Browsing by Subject "Brain Networks"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Causal Network Analysis in the Human Brain: Applications in Cognitive Control and Parkinson’s Disease(2022-04) Avvaru, Satya Venkata SandeepThe 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.Item Geometric and Optimization Methods for Diffusion Magnetic Resonance Imaging(2017-08) Farooq, HamzaThis thesis presents novel mathematical and computational methods aimed at enhancing and improving brain tissue structural imaging techniques that are based on diffusion Magnetic Resonance Imaging (dMRI). The most commonly used dMRI technique is Diffusion Tensor Imaging (DTI), which models water diffusion via a Gaussian pattern and estimates the corresponding covariance, also known as diffusion tensor. DTI forms the basis of brain structural connectivity methods like tractography and sub-cortical region parcellation, and thus provides useful markers for brain white matter integrity. Other, recently proposed dMRI techniques rely on modeling water diffusion in intra-axonal and extra-axonal spaces separately. Thereby, these so-called multi-compartment models hold the promise to provide detailed tissue microstructure information and to identify markers that may be specific to particular tissue development/diseases. In this thesis we address key mathematical challenges encountered by DTI, as well as by these newly proposed dMRI techniques, that pertain to recovering more detailed microstructure information. We begin by focusing on DTI and present novel geometrical methods to improve DTI analysis (Chapters 3, 4, and 5). In particular, (i) we utilize the mathematical theory of Optimal Mass Transport to improve brain parcellation by comparing sub-cortical regions connectivity profiles and compute their corresponding geometric ``average'' connectivity profiles, (ii) we introduce Ricci flow applied to diffusion tensor fields to enhance feature extraction, and finally (iii) we introduce a notion of discrete Ricci curvature in brain connectivity networks as a novel nodal measure to detect critical regions (nodes) of the structural brain networks. This notion of node curvature can be used to identify changes in brain network structure due to disease/development as it supplements information that can be obtained by other conventional network nodal measures. We then study multi-compartment dMRI models, and present a novel model fitting method to such tissue models (Chapter 6). Our proposed method is generic to all multicompartment models and enables for the first time dMRI-imaging in multiple fiber orientations and fiber-crossings situations. In addition to potential improvements in imaging technology, we hope that the advances presented in this work will contribute to the diagnosis and treatment of neurological disorders.