Neurophysiological markers of response to theta burst stimulation in youth depression.

BACKGROUND: Theta burst stimulation (TBS) has recently been proposed as a novel treatment for youth depression. However, the impact of TBS on the youth brain and neurophysiological predictors of response to TBS in this population have not been investigated. METHODS: Cortical reactivity was assessed at baseline and following 2 weeks of bilateral dorsolateral prefrontal cortex (DLPFC) TBS treatment in 16 youth with depression (aged 16-24 years old). In 16 age-matched health youths, cortical reactivity was assessed twice, 2 weeks apart. Transcranial magnetic stimulation (TMS) combined with electroencephalography was used to assess TMS-evoked potentials in bilateral DLPFC, motor cortices, and intraparietal lobules (IPL). Resting-state functional magnetic resonance imaging (fMRI) data was also collected at baseline. RESULTS: Left DLPFC pretreatment cortical reactivity, specifically the negativity at 45 ms (i.e., N45), which is related to GABAA neurotransmission, was associated with changes in depressive symptoms. Furthermore, TBS treatment was found to alter the N45 in the right IPL, a site distal to the treatment sites. The magnitude of the right IPL N45 modulation was correlated with the baseline fMRI connectivity between the right IPL and right DLPFC. CONCLUSIONS: TMS-probed cortical inhibition at the site of TBS application may have potential as a predictor of treatment response in youth depression. Furthermore, pre-treatment functional connectivity may predict the impact of TBS on the neurophysiology of regions distal to the stimulation site. Collectively, these results offer novel neurophysiological insights into the application of TBS for youth depression, which may facilitate its wider use in the youth population.

Prefrontal Cortical Reactivity and Connectivity Markers Distinguish Youth Depression from Healthy Youth.

Up to 50% of youth with depression do not respond to conventional first-line treatments. However, little research has been conducted on the pathophysiology of youth depression, hindering the identification of more effective treatments. Our goal was to identify neurophysiological markers that differentiate youth with depression from healthy youth and could serve as targets of novel treatments. We hypothesized that youth with depression would exhibit network-specific cortical reactivity and connectivity abnormalities compared with healthy youth. Transcranial magnetic stimulation combined with electroencephalography and magnetic resonance imaging was employed in combination with clinical and behavioral assessments to study cortical reactivity and connectivity in bilateral dorsolateral prefrontal cortex (DLPFC), motor cortex, and inferior parietal lobule, sites linked to the frontoparietal network, sensorimotor network, and default mode network, respectively. In youth depression, greater cortical reactivity was observed specific to the left and right DLPFC stimulation only, which correlated with anhedonia scores. Additionally, the connectivity of the right DLPFC was significantly higher in youth depression. Source reconstruction attributed the observed connectivity dysregulation to regions belonging to the default mode network. The neurophysiological signatures identified in this study have high potential to inform the development of more effective and targeted interventions for the youth depression population.

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

Importance: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression. Objective: To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression. Design, Setting, and Participants: This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment. Interventions: All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment. Main Outcomes and Measures: The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%. Results: Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%). Conclusions and Relevance: These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Feasibility and clinical effects of theta burst stimulation in youth with major depressive disorders: An open-label trial.

BACKGROUND: Conventional treatments for youth depression, such as antidepressants, have modest efficacy, side effects, and ongoing controversies regarding safety. Repetitive transcranial magnetic stimulation (rTMS), specifically theta burst stimulation (TBS), applied to the dorsolateral prefrontal cortex (DLPFC) has demonstrated efficacy for the treatment of depression in adults. However, the feasibility and clinical response to TBS for youth depression has yet to be explored. METHODS: Twenty participants between the ages of 16 to 24 years old with MDD were recruited. The intervention consisted of 10 treatment sessions over the course of two weeks, in which participants received intermittent TBS and continuous TBS stimulation to the left and right DLPFC, respectively. Change in the Hamilton Rating Scale for Depression (HRSD-17) score was the primary outcome. Clinical assessments occurred at baseline, after the fifth treatment session, and within a week after treatment completion. RESULTS: Of the twenty participants, eighteen received all TBS sessions, and seventeen completed all clinical assessments. There was a significant reduction in depressive symptoms following treatment completion (p < 0.001). Four of the twenty patients had more than 50% reduction in their depressive symptoms, two of whom achieved remission. All participants received and tolerated at least six daily TBS treatments with no major adverse events. LIMITATIONS: Study was an uncontrolled, open-label design. CONCLUSION: Ten sessions of TBS was feasible, well tolerated, and appeared to have clinical effects for the treatment of depressed youth. Future sham-controlled randomized trials are warranted to validate these findings in a larger cohort of youth depression.

Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression.

