Todd Woodward

Recent decades of neuroimaging have witnessed a rapid increase in network-based research, reflecting the understanding that cognition arises from the interacting activity of multiple large-scale brain networks. However, research has increasingly prioritized studying how activity in resting state (RS) networks relate to cognition, rather than using task-based experimentation to observe the networks supporting cognitive processes. A central assumption of this approach is that task-based activity constitutes a ‘rebalancing’ of RS networks for task conditions. However, no research to date has adequately tested this assumption by evaluating RS and other network sets on their ability to explain task-related BOLD signal. In this dissertation, we introduce a novel method, Spatiotemporal fMRI-CPCA, and demonstrate its utility in characterizing the successes, and shortcomings, of different network models in accounting for the variance and spatiotemporal features of brain networks predictable from task timing. Through a series of quality assurance analyses, we validate the Spatiotemporal fMRI-CPCA method and illustrate the metrics by which network model performance can be evaluated. In order to establish a ‘ground truth’ against which network models could be evaluated, we measured and characterized the data-driven brain networks supporting verbal paired associates learning. Then, we employed Spatiotemporal fMRI-CPCA to examine how well two popular RS network models, and one network model using spatial templates derived from past observations of task-based networks, accounted for these data-driven networks. Overall, our results indicated advantages of the task-derived network model relative to both RS network models in capturing task-specific variance in BOLD signal, and in predicting key spatial and hemodynamic characteristics of the task-based networks. The advantages of the task-derived-templates model also extended to better accuracy characterizing differences in network activation between patients with schizophrenia and healthy controls. We discuss the implications of these findings for efforts to study the neural basis of cognition, and cognitive disorder, through observation of resting state networks.

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Working memory (WM), defined as actively holding and/or manipulating information in mind, is central in guiding behaviour. WM is also a core domain of impairment in schizophrenia which substantially impacts functional outcome. Although the dorsolateral prefrontal cortex (DLPFC) has been implicated as a source of WM deficits in schizophrenia, these deficits may be better characterized within the framework of functional connectivity of distributed brain regions. However, in task-state functional magnetic resonance imaging (fMRI) research, the sluggish hemodynamic response hinders the separation of cognitive sub-processes in WM tasks. Moreover, findings from an individual fMRI task may not be broadly clinically meaningful even if reliable effects are detected. The present research used whole-brain, multi-experiment, functional connectivity analyses to obtain more refined characterizations of WM networks and their activity in schizophrenia across a variety of cognitive tasks. Study 1 demonstrated a novel method of combining a verbal WM task with a thought generation task, which produced a finer delineation of networks than when the WM task was analysed alone. Study 2 reported individual analyses of four tasks (i.e., verbal WM, thought generation, visuospatial WM, and set-switching Stroop tasks), providing basic characterizations of their dominant networks in healthy individuals. Finally, study 3 consolidated all four datasets into a unified analysis to examine differences between healthy controls and schizophrenia patients in the resulting networks, as well as correlations be-tween these networks and task performance. A visual attention network – engaged during encoding of memory sets, and diminished in patients – was associated with accuracy in the verbal and visuospatial WM tasks, and with WM capacity measured in separate out-of-scanner testing sessions. A frontoparietal network including the DLPFC – possibly underlying internally-oriented attention – exhibited hypoactivity in patients as expected, but was not correlated with behavioural WM measures. These findings suggest that dysfunction in a given network cannot be assumed to underlie poor task performance, as this may depend on the cognitive sub-process it supports. This work also demonstrates that a network may be concealed in an individual task when it does not account for a distinct portion of variance, yet may exhibit reliable activity when examined across multiple tasks.

