Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium

Roelofs, Charlotte L.; Krepel, Noralie; Corlier, Juliana; Carpenter, Linda L.; Fitzgerald, Paul B.; Daskalakis, Zafiris J.; Tendolkar, Indira; Wilson, Andrew; Downar, Jonathan; Bailey, Neil W.; Blumberger, Daniel M.; Vila‐Rodriguez, Fidel; Leuchter, Andrew F.; Arns, Martijn

doi:10.1016/j.clinph.2020.10.017

Cited by 43 publications

(39 citation statements)

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“…62 Notably, the effect we detected was mostly driven by high frequency (10 Hz) stimulation at the left DLPFC, which was previously associated with a network synchronizing effect in the alpha frequency band. 40 This protocolspeci c effect was most predominantly found in men. Furthermore, while increasing PF-SM network activity was signi cantly related to improvement of depressive symptoms after rTMS in women, the reverse effect was found in men.…”

Section: Discussionmentioning

confidence: 99%

“…[31][32][33] EEG biomarker research for treatment prediction within MDD has shown that certain EEG patterns or abnormalities are differentially associated with drug-speci c or drug-class speci c antidepressant treatment effects [34][35][36] , as well as rTMS outcome. [37][38][39][40] Such studies have also demonstrated qualitative sex differences in topographic distribution of EEG activity and sex-speci c predictors of treatment response of alpha asymmetry, 35 EEG connectivity, 41 and event-related potentials. 42 Until recently, it was concluded that studies are insu ciently validated and replicated, and do not yet support the use of EEG for clinical decision making.…”

Section: Introductionmentioning

confidence: 95%

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Can polygenic-informed EEG biomarkers predict differential antidepressant treatment response? An EEG stratification marker for rTMS and sertraline

Arns

Meijs

Lin

et al. 2021

Preprint

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The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG biomarkers may help predict differential antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a functional brain network that is sex-specifically associated with polygenic risk for MDD in psychiatric patients (N=1,123). Subsequently, we demonstrate the utility of this network in predicting response to transcranial magnetic stimulation (TMS) and antidepressant medication in two independent datasets (N=196 and N=1,008). A simulation aimed at stratifying patients to TMS, sertraline or escitalopram/venlafaxine based on only this EEG component yields up to >30% improved remission rates. Overall, our findings highlight the power and utility of a combined polygenic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Can polygenic-informed EEG biomarkers predict differential antidepressant treatment response? An EEG stratification marker for rTMS and sertraline

Arns

Meijs

Lin

et al. 2021

Preprint

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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: 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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“…Finally, some studies have shown better outcomes the closer the stimulation frequency is to endogenous alpha rhythm, bringing up the question of whether rTMS frequency should be personalized based on individual EEG signatures. 85 , 86 …”

Section: Obstacles To the Current Care Delivery Model And Effectiveness Issuesmentioning

confidence: 99%

“…A recent secondary analysis from the THREE-D trial 33 has shown different patients’ response trajectories to rTMS. 85 Using a group-based trajectory modeling statistical approach, the authors identified a “nonresponse” group (11%) with minimal improvement by the end of treatment, a “rapid response” group (19%) with almost complete improvement by mid-treatment followed by a plateau, and 2 additional groups (70%) showing steady and linear improvement all throughout treatment and no apparent plateau by the end. This important majority of patients may have required more than 30 sessions to achieve maximum benefits.…”

Section: Obstacles To the Current Care Delivery Model And Effectiveness Issuesmentioning

confidence: 99%

Repetitive transcranial magnetic stimulation for major depressive disorder: basic principles and future directions

Miron

Jodoin

Lespérance

et al. 2021

Therapeutic Advances in Psychopharmacology

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Repetitive transcranial magnetic stimulation (rTMS) is a safe and well-tolerated intervention for major depressive disorder (MDD). Over 150 randomized controlled trials (RCTs) have been carried out, and its efficacy has been confirmed in dozens of meta-analyses. Real world data has also confirmed the effectiveness of rTMS for MDD in clinical practice, with the most recent literature indicating response rates of 40–50% and remission rates of 25–30%. In this review, we first offer an historical perspective, followed by a review of basic principles, such as putative mechanisms, procedures and protocols, stimulation targets, efficacy and durability of response, side effects, and the placebo controversy. In the second part of this review, we first discuss solutions to increase accessibility to rTMS, such as modifications to treatment equipment, protocols and setting. We continue with possible means to further increase effectiveness, such as treatment personalization and extension. We conclude by addressing the scheduling issue, with accelerated rTMS (arTMS) as a possible solution.

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Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium

Cited by 43 publications

References 43 publications

Can polygenic-informed EEG biomarkers predict differential antidepressant treatment response? An EEG stratification marker for rTMS and sertraline

Can polygenic-informed EEG biomarkers predict differential antidepressant treatment response? An EEG stratification marker for rTMS and sertraline

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

Repetitive transcranial magnetic stimulation for major depressive disorder: basic principles and future directions

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