related to arm movements. We used functional magnetic resonance imaging (fMRI) to measure brain signals non-invasively and investigated whether the recent decoding methods were applicable to motor areas
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