Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

TL;DR

This paper develops a comprehensive statistical framework for causal functional connectomics in neural networks, integrating association and causality concepts to compare existing methods and guide future research.

Contribution

It introduces a systematic statistical guide using directed Markov graphical models to define causality in neural connectomics, enabling comparison and development of causal inference methods.

Findings

Introduces the Directed Markov Property for causal analysis

Provides a framework to compare existing causal connectomics approaches

Outlines future properties for causal methodology development

Abstract

Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property -- an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.