Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples

Nieuwenhuis, Mireille; Haren, Neeltje E.M. van; Pol, Hilleke E. Hulshoff; Cahn, Wiepke; Kahn, René S.; Schnack, Hugo G.

doi:10.1016/j.neuroimage.2012.03.079

Cited by 168 publications

(156 citation statements)

References 38 publications

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“…Numerous approaches have been proposed to diagnose diseases, such as Alzheimer's disease (Chyzhyk et al, 2014;López et al, 2011), schizophrenia (Nieuwenhuis et al, 2012), tumor or lesion detection (González-Navarro et al, 2010;Zacharaki et al, 2009) and Huntington's disease (Kassubek et al, 2004). Whereas, in case of PD, the main purpose of MRI acquisition has been to rule out alternative pathologies.…”

Section: Discussionmentioning

confidence: 99%

Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI

Rana

Juneja

Saxena

et al. 2015

Expert Systems with Applications

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Section: Discussionmentioning

confidence: 99%

Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI

Rana

Juneja

Saxena

et al. 2015

Expert Systems with Applications

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“…The predictive models become stable at about 130 participants per group. 48 We included a total of 126 participants. For comparison, a previous study of BD investigated 80 participants, 30 and the first ML study in participants at clinical risk for schizophrenia included 70 individuals.…”

Section: Limitationsmentioning

confidence: 99%

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Hájek

Cooke

Kopeček

et al. 2015

jpn

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IntroductionThe diagnostic system in psychiatry continues to be based on behavioural symptoms rather than biomarkers. This complicates clinical work and research as it introduces marked hetero geneity. Neuroimaging has the unique ability to noninvasively investigate brain structure and function. Yet, the diag nostic promise of neuroimaging in psychiatry has not been fully realized. Brain imaging studies have shown replicated evidence for neuroanatomical changes in groups of participants with psychiatric disorders relative to controls. However, these statistical group differences have low specificity and sensitivity and thus are of limited diagnostic use on the level of individual participants. 1-3The problem of low sensitivity and specificity may be overcome by novel methods of neuroimaging analyses, such as machine learning (ML).1,2 Traditional methods of MRI data analysis focus on relatively large, localized and spatially segregated patterns of between-group differences. 4 In contrast, the multivariate ML techniques target patterns of relatively minor alterations distributed throughout the whole brain, 1 which may better characterize the abnormalities found in individuals with psychiatric disorders.5 These techniques bring neuroimaging analyses to the level of individual participants and potentially allow for their diagnostic use.The use of neuroimaging for diagnostic purposes in psychiatry is further complicated by clinical heterogeneity.6,7 Not all neuroimaging findings in psychiatric patients are of diagnostic use. For example, brain changes in patients with bipolar disorders (BD) may represent biological markers of BD, but also the consequences of illness episodes, 8,9 exposure to medications 10-12 or comorbid conditions. 13,14 The changes Background: Brain imaging is of limited diagnostic use in psychiatry owing to clinical heterogeneity and low sensitivity/specificity of between-group neuroimaging differences. Machine learning (ML) may better translate neuroimaging to the level of individual participants. Studying unaffected offspring of parents with bipolar disorders (BD) decreases clinical heterogeneity and thus increases sensitivity for detection of biomarkers. The present study used ML to identify individuals at genetic high risk (HR) for BD based on brain structure. Methods:We studied unaffected and affected relatives of BD probands recruited from 2 sites (Halifax, Canada, and Prague, Czech Republic). Each participant was individually matched by age and sex to controls without personal or family history of psychiatric disorders. We applied support vector machines (SVM) and Gaussian process classifiers (GPC) to structural MRI. Results: We included 45 unaffected and 36 affected relatives of BD probands matched by age and sex on an individual basis to healthy controls. The SVM of white matter distinguished unaffected HR from control participants (accuracy = 68.9%, p = 0.001), with similar accuracy for the GPC (65.6%, p = 0.002) or when analyzing data from each site separately. Differentiation of the mo...

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“…A limitation of this study might be the slightly limited sample size, as Nieuwenhuis et al [12] recommend to use more than 130 subjects. However, our data set containing 104 subjects is larger than the data sets used in most of the schizophrenia studies [5]- [8], [11], [14]- [15], [17]- [19].…”

Section: Resultsmentioning

confidence: 99%

Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

Janoušová

Schwarz

Montana

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the brain in comparison to the normal template anatomy. The sparse data enables efficient data reduction and classification via the penalised linear discriminant analysis with resampling. The classification accuracy obtained in an experiment with magnetic resonance brain images of first episode schizophrenia patients and healthy controls is comparable to the related state-of-the-art studies.

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Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples

Cited by 168 publications

References 38 publications

Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI

Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

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