Todd Woodward
Relevant Thesis-Based Degree Programs
Affiliations to Research Centres, Institutes & Clusters
Biography
Dr. Woodward is a Professor within the Department of Psychiatry in the Faculty of Medicine, a Research Scientist with the BC Mental Health and Addictions Research Institute (BCMHARI), and Centre Investigator with the Brain Research Centre.
The objective of Dr. Woodward’s research program is to gain a functional and anatomical understanding of the functional brain networks that underlie the primary symptoms of psychosis and schizophrenia. Three lines of research are being pursued.
First, the cognitive correlates of the symptoms of psychosis are being explored by way of originally designed cognitive paradigms assessing specific aspects of memory and reasoning. Translation of these results back to people with schizophrenia in a group setting have led to a promising treatment program called metacognitive training (MCT). Second, functional neuroimaging (fMRI, EEG, MEG) is being utilized to identify the neural underpinnings of these cognitive functions, and how their dysfunction manifests as the symptoms of psychosis, and how they are affected by MCT. Finally, software is being developed for multivariate analysis of functional neuroimaging data (fMRI-CPCA and MEG-CPCA).
Graduate Student Supervision
Doctoral Student Supervision
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
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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Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Functional magnetic resonance imaging (fMRI) studies typically explore the blood-oxygen-level-dependent (BOLD) signal underlying discrete cognitive processes that occur over milliseconds to a few seconds. However, autobiographical cognition is a protracted process and requires fMRI tasks with longer trials to capture the temporal dynamics of the underlying brain networks. In the current study, we provide an updated analysis of the fMRI data obtained from a published autobiographical event simulation task, with a slow-event-related design (34-second trials), that involved participants recalling past events, imagining past/future events, and completing a semantic association control task. Both versions of the analytical approach, called Principal Component Analysis for fMRI (fMRI-CPCA), used to derive the brain networks involved in the autobiographical event simulation task resulted in four brain networks. Two networks, the Default Mode network, which activated during the autobiographical event simulation conditions but deactivated in the semantic association control condition, and the Multiple Demand network, which peaked early in the trials when external focus to solve the task was required, partially reproduced and extended the previously published results. Two networks, the Maintaining Internal Attention network, which activated when participants had to direct their focus internally to engage in autobiographical event simulation and semantic association, and the Response network, which activated when participants had to make motor responses, were novel and provided new information about the BOLD changes underlying autobiographical event simulation. Overall, our findings reveal that the DMN is not uniquely involved in autobiographical event simulation as it coactivates with the MAIN. However, these two networks do not have redundant functions as in the semantic association control condition, the DMN deactivated while the MAIN activated. Thus, our results highlight that the MAIN consistently shows activation during all periods of maintaining attention to internal mental representations, whereas the DMN shows activation during the simulation of internal events that take one’s own perspective – a process unlikely to be engaged during semantic association. The identification of additional networks by fMRI-CPCA demonstrates how different analytical approaches can provide complementary information and, ultimately, lead to more refined cognitive interpretations of task-based fMRI data.
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Impairment in language association and detriments in executive functioning are characteristic features of schizophrenia. Available external measures are often used to describe the profile of schizophrenia, but whether these measures can reflect underlying neuropathology of schizophrenia remains to be answered. In the current research, we attempt to 1) identify functional brain networks underlying a semantic association task and 2) relate functional brain activity to external measures of behaviour, symptomatology, and neurocognition. A data-driven exploratory method was employed to investigate the functional brain networks underlying a semantic association and word recall task, and how these networks may be impaired in schizophrenia. This revealed three distinct networks: a Language Network (LN), Default Mode Network (DMN), and Volitional Attention/Response Network (VAN/RN). All three networks exhibited the greatest magnitude of activation or deactivation under the distant association, forgotten condition. The activation of the DMN significantly differed between patients and controls, where schizophrenia patients showed a greater sensitivity to recall relative to controls, reflected in an increased magnitude of deactivation of the DMN. One interpretation posits that the distant association and forgotten condition was more difficult, and required a greater shift of available cognitive resources away from the DMN (a task-negative network) to the LN and VAN/RN (task-positive networks). A greater shift in resources towards the LN and VAN/RN may have come at a cost of background processes of encoding and memory, where few resources were available for making word pair memories. Analyses relating brain networks to external measures of cognition showed that, while brain networks were able to significantly predict scanner-based behavioural measures of reaction time, accuracy, and recall, they were unable to predict either symptom or neurocognitive measures. Specifically, a desired balance between networks and brain networks acting in conjunction was able to significantly relate to in-scanner behaviour. Taken together, these studies provide evidence that the functional brain networks may be disordered in schizophrenia, and sheds light on how functional network activation relates to external measures of behaviour and symptoms. The results of these series of studies may have implications for informing decisions for personalized targets in future therapeutic treatments of schizophrenia.
