A statistical method for analyzing and comparing spatiotemporal cortical activation patterns

TL;DR

This paper introduces a new statistical method called multi-dimensional cluster statistics (MCS) for analyzing and comparing complex spatiotemporal patterns in multichannel neural data, aiding brain activity understanding and brain-computer interface development.

Contribution

The paper presents a novel MCS method for analyzing and comparing spatiotemporal neural activity patterns across different sensory cortices and species.

Findings

Stimulus-specific activity patterns can be detected using MCS.

Significant differences in activity patterns are identified during different stimuli.

Method is applicable to various multichannel neuronal data types.

Abstract

We present a new statistical method to analyze multichannel steady-state local field potentials (LFP) recorded within different sensory cortices of different rodent species. Our spatiotemporal multi-dimensional cluster statistics (MCS) method enables statistical analyzing and comparing clusters of data points in n-dimensional space. We demonstrate that using this approach stimulus-specific attractor-like spatiotemporal activity patterns can be detected and be significantly different from each other during stimulation with long-lasting stimuli. Our method may be applied to other types of multichannel neuronal data, like EEG, MEG or spiking responses and used for the development of new read-out algorithms of brain activity and by that opens new perspectives for the development of brain-computer interfaces.