Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Abstract

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra-and interregional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra-and interregional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra-and interregional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

A newly developed free software tool set for averaging electroencephalogram implemented in the Perl programming language

“…Recently, an advanced EEG analysis, utilizing artificial intelligence and/or machine learning techniques, reported an up to 74% true-positive rate for detecting seizure waveforms ( Emami et al., 2019 ) or up to 88% correct prediction of antidepressant effects in patients with major depression ( Jaworska et al., 2019 ). Connectivity analysis with a high temporal resolution (milliseconds), utilized with intracranial recording, demonstrated that inter-regional connectivity between the inferior occipital gyrus and the amygdala could be observed during face recognition ( Sato et al., 2018 ).…”