A neurophysiological brain map: Spectral parameterization of the human intracranial electroencephalogram

• Published in: Clinical neurophysiology 2020-03, Vol.131 (3), p.665-675
• Main Authors: Kalamangalam, Giridhar P., Long, Sarah, Chelaru, Mircea I.
• Format: Article
• Language: English
• Subjects: Electrocorticogram; Epilepsy; Fourier analysis; Gaussian mixture model; Log-normal distribution
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/j.clinph.2019.11.061

•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. 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’. 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. Each brain region has its own spectral characteristics that together yield a novel composite intracranial EEG brain map. 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. 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.