Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy

Gärtner, Matti; Ghisu, Elisabetta; Herrera-Meléndez, Ana Lucía; Koslowski, Michael; Aust, Sabine; Asbach, Patrick; Otte, Christian; Regen, Francesca; Heuser, Isabella; Borgwardt, Karsten; Grimm, Simone; Bajbouj, Malek

doi:10.1016/j.expneurol.2020.113505

Cited by 13 publications

(13 citation statements)

References 23 publications

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“…A recent meta-analysis found depression severity at baseline to be a predictor of response in ECT though with only a modest effect size (SMD = 0.19) [ 3 ]. Other studies have found no predictive power for response with respect to depression severity in an ECT population [ 32 , 33 ]. Depression severity based on HAMD scales and, as shown here, the CGI-S may therefore not be a robust predictor of response and remission in ECT.…”

Section: Discussionmentioning

confidence: 94%

Usefulness of Hamilton rating scale for depression subset scales and full versions for electroconvulsive therapy

Fenton

McLoughlin

2021

PLoS ONE

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Objectives We investigated the predictive value of subset scales and full versions of the Hamilton Rating Scale for Depression (HAMD) for therapeutic outcomes in ECT. Methods This secondary analysis of patients with major depression (N = 136; 63% female; age = 56.7 [SD = 14.8]) from the EFFECT-Dep trial (NCT01907217) examined the predictive value of Evans-6, Toronto-7, Gibbons-8 and Maier-Philip 6 HAMD subset scales and three ‘full’ versions (HAMD-17, HAMD-21 and HAMD-24) on therapeutic outcomes. We also examined early improvement on subset scales and full versions as predictors of response and remission and explored predictive abilities of individual HAMD-24 items. Results The subset scales and full scales lacked sufficient predictive ability for response and remission. Receiver operating characteristic curves identified a lack of discriminative capacity of HAMD subset scales and full versions at baseline to predict response and remission. Only the Maier-Philip-6 was significantly associated with percentage reduction in HAMD-24 scores from baseline to end of ECT course. Early improvement on most of the subset scales and full versions was a sensitive and specific predictor of response and remission. Four of the HAMD-24 items were significantly associated with response and one with remission. Conclusions Limited utility of the HAMD subset scales and full versions in this context highlight a need for more tailored depression rating scales for ECT.

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Section: Discussionmentioning

confidence: 94%

Usefulness of Hamilton rating scale for depression subset scales and full versions for electroconvulsive therapy

Fenton

McLoughlin

2021

PLoS ONE

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“…In addition to informing risk, findings on MRI may help predict response. 13,14 Levy et al 14 found that a variety of anomalies in white and gray matter including deep white matter hyperintensities in the basal ganglia and Virchow-Robin spaces, medial temporal atrophy, the ratio of left superior frontal to left rostral middle frontal cortical thickness, and cingulate isthmus thickness asymmetry were predictive of a poorer response to ECT. Interestingly, in a study of 34 elderly patients with late-life depression, no association was found with several markers of aging (including baseline hippocampal volume, white matter hyperintensity volume, and total amyloid load) and rates of response or remission with ECT.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to informing risk, findings on MRI may help predict response 13,14 . Levy et al 14 found that a variety of anomalies in white and gray matter including deep white matter hyperintensities in the basal ganglia and Virchow-Robin spaces, medial temporal atrophy, the ratio of left superior frontal to left rostral middle frontal cortical thickness, and cingulate isthmus thickness asymmetry were predictive of a poorer response to ECT.…”

Section: Discussionmentioning

confidence: 99%

Neuroimaging Prior to ECT

Aloysi¹,

Majeske²,

Soleimani³

et al. 2023

J ECT

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“…Therefore, it is very important to predict a patient's response to particular treatments and to design treatment strategies early at the onset of MDD. Studies have been conducted to identify biomarkers that can predict treatment response, and ML studies are also increasing [9,[65][66][67][68][69][70][71][72]85,86].…”

Section: Structural Characteristics Related With Depression Treatment...mentioning

confidence: 99%

Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders

Lee

Chi

Lee

2022

JPM

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Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.

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Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy

Cited by 13 publications

References 23 publications

Usefulness of Hamilton rating scale for depression subset scales and full versions for electroconvulsive therapy

Usefulness of Hamilton rating scale for depression subset scales and full versions for electroconvulsive therapy

Neuroimaging Prior to ECT

Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders

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