Statistical mechanics of neocortical interactions: Portfolio of Physiological Indicators

TL;DR

This paper introduces a novel approach combining statistical mechanics, copula risk management, and advanced algorithms to integrate diverse brain imaging data, aiming to improve understanding of neural information processing and its relation to behavior.

Contribution

It develops a new framework that applies statistical mechanics and copula methods to integrate multiple neuroimaging modalities for enhanced data resolution.

Findings

Successful integration of EEG, MEG, PET, SPECT, and fMRI data sources.

Improved correlation between integrated data and behavioral phenomena.

Enhanced stability and duration estimates of neural activity distributions.

Abstract

There are several kinds of non-invasive imaging methods that are used to collect data from the brain, e.g., EEG, MEG, PET, SPECT, fMRI, etc. It is difficult to get resolution of information processing using any one of these methods. Approaches to integrate data sources may help to get better resolution of data and better correlations to behavioral phenomena ranging from attention to diagnoses of disease. The approach taken here is to use algorithms developed for the author's Trading in Risk Dimensions (TRD) code using modern methods of copula portfolio risk management, with joint probability distributions derived from the author's model of statistical mechanics of neocortical interactions (SMNI). The author's Adaptive Simulated Annealing (ASA) code is for optimizations of training sets, as well as for importance-sampling. Marginal distributions will be evolved to determine their expected duration and stability using algorithms developed by the author, i.e., PATHTREE and PATHINT codes.