Causal Network Analysis in the Human Brain: Applications in Cognitive Control and Parkinson’s Disease

Abstract

The human brain is an efficient organization of 100 billion neurons anatomically connected by about 100 trillion synapses over multiple scales of space and functionally interactive over multiple scales of time. The recent mathematical and conceptual development of network science combined with the technological advancement of measuring neuronal dynamics motivated the field of network neuroscience. Network science provides a particularly appropriate framework to study several mechanisms in the brain by treating neural elements (a population of neurons, a sub-region) as nodes in a graph and neural interactions (synaptic connections, information flow) as its edges. The central goal of network neuroscience is to link macro-scale human brain network topology to cognitive functions and pathology. Although interactions between any two neural elements are inherently asymmetrical, few techniques characterize directional/causal connectivity. This dissertation proposes model-free techniques to estimate and analyze nonlinear causal interactions in the human brain. The proposed methods were employed to build machine learning models that decode the network organization using electrophysiological signals. Mental disorders constitute a significant source of disability, with few effective treatments. Dysfunctional cognitive control is a common element in various psychiatric disorders. The first part of the dissertation addresses the challenge of decoding human cognitive control. To this end, we analyze local field potentials (LFP) from 10 human subjects to discover network biomarkers of cognitive conflict. We utilize cortical and subcortical LFP recordings from the subjects during a cognitive task known as the Multi-Source Interference Task (MSIT). We propose a novel method called maximal variance node merging (MVNM) that merges nodes within a brain region to construct informative inter-region brain networks. Region-level effective (causal) networks computed using convergent cross-mapping and MVNM differentiate task engagement from background neural activity with 85% median classification accuracy. We also derive task engagement networks (TENs) that constitute the most discriminative inter-region connections. Subsequent graph analysis illustrates the crucial role of the dorsolateral prefrontal cortex (dlPFC) in task engagement, consistent with a widely accepted model for cognition. We also show that task engagement is linked to the theta (4-8 Hz) oscillations in the prefrontal cortex. Thus, we decode the task engagement and discover biomarkers that may facilitate closed-loop neuromodulation to enhance cognitive control.

In the second part of the dissertation, the main goal is to use network features derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson’s disease (PD) patients from healthy controls (HC). We introduce a novel causality measure called frequency-domain convergent cross-mapping (FDCCM) that utilizes frequency-domain dynamics through nonlinear state-space reconstruction. Using synthesized chaotic timeseries, we investigate the general applicability of FDCCM at different causal strengths and noise levels. We show that FDCCM is resilient to additive Gaussian noise, making it suitable for real-world data. We used FDCCM networks estimated from scalp-EEG signals to classify the PD and HC groups with approximately 97% accuracy. The classifiers achieve high accuracy, independent of the patients’ medication status. More importantly, our spectral-based causality measure can significantly improve classification performance and reveal useful network biomarkers of Parkinson’s disease. Overall, this dissertation provides valuable techniques for causal network construction and analysis. Their usage is demonstrated on two applications: decoding cognitive control and detecting Parkinson’s disease. These methods can be extended to other neurological and psychiatric conditions to elucidate their network mechanisms.