Publications

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5–84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT – but in opposite direction for women (p = 0.002) relative to men (p = 0.018) – yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

Transcranial magnetic stimulation (TMS)-induced heart–brain coupling (HBC) has been proposed as a technique capable of validating target engagement of the frontal-vagal pathways, without the need of fMRI-guided neuronavigation. In parallel, recent fMRI-guided, personalized TMS protocols aim to target prefrontal regions that are negatively connected to the subgenual anterior cingulate cortex (sgACC), as these targets may have better antidepressant efficacy. It has never been tested to what extent the HBC-based target selection and these fMRI-guided targets are overlapping. Here we used fMRI-guided TMS to determine whether TMS-induced HBC is specifically affected when targeting regions negatively connected to the sgACC. In this crossover pilot study, we applied neuronavigated TMS in 14 healthy participants to five frontal and five parietal areas positively connected (bilateral), negatively connected (bilateral) or neutrally connected (midline) to the sgACC. The targets were prospectively determined using individual resting-state fMRI. We compared TMS-induced effects on HBC between different TMS targets, for frontal and parietal areas separately. With prefrontal targets, 12 out of 14 participants (86%) showed maximal HBC at TMS sites negatively connected to sgACC. HBC power was significantly higher in left frontal (d = 0.68) and left parietal (d = 0.75) targets negatively connected to sgACC versus respective targets with neutral connections to sgACC. This effect was unrelated to magnitude of negative connectivity strength. By contrast, HBC power was correlated with sgACC connectivity strength at right frontal (r = 0.56) and right parietal (r = 0.72) targets negatively connected to sgACC. We used fMRI-guided TMS to predictably and selectively modulate heart rate measured using HBC. HBC may be used as an agile readout to identify individualized TMS targets that specifically target prefrontal-sgACC connectivity. In this pilot study, the authors detected specific brain regions that can be precisely targeted with transcranial magnetic stimulation to influence heart rate. The heart–brain coupling might serve as a readout to identify optimal individualized transcranial magnetic stimulation targets for depression.

rTMS or TMS is an effective treatment method for the treatment of depression.

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. Voetterl and colleagues examined the application of ‘Brainmarker-I’, an age- and sex-normalized electroencephalogram measure of individual alpha peak frequency, on predicting remission to therapeutic noninvasive neuromodulation protocols (repetitive transcranial magnetic stimulation and electroconvulsive therapy).

A good night’s sleep is vital for normal human cognitive performance. We earlier reported that a home-based tele-neurofeedback program effectively reduced sleep problems (Krepel et al. in Appl Psychophysiol Biofeedback, https://doi.org/10.1007/s10484-021-09525-z, 2021). The present article presents a follow-up on this earlier study and investigates improvements in cognitive functions after sensory-motor rhythm (SMR) neurofeedback. Thirty-seven participants reporting sleep problems received SMR neurofeedback. Cognitive measures were assessed pre- and post-treatment. Measurements included a continuous performance/working memory (CPT/WM) task, Stroop task, and Trailmaking A and B test (from the IntegNeuro cognitive test battery). For neurofeedback-Learners relative to non-Learners significantly improved CPT/WM response time (d = 0.50), omission errors (d = 0.67), and Stroop incongruent performance (d = 0.72) were found. A significant time effect for both groups were found for the Stroop, the Trailmaking test part B (d = 0.52), and the Stroop interference score (d = 0.55). No significant correlations between changes in sleep and changes in cognition (p > 0.05) were found for the sample. SMR neurofeedback specifically improved measures of attention (response time and omission errors in a CPT/WM test) and working memory (Stroop incongruent) for SMR Learners compared to non-Learners with medium effect sizes. Furthermore, overall improvements for the whole sample were found on measures of executive function and visual attention, possibly reflecting non-specific or practice effects. Future better powered randomized control trials are needed to investigate if cognitive improvements are a direct effect of SMR neurofeedback or mediated by sleep improvements.

Non-invasive brain stimulation (NIBS) treatments have gained considerable attention as a potential therapeutic intervention for psychiatric disorders. The identification of reliable biomarkers for predicting clinical response to NIBS has been a major focus of research in recent years. Neuroimaging techniques, such as electroencephalography (EEG) and (functional) magnetic resonance imaging (fMRI), have been used to identify potential biomarkers that could predict response to NIBS. However, identifying clinically actionable brain biomarkers requires robustness. In this systematic review, we aimed to summarize the current state of brain biomarker research for NIBS in depression, focusing only on well-powered studies (N≥88) and/or studies that aimed at independently replicating prior findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies adhered to the inclusion criteria, all focused on repetitive transcranial magnetic stimulation treatment in depression. After reviewing the included studies, we found the following MRI and EEG biomarkers to be most robust: 1) fMRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) fMRI-based network connectivity, 3) task-induced EEG frontal-midline theta, and 4) EEG individual alpha frequency. Future prospective studies should further investigate the clinical actionability of these specific EEG and MRI biomarkers to bring biomarkers closer to clinical reality.

