A Deep Learning-Derived Transdiagnostic Signature Indexing Hypoarousal and Impulse Control: Implications for Treatment Prediction in Psychiatric Disorders.

BACKGROUND: Psychiatric disorders are traditionally classified within diagnostic categories, but this approach has limitations. Research Domain Criteria (RDoC) constitute a research classification system for psychiatric disorders based on dimensions within domains that cut across these psychiatric diagnoses. The overall aim of RDoC is to better understand mental illness in terms of dysfunction in fundamental neurobiological and behavioral systems, leading to better diagnosis, prevention and treatment. METHODS: A unique electroencephalographic (EEG) feature, referred to as spindling excessive beta (SEB), has been studied in relation to impulse control and sleep, as part of the arousal/regulatory systems RDoC domain. Here, we study EEG frontal beta activity as a potential transdiagnostic biomarker capable of diagnosing and predicting impulse control and sleep problems. RESULTS: We show in the first dataset (n=3279) that the probability of having SEB, classified by a deep learning algorithm, is associated with poor sleep maintenance and low daytime impulse control. Furthermore, in two additional, independent datasets (iSPOT-A, n=336; iSPOT-D, n=1008), we revealed that conventional frontocentral beta power and/or SEB probability, referred to as Brainmarker-III, is associated with a diagnosis of attention deficit hyperactivity disorder (ADHD), with remission to methylphenidate in children with ADHD in a sex-specific manner, and with remission to antidepressant medication in adults with a major depressive disorder in a drug-specific manner. CONCLUSION: Our results demonstrate the value of the RDoC approach in psychiatry research for the discovery of biomarkers with diagnostic and treatment prediction capacities.

rTMS as a Next Step in Antidepressant Nonresponders: A Randomized Comparison With Current Antidepressant Treatment Approaches.

OBJECTIVE: Although repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression, little is known about the comparative effectiveness of rTMS and other treatment options, such as antidepressants. In this multicenter randomized controlled trial, rTMS was compared with the next pharmacological treatment step in patients with treatment-resistant depression. METHODS: Patients with unipolar nonpsychotic depression (N=89) with an inadequate response to at least two treatment trials were randomized to treatment with rTMS or to a switch of antidepressants, both in combination with psychotherapy. Treatment duration was 8 weeks and consisted of either 25 high-frequency rTMS sessions to the left dorsolateral prefrontal cortex or a switch of antidepressant medication following the Dutch treatment algorithm. The primary outcome was change in depression severity based on the Hamilton Depression Rating Scale (HAM-D). Secondary outcomes were response and remission rates as well as change in symptom dimensions (anhedonia, anxiety, sleep, rumination, and cognitive reactivity). Finally, expectations regarding treatment were assessed. RESULTS: rTMS resulted in a significantly larger reduction in depressive symptoms than medication, which was also reflected in higher response (37.5% vs. 14.6%) and remission (27.1% vs. 4.9%) rates. A larger decrease in symptoms of anxiety and anhedonia was observed after rTMS compared with a switch in antidepressants, and no difference from the medication group was seen for symptom reductions in rumination, cognitive reactivity, and sleep disorders. Expectations regarding treatment correlated with changes in HAM-D scores. CONCLUSIONS: In a sample of patients with moderately treatment-resistant depression, rTMS was more effective in reducing depressive symptoms than a switch of antidepressant medication. In addition, the findings suggest that the choice of treatment may be guided by specific symptom dimensions.

Challenging the Diagnostic Value of Theta/Beta Ratio: Insights From an EEG Subtyping Meta-Analytical Approach in ADHD.

The frequently reported high theta/beta ratio (TBR) in the electroencephalograms (EEGs) of children with attention-deficit/hyperactivity disorder (ADHD) has been suggested to include at least two distinct neurophysiological subgroups, a subgroup with high TBR and one with slow alpha peak frequency, overlapping the theta range. We combined three large ADHD cohorts recorded under standardized procedures and used a meta-analytical approach to leverage the large sample size (N = 417; age range: 6-18 years), classify these EEG subtypes and investigate their behavioral correlates to clarify their brain-behavior relationships. To control for the fact that slow alpha might contribute to theta power, three distinct EEG subgroups (non-slow-alpha TBR (NSAT) subgroup, slow alpha peak frequency (SAF) subgroup, not applicable (NA) subgroup) were determined, based on a halfway cut-off in age- and sex-normalized theta and alpha, informed by previous literature. For the meta-analysis, Cohen's d was calculated to assess the differences between EEG subgroups for baseline effects, using means and standard deviations of baseline inattention and hyperactivity-impulsivity scores. Non-significant, small Grand Mean effect sizes (-0.212 < d < 0.218) were obtained when comparing baseline behavioral scores between the EEG subgroups. This study could not confirm any association of EEG subtype with behavioral traits. This confirms previous findings suggesting that TBR has no diagnostic value for ADHD. TBR could, however, serve as an aid to stratify patients between neurofeedback protocols based on baseline TBR. A free online tool was made available for clinicians to calculate age- and sex-corrected TBR decile scores (Brainmarker-IV) for stratification of neurofeedback protocols.

Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies.

Noninvasive brain stimulation (NIBS) treatments have gained considerable attention as 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 (MRI), 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 previous findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies met 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) functional MRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) functional MRI-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.

Target Engagement and Brain State Dependence of Transcranial Magnetic Stimulation: Implications for Clinical Practice.

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 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.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS.

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.

Rostral Anterior Cingulate Cortex Oscillatory Power Indexes Treatment-Resistance to Multiple Therapies in Major Depressive Disorder.

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.

Transcranial Magnetic Stimulation-Induced Heart-Brain Coupling: Implications for Site Selection and Frontal Thresholding-Preliminary Findings.

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.

Remote neurocognitive interventions for attention-deficit/hyperactivity disorder - Opportunities and challenges.

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.

Sleep predicts the response to rTMS and CBT in patients with OCD: an open label effectiveness study

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. (AU)

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

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.

For Which Children with ADHD is TBR Neurofeedback Effective? Comorbidity as a Moderator.

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.

Sleep predicts the response to rTMS and CBT in patients with OCD: an open label effectiveness study.

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.