Analysis of hippocampal local field potentials by diffusion mapped delay coordinates

TL;DR

This paper introduces a new method to analyze brain signals in the hippocampus, revealing insights into spatial navigation and age-related differences.

Contribution

The novel contribution is the application of diffusion mapped delay coordinates (DMDC) to hippocampal local field potentials for improved analysis.

Findings

DMDC identifies age differences in hippocampal dynamic states not detected by traditional methods.

The method replicates standard analysis outcomes while avoiding assumptions of traditional signal processing.

DMDC could enhance information yield from local field potential recordings in behavioral studies.

Abstract

Spatial navigation through novel spaces and to known goal locations recruits multiple integrated structures in the mammalian brain. Within this extended network, the hippocampus enables formation and retrieval of cognitive spatial maps and contributes to decision making at choice points. Exploration and navigation to known goal locations produce synchronous activity of hippocampal neurons resulting in rhythmic oscillation events in local networks. Power of specific oscillatory frequencies and numbers of these events recorded in local field potentials correlate with distinct cognitive aspects of spatial navigation. Typically, oscillatory power in brain circuits is analyzed with Fourier transforms or short-time Fourier methods, which involve assumptions about the signal that are likely not true and fail to succinctly capture potentially informative features. To avoid such assumptions, we applied a method that combines manifold discovery techniques with dynamical systems theory, namely diffusion maps and Takens’ time-delay embedding theory, that avoids limitations seen in traditional methods. This method, called diffusion mapped delay coordinates (DMDC), when applied to hippocampal signals recorded from juvenile rats freely navigating a Y-maze, replicates some outcomes seen with standard approaches and identifies age differences in dynamic states that traditional analyses are unable to detect. Thus, DMDC may serve as a suitable complement to more traditional analyses of LFPs recorded from behaving subjects that may enhance information yield.

Figures