EEG biomarker informed prescription of antidepressants in MDD: a feasibility trial

Vinne, Nikita van der; Vollebregt, Madelon A.; Rush, A. John; Eebes, Michiel; Putten, Michel J.A.M. van; Arns, Martijn

doi:10.1016/j.euroneuro.2020.12.005

Cited by 23 publications

(17 citation statements)

References 33 publications

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“…9.3 Clinical efficacy, clinical utility, and additional challenges for assay development Using retrospective data for studying predictive validity comes with a high risk of overfitting, especially when relying on currently popular validation techniques Chekroud et al, 2021;Rutledge et al, 2019). A much more convincing test of prediction accuracy will be the demonstration of clinical efficacy: showing that clinical decision making guided by computational assays leads to improved outcomes compared to treatment as usual (Paulus and Thompson, 2021;van der Vinne et al, 2021;Kingslake et al, 2017). Importantly, this will come with additional practical challenges of how to best integrate the assays into the clinical workflow (Paulus and Thompson, 2021;Kelly et al, 2019).…”

Section: Longitudinal Validity and The Dynamic Nature Of Mental Disor...mentioning

confidence: 99%

Individual Differences in Computational Psychiatry: A Review of Current Challenges

Karvelis¹,

Paulus²,

Diaconescu³

2022

Preprint

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Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. To contextualize this, we also provide a more general perspective on key developments that are needed for translating computational assays to clinical practice. Emerging evidence suggests that most computational measures show poor-to-moderate reliability and often provide little improvement over simple behavioral measures. Furthermore, behavioral and computational measures used to test computational accounts of mental disorders show a lack of convergent validity, which compromises their interpretability. Taken together, these issues pose a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We suggest that cross-sectional single-task designs, which currently dominate the research landscape, are partly to blame for these problems and therefore are not suitable for solving them. Instead, reliability and construct validity need to be studied more systematically using longitudinal designs with batteries of tasks. Finally, to enable clinical applications, it will be necessary to establish predictive and longitudinal validity, and to make the assays more efficient and less burdensome.

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Section: Longitudinal Validity and The Dynamic Nature Of Mental Disor...mentioning

confidence: 99%

Individual Differences in Computational Psychiatry: A Review of Current Challenges

Karvelis¹,

Paulus²,

Diaconescu³

2022

Preprint

View full text Add to dashboard Cite

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“…While this study already successfully validated MPH and multimodal NFB prediction by means of Brainmarker-1, a prospective validation study would be valuable that prospectively strati es patients between the interventions based on baseline iAPF, similar to the feasibility study of van der Vinne 17 that successfully tested the biomarker-based strati cation approach for MDD patients. Since the relationship between iAPF and MDD treatment outcome has already been established 8,58,59 , a next step will involve expanding Brainmarker-1 that was applied to ADHD samples in the current study into a transdiagnostic marker, incorporating different pharmacological and non-pharmacological interventions for MDD.…”

Section: Discussionmentioning

confidence: 95%

“…Treatment strati cation has already been implemented in the treatment of different cancer types [14][15][16] and recently also MDD, where strati cation to different antidepressant medications was informed by pretreatment EEG biomarkers, resulting in improved remission rates relative to treatment-as-usual 17 .…”

Section: Introductionmentioning

confidence: 99%

Biomarker-based treatment stratification in ADHD: An out-of-sample validation

Voetterl

Wingen

Michelini

et al. 2021

Preprint

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Attention-deficit/hyperactivity disorder (ADHD) is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.The present study introduces a clinically interpretable and actionable, age- and sex-standardized biomarker based on individual alpha peak frequency (iAPF) assessed during resting-state electroencephalography (EEG). The biomarker was developed in a heterogeneous sample (N=4249), and stratifies patients with a higher iAPF to Methylphenidate (MPH; N=336) and those with a lower iAPF to Neurofeedback (NFB; N=136), resulting in a predicted gain in normalized remission of 17-30%. Blinded out-of-sample validation studies for MPH (N=58) and NFB (N=96) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.These findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

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“…The ultimate test of treatment response prediction will require prospective validation studies to assess the actual improvement of outcomes following prediction-based treatment selection as compared to treatment-as-usual (van der Vinne et al, 2021). Such studies will also require careful consideration of how treatment response predictions can be most optimally integrated into the clinical workflow (Kelly et al, 2019).…”

Section: Reliability Of Prediction Accuracy: Sample Size and Validationmentioning

confidence: 99%

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Karvelis

Charlton

Allohverdi

et al. 2022

Network Neuroscience

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Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models which attempt to predict treatment response based on various pre-treatment measures. In this paper, we review studies which use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain-knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modelling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

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EEG biomarker informed prescription of antidepressants in MDD: a feasibility trial

Cited by 23 publications

References 33 publications

Individual Differences in Computational Psychiatry: A Review of Current Challenges

Individual Differences in Computational Psychiatry: A Review of Current Challenges

Biomarker-based treatment stratification in ADHD: An out-of-sample validation

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

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