We examined psychiatric comorbidities moderation of a 2-site double-blind randomized clinical trial of theta/beta-ratio (TBR) neurofeedback (NF) for attention deficit hyperactivity disorder (ADHD). Seven-to-ten-year-olds with ADHD received either NF (n = 84) or Control (n = 58) for 38 treatments. Outcome was change in parent-/teacher-rated inattention from baseline to end-of-treatment (acute effect), and 13-month-follow-up. Seventy percent had at least one comorbidity: oppositional defiant disorder (ODD) (50%), specific phobias (27%), generalized anxiety (23%), separation anxiety (16%). Comorbidities were grouped into anxiety alone (20%), ODD alone (23%), neither (30%), or both (27%). Comorbidity (p = 0.043) moderated acute effect; those with anxiety-alone responded better to Control than to TBR NF (d = − 0.79, CI − 1.55– − 0.04), and the other groups showed a slightly better response to TBR NF than to Control (d = 0.22 ~ 0.31, CI − 0.3–0.98). At 13-months, ODD-alone group responded better to NF than Control (d = 0.74, CI 0.05–1.43). TBR NF is not indicated for ADHD with comorbid anxiety but may benefit ADHD with ODD. Clinical Trials Identifier: NCT02251743, date of registration: 09/17/2014

Introduction: Currently, major depressive disorder (MDD) treatment plans are based on trial-and-error, and remission rates remain low. A strategy to replace trial-and-error and increase remission rates could be treatment stratification. We explored the heartbeat-evoked potential (HEP) as a biomarker for treatment stratification to either antidepressant medication or rTMS treatment. Methods: Two datasets were analyzed: (1) the International Study to Predict Optimized Treatment in Depression (iSPOT-D; n = 1,008 MDD patients, randomized to escitalopram, sertraline, or venlafaxine, and n = 336 healthy controls) and (2) a multi-site, open-label rTMS study (n = 196). The primary outcome measure was remission. Cardiac field artifacts were removed from the baseline EEG using independent component analysis (ICA). The HEP-peak was detected in a bandwidth of 20 ms around 8 ms and 270 ms (N8, N270) after the R-peak of the electrocardiogram signal. Differences between remitters and non-remitters were statistically assessed by repeated-measures ANOVAs for electrodes Fp1, Cz, and Oz. Results: In the venlafaxine subgroup, remitters showed a lower HEP around the N8 peak than non-remitters on electrode site Cz (p = 0.004; d = 0.497). The rTMS group showed a non-significant difference in the opposite direction (d = −0.051). Retrospective stratification to one of the treatments based on the HEP resulted in enhanced treatment outcome prediction for venlafaxine (+22.98%) and rTMS (+10.66%). Conclusion: These data suggest that the HEP could be used as a stratification biomarker between venlafaxine and rTMS; however, future out-of-sample replication is warranted.

Improving neurocognitive functions through remote interventions has been a promising approach to developing new treatments for attention-deficit/hyperactivity disorder (AD/HD). Remote neurocognitive interventions may address the shortcomings of the current prevailing pharmacological therapies for AD/HD, e.g., side effects and access barriers. Here we review the current options for remote neurocognitive interventions to reduce AD/HD symptoms, including cognitive training, EEG neurofeedback training, transcranial electrical stimulation, and external cranial nerve stimulation. We begin with an overview of the neurocognitive deficits in AD/HD to identify the targets for developing interventions. The role of neuroplasticity in each intervention is then highlighted due to its essential role in facilitating neuropsychological adaptations. Following this, each intervention type is discussed in terms of the critical details of the intervention protocols, the role of neuroplasticity, and the available evidence. Finally, we offer suggestions for future directions in terms of optimizing the existing intervention protocols and developing novel protocols.

Introduction: High rostral anterior cingulate cortex (rACC) activity is proposed as a nonspecific prognostic marker for treatment response in major depressive disorder, independent of treatment modality. However, other studies report a negative association between baseline high rACC activation and treatment response. Interestingly, these contradictory findings were also found when focusing on oscillatory markers, specifically rACC-theta power. An explanation could be that rACC-theta activity dynamically changes according to number of previous treatment attempts and thus is mediated by level of treatment-resistance. Methods: Primarily, we analyzed differences in rACC- and frontal-theta activity in large national cross-sectional samples representing various levels of treatment-resistance and resistance to multimodal treatments in depressed patients (psychotherapy [n = 175], antidepressant medication [AD; n = 106], repetitive transcranial magnetic stimulation [rTMS; n = 196], and electroconvulsive therapy [ECT; n = 41]), and the respective difference between remitters and non-remitters. For exploratory purposes, we also investigated other frequency bands (delta, alpha, beta, gamma). Results: rACC-theta activity was higher (p < 0.001) in the more resistant rTMS and ECT patients relative to the less resistant psychotherapy and AD patients (psychotherapy-rTMS: d = 0.315; AD-rTMS: d = 0.320; psychotherapy-ECT: d = 1.031; AD-ECT: d = 1.034), with no difference between psychotherapy and AD patients. This association was even more pronounced after controlling for frontal-theta. Post hoc analyses also yielded effects for delta, beta, and gamma bands. Conclusion: Our findings suggest that by factoring in degree of treatment-resistance during interpretation of the rACC-theta biomarker, its usefulness in treatment selection and prognosis could potentially be improved substantially in future real-world practice. Future research should however also investigate specificity of the theta band.

