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- Title
Functional Semi-Blind Source Separation Identifies Primary Motor Area Without Active Motor Execution.
- Authors
Porcaro, Camillo; Cottone, Carlo; Cancelli, Andrea; Salustri, Carlo; Tecchio, Franca
- Abstract
High time resolution techniques are crucial for investigating the brain in action. Here, we propose a method to identify a section of the upper-limb motor area representation (FS_M1) by means of electroencephalographic (EEG) signals recorded during a completely passive condition (FS_M1bySS). We delivered a galvanic stimulation to the median nerve and we applied to EEG the semi-Blind Source Separation (s-BSS) algorithm named Functional Source Separation (FSS). In order to prove that FS_M1bySS is part of FS_M1, we also collected EEG in a motor condition, i.e. during a voluntary movement task (isometric handgrip) and in a rest condition, i.e. at rest with eyes open and closed. In motor condition, we show that the cortico-muscular coherence (CMC) of FS_M1bySS does not differ from FS_ M1 CMC (0.04 for both sources). Moreover, we show that the FS_M1bySS's ongoing whole band activity during Motor and both rest conditions displays high mutual information and time correlation with FS_M1 (above 0.900 and 0.800, respectively) whereas much smaller ones with the primary somatosensory cortex (about 0.300 and 0.500, ). FS_M1bySS as a marker of the upper-limb FS_M1 representation obtainable without the execution of an active motor task is a great achievement of the FSS algorithm, relevant in most experimental, neurological and psychiatric protocols.
- Subjects
BRAIN function localization; ELECTROENCEPHALOGRAPHY; AFFERENT pathways; NEURAL stimulation; ISOMETRIC exercise
- Publication
International Journal of Neural Systems, 2018, Vol 28, Issue 3, p-1
- ISSN
0129-0657
- Publication type
Article
- DOI
10.1142/S0129065717500472