Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting‐state fMRI

Abstract

Background and Purpose:The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. Methods:In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality).Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. Results:We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network.Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network.An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. Conclusion:Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.

Wavelet Multiscale Granger Causality Analysis Based on State Space Models

“…Since Granger causality is related to the flow of information across variables, it has been used in a growing number of studies in various fields, including the inference of information flow in the human brain as well as the analysis of economic and engineering data [4][5][6][7][8]. With the growing number of potential applications, Granger causality analysis is increasingly being employed to investigate multivariate and multiscale data scenarios.…”