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Probing neural circuitry and large-scale brain dynamics underlying cognitive deficits associated with schizophrenia


Schizophrenia is a complex neuropsychiatric disorder characterized by a wide range of clinical manifestations. Even though the etiology of schizophrenia is not known, the heterogeneous nature of the disorder strongly suggests that multiple pathways and brain areas are affected by a combination of internal and external factors. These factors include and not limited to: psychological, genetic, social, and environmental determinants. Cognitive impairment is one of the commonly observed clinical manifestations of schizophrenia. Working memory, which is an ability to encode and hold information over a short period, is severely impaired in schizophrenia. (1) Characterizing how such deficits manifest in large-scale dynamics and (2) understanding the pathophysiology and circuit mechanisms behind working memory deficits associated with schizophrenia are the two main questions that I address in my dissertation.

To answer the first question, I employed a method based on nonlinear systems theory to quantify large-scale dynamical states of time-series data and to identify dynamically distinct subgroups (Chapter 2). I demonstrate that the method, which utilizes delay differential analysis (DDA), can effectively extract features reflective of significant state changes and detect subgroups with similar features. Applying the method to brain signals obtained from a large cohort of schizophrenia patients further revealed subgroups with distinct dynamical characteristics aligned with neurophysiological and clinical parameters.

To answer the second question, I first developed a biologically realistic computational model based on spiking recurrent neural networks (RNNs) capable of learning cognitive tasks that involve working memory (Chapter 3). By taking advantage of a close relationship between continuous and spike RNNs that emerges under certain conditions, the method provides an extremely simple platform that can be utilized to investigate how power-efficient network dynamics lead to complex cognitive computations. By employing the framework, I uncover and characterize important circuit mechanisms critical for working memory maintenance in Chapter 4. The uncovered microcircuitry underscores the importance of disinhibitory gating exerted by specific subtypes of inhibitory interneurons, further confirming recent experimental findings.

Overall, my dissertation provides important computational tools for probing both micro- and macro-scale circuit dynamics associated with cognitive deficits in schizophrenia.

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