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.

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.

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.

Cardiovascular biomarkers of response to accelerated low frequency repetitive transcranial magnetic stimulation in major depression.

BACKGROUND AND OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) is an effective and safe treatment for major depressive disorder (MDD). rTMS is in need of a reliable biomarker of treatment response. High frequency (HF) dorsolateral prefrontal cortex (DLPFC) rTMS has been reported to induce significant changes in the cardiac activity of MDD patients. Low frequency DLPFC rTMS has many advantages over HF-DLPFC rTMS and thus this study aims to further investigate the effect of low frequency 1 Hz right hemisphere (R)-DLPFC rTMS on the cardiac activity of MDD patients, as well as the potential of using electrocardiogram (ECG) parameters as biomarkers of treatment outcome. METHODS: Baseline ECG sessions were performed for 19 MDD patients. All patients then underwent 40 sessions of accelerated 1 Hz R-DLPFC rTMS one week after the baseline session. RESULTS: Heart rate (HR) significantly decreased from the resting period to the first and third minute of the 1 Hz R-DLPFC rTMS period. Resting HR was found to have a significant negative association with treatment outcome. Prior to Bonferroni correction, HR during stimulation and the degree of rTMS-induced HR reduction were significantly negatively associated with treatment outcome. No significant changes were observed for the heart rate variability (HRV) parameters. LIMITATIONS: Sample size (n = 19); the use of electroencephalography equipment for ECG; lack of respiration monitoring; relatively short recording duration for HRV parameters. CONCLUSION: This novel study provides further preliminary evidence that ECG may be utilized as a biomarker of rTMS treatment response in MDD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04376697.

Optimized repetitive transcranial magnetic stimulation techniques for the treatment of major depression: A proof of concept study.

Although effective in major depressive disorder (MDD), repetitive transcranial magnetic stimulation (rTMS) is costly and complex, limiting accessibility. To address this, we tested the feasibility of novel rTMS techniques with cost-saving opportunities, such as an open-room setting, large non-focal parabolic coils, and custom-built coil arms. We employed a low-frequency (LF) 1 Hz stimulation protocol (360 pulses per session), delivered on the most affordable FDA-approved device. MDD participants received an initial accelerated rTMS course (arTMS) of 6 sessions/day over 5 days (30 total), followed by a tapering course of daily sessions (up to 25) to decrease the odds of relapse. The self-reported Beck Depression Inventory II (BDI-II) was used to measure severity of depression. Forty-eight (48) patients completed the arTMS course. No serious adverse events occurred, and all patients reported manageable pain levels. Response and remission rates were 35.4% and 27.1% on the BDI-II, respectively, at the end of the tapering course. Repeated measures ANOVA showed significant changes of BDI-II scores over time. Even though our protocol will require further improvements, some of the concepts we introduced here could help guide the design of future trials aiming at increasing accessibility to rTMS.