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.

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.

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.

Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction.

Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67 %, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators' brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.

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.

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.

Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study.

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.

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.

The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database.

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 .

Stratified psychiatry: Tomorrow's precision psychiatry?

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.

To spindle or not to spindle: A replication study into spindling excessive beta as a transdiagnostic EEG feature associated with impulse control.

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 = 0.87) and false positive errors (d = 0.55) 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.

Neuro-Cardiac-Guided TMS (NCG TMS): A replication and extension study.

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.

Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in depression: A non-replication from the ICON-DB consortium.

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, ηp2 = 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.

Different Spectral Analysis Methods for the Theta/Beta Ratio Calculate Different Ratios But Do Not Distinguish ADHD from Controls.

There has been ongoing research on the ratio of theta to beta power (Theta/Beta Ratio, TBR) as an EEG-based test in the diagnosis of ADHD. Earlier studies reported significant TBR differences between patients with ADHD and controls. However, a recent meta-analysis revealed a marked decline of effect size for the difference in TBR between ADHD and controls for studies published in the past decade. Here, we test if differences in EEG processing explain the heterogeneity of findings. We analyzed EEG data from two multi-center clinical studies. Five different EEG signal processing algorithms were applied to calculate the TBR. Differences between resulting TBRs were subsequently assessed for clinical usability in the iSPOT-A dataset. Although there were significant differences in the resulting TBRs, none distinguished between children with and without ADHD, and no consistent associations with ADHD symptoms arose. Different methods for EEG signal processing result in significantly different TBRs. However, none of the methods significantly distinguished between ADHD and healthy controls in our sample. The secular effect size decline for the TBR is most likely explained by factors other than differences in EEG signal processing, e.g. fewer hours of sleep in participants and differences in inclusion criteria for healthy controls.

Auditory cortex sensitivity to the loudness attribute of verbs.

The auditory cortex was shown to be activated during the processing of words describing actions with acoustic features. The present study further examines whether processing visually presented action words characterized by different levels of loudness, i.e. "loud" (to shout) and "quiet" actions (to whisper), differentially engage the auditory cortex. Twenty healthy participants were measured with magnetoencephalography (MEG) while reading inflected verbs followed by a short tone and semantic tasks. Based on the results of a localizer task, loudness sensitive temporal Brodmann areas A22, A41/42, and pSTS were inspected in the word paradigm. "Loud" actions induced significantly stronger beta power suppression compared to "quiet" actions in the left hemisphere. Smaller N100m amplitude related to tones following "loud" compared to "quiet" actions confirmed that auditory cortex sensitivity was modulated by action words. Results point to possible selective auditory simulation mechanisms involved in verb processing and support embodiment theories.