Classification of bipolar disorder using basal-ganglia-related functional connectivity in the resting state

Teng, Shin; Lu, Chia‐Feng; Wang, Po-Shan; Hung, Chih-I; Li, Cheng Ta; Tu, Pei-Chi; Su, Tung‐Ping; Wu, Yu‐Te

doi:10.1109/embc.2013.6609686

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…Nearly half of the models (48.3% [268 of 555]) were found in studies authored by those with academic training in computers and data science (eTable 2 in Supplement 1). Schizophrenia (25.4% [141 of 555 models])…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…In addition, 378 of 555 models (68.1%; 95% CI, 64.2%-72.0%) lacked reports for quality control (eg, signaling question: was how to handle neuroimaging artifacts reported? ), incurring a low reporting quality rating …”

Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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“…In the largest resting-state study to date, increased connectivity between a medial paralimbic resting-state network, involving the ventromedial prefrontal cortex and the ventral striatum, with a network thought to subserve emotion regulation and executive function, including subgenual cingulate and insula, distinguished bipolar patients from schizophrenia patients and healthy controls, suggesting that interactions between cognitive and emotional networks may be specifically disrupted in bipolar disorder 24 . Similarly, another analysis found that functional connectivity between the basal ganglia, thalamus and cortical areas implicated in cognition were the most distinguishing feature between bipolar patients and healthy controls, achieving a classification accuracy of 90% 25 . Furthermore, altered functional connectivity in the cognitive and affective subdivisions of the striatum has been shown to differentiate manic patients from depressed bipolar patients 26 .…”

Section: Introductionmentioning

confidence: 90%

Differential effect of quetiapine and lithium on functional connectivity of the striatum in first episode mania

et al. 2018

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Mood disturbances seen in first-episode mania (FEM) are linked to disturbed functional connectivity of the striatum. Lithium and quetiapine are effective treatments for mania but their neurobiological effects remain largely unknown. We conducted a single-blinded randomized controlled maintenance trial in 61 FEM patients and 30 healthy controls. Patients were stabilized for a minimum of 2 weeks on lithium plus quetiapine then randomly assigned to either lithium (serum level 0.6 mmol/L) or quetiapine (dosed up to 800 mg/day) treatment for 12 months. Resting-state fMRI was acquired at baseline, 3 months (patient only) and 12 months. The effects of treatment group, time and their interaction, on striatal functional connectivity were assessed using voxel-wise general linear modelling. At baseline, FEM patients showed reduced connectivity in the dorsal (p = 0.05) and caudal (p = 0.008) cortico-striatal systems when compared to healthy controls at baseline. FEM patients also showed increased connectivity in a circuit linking the ventral striatum with the medial orbitofrontal cortex, cerebellum and thalamus (p = 0.02). Longitudinally, we found a significant interaction between time and treatment group, such that lithium was more rapid, compared to quetiapine, in normalizing abnormally increased functional connectivity, as assessed at 3-month and 12-month follow-ups. The results suggest that FEM is associated with reduced connectivity in dorsal and caudal corticostriatal systems, as well as increased functional connectivity of ventral striatal systems. Lithium appears to act more rapidly than quetiapine in normalizing hyperconnectivity of the ventral striatum with the cerebellum. The study was registered on the Australian and New Zealand Clinical Trials Registry (ACTRN12607000639426). http://www.anzctr.org.au

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“… 16 17 Structural magnetic resonance imaging (MRI) studies have indicated volumes reduction and shape changes in these regions in the two disorders. 16 18 19 20 21 22 Functional MRI studies have demonstrated functional impairment in these structures in BD 23 and UD. 24 However, because of the limits of conventional MRI technology, far too little attention has been paid to the microstructural changes and perfusional abnormalities in basal ganglia and thalamus which might help to clarify the neuropathological mechanisms of the two disorders.…”

Section: Introductionmentioning

confidence: 99%

Microstructural Abnormalities of Basal Ganglia and Thalamus in Bipolar and Unipolar Disorders: A Diffusion Kurtosis and Perfusion Imaging Study

Zhao

Wang

Jia

et al. 2017

Psychiatry Investig

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ObjectiveBipolar disorder (BD) is often misdiagnosed as unipolar depression (UD), leading to mistreatment and poor clinical outcomes. However, little is known about the similarities and differences in subcorticalgray matter regions between BD and UD.MethodsThirty-five BD patients, 30 UD patients and 40 healthy controls underwent diffusional kurtosis imaging (DKI) and three dimensional arterial spin labeling (3D ASL). The parameters including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr) and cerebral blood flow (CBF) were measured by using regions-of-interest analysis in the caudate, putamen and thalamus of the subcortical gray matter regions.ResultsUD exhibited differences from controls for DKI measures and CBF in the left putamen and caudate. BD showed differences from controls for DKI measures in the left caudate. Additionally, BD showed lower Ka in right putamen, higher MD in right caudate compared with UD. Receiver operating characteristic analysis revealed the Kr of left caudate had the highest predictive power for distinguishing UD from controls.ConclusionThe two disorders may have overlaps in microstructural abnormality in basal ganglia. The change of caudate may serve as a potential biomarker for UD.

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Classification of bipolar disorder using basal-ganglia-related functional connectivity in the resting state

Cited by 11 publications

References 12 publications

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

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

Differential effect of quetiapine and lithium on functional connectivity of the striatum in first episode mania

Microstructural Abnormalities of Basal Ganglia and Thalamus in Bipolar and Unipolar Disorders: A Diffusion Kurtosis and Perfusion Imaging Study

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