Biomarkers predicting treatment outcome in major depressive disorder could enhance clinical improvement. Here this observational and prospective accuracy study investigates whether an age- and sex-normalized electroencephalography biomarker, based on the individual alpha frequency (iAF), can successfully stratify patients to different interventions such as repetitive transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT). Differential iAF directions were explored for sertraline, as well as rTMS ( N = 196) and ECT ( N = 41). A blinded out-of-sample validation (EMBARC; N = 240) replicated the previously found association between low iAF and better sertraline response. The subgroup of patients with an iAF around 10 Hz had a higher remission rate following 10 Hz rTMS compared with the group level, while the high-iAF subgroup had highest remission to 1 Hz rTMS and the low-iAF subgroup to ECT. Blinded out-of-sample validations for 1 Hz ( N = 39) and ECT ( N = 51) corroborated these findings. The present study suggests a clinically actionable electroencephalography biomarker that can successfully stratify between various antidepressant treatments.

BACKGROUND: Neurocardiac-guided transcranial magnetic stimulation (TMS) uses repetitive TMS (rTMS)-induced heart rate deceleration to confirm activation of the frontal-vagal pathway. Here, we test a novel neurocardiac-guided TMS method that utilizes heart-brain coupling (HBC) to quantify rTMS-induced entrainment of the interbeat interval as a function of TMS cycle time. Because prior neurocardiac-guided TMS studies indicated no association between motor and frontal excitability threshold, we also introduce the approach of using HBC to establish individualized frontal excitability thresholds for optimally dosing frontal TMS. METHODS: In studies 1A and 1B, we validated intermittent theta burst stimulation (iTBS)-induced HBC (2 seconds iTBS on; 8 seconds off: HBC = 0.1 Hz) in 15 (1A) and 22 (1B) patients with major depressive disorder from 2 double-blind placebo-controlled studies. In study 2, HBC was measured in 10 healthy subjects during the 10-Hz "Dash" protocol (5 seconds 10-Hz on; 11 seconds off: HBC = 0.0625 Hz) applied with 15 increasing intensities to 4 evidence-based TMS locations. RESULTS: Using blinded electrocardiogram-based HBC analysis, we successfully identified sham from real iTBS sessions (accuracy: study 1A = 83%, study 1B = 89.5%) and found a significantly stronger HBC at 0.1 Hz in active compared with sham iTBS (d = 1.37) (study 1A). In study 2, clear dose-dependent entrainment (p = .002) was observed at 0.0625 Hz in a site-specific manner. CONCLUSIONS: We demonstrated rTMS-induced HBC as a function of TMS cycle time for 2 commonly used clinical protocols (iTBS and 10-Hz Dash). These preliminary results supported individual site specificity and dose-response effects, indicating that this is a potentially valuable method for clinical rTMS site stratification and frontal thresholding. Further research should control for TMS side effects, such as pain of stimulation, to confirm these findings.

In the treatment of depression using repetitive Transcranial Magnetic Stimulation (rTMS), the intensity of stimulation is based on the motor threshold (MT). However, prior work demonstrated that frontal excitability thresholds are not related to MT but to percentage machine output (%MSO), suggestive of relevant differences between MT and frontal excitability thresholds [1]. Neuro-cardiac-guided TMS (NCG-TMS) employs rTMS-induced heart rate deceleration to confirm activation of the frontal-vagal pathway [2], and thereby represents a first immediate output of frontal stimulation.

Fitzgerald [1] reviewed the question ‘Targeting repetitive transcranial magnetic stimulation in depression: do we really know what we are stimulating and how best to do it?‘. Two clusters of stimulation targets in use for rTMS treatment in major depressive disorder (MDD) emerge, one surrounding the more posterior ‘5-cm’ rule and another surrounding the more anterior ‘Beam-F3’ target. Given the fact that large effectiveness studies have demonstrated comparable response (47–58 %) and remission (29–37 %) rates for the ‘Beam-F3’ cluster [2] and ‘5-cm’ cluster [3] respectively, we hypothesize if these comparable rates could be explained by inter-individual differences.

Despite a variety of different treatment options for major depressive disorder (MDD), many patients do not experience adequate symptom relief.Moving from the standard one-size-fits-all treatment prescription towards stratifying patients to different interventions by means of biomarkers, could aid in increasing clinical remission.We recently developed a clinically implementable and easily interpretable biomarker (Brainmarker-I) based on the individual alpha peak frequency (iAPF) measured during resting-state electroencephalography (EEG) in a large heterogeneous dataset (N¼4249), and conducted blinded out-ofsample validations in two independent samples, successfully predicting remission to different pharmaceutical and non-pharmaceutical interventions of attention-deficit/hyperactivity-disorder.Next, we applied Brainmarker-I to several datasets to predict remission to different MDD treatments including rTMS (10Hz left DLPFC and 1Hz right DLPFC) and pharmaceutical interventions (sertraline, escitalopram, venlafaxine).Positive predictive values (PPVs) were employed to indicate the direction of treatment stratification.Normalized PPVs were calculated to improve comparability of predicted increase in remission rates across datasets.As demonstrated in earlier work, an iAPF close to the stimulation frequency of 10Hz at the site of stimulation best predicted remission to 10Hz rTMS, with an increase in predicted normalized remission rate (normalized PPV) of 24%.A relatively lower iAPF suggested an increased likelihood of remission to sertraline, while individuals with a relatively higher iAPF were more likely to remit to 1Hz rTMS.Escitalopram and venlafaxine were exploratively examined in the same way, and results are discussed.Here we present a transdiagnostic treatment stratification biomarker that is capable of predicting differential treatment outcome in patient subgroups, and that is ready for implementation in clinical practice.Brainmarker-I represents a first step from a one-size-fits-all treatment approach towards personalized psychiatry in depression treatment.