Background Although many OCD patients benefit from repetitive transcranial magnetic stimulation (rTMS) as treatment, there is still a large group failing to achieve satisfactory response. Sleep problems have been considered transdiagnostic risk factors for psychiatric disorders, and prior work has shown comorbid sleep problems in OCD to be associated with non-response to rTMS in OCD. We therefore set out to investigate the utility of sleep problems in predicting response to rTMS in treatment resistant OCD. Method A sample of 61 patients (treated with 1-Hz SMA or sequential 1-Hz SMA+DLPFC rTMS, combined with cognitive behavioral therapy) were included. Sleep disturbances were measured using the PSQI, HSDQ and actigraphy. Treatment response was defined as a decrease of at least 35% in symptom severity as measured with the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). Results 32 of 61 patients (52.5%) responded to rTMS, and trajectories of response were similar for both rTMS protocols. Three PSQI items (Subjective Sleep Quality; Sleep Latency and Daytime Dysfunction) and the HSDQ-insomnia scale were found to predict TMS response. A discriminant model yielded a significant model, with an area under the curve of 0.813. Conclusion Future replication of these predictors could aid in a more personalized treatment for OCD.

Transcranial Magnetic Stimulation (TMS) is capable of noninvasively inducing lasting neuroplastic changes when applied repetitively across multiple treatment sessions. In recent years, repetitive TMS has developed into an established evidence-based treatment for various neuropsychiatric disorders such as depression. Despite significant advancements in our understanding of the mechanisms of action of TMS, there is still much to learn about how these mechanisms relate to the clinical effects observed in patients. If there is one thing about TMS that we know for sure, it is that TMS effects are state-dependent. In this review, we will describe how the effects of TMS on brain networks depend on various factors, including cognitive brain state, oscillatory brain state, and recent brain state history. These states play a crucial role in determining the effects of TMS at the moment of stimulation and are therefore directly linked to what is referred to as target engagement in TMS therapy. There is no control over target engagement without considering the different brain state dependencies of our TMS intervention. Clinical TMS protocols are largely ignoring this fundamental principle, which may explain the large variability and often still limited efficacy of TMS treatments. We propose that after almost 30 years of research on state-dependency of TMS, it is time to change standard clinical practice by taking advantage of this fundamental principle. Rather than ignoring TMS state-dependency, we can use it to our clinical advantage to improve the effectiveness of TMS treatments.

Background Neuro-cardiac-guided transcranial magnetic stimulation (NCG-TMS) employs repetitive TMS (rTMS)-induced heart rate deceleration to confirm activation of the frontal-vagal pathway. Here we test a novel NCG-TMS method that utilizes Heart-Brain-Coupling (HBC) in order to quantify rTMS-induced entrainment of the inter-beat-interval as a function of TMS cycle-time. Since prior NCG-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 study 1A and 1B we validated intermittent theta-burst stimulation (iTBS)-induced HBC (2s iTBS-on; 8s off: HBC=0.1Hz) in fifteen (1A) and twenty-two (1B) patients with major depressive disorder from two double-blind placebo-controlled studies. In study 2, HBC was measured in ten healthy subjects during the 10Hz "dash”-protocol (5s 10Hz-on; 11s off: HBC=0.0625Hz) applied with fifteen increasing intensities to four 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 0.1Hz in active compared to sham iTBS (d=1.37) (Study 1A). In study 2, clear dose-dependent entrainment (p=.002) was observed at 0.0625Hz in a site-specific manner. Conclusions We demonstrated rTMS-induced HBC as a function of TMS cycle-time for two commonly used clinical protocols (iTBS and 10Hz-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.

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 signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

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 17-30%. 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. Conclusion The present 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.

