Influence of Large-Scale Brain State Dynamics on the Evoked Response to Brain Stimulation.

Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms.

Integrative Genetic Variation, DNA Methylation, and Gene Expression Analysis of Escitalopram and Aripiprazole Treatment Outcomes in Depression: A CAN-BIND-1 Study.

INTRODUCTION: Little is known about the interplay between genetics and epigenetics on antidepressant treatment (1) response and remission, (2) side effects, and (3) serum levels. This study explored the relationship among single nucleotide polymorphisms (SNPs), DNA methylation (DNAm), and mRNA levels of four pharmacokinetic genes, CYP2C19, CYP2D6, CYP3A4, and ABCB1, and its effect on these outcomes. METHODS: The Canadian Biomarker Integration Network for Depression-1 dataset consisted of 177 individuals with major depressive disorder treated for 8 weeks with escitalopram (ESC) followed by 8 weeks with ESC monotherapy or augmentation with aripiprazole. DNAm quantitative trait loci (mQTL), identified by SNP-CpG associations between 20 SNPs and 60 CpG sites in whole blood, were tested for associations with our outcomes, followed by causal inference tests (CITs) to identify methylation-mediated genetic effects. RESULTS: Eleven cis-SNP-CpG pairs (q<0.05) constituting four unique SNPs were identified. Although no significant associations were observed between mQTLs and response/remission, CYP2C19 rs4244285 was associated with treatment-related weight gain (q=0.027) and serum concentrations of ESCadj (q<0.001). Between weeks 2-4, 6.7% and 14.9% of those with *1/*1 (normal metabolizers) and *1/*2 (intermediate metabolizers) genotypes, respectively, reported ≥2 lbs of weight gain. In contrast, the *2/*2 genotype (poor metabolizers) did not report weight gain during this period and demonstrated the highest ESCadj concentrations. CITs did not indicate that these effects were epigenetically mediated. DISCUSSION: These results elucidate functional mechanisms underlying the established associations between CYP2C19 rs4244285 and ESC pharmacokinetics. This mQTL SNP as a marker for antidepressant-related weight gain needs to be further explored.

Influence of CYP2C19, CYP2D6, and ABCB1 Gene Variants and Serum Levels of Escitalopram and Aripiprazole on Treatment-Emergent Sexual Dysfunction: A Canadian Biomarker Integration Network in Depression 1 (CAN-BIND 1) Study.

OBJECTIVES: Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6, and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample. METHODS: A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n = 91), while responders continued ESC (i.e., ESC-Only, n = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models. RESULTS: In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F(2,54) = 8.00, p < 0.001, q = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF (r = -0.42, p = 0.004, q = 0.034) and SS (r = -0.43, p = 0.003, q = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction (r = -0.39, p = 0.009, q = 0.052). CONCLUSIONS: CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.

Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report.

BACKGROUND: Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers. METHODS: In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively. RESULTS: A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction. CONCLUSIONS: A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Identification of Endocannabinoid Predictors of Treatment Outcomes in Major Depressive Disorder: A Secondary Analysis of the First Canadian Biomarker Integration Network in Depression (CAN-BIND 1) Study.

INTRODUCTION: An increasing number of studies are examining the link between the endocannabinoidome and major depressive disorder (MDD). We conducted an exploratory analysis of this system to identify potential markers of treatment outcomes. METHODS: The dataset of the Canadian Biomarker Integration Network in Depression-1 study, consisting of 180 patients with MDD treated for eight weeks with escitalopram followed by eight weeks with escitalopram alone or augmented with aripiprazole was analyzed. Association between response Montgomery-Asberg Depression Rating Scale (MADRS; score reduction≥50%) or remission (MADRS score≤10) at weeks 8 and 16 and single nucleotide polymorphisms (SNPs), methylation, and mRNA levels of 33 endocannabinoid markers were examined. A standard genome-wide association studies protocol was used for identifying SNPs, and logistic regression was used to assess methylation and mRNA levels. RESULTS: Lower methylation of CpG islands of the diacylglycerol lipase alpha gene (DAGLA) was associated with non-remission at week 16 (DAGLA; OR=0.337, p<0.003, q=0.050). Methylation of DAGLA was correlated with improvement in Clinical Global Impression (p=0.026), Quick Inventory of Depressive Symptomatology (p=0.010), and Snaith-Hamilton Pleasure scales (p=0.028). We did not find any association between SNPs or mRNA levels and treatment outcomes. DISCUSSION: Methylation of DAGLA is a promising candidate as a marker of treatment outcomes for MDD and needs to be explored further.

Transcranial magnetic stimulation in the treatment of adolescent depression: a systematic review and meta-analysis of aggregated and individual-patient data from uncontrolled studies.

