Brodmann’s pioneering work resulted in the classification of cortical areas based on their cytoarchitecture and topology. Here, we aim at documenting that diverse cortical areas also display different neuronal electric activities. We investigated this notion in the hand-controlling sections of the primary somatosensory (S1) and motor (M1) areas, in both hemispheres. We identified S1 and M1 in 20 healthy volunteers by applying functional source separation (FSS) to their recorded electroencephalograms (EEG). Our results show that S1 and M1 can be clearly differentiated by their neuroelectric activities in both hemispheres and independently of the subject’s state (i.e., at rest or performing movements or receiving external stimulations). In particular, S1 displayed higher relative power than M1 in the alpha and low beta frequency ranges (8–25 Hz, p < .003), whereas the opposite occurred in the high gamma band (52–90 Hz, p = .006). In addition, S1’s activity had a smaller Higuchi’s fractal dimensions (HFD) than M1’s (p < .00001) in all subjects, permitting a reliable classification of the two areas. Moreover, HFD of M1’s activity resulted correlated with the hand’s fine motor control, as expressed by the 9-hole peg test scores. The present work is a first step toward the identification and classification of brain cortical areas based on neuronal dynamics rather than on cytoarchitectural features. We deem this step to be an improvement of our knowledge of the brain’s structural–functional unity.

Neuronal electrical ongoing activity as a signature of cortical areas

Porcaro C.;
2017

Abstract

Brodmann’s pioneering work resulted in the classification of cortical areas based on their cytoarchitecture and topology. Here, we aim at documenting that diverse cortical areas also display different neuronal electric activities. We investigated this notion in the hand-controlling sections of the primary somatosensory (S1) and motor (M1) areas, in both hemispheres. We identified S1 and M1 in 20 healthy volunteers by applying functional source separation (FSS) to their recorded electroencephalograms (EEG). Our results show that S1 and M1 can be clearly differentiated by their neuroelectric activities in both hemispheres and independently of the subject’s state (i.e., at rest or performing movements or receiving external stimulations). In particular, S1 displayed higher relative power than M1 in the alpha and low beta frequency ranges (8–25 Hz, p < .003), whereas the opposite occurred in the high gamma band (52–90 Hz, p = .006). In addition, S1’s activity had a smaller Higuchi’s fractal dimensions (HFD) than M1’s (p < .00001) in all subjects, permitting a reliable classification of the two areas. Moreover, HFD of M1’s activity resulted correlated with the hand’s fine motor control, as expressed by the 9-hole peg test scores. The present work is a first step toward the identification and classification of brain cortical areas based on neuronal dynamics rather than on cytoarchitectural features. We deem this step to be an improvement of our knowledge of the brain’s structural–functional unity.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3405390
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