Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images

Qiu, Lihua; Huang, Xiaoqi; Zhang, Junran; Wang, Yuqing; Kuang, Weihong; Li, Jing; Wang, Xiuli; Wang, Limin; Yang, Xun; Lui, Su; Mechelli, Andrea; Gong, Qiyong

doi:10.1503/jpn.130034

Cited by 19 publications

(15 citation statements)

References 59 publications

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“…Unlike previous findings on adults, only cortical thickness could provide the best accuracy in three adolescent classification models in our study. This is consistent with the findings by Qiu et al, who used an SVM based on various brain morphometric features to distinguish between 32 adult patients with first-episode MDD and 32 HC [ 61 ]. They reported that multiple cortical features could discriminate them with cortical thickness providing the highest accuracy.…”

Section: Discussionsupporting

confidence: 90%

Cortical thickness distinguishes between major depression and schizophrenia in adolescents

et al. 2021

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Background Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. Methods We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. Results We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. Conclusions Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

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Section: Discussionsupporting

confidence: 90%

Cortical thickness distinguishes between major depression and schizophrenia in adolescents

et al. 2021

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“… 11 , 12 We also found that WM regions in the bilateral cerebellum contributed towards diagnostic classification, which has been previously observed 12 but more frequently has not been examined. 14 – 16 The cerebellum is involved in the regulation of emotional responses, 39 and GM 40 as well as WM 41 deficits have been reported in depression. These findings underline the wide distribution of GM and WM abnormalities within fronto-limbic networks in depression, supporting the necessity of analyses which are capable of combining these features, rather than examining each region individually.…”

Section: Discussionmentioning

confidence: 99%

“…As we achieved converging results from both an SVM model on the imaging data and the COMPARE high-dimensional feature extraction and classification method, and we applied proper cross-validation in the analysis, we believe that our models provide unbiased and robust findings. Advantages of the current sample include that all patients were medication-free status while in an acute depressive episode and were recruited from the community, as one of the highest classification accuracy was observed in patients who were already on antidepressant medications; 13 patients were in their first episode 11 , 16 as well as having a history of recurrent episodes; 10 and there was wide ethnicity which included Asian, African and Caucasian participants. Another concern is the access and cost of the MRI scan and the computational requirements of the analysis.…”

Section: Discussionmentioning

confidence: 99%

“… 9 Using structural MRI scans, the accuracy of diagnosis for depression has ranged from 48% to 91% ( Table 1 ). 10 – 16 We would not expect to achieve a perfect accuracy with current diagnostic criteria that are based on clinical signs and symptoms which are not necessarily linked to aetiology. 17 Rather we would expect that there would be an iterative process by which the factors contributing to the neuroimaging classification would be delineated and the classification findings would in turn lead to new hypotheses.…”

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confidence: 99%

“… 2 , 19 However, studies to date had included individuals who were predominantly from the same ethnicity: Caucasian 10 , 13 and Chinese. 11 , 12 , 16 Ethnicity is moderating factor for functional brain responses in healthy participants, 20 while an effect on structural neural features may be more mixed with evidence of differences in cortical thickness in frontal and parietal regions 21 but not in hippocampal asymmetry. 22 Ethnicity could thus impact on potential functional MRI-based markers while structural MRI-based markers may be more robust.…”

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confidence: 99%

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Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample

et al. 2016

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BackgroundAt present, we do not have any biological tests which can contribute towards a diagnosis of depression. Neuroimaging measures have shown some potential as biomarkers for diagnosis. However, participants have generally been from the same ethnic background while the applicability of a biomarker would require replication in individuals of diverse ethnicities.AimsWe sought to examine the diagnostic potential of the structural neuroanatomy of depression in a sample of a wide ethnic diversity.MethodStructural magnetic resonance imaging (MRI) scans were obtained from 23 patients with major depressive disorder in an acute depressive episode (mean age: 39.8 years) and 20 matched healthy volunteers (mean age: 38.8 years). Participants were of Asian, African and Caucasian ethnicity recruited from the general community.ResultsStructural neuroanatomy combining white and grey matter distinguished patients from controls at the highest accuracy of 81% with the most stable pattern being at around 70%. A widespread network encompassing frontal, parietal, occipital and cerebellar regions contributed towards diagnostic classification.ConclusionsThese findings provide an important step in the development of potential neuroimaging-based tools for diagnosis as they demonstrate that the identification of depression is feasible within a multi-ethnic group from the community.Declaration of interestsC.H.Y.F. has held recent research grants from Eli Lilly and Company and GlaxoSmithKline. L.M. is a former employee and stockholder of Eli Lilly and Company.Copyright and usage© The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.

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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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Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images

Cited by 19 publications

References 59 publications

Cortical thickness distinguishes between major depression and schizophrenia in adolescents

Cortical thickness distinguishes between major depression and schizophrenia in adolescents

Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample

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

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