Browsing by Subject "Cognitive Control"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Causal Network Analysis in the Human Brain: Applications in Cognitive Control and Parkinson’s Disease
    (2022-04) Avvaru, Satya Venkata Sandeep
    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.
  • Thumbnail Image
    Item
    Multiple Perspectives on the Impact of Cognitive Control Processes on Disorganization in Psychosis
    (2023-06) Kwashie, Anita
    Disorganization, a psychosis symptom factor denoting abnormal speech and behavior, is thought to reflect impaired cognitive processing. However, despite their theoretical similarities, there appears to be little consistent evidence of a distinctive relationship between disorganization and cognitive control. This dissertation examined the relationship between disorganization and two key cognitive control mechanisms, context processing and prepotent response inhibition, also known as proactive control and reactive control. Chapter 2 compared the functional neuroanatomy of cognitive control in schizophrenia patients and healthy controls. Both context processing and prepotent response inhibition marshaled activity in prefrontal, anterior cingulate, posterior parietal, and middle temporal structures. Controls demonstrated greater prefrontal activity during context processing than did patients. However, BOLD signal did not predict disorganization severity. Due to potential bias in symptom ratings, Chapter 3 controlled for ethnic minority identity while examining the relationship between disorganization and cognitive control in a psychosis sample. Disorganization predicted variance in context processing and prepotent response inhibition, unlike other symptoms. However, the context processing model also found ethnic minority identity predictive, particularly if identifying as Black. Chapter 4 explored the potential influence of visual processing within a new cognitive control task. Behavioral metrics were uncorrelated with either self-report or experimenter-rated disorganization. However, context processing trended with general cognitive impairment in psychosis patients. Visual processing was associated with context processing and prepotent response inhibition in controls, while only the Middle Frontal Gyrus activated in patients. Generalized smooth models suggest visual processing regions may predict cognitive impairment better than traditional regions of interest. Implications and future directions are considered.
  • Thumbnail Image
    Item
    Testing the effects of adolescent alcohol use on adult conflict-related theta dynamics
    (2017-02) Harper, Jeremy
    While adolescent alcohol use (AAU) has been associated with poor neurocognitive outcomes, few studies have utilized prospective samples, leaving uncertain any potential long-term neurocognitive effects of AAU. In addition, despite theoretical models linking AAU to diminished cognitive control, empirical work testing the relationship between AAU and specific neural correlates of cognitive control remains scarce. Recent work indicates that demands of cognitive control (e.g., response conflict) involve EEG theta-band dynamics, including medial frontal cortex (MFC) power and MFC-dorsal prefrontal cortex (dPFC) functional connectivity, which may be related to AAU-related neurocognitive dysfunction. The present study tested the hypothesis that greater AAU is associated with diminished adult conflict-related EEG theta-band dynamics in a large (N = 718) population-based prospective twin sample assessed at the target ages of 11, 14, 17, and 29. Two complementary analytic methods (cotwin control design; bivariate biometric modeling) were used to disentangle the genetic/shared environmental premorbid risk towards AAU from the potentially causal nonshared environmental effects of alcohol exposure. AAU was negatively associated with adult (age 29) theta MFC power and MFC-dPFC connectivity during a flanker task, suggesting that early drinking is associated with diminished cognitive control-related theta dynamics in adulthood. Both the CTC and biometric modeling results indicated that genetic influences primarily accounted for the association between AAU and reduced theta-band dynamics. Taken together, these findings suggest that the link between AAU and diminished adult cognitive control-related theta dynamics is likely a consequence of heritable genetic factors, rather than causal nonshared environmental effects.