November 16, 2022

Computational Neuroimaging for Personalised Psychiatry

1st Call, TPdF

Short Summary

For psychiatric diseases, diagnosis, management and treatment are all currently symptom-based. What are urgently needed are techniques that are sensitive, in individual patients, to the different underlying mechanisms that give rise to similar symptoms. The goal of this Transition Postdoc Fellowship is to develop personalised psychiatry techniques that are able to predict the clinical development of psychiatric disorders from a single baseline neuroimaging dataset, for a time span of up to several years.

Goals

The aim of this project is to predict the progression of depression and schizophrenia in individual patients using neuroimaging data. Our focus for this ‘personalised psychiatry’ approach is on combining the advanced computational models of brain function developed and used at the Translational Neuromodeling Unit with modern machine learning techniques. The advantage of basing this approach on computational models is that, by virtue of their reduction of the neuroimaging data to a small number of informative mechanistic parameters, they should make our predictions more accurate, more interpretable and better suited for treatment guidance.

Significance

Clinical management of psychiatric disorders is hindered by the fact that a patient’s symptoms are typically poor predictors of either the likely progression of the disorder, or the response to treatment. This Transition Postdoc Fellowship aims to develop techniques, based on computational models of neuroimaging data that may ultimately improve the quality of the information available to clinicians. Furthermore, the aim is that this approach generates interpretable representations of brain structure and function, which would facilitate the integration of neuroimaging data with multi-omic datasets as, for example, collected by the Swiss Personalised Health Network (SPHN).

Background

Modern neuroimaging techniques, by virtue of their non-invasive, multi-modal nature, have the potential to transform the clinical management of psychiatric and neurological disorders. However, despite their obvious promise, progress towards a personalised medicine framework has been slow, with limited clinical impact. For example, there are, as yet, no established methods that are able to predict the clinical progression of psychiatric disorders from a set of baseline measurements, despite the obvious benefits it would afford in terms of more informed choices about disease management and treatment.

Publications

Bijsterbosch, J. D.; Harrison, S. J.; Jbabdi, S.; Woolrich, M. W.; Beckmann, C. F., Smith, S. M. & Duff, E.P.; Challenges and future directions for representations of functional brain organization; Nature Neuroscience 23, pp. 1484–1495; 2020.
Farahibozorg, R.-S.; Bijsterbosch, J. D.; Gong, W.; Jbabdi, S.; Harrison, S. J.; Smith, S. M. & Woolrich, M. W.; Hierarchical modelling of functional brain networks in population and individuals from big fMRI data; bioRxiv; 2021.
Fitzgibbon, S. P.; Harrison, S. J.; Jenkinson, M.; Baxter, L.; Robinson, Emma C. & others; The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants; NeuroImage 223, p.117303; 2020.
Frässle, S.; Harrison, S. J.; Heinzle, J.; Clementz, B. A.; Tamminga, C. A.; Sweeney, J. A.; Gershon, E. S.; Keshavan, M. S.; Pearlson, G. D.; Powers, A. & Stephan, K. E.; Regression dynamic causal modeling for resting-state fMRI; Human Brain Mapping (in press); 2021.
Glasser, M. F.; Coalson, T. S.; Bijsterbosch, J. D.; Harrison, S. J.; Harms, M. P.; Anticevic, A.; Van Essen, D. C. & Smith, S. M.; Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power; NeuroImage 197, pp. 435–438; 2019.
Harrison, O. K.; Garfinkel, S. N.; Marlow, L.; Finnegan, S.; Marino, S.; Nanz, L.; Allen, M.; Finnemann, J.; Keur-Huizinga, L.; Harrison, S. J.; Stephan, K. E.; Pattinson, K. T. S. & Fleming, S. M.; The Filter Detection Task for measurement of breathing-related interoception and metacognition; bioRxiv; 2020.
Harrison, S. J.; Bianchi, S.; Heinzle, J.; Stephan, K. E.; Iglesias, S. & Kasper, L.; A Hilbert-based method for processing respiratory timeseries; NeuroImage (in press); 2021.
Harrison, S. J.; Bijsterbosch, J. D.; Segerdahl, A. R.; Fitzgibbon, S. P.; Farahibozorg, R.-S.; Duff, E.; Smith, S. M. & Woolrich, M. W.; Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes; NeuroImage 222, p. 117226; 2020.
Iglesias, S.; Kasper, L.; Harrison, S. J.; Manka, R.; Mathys, C. & Stephan, K. E.; Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning; NeuroImage 226, p. 117590; 2021.