Transcranial magnetic stimulation (TMS) is a non-invasive treatment for adolescent major depressive disorder (MDD). Existing evidence on the efficacy of TMS in adolescent MDD awaits quantitative synthesis. A systematic literature search was conducted, and data from eligible studies were synthesized using random-effects models. Treatment-covariate interactions were examined in exploratory analyses of individual-patient data (IPD). Systematic search of the literature yielded 1264 hits, of which 10 individual studies (2 randomized trials) were included for quantitative synthesis of mainly uncontrolled studies. Individual patient data (IPD) were available from five trials (all uncontrolled studies). Quantitative synthesis of aggregated data revealed a statistically significant negative overall standardized mean change (pooled SMCC = 2.04, 95% CI [1.46; 2.61], SE = 0.29, p < .001), as well as a significant overall treatment response rate (Transformed Proportion = 41.30%, 95% CI [31.03; 51.57], SE = 0.05; p < 0.001), considering data from baseline to post-treatment. Exploratory IPD analyses suggests TMS might be more effective in younger individuals and individuals with more severe depression, and efficacy might be enhanced with certain treatment modality settings, including higher number of TMS sessions, longer treatment durations, and unilateral and not bilateral stimulation. Existing studies exhibit methodological shortcomings, including small-study effects and lack of control group, blinding, and randomization-compromising the credibility of the present results. To date, two randomized controlled trials on TMS in adolescent depression have been published, and the only large-scale randomized trial suggests TMS is not more effective than sham stimulation. Future large-scale, randomized, and sham-controlled trials are warranted. Future trials should ensure appropriate selection of patients for TMS treatment and guide precision medicine approaches for stimulation protocols.

Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis.

BACKGROUND: Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes. METHODS: Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment. RESULTS: Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy. CONCLUSIONS: The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

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.

Beyond the target area: an integrative view of tDCS-induced motor cortex modulation in patients and athletes.

Transcranial Direct Current Stimulation (tDCS) is a non-invasive technique used to modulate neural tissue. Neuromodulation apparently improves cognitive functions in several neurologic diseases treatment and sports performance. In this study, we present a comprehensive, integrative review of tDCS for motor rehabilitation and motor learning in healthy individuals, athletes and multiple neurologic and neuropsychiatric conditions. We also report on neuromodulation mechanisms, main applications, current knowledge including areas such as language, embodied cognition, functional and social aspects, and future directions. We present the use and perspectives of new developments in tDCS technology, namely high-definition tDCS (HD-tDCS) which promises to overcome one of the main tDCS limitation (i.e., low focality) and its application for neurological disease, pain relief, and motor learning/rehabilitation. Finally, we provided information regarding the Transcutaneous Spinal Direct Current Stimulation (tsDCS) in clinical applications, Cerebellar tDCS (ctDCS) and its influence on motor learning, and TMS combined with electroencephalography (EEG) as a tool to evaluate tDCS effects on brain function.

Impaired neuroplasticity in the prefrontal cortex in depression indexed through paired associative stimulation.

BACKGROUND: Dysfunctional neuroplasticity may be one of the pathophysiological mechanisms underlying major depression. We have previously established methods to assess neuroplasticity from the dorsolateral prefrontal cortex (DLPFC) using a paired associative stimulation (PAS) paradigm, which pairs a preceding peripheral nerve stimulation with subsequent transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG). We aimed to investigate neuroplasticity through the PAS paradigm in the DLPFC in patients with depression compared to healthy subjects. METHODS: Twenty-nine patients with depression and 28 healthy controls participated in this study. There were no significant age or sex differences between the two groups. All participants received PAS paradigm in the DLPFC. We analyzed PAS induced potentiation from the DLPFC in both groups calculating the power of TMS-evoked potentials (TEP). A two-way ANOVA with PAS effect as a within-subject factor and diagnostic group as a between-subject factor was performed to examine the group differences in the PAS paradigm. RESULTS: DLPFC-PAS induced a significant potentiation at the stimulation site in both patients and healthy subjects (mean ± SD: 1.24 ± 0.33 [µV] vs. 1.48 ± 0.28 [µV]). However, when we compared PAS potentiation between patients and healthy subjects, there were significant main effects of PAS (F1,53  = 68.63, p < 0.0001) and PAS-by-diagnostic group interaction (F1,53  = 25.05, p < 0.0001). Post hoc analysis demonstrated that patients had a significantly lower PAS potentiation compared to healthy subjects (t55  = 3.128, p = 0.003). CONCLUSTIONS: Our findings provide evidence for impaired neuroplasticity in DLPFC in patients with depression compared to healthy subjects. Such findings may ultimately help us understand the pathophysiology of MDD and mechanisms involved in its treatment.

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.

Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software.

