# Inferring the temporal structure of directed functional connectivity in neural systems: some extensions to Granger causality

## TL;DR

This paper extends Granger causality methods to better analyze the temporal dynamics of neural connectivity, enabling more detailed insights into brain information flow across different time scales.

## Contribution

It introduces new variants of Granger causality based on multi-step, infinite-future, and single-lag prediction for enhanced temporal analysis.

## Key findings

- New Granger causality variants for temporal analysis
- Improved understanding of neural information flow
- Systematic approach to neural connectivity timing

## Abstract

Neural processes in the brain operate at a range of temporal scales. Granger causality, the most widely-used neuroscientific tool for inference of directed functional connectivity from neurophsyiological data, is traditionally deployed in the form of one-step-ahead prediction regardless of the data sampling rate, and as such yields only limited insight into the temporal structure of the underlying neural processes. We introduce Granger causality variants based on multi-step, infinite-future and single-lag prediction, which facilitate a more detailed and systematic temporal analysis of information flow in the brain.