Background Research on the electroencephalographic (EEG) signatures of attention-deficit/hyperactivity disorder (ADHD) has historically concentrated on its frequency spectrum or event-related evoked potentials. In this work, we investigate EEG microstates (MSs), an alternative framework defined by the clustering of recurring topographical patterns, as a novel approach for examining large-scale cortical dynamics in ADHD. Methods Using k-means clustering, we studied the spatiotemporal dynamics of ADHD during the rest condition by comparing the MS segmentations between adult patients with ADHD and neurotypical control subjects across two independent datasets: the first dataset consisted of 66 patients with ADHD and 66 control subjects, and the second dataset comprised 22 patients with ADHD and 22 control subjects and was used for out-of-sample validation. Results Spatially, patients with ADHD and control subjects displayed equivalent MS topographies (canonical maps), indicating the preservation of prototypical EEG generators in patients with ADHD. However, this concordance was accompanied by significant differences in temporal dynamics. At the group level, and across both datasets, ADHD diagnosis was associated with longer mean durations of a frontocentral topography (MS D), indicating that its electrocortical generator(s) could be acting as pronounced attractors of global cortical dynamics. In addition, its spatiotemporal metrics were correlated with sleep disturbance, the latter being known to have a strong relationship with ADHD. Finally, in the first (larger) dataset, we also found evidence of decreased time coverage and mean duration of a left-right diagonal topography (MS A), which inversely correlated with ADHD scores. Conclusions Overall, our study underlines the value of EEG MSs as promising functional biomarkers for ADHD, offering an additional lens through which to examine its neurophysiological mechanisms.

Background Electroconvulsive therapy (ECT) is the most effective treatment for severe major depressive episodes (MDEs). Nonetheless, firmly established associations between ECT outcomes and biological variables are currently lacking. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response in psychiatry are seldom reported. Here, we examined whether PRSs for major depressive disorder, schizophrenia (SCZ), cross-disorder, and pharmacological antidepressant response are associated with ECT effectiveness. Methods A total of 288 patients with MDE from 3 countries were included. The main outcome was a change in the 17-item Hamilton Depression Rating Scale scores from before to after ECT treatment. Secondary outcomes were response and remission. Regression analyses with PRSs as independent variables and several covariates were performed. Explained variance (R 2) at the optimal p-value threshold is reported. Results In the 266 subjects passing quality control, the PRS-SCZ was positively associated with a larger Hamilton Depression Rating Scale decrease in linear regression (optimal p-value threshold = .05, R 2 = 6.94%, p < .0001), which was consistent across countries: Ireland (R 2 = 8.18%, p = .0013), Belgium (R 2 = 6.83%, p = .016), and the Netherlands (R 2 = 7.92%, p = .0077). The PRS-SCZ was also positively associated with remission (R 2 = 4.63%, p = .0018). Sensitivity and subgroup analyses, including in MDE without psychotic features (R 2 = 4.42%, p = .0024) and unipolar MDE only (R 2 = 9.08%, p < .0001), confirmed the results. The other PRSs were not associated with a change in the Hamilton Depression Rating Scale score at the predefined Bonferroni-corrected significance threshold. Conclusions A linear association between PRS-SCZ and ECT outcome was uncovered. Although it is too early to adopt PRSs in ECT clinical decision making, these findings strengthen the positioning of PRS-SCZ as relevant to treatment response in psychiatry.

In the field of psychiatry diagnoses are primarily based on the report of symptoms from either the patient, parents, or both, and a psychiatrist’s observations. A psychiatric diagnosis is currently the most widely used basis for medication selection and the brain is seldom investigated directly as a source of those symptoms. This study addresses the request from the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC) for scientific research into neurological abnormalities that can be linked to psychiatric symptoms for the purpose of predicting medication response. One such neurological abnormality that has been the focus of many studies over the last three decades is isolated epileptiform discharges (IEDs) in children and adolescents without seizures. We conducted a systematic review of the literature to determine prevalence rates of IEDs within diagnostic categories. We then compared the prevalence of IEDs in the selected literature to our IRB-approved data archive. Our study found a consistent high prevalence of IEDs specifically for ADHD (majority > 25%) and ASD (majority > 59%), and consistent low prevalence rates were found for Depression (3%). If children and adolescents have failed multiple medication attempts, and more than one-third of them have IEDs, then an EEG would be justified within the RDoC paradigm.

Objective To examine delayed effects of theta-beta ratio (TBR) neurofeedback (NF) for attention-deficit/hyperactivity disorder (ADHD) 25 months after baseline, ∼21 months after end of treatment. Method Children aged 7-10 with rigorously diagnosed ADHD had been randomized to 38 sessions of TBR NF (n=84) or control treatment (n=58) of identical appearance, intensity/frequency, and duration, differing only in that reinforcement for controls was based on a pre-recorded EEG of another child. Child, parent, and all site staff were blind until after 25-month assessments, with only one-fourth able to guess the control treatment correctly. Baseline assessments were repeated off medication after 25 months. Results Of the 142, 120 had 25-month follow-up (84.5% retention). Only 12 (6 controls) had NF after the study treatment, greatly retaining the randomization. The primary outcome, parent-rated inattention, was not significantly different between treatments despite large pre-post effect sizes (NF, d=1.63; controls, d=1.42). Most secondary measures showed the same pattern. Response rates (CGI-I≤2) were 58.6% of NF and 66% of controls (non-significant). Marginally more controls than NF recipients needed medication (57.1% vs 38.6%, p=0.059); specifically, 7.1% of NF and 4% of controls had reduced medication need while 34.3% of NF and 50% of controls needed more medication (p=0.084). Conclusion Most of the large within-group improvement from the NF treatment package reported by unblinded studies and replicated in this blinded study reflects nonspecific effects, not specific effects of deliberate downtraining of EEG theta-beta power ratio. At 25-month follow-up it appears comparable to the evidence-based MTA treatments, suggesting a psychotherapeutic/behavioral effect.