The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS-EEG) is growing in popularity as a method for assessing various cortical properties such as excitability, oscillations and connectivity. However, this combination of methods is technically challenging, resulting in artifacts both during recording and following typical EEG analysis methods, which can distort the underlying neural signal. In this article, we review the causes of artifacts in EEG recordings resulting from TMS, as well as artifacts introduced during analysis (e.g. as the result of filtering over high-frequency, large amplitude artifacts). We then discuss methods for removing artifacts, and ways of designing pipelines to minimise analysis-related artifacts. Finally, we introduce the TMS-EEG signal analyser (TESA), an open-source extension for EEGLAB, which includes functions that are specific for TMS-EEG analysis, such as removing and interpolating the TMS pulse artifact, removing and minimising TMS-evoked muscle activity, and analysing TMS-evoked potentials. The aims of TESA are to provide users with easy access to current TMS-EEG analysis methods and to encourage direct comparisons of these methods and pipelines. It is hoped that providing open-source functions will aid in both improving and standardising analysis across the field of TMS-EEG research.

EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training.

The neurobiological correlates of human fluid intelligence (Gf) remain elusive. Here, we demonstrate that spatiotemporal dynamics of EEG activity correlate with baseline measures of Gf and with its modulation by cognitive training. EEG dynamics were assessed in 74 healthy participants by examination of fast-changing, recurring, topographically-defined electric patterns termed "microstates", which characterize the electrophysiological activity of distributed cortical networks. We find that the frequency of appearance of specific brain topographies, spatially associated with visual (microstate B) and executive control (microstate C) networks, respectively, is inversely related to Gf scores. Moreover, changes in Gf scores with cognitive training are inversely correlated with changes in microstate properties, indicating that the changes in brain network dynamics are behaviorally relevant. Finally, we find that cognitive training that increases Gf scores results in a posterior shift in the topography of microstate C. These results highlight the role of fast-changing brain electrical states in individual variability in Gf and in the response to cognitive training.

A combined TMS-EEG study of short-latency afferent inhibition in the motor and dorsolateral prefrontal cortex.

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) enables noninvasive neurophysiological investigation of the human cortex. A TMS paradigm of short-latency afferent inhibition (SAI) is characterized by attenuation of the motor-evoked potential (MEP) and modulation of N100 of the TMS-evoked potential (TEP) when TMS is delivered to motor cortex (M1) following median nerve stimulation. SAI is a marker of cholinergic activity in the motor cortex; however, the SAI has not been tested from the prefrontal cortex. We aimed to explore the effect of SAI in dorsolateral prefrontal cortex (DLPFC). SAI was examined in 12 healthy subjects with median nerve stimulation and TMS delivered to M1 and DLPFC at interstimulus intervals (ISIs) relative to the individual N20 latency. SAI in M1 was tested at the optimal ISI of N20 + 2 ms. SAI in DLPFC was investigated at a range of ISI from N20 + 2 to N20 + 20 ms to explore its temporal profile. For SAI in M1, the attenuation of MEP amplitude was correlated with an increase of TEP N100 from the left central area. A similar spatiotemporal neural signature of SAI in DLPFC was observed with a marked increase of N100 amplitude. SAI in DLPFC was maximal at ISI N20 + 4 ms at the left frontal area. These findings establish the neural signature of SAI in DLPFC. Future studies could explore whether DLPFC-SAI is neurophysiological marker of cholinergic dysfunction in cognitive disorders.

Evidence for inhibitory deficits in the prefrontal cortex in schizophrenia.

Abnormal gamma-aminobutyric acid inhibitory neurotransmission is a key pathophysiological mechanism underlying schizophrenia. Transcranial magnetic stimulation can be combined with electroencephalography to index long-interval cortical inhibition, a measure of GABAergic receptor-mediated inhibitory neurotransmission from the frontal and motor cortex. In previous studies we have reported that schizophrenia is associated with inhibitory deficits in the dorsolateral prefrontal cortex compared to healthy subjects and patients with bipolar disorder. The main objective of the current study was to replicate and extend these initial findings by evaluating long-interval cortical inhibition from the dorsolateral prefrontal cortex in patients with schizophrenia compared to patients with obsessive-compulsive disorder. A total of 111 participants were assessed: 38 patients with schizophrenia (average age: 35.71 years, 25 males, 13 females), 27 patients with obsessive-compulsive disorder (average age: 36.15 years, 11 males, 16 females) and 46 healthy subjects (average age: 33.63 years, 23 females, 23 males). Long-interval cortical inhibition was measured from the dorsolateral prefrontal cortex and motor cortex through combined transcranial magnetic stimulation and electroencephalography. In the dorsolateral prefrontal cortex, long-interval cortical inhibition was significantly reduced in patients with schizophrenia compared to healthy subjects (P = 0.004) and not significantly different between patients with obsessive-compulsive disorder and healthy subjects (P = 0.5445). Long-interval cortical inhibition deficits in the dorsolateral prefrontal cortex were also significantly greater in patients with schizophrenia compared to patients with obsessive-compulsive disorder (P = 0.0465). There were no significant differences in long-interval cortical inhibition across all three groups in the motor cortex. These results demonstrate that long-interval cortical inhibition deficits in the dorsolateral prefrontal cortex are specific to patients with schizophrenia and are not a generalized deficit that is shared by disorders of severe psychopathology.

