Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy

TL;DR

This study introduces a novel graph analysis technique called Modular Dirichlet Energy to analyze EEG data, revealing temporally precise differences in brain connectivity during visual memory tasks, which could aid early Alzheimer's detection.

Contribution

The paper presents a new method, Modular Dirichlet Energy, for analyzing functional brain networks with high temporal resolution during memory tasks.

Findings

Connectivity in the binding condition is less integrated than in shape-only during encoding.

Distinct driving effects in occipital and frontal modules at specific time windows.

Temporal differences in brain module activity relate to information processing differences.

Abstract

Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function. Electroencephalogram recordings were obtained from encoding and maintenance periods of tasks performed by healthy young volunteers. We probe the task's transient physiological underpinnings by contrasting shape only (Shape) and shape-colour binding (Bind) conditions, displayed in the left and right sides of the screen, separately. Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the functional network with unprecedented temporal precision. We find that connectivity in the Bind condition is less integrated with the global network than in the Shape condition in occipital and frontal modules during the encoding period of the right screen condition. Using MDE we are able to discern driving effects in the occipital module between 100-140ms, coinciding with the P100 visually evoked potential, followed by a driving effect in the frontal module between 140-180ms, suggesting that the differences found constitute an information processing difference between these modules. This provides temporally precise information over a heterogeneous population in promising tasks for the detection of AD.