Abstract
Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.
Highlights
The underlying neural correlates of movement have captivated neuroscientists throughout history; research has typically focused on the primary, supplementary, and pre-motor cortices
We found similar regions were selected as features across subjects, including right hippocampus, middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus
Linear decoding models were constructed from the data to find the relationship between the movement speed as a function of spectral content in measured neural activity during movement
Summary
The underlying neural correlates of movement have captivated neuroscientists throughout history; research has typically focused on the primary, supplementary, and pre-motor cortices. Movements that would largely engage non-motor regions may be more complex and require cumbersome experimental setups, and/or capturing activity in these regions requires a recording modality with large brain coverage and fine spatial and temporal resolution (Diedrichsen et al, 2005; Logothetis, 2008; González-Martínez et al, 2015). In Grave de Peralta et al (2009), researchers demonstrated that they could accurately classify which hand subjects would use on a trial-by-trial basis by first estimating intracranial potentials from the scalp EEG (Grave de Peralta-Menendez and Gonzalez-Andino, 2008) and relating high frequency spectral power of these estimated signals to hand movement intention. There are several approaches to estimating activity from deeper structures in the brain from scalp EEG, including source localization; but, these approaches suffer from smearing of the signals due to skull conductivity, making them less desirable than directly recording from the source
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