Background: We evaluated the efficacy and safety of repetitive transcranial magnetic stimulation (rTMS) for obsessive-compulsive disorder (OCD), and ranked the relative efficacy of different stimulation protocols. Methods: We performed a search for randomised, sham-controlled trials of rTMS for OCD. The primary analysis included both a pairwise meta-analysis and a series of frequentist network meta-analyses (NMA) of OCD symptom severity. Secondary analyses were carried out on relevant clinical factors and safety. Results: 21 studies involving 662 patients were included. The pairwise meta-analysis showed that rTMS for OCD is efficacious across all protocols (Hedges’ g=-0.502 [95%CI= -0.708, -0.296]). The first NMA, with stimulation protocols clustered only by anatomical location, showed that both dorsolateral prefrontal cortex (dlPFC) stimulation and medial frontal cortex stimulation were efficacious. In the second NMA, considering each unique combination of frequency and location separately, low frequency (LF) pre-supplementary motor area (preSMA) stimulation, high frequency (HF) bilateral dlPFC stimulation, and LF right dlPFC stimulation were all efficacious . LF right dlPFC was ranked highest in terms of efficacy, although the corresponding confidence intervals overlapped with the other two protocols. Limitations: Evidence base included mostly small studies, with only a few studies using similar protocols, giving a sparse network. Studies were heterogeneous, and a risk of publication bias was found. Conclusions: rTMS for OCD was efficacious compared with sham stimulation. LF right dlPFC, HF bilateral dlPFC and LF preSMA stimulation were all efficacious protocols with significant and comparable clinical improvements. Future studies should further investigate the relative merits of these three protocols.

Major depressive disorder (MDD) is one of the most common psychiatric disorders, however, current treatment options are insufficiently effective for about 35% of patients, resulting in treatment-resistant depression (TRD). Repetitive transcranial magnetic stimulation (rTMS) is a form of non-invasive neuromodulation that is effective in treating TRD. Not much is known about the comparative efficacy of rTMS and other treatments and their timing within the treatment algorithm, making it difficult for the treating physician to establish when rTMS is best offered as a treatment option. This study aims to investigate the (cost-)effectiveness of rTMS (in combination with cognitive behavioral therapy (CBT) and continued antidepressant medication), compared to the next step in the treatment algorithm. This will be done in a sample of patients with treatment resistant non-psychotic unipolar depression. In this pragmatic multicenter randomized controlled trial 132 patients with MDD are randomized to either rTMS or the next pharmacological step within the current treatment protocol (a switch to a tricyclic antidepressant or augmentation with lithium or a second-generation antipsychotic). Both groups also receive CBT. The trial consists of 8 weeks of unblinded treatment followed by follow-up of the cohort at four and 6 months. A subgroup of patients (n = 92) will have an extended follow-up at nine and 12 months to assess effect decay or retention. We expect that rTMS is more (cost-)effective than medication in reducing depressive symptoms in patients with TRD. We will also explore the effects of both treatments on symptoms associated with depression, e.g. anhedonia and rumination, as well as the effect of expectations regarding the treatments on its effectiveness. The present trial aims to inform clinical decision making about whether rTMS should be considered as a treatment option in patients with TRD. The results may improve treatment outcomes in patients with TRD and may facilitate adoption of rTMS in the treatment algorithm for depression and its implementation in clinical practice. This trial is registered within the Netherlands Trial Register (code: NL7628, date: March 29th 2019).

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.

In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5–89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main indications included are Major Depressive Disorder (MDD; N = 426), attention deficit hyperactivity disorder (ADHD; N = 271), Subjective Memory Complaints (SMC: N = 119) and obsessive-compulsive disorder (OCD; N = 75). Demographic-, personality- and day of measurement data are included in the database. Thirty percent of clinical and treatment outcome data will remain blinded for prospective validation and replication purposes. The TDBRAIN database and code are available on the Brainclinics Foundation website at www.brainclinics.com/resources and on Synapse at www.synapse.org/TDBRAIN.

