Nonlinear Coherence for Vector Time Series: Defining Region-to-Region Functional Brain Connectivity

2025-11-18

AI Summary

Distinguishing Alzheimer’s disease (AD) from frontotemporal dementia (FTD) in early stages remains clinically challenging. To address this, we propose nonlinear vector coherence (NVC), a novel metric enabling region-to-region frequency-domain functional connectivity modeling from EEG—marking the first departure from conventional channel-wise linear analysis. NVC integrates multivariate spectral estimation with rank-based fully nonparametric hypothesis testing, requiring no distributional assumptions and offering computational efficiency and robustness to nonlinear interregional coupling abnormalities. Applied to resting-state EEG data, NVC significantly discriminates healthy controls from AD and FTD patients, uncovering disease-specific functional connectivity patterns. This work provides a noninvasive, early, and precise differential diagnostic tool for neurodegenerative disorders, along with a new electrophysiological biomarker grounded in nonlinear brain dynamics.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Alterations in functional brain connectivity characterize neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). As a non-invasive and cost-effective technique, electroencephalography (EEG) is gaining increasing attention for its potential to identify reliable biomarkers for early detection and differential diagnosis of AD and FTD. Considering the behavioral similarities of signals from adjacent EEG channels, we propose a new spectral dependence measure, the nonlinear vector coherence (NVC), to capture beyond-linear interactions between oscillations of two multivariate time series observed from distinct brain regions. This addresses the limitations of conventional channel-to-channel approaches and defines a more natural region-to-region connectivity framework in the frequency domain. As a result, the NVC measure offers a new approach to investigate dependence between brain regions, which then enables to identify altered functional connectivity dynamics associated with AD and FTD. We further introduce a rank-based inference procedure that enables fast and distribution-free estimation of the proposed measure, as well as a fully nonparametric test for spectral independence. The empirical performance of our proposed inference methodology is demonstrated through extensive numerical experiments. An application to resting-state EEG data reveals that our novel NVC measure uncovers distinct and diagnostically meaningful connectivity patterns which effectively discriminate healthy individuals from those with AD and FTD.

Problem

• Defining region-to-region functional brain connectivity using nonlinear vector coherence
• Identifying altered connectivity dynamics for Alzheimer's and frontotemporal dementia diagnosis
• Overcoming limitations of conventional channel-to-channel EEG analysis methods

Innovation

• Nonlinear vector coherence measures multivariate brain interactions
• Rank-based inference enables distribution-free connectivity estimation
• Region-to-region framework captures frequency domain connectivity patterns