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort.

BACKGROUND: Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. METHODS: CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. DISCUSSION: From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01655706 . Registered July 27, 2012.

Intermittent theta-burst stimulation of the lateral cerebellum increases functional connectivity of the default network.

Cerebral cortical intrinsic connectivity networks share topographically arranged functional connectivity with the cerebellum. However, the contribution of cerebellar nodes to distributed network organization and function remains poorly understood. In humans, we applied theta-burst transcranial magnetic stimulation, guided by subject-specific connectivity, to regions of the cerebellum to evaluate the functional relevance of connections between cerebellar and cerebral cortical nodes in different networks. We demonstrate that changing activity in the human lateral cerebellar Crus I/II modulates the cerebral default mode network, whereas vermal lobule VII stimulation influences the cerebral dorsal attention system. These results provide novel insights into the distributed, but anatomically specific, modulatory impact of cerebellar effects on large-scale neural network function.

Disrupted cortical conductivity in schizophrenia: TMS-EEG study.

Schizophrenia is conceptualized as a failure of cognitive integration, and altered oscillatory properties of neurocircuits are associated with its symptoms. We hypothesized that abnormal characteristics of neural networks may alter functional connectivity and distort propagation of activation in schizophrenic brains. Thus, electroencephalography (EEG) responses to transcranial magnetic stimulation (TMS) of motor cortex were compared between schizophrenia and healthy subjects. There was no difference in the initial response. However, TMS-induced waves of recurrent excitation spreading across the cortex were observed in schizophrenia, while in healthy subjects the activation faded away soon after stimulation. This widespread activation in schizophrenia was associated with increased oscillatory activities in the proximal central leads and in fronto-temporo-parietal leads bilaterally. A positive correlation was found between increased TMS-induced cortical activation in gamma frequency and positive symptoms of schizophrenia, while negative symptoms were correlated with activation in theta and delta bands. We suggest that excessive activation in response to stimulation in schizophrenia brains may lead to abnormal propagation of the signal that could potentially result in aberrant activity in areas remote from the activation origin. This mechanism may account for the positive symptoms of schizophrenia and could worsen signal to noise deficits, jeopardizing adequate information processing with ensuing cognitive deficits.

Removing artefacts from TMS-EEG recordings using independent component analysis: importance for assessing prefrontal and motor cortex network properties.

INTRODUCTION: The combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is emerging as a powerful tool for causally investigating cortical mechanisms and networks. However, various artefacts contaminate TMS-EEG recordings, particularly over regions such as the dorsolateral prefrontal cortex (DLPFC). The aim of this study was to substantiate removal of artefacts from TMS-EEG recordings following stimulation of the DLPFC and motor cortex using independent component analysis (ICA). METHODS: 36 healthy volunteers (30.8 ± 9 years, 9 female) received 75 single TMS pulses to the left DLPFC or left motor cortex while EEG was recorded from 57 electrodes. A subset of 9 volunteers also received 50 sham pulses. The large TMS artefact and early muscle activity (-2 to ~15 ms) were removed using interpolation and the remaining EEG signal was processed in two separate ICA runs using the FastICA algorithm. Five sub-types of TMS-related artefacts were manually identified: remaining muscle artefacts, decay artefacts, blink artefacts, auditory-evoked potentials and other noise-related artefacts. The cause of proposed blink and auditory-evoked potentials was assessed by concatenating known artefacts (i.e. voluntary blinks or auditory-evoked potentials resulting from sham TMS) to the TMS trials before ICA and evaluating grouping of resultant independent components (ICs). Finally, we assessed the effect of removing specific artefact types on TMS-evoked potentials (TEPs) and TMS-evoked oscillations. RESULTS: Over DLPFC, ICs from proposed muscle and decay artefacts correlated with TMS-evoked muscle activity size, whereas proposed TMS-evoked blink ICs combined with voluntary blinks and auditory ICs with auditory-evoked potentials from sham TMS. Individual artefact sub-types characteristically distorted each measure of DLPFC function across the scalp. When free of artefact, TEPs and TMS-evoked oscillations could be measured following DLPFC stimulation. Importantly, characteristic TEPs following motor cortex stimulation (N15, P30, N45, P60, N100) could be recovered from artefactual data, corroborating the reliability of ICA-based artefact correction. CONCLUSIONS: Various different artefacts contaminate TMS-EEG recordings over the DLPFC and motor cortex. However, these artefacts can be removed with apparent minimal impact on neural activity using ICA, allowing the study of TMS-evoked cortical network properties.