Introduction A recent study showed hypoactivity in the beta/gamma band in female suicide ideators and suicide attempters diagnosed with depression, relative to a low-risk group. The current study aimed to conceptually replicate these results. Methods In the iSPOT-D sample (n = 402), suicide ideators and low-risk individuals were identified. Confining analyses to females only, differences between low-risk individuals and suicide ideators were tested for using the electroencephalogram (EEG) frequency bands SMR (Sensori-Motor-Rhythm; 12-15 Hz), beta (14.5-30 Hz), beta I (14.5-20 Hz), beta II (20-25 Hz), beta III (25-30 Hz), gamma I (31-49 Hz) using LORETA-software. Results None of the tested frequency bands showed to be significantly different between suicide ideators and low-risk individuals. Conclusions The current study could not conceptually replicate the earlier published results. Several reasons could explain this non-replication, among which possible electromyographic (EMG) contamination in the beta/gamma band in the original study. Trial Registration ClinicalTrials.gov identifier: NCT00693849 URL: http://clinicaltrials.gov/ct2/show/NCT00693849.

Objective To determine whether theta/beta-ratio (TBR) electroencephalographic biofeedback (neurofeedback [NF]) has a specific effect on attention-deficit/hyperactivity disorder (ADHD) beyond nonspecific benefit. Method In a 2-site double-blind randomized clinical trial, 144 children aged 7 to 10 years with rigorously diagnosed moderate/severe ADHD and theta/beta-ratio (TBR) ≥4.5 were randomized 3:2 to deliberate TBR downtraining versus a control of equal duration, intensity, and appearance. Two early dropouts left 142 children for modified intent-to-treat analysis. The control used prerecorded electroencephalograms with the participant’s artifacts superimposed. Treatment was programmed via Internet by an off-site statistician-guided co-investigator. Fidelity was 98.7% by trainers/therapists and 93.2% by NF expert monitor. The primary outcome was parent- and teacher-rated inattention; analysis was mixed-effects regression. Because the expense and effort of NF can be justified only by enduring benefit, follow-ups were integrated. Results Blinding was excellent. Although both groups showed significant improvement (p < .001, d = 1.5) in parent/teacher-rated inattention from baseline to treatment end and 13-month follow-up, NF was not significantly superior to the control condition at either time point on this primary outcome (d = 0.01, p = .965 at treatment end; d = 0.23, p = .412 at 13-month follow-up). Responders (Clinical Global Impression−Improvement [CGI-I] = 1−2) were 61% of NF and 54% of controls (p = .36). Adverse events were distributed proportionally between treatments. The 13-month follow-up found nonsignificant improvement from treatment end for NF (d = 0.1), with mild deterioration for controls (d = −0.07). NF required significantly less medication at follow-up (p = .012). Conclusion This study does not support a specific effect of deliberate TBR NF at either treatment end or 13-month follow-up. Participants will be reassessed at 25-month follow-up. Clinical trial registration information Double-Blind 2-Site Randomized Clinical Trial of Neurofeedback for ADHD; https://clinicaltrials.gov/; NCT02251743.

Using pre-treatment biomarkers to guide patients to the preferred antidepressant medication treatment could be a promising approach to enhance its current modest response and remission rates. This open-label prospective study assessed the feasibility of using such pre-treatment biomarkers, by using previously identified EEG features (paroxysmal activity; alpha peak frequency; frontal alpha asymmetry) to inform the clinician in selecting among three different antidepressants (ADs; escitalopram, sertraline, venlafaxine) as compared to Treatment As Usual (TAU). EEG data were obtained from 195 outpatients with major depressive disorder prior to eight weeks of AD treatment. Primary outcome measure was the percentage change between before and after treatment on the Beck Depression Inventory-II (BDI-II). We compared TAU and EEG-informed prescription through AN(C)OVAs. Recruitment started with patients receiving TAU to establish baseline effectiveness, after which we recruited patients receiving EEG-informed prescription. 108 patients received EEG-informed prescription and 87 patients received TAU. Clinicians and patients were satisfied with the protocol. Overall, 70 (65%) of the EEG-informed clinicians followed recommendations (compared to 52 (60%) following prescriptions in the TAU group), establishing feasibility. We here confirm that treatment allocation informed by EEG variables previously reported in correlational studies, was feasible.

