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Both 10 Hz repetitive transcranial magnetic stimulation (rTMS) as well as 18 Hz deep TMS (dTMS) constitute effective, FDA-approved TMS treatment protocols for depression. However, not all patients experience sufficient symptom relief after either of these protocols. Biomarker-guided treatment stratification could aid in personalizing treatment and thereby enhancing improvement. An individual alpha frequency (iAF)-based EEG-biomarker, Brainmarker-I, can differentially stratify patients to depression treatments. For instance, an iAF close to 10 Hz was associated with better improvement to 10 Hz rTMS, possibly reflecting entrainment of endogenous oscillations to the stimulation frequency. Accordingly, we examined whether 18 Hz dTMS would result in better improvement in individuals whose iAF lies around 9 Hz, a harmonic frequency of 18 Hz. Curve fitting and regression analyses were conducted to assess the relation between iAF and improvement. For treatment stratification purposes, correlations with iAF-distance to 10 Hz compared 18 Hz dTMS (N = 114) to 10 Hz rTMS (N = 72). We found a robust quadratic effect, indicating that patients with an iAF around 9 Hz exhibited least symptom improvement (r 2 =0.126, p<.001). Improvement correlated positively with iAF-distance to 10 Hz (p=.003). A secondary analysis in 20 Hz figure-of-eight data confirmed this direction. A significant interaction of iAF-distance and stimulation frequency between 10 and 18 Hz datasets emerged (p=.026). These results question entrainment of endogenous oscillations by their harmonic frequency for 18 Hz, and suggest that 10 Hz and 18 Hz TMS target different subgroups of depression patients. This study adds to iAF stratification, augmenting Brainmarker-I with alternative TMS protocols (18 Hz/20 Hz) for patients with a slower iAF, thereby broadening clinical applicability and relevance of the biomarker., Competing Interests: Declaration of competing interest Dr. Alyagon is an EEG consultant for BrainsWay Ltd. Dr. Downar has received research support from NIH, CIHR, Brain Canada, Ontario Brain Institute, the Klarman Family Foundation, the Arrell Family Foundation, and the Buchan Family Foundation, in-kind equipment support for investigator-initiated trials from MagVenture, is an advisor for BrainCheck, Arc Health Partners and Salience Neuro Health, and is a co-founder of Ampa Health. Dr. Zangen is an inventor of Deep TMS coils and has financial interest in BrainsWay Ltd. Dr. Sack is chief scientific advisor at PlatoScience Medical, scientific advisor at Alpha Brain Technologies, Founder and CEO of Neurowear Medical, Director of the International Clinical TMS Certification Course (www.tmscourse.eu), and receives equipment support from MagVenture, Magstim, and Deymed. Victoria Middleton and Aimee Halloran are employees of Salience Health. Dr. Donachie is Chief Medical Officer at Salience Health. Dr. Arns holds equity/stock in neurocare and Sama Therapeutics, serves as consultant to Synaeda, Sama Therapeutics and Roche and is named inventor on patents and intellectual property but receives no royalties. Brainclinics Foundation received equipment support from MagVenture and Deymed. All other authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

The registry project led by the Japanese Society for Clinical Transcranial Magnetic Stimulation (TMS) Research aims to establish a centralized database of epidemiological, clinical, and biological data on TMS therapy for refractory psychiatric disorders, including treatment-resistant depression, as well as to contribute to the elucidation of the therapeutic mechanism of TMS therapy and to the validation of its efficacy by analyzing and evaluating these data in a systematic approach. The objective of this registry project is to collect a wide range of complex data linked to patients with various neuropsychiatric disorders who received TMS therapy throughout Japan, and to make effective use of these data to promote cross-sectional and longitudinal exploratory observational studies. Research utilizing this registry project will be conducted in a multicenter, non-invasive, retrospective, and prospective observational research study design, regardless of the framework of insurance medical care, private practice, or clinical research. Through the establishment of the registry, which aims to make use of data, we will advance the elucidation of treatment mechanisms and identification of predictors of therapeutic response to TMS therapy for refractory psychiatric disorders on a more real-world research basis. Furthermore, as a future vision, we aim to develop novel neuromodulation medical devices, algorithms for predicting treatment efficacy, and digital therapeutics based on the knowledge generated from this TMS registry database. [ABSTRACT FROM AUTHOR]