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Integrating evidence that contradicts a belief is a fundamental aspect of belief revision and is closely linked to delusions in schizophrenia. In this research, we examined the cognitive and brain mechanisms underlying disconfirmatory evidence integration, their relation to delusions and the bias against disconfirmatory evidence (BADE) in schizophrenia, as well as associations between changes in delusion severity, BADE, and functional brain activity related to evidence integration. Across three neuroimaging studies, two functional brain networks emerged as central to disconfirmatory evidence integration: a visual attention network (VsAN) including dorsal anterior cingulate cortex and bilateral insula; and a cognitive evaluation network (CEN) involving rostrolateral/orbitofrontal cortex, inferior frontal gyrus, and inferior parietal lobule. In study 1, these networks showed sequential activation and increased activity during disconfirmatory evidence integration, suggesting they were involved in distinct evidence detection and integration processes. In study 2, we found that activity in these networks was differentially associated with delusions, with delusional schizophrenia patients showing VsAN hyperactivity and CEN hypoactivity relative to controls. Subsequent analyses examining associations between activity in these networks and behaviour revealed that BADE was positively associated with VsAN activity during confirmatory evidence integration, and negatively associated with CEN activity during disconfirmatory evidence integration. These findings indicate that VsAN hyperactivity underlies the focus on confirmatory evidence, and CEN hypoactivity the avoidance of disconfirmatory evidence, that contributes to impaired evidence integration and delusion maintenance in schizophrenia. Finally, in study 3, we demonstrated that poorer evidence integration over time was related to greater hyperactivity in the VsAN and hypoactivity in the CEN, from time 1 to time 2, and that improved positive symptoms (including delusions) were associated with normalization of activity in the CEN, showing that activity in these networks fluctuates as a function of changes in behavioural BADE evidence integration and symptoms. This research represents the first comprehensive study of the cognitive and brain mechanisms underlying disconfirmatory evidence integration and behavioural BADE in schizophrenia patients with delusions, and highlights brain networks underlying cognitive biases related to important aspects of delusion maintenance in schizophrenia: the focus on confirmatory evidence; and the avoidance of disconfirmatory evidence.

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Schizophrenia is a serious, chronic mental illness that is characterized by perceptual abnormalities and cognitive deficits. Although the illness is commonly associated with perceptual abnormalities, the cognitive deficits have the greater impact on functional outcomes in patients. Some of the most profound deficits in schizophrenia have been observed in a domain referred to as cognitive control. Cognitive control is defined as the ability to adaptively adjust behaviour in response to environmental changes. Given the broadness of this definition, cognitive control is often fractionated into constituent cognitive operations, such as goal representation and maintenance, attentional biasing, conflict resolution, and stimulus-response mapping. In this study, the goal was to examine the brain basis for deficits in the attentional biasing aspect of cognitive control in schizophrenia. Behavioural and brain mechanisms of attentional biasing were assessed by manipulating the number of features that participants would have to ignore for each experimental trial. As schizophrenia is characterized by changes to both brain structure and function, a further aim was to use multi-modal brain imaging to develop a comprehensive picture of changes that underlie difficulties in attentional biasing. The results of this study indicated that although schizophrenia patients exhibit changes in brain structure, they still utilized the same brain networks as neurologically healthy individuals to bias attention towards relevant stimulus features. For the functional magnetic resonance imaging results, a functional brain network underlying attentional biasing, which included the dorsal anterior cingulate cortex, was identified and showed a positive relationship between the number of irrelevant stimulus features and increases in brain activity. Patients, however, showed reduced compensatory modulations in brain activity as the number of irrelevant stimulus dimensions increased. The magnetoencephalography results showed differences between the schizophrenia patients and healthy participants, but these differences were not as hypothesized, and may reflect cognitive differences related to language processing in schizophrenia. This work suggests that brain activity in patients is less efficient at higher levels of task difficulty when performing an attentional biasing task but these results are clouded by underlying changes in brain structure and a high variability in task activity in the patients.

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The manner in which we judge multiple hypotheses and consider multiple items of evidence is fundamental to diverse aspects of behaviour. One goal of the studies reported here was to identify cognitive biases in this process. A probabilistic reasoning paradigm involving objectively quantifiable evidence allowed the manipulation of factors biasing hypothesis judgment while mathematically normative responses were kept constant. This revealed two cognitive biases. The first was a tendency to overestimate the strength of gradually accumulated evidence. The second was a tendency to judge a self-selected hypothesis as being more probable than an externally selected one, despite equivalent supporting evidence. This selection bias was exacerbated in delusional schizophrenia patients. Our second goal was to describe brain networks involved in hypothesis judgment. To this purpose, we collected functional magnetic resonance imaging (fMRI) data during performance of a probabilistic reasoning task. Functionally connected brain networks were identified using constrained principal component analysis (CPCA). The fMRI results showed task-related activity in a network including the dorsal anterior cingulate cortex (dACC) and bilateral parietal cortex. The activity of this dACC-based network was strongest when the evidence was consistent with the hypothesis being judged (evidence-hypothesis matches). This result is discussed in terms of functional connectivity between the dACC and other brain regions as a possible mechanism for coherence between components of a mental representation. Both our behavioural results and our neuroimaging results show evidence of processing unique to situations involving cognitive coherence between the hypothesis being judged and the relevant evidence.

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