A neurophysiological brain map: Spectral parameterization of the human intracranial electroencephalogram. Issue 3 (March 2020)

• Record Type: Journal Article
• Authors: Kalamangalam, Giridhar P.; Long, Sarah; Chelaru, Mircea I.
• Is Part Of: Clinical neurophysiology. Volume 131:Issue 3(2020:Mar.)
• Page Start: 665
• Page End: 675
• Publication Date: 2020-03
• Subjects: Epilepsy -- Electrocorticogram -- Gaussian mixture model -- Fourier analysis -- Log-normal distribution; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2019.11.061
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: We develop an accurate spectral model of an open-access normative atlas of human intracranial EEG. Color-coding the model on a brain surface yields a novel visual neurophysiological brain map. Our results imply a certain 'universality' to brain rhythm generation across the cortex. Abstract: Objective: A library of intracranial electroencephalography (iEEG) from the normal human brain has recently been made publicly available (Frauscher et al., 2018). The library – which we term the Montreal Neurological Institute Atlas (MNIA) – comprises 30 hours of iEEG from over a hundred epilepsy patients. We present a Fourier spectrum-based model of low dimension that summarizes all of MNIA into a neurophysiological 'brain map'. Methods: Normalized amplitude spectra of the MNIA data were modelled as log-normal distributions around individual canonical Berger frequencies. The latter were concatenated to yield the composite spectrum with high accuracy. Key model parameters were color-coded into a visual representation on cortical surface models. Results: Each brain region has its own spectral characteristics that together yield a novel composite intracranial EEG brain map. Conclusions: iEEG from normal brain regions can be accurately modelled with a small number of independent parameters. Our model is based in the canonical Berger bands and naturally suits clinical electroencephalography. Significance: Due to its applicability to iEEG from all sampled regions, the model suggests a certain universality to brain rhythm generation that is independent of precise cortical location. More generally, our results are a novel abstraction of resting cortical dynamics that may help diagnostics in epileptology, in addition to informing structure-function relationships in the field of human brain mapping.