Here we review the paradigm-change from one-size-fits-all psychiatry to more personalized-psychiatry, where we distinguish between 'precision psychiatry' and 'stratified psychiatry'. Using examples in Depression and ADHD we argue that stratified psychiatry, using biomarkers to facilitate patients to best 'on-label' treatments, is a more realistic future for implementing biomarkers in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Stratified or personalised medicine targets treatments for groups of individuals with a disorder based on individual heterogeneity and shared factors that influence the likelihood of response. Psychiatry has traditionally defined diagnoses by constellations of co-occurring signs and symptoms that are assigned a categorical label (e.g. schizophrenia). Trial methodology in psychiatry has evaluated interventions targeted at these categorical entities, with diagnoses being equated to disorders. Recent insights into both the nosology and neurobiology of psychiatric disorder reveal that traditional categorical diagnoses cannot be equated with disorders. We argue that current quantitative methodology (1) inherits these categorical assumptions, (2) allows only for the discovery of average treatment response, (3) relies on composite outcome measures and (4) sacrifices valuable predictive information for stratified and personalised treatment in psychiatry.Methods and Findings: To achieve a truly 'stratified psychiatry' we propose and then operationalise two necessary steps: first, a formal multi-dimensional representation of disorder definition and clinical state, and second, the similar redefinition of outcomes as multidimensional constructs that can expose within- and between-patient differences in response. We use the categorical diagnosis of schizophrenia-conceptualised as a label for heterogeneous disorders-as a means of introducing operational definitions of stratified psychiatry using principles from multivariate analysis. We demonstrate this framework by application to the Clinical Antipsychotic Trials of Intervention Effectiveness dataset, showing heterogeneity in both patient clinical states and their trajectories after treatment that are lost in the traditional categorical approach with composite outcomes. We then systematically review a decade of registered clinical trials for cognitive deficits in schizophrenia highlighting existing assumptions of categorical diagnoses and aggregate outcomes while identifying a small number of trials that could be reanalysed using our proposal.Conclusion: We describe quantitative methods for the development of a multi-dimensional model of clinical state, disorders and trajectories which practically realises stratified psychiatry. We highlight the potential for recovering existing trial data, the implications for stratified psychiatry in trial design and clinical treatment and finally, describe different kinds of probabilistic reasoning tools necessary to implement stratification. [ABSTRACT FROM AUTHOR]

Objectives: Clinical neuroscience is increasingly turning to imaging the human brain for answers to a range of questions and challenges. To date, the majority of studies have focused on the neural basis of current psychiatric symptoms, which can facilitate the identification of neurobiological markers for diagnosis. However, the increasing availability and feasibility of using imaging modalities, such as diffusion imaging and resting-state fMRI, enable longitudinal mapping of brain development. This shift in the field is opening the possibility of identifying predictive markers of risk or prognosis, and also represents a critical missing element for efforts to promote personalized or individualized medicine in psychiatry (i.e., stratified psychiatry). Methods: The present work provides a selective review of potentially high-yield populations for longitudinal examination with MRI, based upon our understanding of risk from epidemiologic studies and initial MRI findings. Results: Our discussion is organized into three topic areas: (1) practical considerations for establishing temporal precedence in psychiatric research; (2) readiness of the field for conducting longitudinal MRI, particularly for neurodevelopmental questions; and (3) illustrations of high-yield populations and time windows for examination that can be used to rapidly generate meaningful and useful data. Particular emphasis is placed on the implementation of time-appropriate, developmentally informed longitudinal designs, capable of facilitating the identification of biomarkers predictive of risk and prognosis. Conclusions: Strategic longitudinal examination of the brain at-risk has the potential to bring the concepts of early intervention and prevention to psychiatry. (JINS, 2016, 22, 164–179) [ABSTRACT FROM AUTHOR]

Background: Attention-deficit/hyperactivity disorder 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., Methods: The biomarker in this study was developed in a heterogeneous clinical sample (N = 4249) and first applied to two large transfer datasets, a priori stratifying young males (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored., Results: Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively., Conclusions: This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)