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Cited by 56Schizophrenia research and treatmentDementia and Cognitive Impairment ResearchPsychopathy, Forensic Psychiatry, Sexual Offending

Using Structural Neuroimaging to Make Quantitative Predictions of Symptom Progression in Individuals at Ultra-High Risk for Psychosis

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Tognin, Stefania, Pettersson-Yeo, William, Valli, Isabel, Hutton, Chloe, Woolley, James, Allen, Paul, McGuire, Philip, Mechelli, Andrea·2014·Frontiers in Psychiatry
DOI 10.3389/fpsyt.2013.00187PMC PMC3905239
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Abstract

Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups, which do not permit accurate inferences at the level of the individual. We examined the potential of structural Magnetic Resonance Imaging (MRI) data for making accurate quantitative predictions about symptom progression in individuals at ultra-high risk for developing psychosis. Forty people at ultra-high risk for psychosis were scanned using structural MRI at first clinical presentation and assessed over a period of 2 years using the Positive and Negative Syndrome Scale. Using a multivariate machine learning method known as relevance vector regression (RVR), we examined the relationship between brain structure at first clinical presentation, characterized in terms of gray matter (GM) volume and cortical thickness (CT), and symptom progression at 2-year follow-up. The application of RVR to whole-brain CT MRI data allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation = 0.34, p = 0.026; Mean Squared-Error = 249.63, p = 0.024). This prediction was informed by regions traditionally associated with schizophrenia, namely the right lateral and medial temporal cortex and the left insular cortex. In contrast, the application of RVR to GM volume did not allow prediction of symptom progression with statistically significant accuracy. These results provide proof-of-concept that it could be possible to use structural MRI to inform quantitative prediction of symptom progression in individuals at ultra-high risk of developing psychosis. This would enable clinicians to target those individuals at greatest need of preventative interventions thereby resulting in a more efficient use of health care resources.

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  • Biochemical, imaging, and electrophysiological measurements may help segregating patients into higher- and lower-risk groups.

    “In addition, biochemical 140,141 , imaging 52,142,143 , and electrophysiological measurements 144 may help segregating patients into higher- and lower-risk groups.”

    From Developmental timing and critical windows for the treatment of psychiatric disorders (2016) · cites this paper as [143]

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Cited by

Sorted by citations

Authors
Tognin, S., Pettersson-Yeo, W., Valli, I., Hutton, C., Woolley, J., Allen, P., McGuire, P., & Mechelli, A.
Affiliations
  • King's College London (GB)
  • Wellcome Centre for Human Neuroimaging (GB)
  • Imperial College London (GB)
Corresponding author
Mechelli, Andrea
ORCID
Venue
Frontiers in Psychiatry
Publisher
Frontiers Media SA
Published
2014
Volume
4
Pages
187-187
ISSN
1664-0640 (ISSN-L)
DOI
10.3389/fpsyt.2013.00187
PMC
PMC3905239
OpenAlex
W2086381828
Cited by
56
References
1
Citations by year
2025: 1 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 8 · 2019: 4 · 2018: 5 · 2017: 3 · 2016: 15 · 2015: 5 · 2014: 4
FWCI
6.87
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Psychiatry (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Wellcome Trust (091593, 091593/Z/10/, 091593/Z/10/Z)
  • National Institute for Health and Care Research (CL-2011-17-004)
  • Medical Research Council (99859)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
Keywords
clinical high risk for psychosis, structural MRI, cortical thickness, gray matter volume, relevance vector regression, machine learning prediction, schizophrenia, temporal cortex, insula

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