SMR neurofeedback shows potential as a therapeutic tool for reducing sleep problems. It is hypothesized that SMR neurofeedback trains the reticulo-thalamocortical-cortical circuit involved in sleep-spindle generation. As such, strengthening this circuit is hypothesized to reduce sleep problems. The current study aims to investigate the effectiveness of a home-based device that uses SMR neurofeedback to help reduce sleep problems. Thirty-seven participants reporting sleep problems received the SMR neurofeedback-based program for 40 (n = 21) or 60 (n = 16) sessions. The Pittsburgh Sleep Quality Index (PSQI) and Holland Sleep Disorders Questionnaire (HSDQ) were assessed at baseline, session 20, outtake, and follow-up (FU). Actigraphy measurements were taken at baseline, session 20, and outtake. Significant improvements were observed in PSQI Total (d = 0.78), PSQI Sleep Duration (d = 0.52), HSDQ Total (d = 0.80), and HSDQ Insomnia (d = 0.79). Sleep duration (based on PSQI) increased from 5.3 h at baseline to 5.8 after treatment and 6.0 h. at FU. No effects of number of sessions were found. Participants qualified as successful SMR-learners demonstrated a significantly larger gain in sleep duration (d = 0.86 pre-post; average gain = 1.0 h.) compared to non-learners. The home-based SMR tele-neurofeedback device shows the potential to effectively reduce sleep problems, with SMR-learners demonstrating significantly better improvement. Although randomized controlled trials (RCTs) are needed to further elucidate the specific effect of this device on sleep problems, this is the first home-based SMR neurofeedback device using dry electrodes demonstrating effectiveness and feasibility.

Neuro-Cardiac-Guided Transcranial Magnetic Stimulation (NCG-TMS) was studied for its potential to specifically target the frontal-vagal network. Previous research demonstrated that prefrontal stimulation led to significant heartrate slowing. We aimed to replicate these results in a larger sample and extend the findings to investigate dose-response relationships, reproducibility and stimulation frequency (10 Hz and intermittent theta burst (iTBS)). Data of forty-five healthy controls were analyzed, of which 28 received 10 Hz TMS (NCG-TMS) and 27 iTBS (NCG-iTBS; 10 received both protocols) at different stimulation sites according to the 10-20-EEG system. NCG-TMS yielded a relative heartrate deceleration at the F3/4 coil position replicating earlier studies. Both internal consistency and dose-response relationships were found. For NCG-iTBS adverse events were reported and topography for frontal-vagal activation was more lateralised relative to NCG-TMS. These results indicate that we were able to transsynaptically stimulate the frontal-vagal network and that excitability thresholds for the prefrontal cortex may differ relative to motor cortex.

Objective Our previous research showed high predictive accuracy at differentiating responders from non-responders to repetitive transcranial magnetic stimulation (rTMS) for depression using resting electroencephalography (EEG) and clinical data from baseline and one-week following treatment onset using a machine learning algorithm. In particular, theta (4–8 Hz) connectivity and alpha power (8–13 Hz) significantly differed between responders and non-responders. Independent replication is a necessary step before the application of potential predictors in clinical practice. This study attempted to replicate the results in an independent dataset. Methods We submitted baseline resting EEG data from an independent sample of participants who underwent rTMS treatment for depression (N = 193, 128 responders) (Krepel et al., 2018) to the same between group comparisons as our previous research (Bailey et al., 2019). Results Our previous results were not replicated, with no difference between responders and non-responders in theta connectivity (p = 0.250, Cohen’s d = 0.1786) nor alpha power (p = 0.357, ηp 2 = 0.005). Conclusions These results suggest that baseline resting EEG theta connectivity or alpha power are unlikely to be generalisable predictors of response to rTMS treatment for depression. Significance These results highlight the importance of independent replication, data sharing and using large datasets in the prediction of response research.

Objective Sleep disturbances are highly frequent features in a range of child and adolescent psychiatric conditions. However, it is commonly not clear if such sleep problems represent symptomatic features, co-morbidities or risk factors for these conditions. It is believed that underlying dysfunction in the daily biological (circadian) clock may play important roles in the etiology of many sleep disorders, and circadian rhythm changes are also reported in a number of neuropsychiatric conditions. Here we explore the key identifying features of circadian rhythm disorders in child and adolescent psychiatry, and address how such disorders may be managed in the clinic. Method A narrative review of the extant literature of circadian rhythm disorders in children and adolescents with psychiatric conditions. Results We identify key biological and social factors that contribute to circadian rhythm disorders in children and adolescents, as well as outlining the cognitive and neurobehavioral consequences resulting from insufficient sleep. We outline the roles of melatonin and other chronotherapeutic and behavioral interventions for the management of circadian rhythm disorders. Further, we highlight the importance of careful investigation of circadian rhythm abnormalities in shaping the most effective treatment plan according to chronobiological principles. Conclusion Circadian rhythm disorders are common in children and adolescents with psychiatric conditions, and arise out of complex interactions between biological and social factors. Careful clinical attention to, and management of, circadian rhythm disorders in child and adolescent psychiatry has the potential for significant benefit not only in the domain of sleep but also in a range of cognitive, affective and behavioral outcomes.

