HoloBrain: A Harmonic Holography for Self-organized Brain Function

Abstract

Functional neuroimaging technology offers a new window to snapshot the transient neural activity in-vivo. Although tremendous efforts have been made to characterize spontaneous functional fluctuations, little attention has been paid to the functional mechanisms of neural interactions. Inspired by the notion of holography, we propose an explainable machine learning approach to establishing a novel underpinning of self-organized cross-frequency coupling (CFC) through the lens of brain wave interference on top of the network topology. Specifically, we conceptualize that the interaction between ubiquitous neural activities and a collection of reference harmonic wavelets forms a region-adaptive interference pattern that captures cross-frequency coupling of remarkable neuronal oscillations. In this regard, assembling whole-brain CFC patterns under the constraint of brain wiring mechanisms constitutes a HoloBrain mapping that records a wide spectrum of spontaneous neural activities. Since each local interference pattern is a symmetric and positive-definite (SPD) matrix, we tailor a deep model of HoloBrain (coined DeepHoloBrain) to infer the latent feature representations on the Riemannian manifold of SPD matrices for predicting brain states and recognizing disease connectomes. We have applied DeepHoloBrain to the Human Connectome Project and several dementia-related datasets. Compared with current state-of-the-art deep models, our DeepHoloBrain not only improves the recognition/prediction accuracy but also sheds new light on understanding the neurobiological mechanisms of brain function and cognition.

H. Liu and T. Dan—These authors contributed equally to this work.