Todd Woodward

Professor

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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 (Jan 2008 - May 2019)
Cognitive biases and functional brain networks underlying delusions in schizophrenia (2018)

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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Multimodal examination of brain networks involved in attentional biasing in schizophrenia (2017)

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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Cognitive processes involved in hypothesis judgment and underlying functional brain networks (2013)

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 (2010 - 2018)
Symptom-related attributional biases in schizophrenia and bipolar disorder (2014)

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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Brain networks involved in source monitoring in schizophrenia (2011)

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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