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The predictive properties of white matter network diffusion on the development and progression of psychotic illness

Principal Supervisor: Dr Renate Reniers

Secondary Supervisor(s): Prof Stephen Wood

University of Registration: University of Birmingham

BBSRC Research Themes: Understanding the Rules of Life (Neuroscience and Behaviour)

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Deadline: 4 January, 2024


Project Outline

It is possible to identify young people at risk for psychotic illnesses such as schizophrenia through a combination of symptoms and personal or family history. Around 20% of such people develop psychosis within 12 months of being identified. There are differences in the brains of at risk cases when compared to similar individuals not at risk and these differences get greater with the onset of psychotic illness. We do not yet know, however, when in the progression these changes occur. They may come before (and somehow cause) the increase in symptoms, implying that trying to prevent these brain changes could prevent the illness.

The human brain is a complex patchwork of interconnected regions, and it has been shown that deficits in access, engagement and disengagement of large-scale neurocognitive networks play a critical role in disorders such as schizophrenia, depression, and anxiety. Diffusion Tensor Imaging (DTI) is a particularly useful imaging technique for modelling brain connectivity as it allows mapping of the diffusion process of molecules thereby revealing microscopic details of white matter fiber structure. Longitudinal imaging studies in dementia have shown that atrophy patterns associated with illness progression occur along vulnerable fiber pathways rather than by proximity. These findings suggest that various dementias selectively target distinct intrinsic brain networks. Whether this approach can be used to map illness development and progression in psychotic disorders remains to be determined and forms the main aim of this PhD project.

We have conducted a longitudinal, MRC funded study to investigate the course of brain changes from identification of being at risk for psychosis to one year after identification. Data collection included frequent administration of MRI scans, clinical assessments, and self-report measures over the duration of a year. This PhD project will focus on the analysis of the DTI data to investigate atrophy patterns associated with illness development and progression. The student will be able to capitalise on the frequent assessment method employed by this study (DTI data was collected at baseline, 6, and 12 months follow up) which will give us a unique insight in the dynamic association between brain structure/functioning and clinical presentation, thereby informing us about illness development and progression, possible causal relationships and direction, and the potential for recovery.

A follow up study will map the identified atrophy patterns onto a sample of individuals at risk for developing psychosis and a sample of those with a first episode of psychosis to test the hypothesis that transition to psychosis selectively targets distinct intrinsic brain networks compared to those who are at risk for developing psychosis. Data will be newly collected with scans being conducted at the University’s Centre for Human Brain Health. In addition, use will be made of existing data sets through collaborations with experts in the field (e.g. Enigma consortium).

Taken together, the aim is to significantly improve our ability to predict development and progression of psychotic illness by utilising baseline and follow up data on network diffusion in white matter tracts in the brain. We make the following hypotheses:

  1. Longitudinal changes in white matter diffusion networks will be seen primarily in frontal and temporal lobes over the progression of illness and will closely match those areas identified by T1-weighted volumetric analyses.
  2. Diffusion networks of white matter around the time of identification as at risk for developing psychosis will provide significant predictive power for determining development of psychotic illness.

References

Raj et al. (2012). A network diffusion model of disease progression in dementia. Neuron, 73(6), 1204-1215.

WaszczukLink opens in a new window et al. (2021). Disturbances in White Matter Integrity in the Ultra-High-Risk Psychosis State—A Systematic Review. J. Clin. Med., 10(11), 2515

Techniques

Raj et al. (2012) used whole brain tractography of diffusion MRI scans to derive behaviour of transsynaptic transmission of disease agents. Graph theoretic analysis was used to provide a fully quantitative and testable predictive model of dementia. We propose to adopt these novels methods described by Raj et al. in their work on illness progression in dementia to derive a network diffusion model of the development and progression of psychotic illness. Adoption of this approach will determine whether the macroscopic consequences of diffusive propagation on the whole-brain network are predictive of large-scale patterns of disease associated with the development and progression of psychotic illness.

This network diffusion model implies the presence of so-called eigenmodes that represent distinct spatial patterns that are associated with illness development and progression. These eigenmodes can take on the role of biomarkers and provide a clear path for predicting future atrophy in individuals starting from baseline scans. If the measured (follow up scans at 6 and 12 months) and predicted atrophy (from the baseline scan) are statistically close, this will provide evidence for the predictive properties of the proposed network diffusion model and have valuable prognostic applications. Being able to predict an individual’s neuroanatomical state, and together with this the likely clinical state, will assist clinicians in determining the most suitable course of treatment and allow patients to make informed choices regarding their lifestyle and therapeutic interventions. An increased understanding of the brain changes that accompany development and progression of psychotic illness, and how they can be used to improve predictive models, will also contribute to public understanding of the nature of psychotic illnesses and the way in which they develop.