Conference on Cognitive Computational Neuroscience

San Francisco • August 25 - 28, 2022

P-2.28

The aperiodic activity of LFPs from the human basal ganglia and thalamus show no knee and lower exponent compared to neocortex

Alan Bush, Vasileios Kokkinos, Mark Richardson, Massachusetts General Hospital, Harvard Medical School, United States; Jasmine Zou, Massachusetts Institute of Technology, United States; Witold Lipski, University of Pittsburgh, United States
Session:
Posters 2 Poster
Track:
Cognitive science
Location:
Pacific Ballroom H-O
Presentation Time:
Fri, 26 Aug, 19:30 - 21:30 Pacific Time (UTC -8)
Abstract:
The 1-over-f^χ aperiodic component of the power spectra of local field potentials (LFPs) tracks cortical anatomy, arousal level and correlates with (patho)physiological states. Previous research has focus almost exclusively on cortical aperiodic activity. Here we explore the aperiodic component in subcortical nuclei from the human thalamus and basal ganglia in relation to neocortex. To this end we elaborated on previous methods by proposing a new parameterization of the aperiodic component with independent, identifiable and easily interpretable parameters, which allows to fit spectra with and without an aperiodic knee using the same model. Analysis of subcortical recordings from the ventral thalamus (VIM), the globus pallidus internus (GPi) and the subthalamic nucleus (STN) shows no detectable aperiodic knee and a relatively low aperiodic exponent (χ_STN=1.3±0.2, χ_VIM=1.4±0.1, χ_GPi=1.4±0.1), which are markedly different from cortical aperiodic parameters (χ_cortex=3.2±0.4, f_(knee,cortex)=17±5 Hz). We replicated these results in a second dataset from epilepsy patients undergoing stereo-EEG monitoring that included thalamic recordings. These differences likely reflect cytoarchitectonic and/or functional differences. Taken together with the modulation of the aperiodic parameters across cortical regions and physiological states, our results suggest that the aperiodic knee and high aperiodic exponent reflect characteristic dynamics of cortical activity.
Manuscript:
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License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2022.1085-0
Publication:
2022 Conference on Cognitive Computational Neuroscience
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