Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects

Schnack, Hugo G.; Nieuwenhuis, Mireille; Haren, Neeltje E.M. van; Abramovic, Lucija; Scheewe, Thomas W.; Brouwer, Rachel M.; Pol, Hilleke E. Hulshoff; Kahn, René S.

doi:10.1016/j.neuroimage.2013.08.053

Cited by 197 publications

(140 citation statements)

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“…Similar to our findings, Schnack and colleagues 38 also reported 59% accuracy when distinguishing participants with BD from healthy controls. The lower …”

Section: Discussionsupporting

confidence: 92%

“…Interestingly, a previous ML study using structural MRI was able to accurately and significantly differentiate patients with BD from participants with schizophrenia, but not from healthy controls. 38 We were not able to distinguish healthy participants at high genetic risk for BD or affected offspring from healthy low-risk controls based on grey matter structure. This is congruent with another study in which SVM of grey matter distinguished BD from control participants with an accuracy of 59% and sensitivity of 53% 38 as compared with 57% accuracy and 53% sensitivity in our study.…”

Section: J Psychiatry Neurosci 2015;40(5)mentioning

confidence: 78%

“…1,30 Technical descriptions of GPC and SVM inference have been presented elsewhere. 31,36,37,38 Similar to other studies, 30,32 we used the default parameters for the SVM and GPC analyses. This reduces methodological heterogeneity, ensures comparability between the studies and reduces the risk of overfitting.…”

Section: Support Vector Machines and Gaussian Process Classifiersmentioning

confidence: 99%

“…Some of the brain alterations in participants with BD may accumulate with illness burden. It is thus relevant that, relative to our sample or even that of Schnack and colleagues, 38 Rocha-Rego and colleagues 30 recruited participants with longer duration of illness, in whom the secondary, neuroprogressive changes may have been more pronounced and could have contributed to the differentiation from healthy controls. Indeed, a previous study has shown that among participants with psychotic episodes, ML was sensitive to differences in illness course.…”

Section: J Psychiatry Neurosci 2015;40(5)mentioning

confidence: 99%

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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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“…Similar to our findings, Schnack and colleagues 38 also reported 59% accuracy when distinguishing participants with BD from healthy controls. The lower …”

Section: Discussionsupporting

confidence: 92%

Section: J Psychiatry Neurosci 2015;40(5)mentioning

confidence: 78%

Section: Support Vector Machines and Gaussian Process Classifiersmentioning

confidence: 99%

Section: J Psychiatry Neurosci 2015;40(5)mentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…Can we use them to assist diagnosis, predict risk and illness development, treatment response and outcome? Pattern recognition methods may prove useful in diagnosis at least vs bipolar disorder 14 conversion to illness 15 and treatment response, 16 but studies so far have been relatively small, although some large EU-funded studies, such as Psyscan and Pronia are now ongoing. Such studies also need to include other variables such as social and cognitive performance, presence and severity of signs and symptoms 17 and possibly-if relevantblood markers.…”

mentioning

confidence: 99%

The Contribution of Neuroimaging to Understanding Schizophrenia; Past, Present, and Future

Sommer

Kahn

2014

Schizophrenia Bulletin

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Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects

Cited by 197 publications

References 44 publications

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

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

The Contribution of Neuroimaging to Understanding Schizophrenia; Past, Present, and Future

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

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