Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder

BACKGROUND

Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD).

METHODS

The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed.

RESULTS

For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores.

CONCLUSIONS

Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.

Stability and synchronization in neural network with delayed synaptic connections

In this paper, we investigate the stability of the synchronous state in a complex network using the master stability function technique. We use the extended Hindmarsh-Rose neuronal model including time delayed electrical, chemical, and hybrid couplings. We find the corresponding master stability equation that describes the whole dynamics for each coupling mode. From the maximum Lyapunov exponent, we deduce the stability state for each coupling mode. We observe that for electrical coupling, there exists a mixing between stable and unstable states. For a good setting of some system parameters, the position and the size of unstable areas can be modified. For chemical coupling, we observe difficulties in having a stable area in the complex plane. For hybrid coupling, we observe a stable behavior in the whole system compared to the case where these couplings are considered separately. The obtained results for each coupling mode help to analyze the stability state of some network topologies by using the corresponding eigenvalues. We observe that using electrical coupling can involve a full or partial stability of the system. In the case of chemical coupling, unstable states are observed whereas in the case of hybrid interactions a full stability of the network is obtained. Temporal analysis of the global synchronization is also done for each coupling mode, and the results show that when the network is stable, the synchronization is globally observed, while in the case when it is unstable, its nodes are not globally synchronized.

Clinical EEG slowing induced by electroconvulsive therapy is better described by increased frontal aperiodic activity

Electroconvulsive therapy (ECT) is one of the most efficacious interventions for treatment-resistant depression. Despite its efficacy, ECT's neural mechanism of action remains unknown. Although ECT has been associated with "slowing" in the electroencephalogram (EEG), how this change relates to clinical improvement is unresolved. Until now, increases in slow-frequency power have been assumed to indicate increases in slow oscillations, without considering the contribution of aperiodic activity, a process with a different physiological mechanism. In this exploratory study of nine MDD patients, we show that aperiodic activity, indexed by the aperiodic exponent, increases with ECT treatment. This increase better explains EEG "slowing" when compared to power in oscillatory peaks in the delta (1-3 Hz) range and is correlated to clinical improvement. In accordance with computational models of excitation-inhibition balance, these increases in aperiodic exponent are linked to increasing levels of inhibitory activity, suggesting that ECT might ameliorate depressive symptoms by restoring healthy levels of inhibition in frontal cortices.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

Importance

Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging.

Objective

To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications.

Design, Setting, and Participants

This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022.

Exposures

In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment.

Main Outcomes and Measures

The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity.

Results

The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response.

Conclusions and Relevance

In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Brain function changes reveal rapid antidepressant effects of nitrous oxide for treatment-resistant depression:Evidence from task-state EEG

Nitrous oxide has rapid antidepressant effects in patients with treatment-resistant depression (TRD), but its underlying mechanisms of therapeutic actions are not well understood. Moreover, most of the current studies lack objective biological indicators to evaluate the changes of nitrous oxide-induced brain function for TRD. Therefore, this study assessed the effect of nitrous oxide on brain function for TRD based on event-related potential (ERP) components and functional connectivity networks (FCNs) methods. In this randomized, longitudinal, placebo-controlled trial, all TRD participants were divided into two groups to receive either a 1-hour inhalation of nitrous oxide or a placebo treatment, and they took part in the same task-state electroencephalogram (EEG) experiment before and after treatment. The experimental results showed that nitrous oxide improved depressive symptoms better than placebo in terms of 17-Hamilton Depression Rating Scale score (HAMD-17). Statistical analysis based on ERP components showed that nitrous oxide-induced significant differences in amplitude and latency of N1, P1, N2, P2. In addition, increased brain functional connectivity was found after nitrous oxide treatment. And the change of network metrics has a significant correlation with decreased depressive symptoms. These findings may suggest that nitrous oxide improves depression symptoms for TRD by modifying brain function.

Brain Complexity Predicts Response to Adrenocorticotropic Hormone in Infantile Epileptic Spasms Syndrome: A Retrospective Study

INTRODUCTION

Infantile epileptic spasms syndrome (IESS) is an age-specific and severe epileptic encephalopathy. Although adrenocorticotropic hormone (ACTH) is currently considered the preferred first-line treatment, it is not always effective and may cause side effects. Therefore, seeking a reliable biomarker to predict the treatment response could benefit clinicians in modifying treatment options.

METHODS

In this study, the complexities of electroencephalogram (EEG) recordings from 15 control subjects and 40 patients with IESS before and after ACTH therapy were retrospectively reviewed using multiscale entropy (MSE). These 40 patients were divided into responders and nonresponders according to their responses to ACTH.

RESULTS

The EEG complexities of the patients with IESS were significantly lower than those of the healthy controls. A favorable response to treatment showed increasing complexity in the γ band but exhibited a reduction in the β/α-frequency band, and again significantly elevated in the δ band, wherein the latter was prominent in the parieto-occipital regions in particular. Greater reduction in complexity was significantly linked with poorer prognosis in general. Occipital EEG complexities in the γ band revealed optimized performance in recognizing response to the treatment, corresponding to the area under the receiver operating characteristic curves as 0.8621, while complexities of the δ band served as a fair predictor of unfavorable outcomes globally.

CONCLUSION

We suggest that optimizing frequency-specific complexities over critical brain regions may be a promising strategy to facilitate predicting treatment response in IESS.

Cardiovascular Effects of High-Frequency Magnetic Seizure Therapy Compared With Electroconvulsive Therapy

BACKGROUND

Magnetic seizure therapy (MST) is a novel convulsive therapy that has been shown to have antidepressant efficacy comparable to electroconvulsive therapy (ECT) with fewer cognitive side effects. However, the cardiovascular (CVS) effects of high frequency MST in comparison to ECT have not been investigated.

