Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder

Rubin‐Falcone, Harry; Zanderigo, Francesca; Thapa-Chhetry, Binod; Lan, Martin; Miller, Jeffrey M.; Sublette, M. Elizabeth; Oquendo, María A.; Hellerstein, David J.; McGrath, Patrick J.; Stewart, Johnathan W.

doi:10.1016/j.jad.2017.11.043

Cited by 61 publications

(42 citation statements)

References 76 publications

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“…Regarding the interest of combining the features, Jie et al 65 also reported higher ACC when integrating multimodal features (ie, 92.07%) in comparison with single modality (90.89% for functional connectivity and 77.83% for grey matter volumes solely). Notably, the ACC of the 3 studies including more than 100 subjects 44,50,67 was below the median ACC of this category (86%).…”

Section: Literature Reviewmentioning

confidence: 90%

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Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

et al. 2020

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Bipolar Disorders. 2020;22:334-355. wileyonlinelibrary.com/journal/bdi | INTRODUC TI ONBipolar disorder (BD) is a severe chronic mood disorder affecting more than 1% of the general adult population 1 and is associated with a high socio-economic burden. 2,3 Many challenges persist regarding BD management and growing evidence suggests that BD is under-recognized in clinical practice. Currently, the establishment of the diagnosis is solely based on clinical assessments. Therefore, the Abstract Objectives: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. Method:We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. Result:We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. Conclusions:Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD. K E Y W O R D Sbipolar disorders, machine learning, magnetic resonance imaging, precision medicine | 335 CLAUDE Et AL.

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Section: Literature Reviewmentioning

confidence: 90%

“…Reported ACC for discriminating BD vs MDD ranged between 49.5% and 93.1% (Figure 4). Six studies used only sMRI as input features, 44,48,50,54,66,67 5 studies used grey matter input features reaching 59.45%‐75.9% ACC 44,48,50,66,67 and 1 used both grey and white matter input features reaching 54.76% ACC 54 …”

Section: Literature Reviewmentioning

confidence: 99%

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

et al. 2020

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“…Almost all of the selected studies used SVM or its variant method as the primary classification method 22,[40][41][42][44][45][46][47][48][49][50]52,53,82,84,87,[89][90][91]98 and use LOOCV for cross validation. The reason why SVM is the most popular choice among depression classification is because of its useful strengths on including a reliable theoretical foundation and its flexible response to high-dimensional data.…”

Section: Classification Methods and Cross-validationmentioning

confidence: 99%

Machine learning in major depression: From classification to treatment outcome prediction

2018

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“…Small studies may also yield a wide range of classification performances and inconsistencies in regions, which contribute to the overall classification [25][26][27]. Previous ML structural MRI studies in BD have typically included <50 BD participants recruited in a single site [23,[28][29][30][31][32][33][34]. The largest currently available neurostructural ML studies investigated 128-190 BD and 127-284 control participants [35][36][37], from up to two sites [22,23,38].…”

Section: Introductionmentioning

confidence: 99%

Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

et al. 2018

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Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

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Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder

Abstract: Our results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.

Cited by 61 publications

References 76 publications

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

Machine learning in major depression: From classification to treatment outcome prediction

Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

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