Background Frontocentral Spindling Excessive Beta (SEB), a spindle-like beta-activity observed in the electroencephalogram (EEG), has been transdiagnostically associated with more problems with impulse control and sleep maintenance. The current study aims to replicate and elaborate on these findings. Methods Participants reporting sleep problems (n=31) or Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms (n=48) were included. Baseline ADHD-Rating Scale (ADHD-RS), Pittsburgh Sleep Quality Index (PSQI), Holland Sleep Disorder Questionnaire (HSDQ), and EEG were assessed. Analyses were confined to adults with frontocentral SEB. Results Main effects of SEB showed more impulse control problems (d=.87) and false positive errors (d=.55) in participants with SEB. No significant associations with sleep or interactions with Sample were observed. Discussion This study partially replicates an earlier study and demonstrates that participants exhibiting SEB report more impulse control problems, independent of diagnosis. Future studies should focus on automating SEB classification and further investigate the transdiagnostic nature of SEB.

Recent reviews have proposed that scientifically validated standard EEG neurofeedback (NF) protocols are an efficacious and specific treatment for attention-deficit hyperactivity disorder (ADHD). Here, we review the current evidence for the treatment efficacy and clinical effectiveness of NF in ADHD to investigate whether NF treatment personalization (standard protocols matched to the electrophysiological features of ADHD) and combination with other interventions (psychosocial, sleep hygiene and nutritional advice) might yield superior long-term treatment outcomes relative to non-personalized NF and medication monotreatments.

Major Depressive Disorder (MDD) is a psychiatric disorder characterized by high comorbidity with cardiovascular disease. Furthermore, a combination of high heart rate (HR) and low heart rate variability (HRV) has been frequently reported in depressed patients. The present review proposes a frontal-vagal (brain-heart) network that overlaps with functional nodes of the depression network. Moreover, we summarize neuromodulation studies that have targeted key nodes in this depression network, with subsequent impact on heart rate (HR) or heart-rate-variability (HRV), such as the dorsolateral prefrontal cortex (DLPFC), subgenual anterior cingulate cortex (sgACC), and the vagus nerve (VN). Based on the interplay of this frontal-vagal network, we emphasize the importance of including HR and HRV measurements in human depression studies, in particular those that conduct neuromodulation, in order to obtain a better understanding of the pathways that are affected, and we explore the possibilities of using this frontal-vagal interplay as a method for target engagement in neuromodulation treatments. This frontal-vagal network theory opens-up the possibility for individualizing neuromodulation treatments such as rTMS. A recent development called Neuro-Cardiac-Guided TMS (NCG-TMS), was developed based on this theory, and an individual-participant meta-analysis is presented. Four studies provide consistent and replicable support for NCG-TMS as a target engagement method, with consistent HR deceleration during frontal TMS and HR acceleration during motor strip TMS.

Introduction Quantitative Electroencephalogram- (QEEG-)informed neurofeedback is a method in which standard neurofeedback protocols are assigned, based on individual EEG characteristics in order to enhance effectiveness. Thus far clinical effectiveness data have only been published in a small sample of 21 ADHD patients. Therefore, this manuscript aims to replicate this effectiveness in a new sample of 114 patients treated with QEEG-informed neurofeedback, from a large multicentric dataset and to investigate potential predictors of neurofeedback response. Methods A sample of 114 patients were included as a replication sample. Patients were treated with standard neurofeedback protocols (Sensori-Motor-Rhythm (SMR), Theta-Beta (TBR), or Slow Cortical Potential (SCP) neurofeedback), in combination with coaching and sleep hygiene advice. The ADHD Rating Scale (ADHD-RS) and Pittsburgh Sleep Quality Index (PSQI) were assessed at baseline, every 10th session, and at outtake. Holland Sleep Disorder Questionnaire (HSDQ) was assessed at baseline and outtake. Response was defined as ≥25% reduction (R25), ≥50% reduction (R50), and remission. Predictive analyses were focused on predicting remission status. Results In the current sample, response rates were 85% (R25), 70% (R50), and remission was 55% and clinical effectiveness was not significantly different from the original 2012 sample. Non-remitters exhibited significantly higher baseline hyperactivity ratings. Women who remitted had significantly shorter P300 latencies and boys who remitted had significantly lower iAPF’s. Discussion In the current sample, clinical effectiveness was replicated, suggesting it is possible to assign patients to a protocol based on their individual baseline QEEG to enhance signal-to-noise ratio. Furthermore, remitters had lower baseline hyperactivity scores. Likewise, female remitters had shorter P300 latencies, whereas boys who remitted have a lower iAPF. Our data suggests initial specificity in treatment allocation, yet further studies are needed to replicate the predictors of neurofeedback remission.

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression. The efficacy of an antidepressant is predicted from an EEG signature.

Heart rate in MDD is often dysregulated, expressed in overall higher heart rates (HR) and lower heart rate variability (HRV). Interestingly, HR decelerations have been reported after stimulation of the DLPFC using rTMS, suggesting connectivity between the DLPFC and the heart. Recently, a new form of rTMS called theta burst stimulation (TBS) has been developed. One form of TBS, intermittent TBS (iTBS), delivers 600 pulses in just 3 min.

Abstract Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.