BACKGROUND: Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive impairment. Given the neuroimaging evidence for disrupted resting-state network dynamics in depression, we investigated whether seizure therapy (ECT and MST) selectively modifies brain network dynamics for therapeutic efficacy. METHODS: EEG microstate analysis was used to evaluate resting-state network dynamics in patients at baseline and following seizure therapy, and in healthy controls. Microstate analysis defined four classes of brain states (labelled A, B, C, D). Source localization identified the brain regions associated with these states. RESULTS: An increase in duration and decrease in frequency of microstates was specific to responders of seizure therapy. Significant changes in the dynamics of States A, C and D were observed and predicted seizure therapy outcome (specifically ECT). Relative change in the duration of States C and D was shown to be a strong predictor of ECT response. Source localization partly associated C and D to the salience and frontoparietal networks, argued to be impaired in depression. An increase in duration and decrease in frequency of microstates was also observed following MST, however it was not specific to responders. CONCLUSION: This study presents the first evidence for the modulation of global brain network dynamics by seizure therapy. Successful seizure therapy was shown to selectively modulate network dynamics for therapeutic efficacy.

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.

Brain temporal complexity in explaining the therapeutic and cognitive effects of seizure therapy.

Over 350 million people worldwide suffer from depression, a third of whom are medication-resistant. Seizure therapy remains the most effective treatment in depression, even when many treatments fail. The utility of seizure therapy is limited due to its cognitive side effects and stigma. The biological targets of seizure therapy remain unknown, hindering design of new treatments with comparable efficacy. Seizures impact the brains temporal dynamicity observed through electroencephalography. This dynamicity reflects richness of information processing across distributed brain networks subserving affective and cognitive processes. We investigated the hypothesis that seizure therapy impacts mood (depressive symptoms) and cognition by modulating brain temporal dynamicity. We obtained resting-state electroencephalography from 34 patients (age = 46.0 ± 14.0, 21 females) receiving two types of seizure treatments-electroconvulsive therapy or magnetic seizure therapy. We used multi-scale entropy to quantify the complexity of the brain's temporal dynamics before and after seizure therapy. We discovered that reduction of complexity in fine timescales underlined successful therapeutic response to both seizure treatments. Greater reduction in complexity of fine timescales in parieto-occipital and central brain regions was significantly linked with greater improvement in depressive symptoms. Greater increase in complexity of coarse timescales was associated with greater decline in cognition including the autobiographical memory. These findings were region and timescale specific. That is, change in complexity in occipital regions (e.g. O2 electrode or right occipital pole) at fine timescales was only associated with change in depressive symptoms, and not change in cognition, and change in complexity in parieto-central regions (e.g. Pz electrode or intra and transparietal sulcus) at coarser timescale was only associated with change in cognition, and not depressive symptoms. Finally, region and timescale specific changes in complexity classified both antidepressant and cognitive response to seizure therapy with good (80%) and excellent (95%) accuracy, respectively. In this study, we discovered a novel biological target of seizure therapy: complexity of the brain resting state dynamics. Region and timescale dependent changes in complexity of the brain resting state dynamics is a novel mechanistic marker of response to seizure therapy that explains both the antidepressant response and cognitive changes associated with this treatment. This marker has tremendous potential to guide design of the new generation of antidepressant treatments.

TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation.

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research.

Unbiased cluster estimation of electrophysiological brain response.

BACKGROUND: Recent increase in the size and complexity of electrophysiological data from multidimensional electroencephalography (EEG) and magnetoencephalography (MEG) studies has prompted the development of sophisticated statistical frameworks for data analysis. One of the main challenges for such frameworks is the multiple comparisons problem, where the large number of statistical tests performed within a high-dimensional dataset lead to an increased risk of Type I errors (false positives). A solution to this problem, cluster analysis, applies the biologically-motivated knowledge of correlation between adjacent voxels in one or more dimensions of the dataset to correct for the multiple comparisons problem and detect true neurophysiological effects. Cluster-based methods provide increased sensitivity towards detecting neurophysiological events compared to conservative methods such as Bonferroni correction, but are limited by their dependency on an initial cluster-forming statistical threshold (e.g. t-score, alpha) obstructing precise comparisons of results across studies. NEW METHOD: Rather than selecting a single threshold value, unbiased cluster estimation (UCE) computes a significance distribution across all possible threshold values to provide an unbiased overall significance value. COMPARISON TO EXISTING METHODS: UCE functions as a novel extension to existing cluster analysis methods. RESULTS: Using data from EEG combined with brain stimulation study, we showed the impact of statistical threshold on outcome measures and introduction of bias. We showed the application of UCE for different study designs (e.g., within-group, between-group comparisons). CONCLUSION: We propose that researchers consider employing UCE for multidimensional EEG/MEG datasets toward an unbiased comparison of results between subjects, groups, and studies.