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Impairments in social cognition are known to have severe impacts on functional outcome in schizophrenia. Attributional biases (i.e., biases towards assigning blame/credit to oneself, other people, or to the situation) are of particular interest due to their direct relevance to paranoid delusions and potential implication in cognitive-based treatments. A "defense" model of paranoid delusions proposed by Bentall, Kinderman, and Kaney (1994) suggests that paranoid individuals possess an extreme self-serving bias, with a specific tendency to blame other people as opposed to the situation (i.e., personalizing bias). In addition, an inability to integrate belief-disconfirming information is thought to underlie the fixedness of delusions. However, these biases have not been investigated simultaneously to check for additive or multiplicative effects on associations with symptoms. Moreover, previous studies have failed to take into account the heterogeneity of the symptoms of psychosis. The present research employed structural equation modelling and constrained principal component analysis in schizophrenia patients, bipolar disorder patients, and healthy individuals to examine the extent to which group differences and symptom severity could predict patterns of responding on a novel attributional bias task, designed to assess an individual's ability to integrate contextual information in conjunction with attributional reasoning. In line with the defense model of paranoia, it was predicted that schizophrenia patients with severe paranoid delusions would display enhanced self-serving and personalizing biases. However, no differences between diagnostic- or symptom-based participant groups were found. Conversely, the severity of symptoms of overactive disorganization in schizophrenia and bipolar disorder patients predicted higher situation attributions and self-blame (specifically when such attributions were unsupported by the available evidence), while higher depression in healthy participants was negatively related to situation attributions and lower self-credit. These findings suggest that non-self-serving bases may be non-specifically related to high psychopathology, while an ability to integrate socially-relevant contextual information to consider other people’s roles in events may be reduced in overactive disorganization in mental illness, and is negatively related to depression in healthy individuals. The absence of noticeable group differences in attributional biases illustrates the importance of employing a multivariate symptom-based approach when studying complex cognitive processes in psychosis.
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The goals of the present research were two-fold: (1) to examine whether diagnosis-dependent group differences in cognitive performance among schizophrenia patients, their unaffected siblings and healthy controls are fundamentally the result of a general cognitive impairment and/or of domain-specific deficits in schizophrenia; and (2) to examine the cognitive domains that characterize family membership-dependent and family membership-independent group differences in cognitive performance between schizophrenia patients and their siblings. In Study 1, results from a traditional statistical analysis method suggested impairment in all five cognitive domains tested, whereas constrained principal component analysis (CPCA) revealed a single cognitive domain accounting for group differences that extended across all five traditional domains. This component reflected impairment in a generalized cognitive domain in schizophrenia patients and, to a lesser degree, siblings, and was dominated by WAIS-R Digit Symbol and WMS-R Logical Memory subscales, a finding in line with literature reporting most severe impairment in information processing speed and verbal memory in schizophrenia. In Study 2, CPCA with hierarchical regression was used to examine the cognitive domains that accounted for the interaction between group and family membership, revealing three cognitive domains (Working Memory/Attention, Visual Memory, and Verbal Memory) where differences between patients and their siblings depended on family membership. A subsequent cluster analysis revealed several family clusters differing on patients’ and siblings’ performance across these three cognitive domains. The results of the current research suggest that (1) diagnosis-based group differences in cognitive performance are due to impairment in a generalized cognitive domain (and not primarily within more specific cognitive domains) that is common to all families, (2) this general impairment is best captured by measures of information processing speed and verbal memory, and that (3) family membership-dependent group differences are present in more specific cognitive domains that are distinguishable from the general domain describing overall group differences. This research helps synthesize the two sides of the debate surrounding the nature of cognitive impairment in schizophrenia, by suggesting that there is impairment in both a generalized cognitive domain and in more specific domains, but that the latter may depend on moderating factors, such as family membership.
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Schizophrenia is characterized by cognitive deficits in many domains. One of the domains in which these deficits are commonly found is in self-other source monitoring. Source monitoring refers to the set of processes by which individuals recall the conditions and contextual details surrounding the encoding of a memory episode, and self-other source monitoring specifically involves differentiating between actions performed by oneself versus those performed by another person. In this study, the goal was to investigate the neural basis of self-other source monitoring, and to discover how this neural activity differs in schizophrenia. The results of this study indicate that schizophrenia patients and healthy control subjects utilize essentially the same neural network for self-other source monitoring, and that this network involves brain areas that have been described as belonging to the task-positive and task-negative networks. Multiple statistical methods were used to analyze this dataset in order to provide a comprehensive set of results, as well as to determine the agreement between them. Although differences exist between the methods employed herein, in both the matrices that are used as input, and the mathematical operations performed on them, the results suggest that all the methods identified a common signal in the data.
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