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TUTCRIS

Recurrence network analysis of wide band oscillations of local field potentials from the primary motor cortex reveals rich dynamics.

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoInternational IEEE/EMBS Conference on Neural Engineering, NER
KustantajaIEEE COMPUTER SOCIETY PRESS
Sivut960-963
Sivumäärä4
ISBN (painettu)9781467363891
DOI - pysyväislinkit
TilaJulkaistu - 1 heinäkuuta 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational IEEE/EMBS Conference on Neural Engineering -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceInternational IEEE/EMBS Conference on Neural Engineering
Ajanjakso1/01/00 → …

Tiivistelmä

Aggregate signals that reflect activities of a large number of neurons in the cerebral cortex, local field potentials (LFPs) have been observed to mediate gross functional activities of a relatively small volume of the brain tissues. There are several bands of the oscillations frequencies in LFPs that have been observed across multiple brain areas. The signature oscillation band of the LFPs in the primary motor cortex (MI) is over β range and it has been consistently observed both in human and non-human primates around the time of visual cues and movement onsets. However, its dynamical behavior has not been well characterized. Furthermore, dynamics of β oscillations has been documented based on the phase locking of β oscillations, but not in terms of the inherent dynamics of the oscillations themselves. Here, we used the complexity measure derived from cluster coefficients of a recurrence network and analyzed a pair of wide-band signals, one including β band of the LFPs and the other ranging the low γ band in MI recorded from a non-human primate. We show rather unique temporal profiles of the evoked responses using complexity of the dynamical behavior in both bands of the oscillation, either of which is not simply resembling either the power of the oscillation or the phase locking of β oscillations. Therefore, the current method can reveal a new type of dynamics of the underlying network complexity during the task simply based on event evoked potentials of wide-band oscillatory signals.

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