MATERIALS AND METHODS

Forty-five patients with depression received 6 treatment sessions of 100 Hz MST versus 6 bifrontal ECT treatments in a nonrandomized comparative clinical design. Data on CVS function including heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and rate pressure product (RPP) were collected at baseline (T0), after the induction of anesthesia but before the electrical stimulation (T1), during convulsion (T2), 2 minutes after cessation of motor seizure (T3), 5 minutes after cessation of motor seizure (T4), and 10 minutes after cessation of motor seizure (T5). Comparisons were made with baseline data and between MST and ECT groups.

RESULTS

There were statistically significant elevations in the maximum HR, SBP, DBP, and RPP in patients receiving ECT compared with MST both in the initial and sixth treatments (all P < 0.05). Particularly, at T2, the ECT group had significantly higher HR, SBP, DBP, and RPP than those in MST group both in initial and sixth treatment (all P < 0.001). At the sixth treatment, the ECT group had significantly higher SBP, DBP, and RPP during the treatment than in the MST group (all P < 0.001).

LIMITATIONS

The anesthetic choices for this study may limit the generalizability of our findings. The sample size was relatively small.

CONCLUSIONS

Compared with ECT, high-frequency MST has fewer CVS side effects and may be a safer option for depression patients with CVS disorders.

Ongoing Brain Activity and Its Role in Cognition: Dual versus Baseline Models

What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.

Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study

Objectives

Several studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated.

Methods

Nineteen-channel resting-state eyes-closed EEG signals were collected from 24 MDD patients pre- and post-ECT treatment. Functional brain networks were constructed by using various coupling methods and binarization techniques. Changes in functional connectivity and network metrics after ECT treatment and relationships between network metrics and clinical symptoms were explored.

Results

ECT significantly increased global efficiency, edge betweenness centrality, local efficiency, and mean degree of alpha band after ECT treatment, and an increase in these network metrics had significant correlations with decreased depressive symptoms in repeated measures correlation. In addition, ECT regulated the distribution of hubs in frontal and occipital lobes.

Conclusion

ECT modulated the brain’s global and local information-processing patterns. In addition, an ECT-induced increase in network metrics was associated with clinical remission.

Significance

These findings might present the evidence for us to understand how ECT regulated the topology organization in functional brain networks of clinically remitted depressive patients.

Disrupted Spatiotemporal Complexity of Resting-State Electroencephalogram Dynamics Is Associated With Adaptive and Maladaptive Rumination in Major Depressive Disorder

Patients with major depressive disorder (MDD) exhibit abnormal rumination, including both adaptive and maladaptive forms. However, the neural substrates of rumination in depression remain poorly understood. We hypothesize that divergent spatiotemporal complexity of brain oscillations would be associated with the levels of rumination in MDD. We employed the multi-scale entropy (MSE), power and phase-amplitude coupling (PAC) to estimate the complexity of rhythmic dynamics from the eye-closed high-density electroencephalographic (EEG) data in treatment-naive patients with MDD (n = 24) and healthy controls (n = 22). The depressive, brooding, and reflective subscales of the Ruminative Response Scale were assessed. MDD patients showed higher MSE in timescales finer than 5 (cluster P = 0.038) and gamma power (cluster P = 0.034), as well as lower PAC values between alpha/low beta and gamma bands (cluster P = 0.002- 0.021). Higher reflective rumination in MDD was region-specifically associated with the more localized EEG dynamics, including the greater MSE in scales finer than 8 (cluster P = 0.008), power in gamma (cluster P = 0.018) and PAC in low beta-gamma (cluster P = 0.042), as well as weaker alpha-gamma PAC (cluster P = 0.016- 0.029). Besides, the depressive and brooding rumination in MDD showed the lack of correlations with global long-range EEG variables. Our findings support the disturbed neural communications and point to the spatial reorganization of brain networks in a timescale-dependent migration toward local during adaptive and maladaptive rumination in MDD. These findings may provide potential implications on probing and modulating dynamic neuronal fluctuations during the rumination in depression.

Confirmatory Efficacy and Safety Trial of Magnetic Seizure Therapy for Depression (CREST-MST): protocol for identification of novel biomarkers via neurophysiology

Background

Electroconvulsive therapy (ECT) is the most effective treatment for treatment-resistant depression (TRD), especially for acute suicidal ideation, but the associated cognitive adverse effects and negative stigma limit its use. Another seizure therapy under development is magnetic seizure therapy (MST), which could potentially overcome the restrictions associated with ECT with similar efficacy. The neurophysiological targets and mechanisms of seizure therapy, however, remain poorly understood.

Methods/design

This neurophysiological study protocol is published as a companion to the overall Confirmatory Efficacy and Safety Trial of Magnetic Seizure Therapy for Depression (CREST-MST) protocol that describes our two-site, double-blind, randomized, non-inferiority clinical trial to develop MST as an effective and safe treatment for TRD. Our aim for the neurophysiological component of the study is to evaluate two biomarkers, one to predict remission of suicidal ideation (primary outcome) and the other to predict cognitive impairment (secondary outcome). Suicidal ideation will be assessed through cortical inhibition, which according to our preliminary studies, correlates with remission of suicidal ideation. Cortical inhibition will be measured with simultaneous transcranial magnetic stimulation (TMS) and electroencephalography (EEG), TMS-EEG, which measures TMS-evoked EEG activity. Cognitive adverse effects associated with seizure therapy, on the contrary, will be evaluated via multiscale entropy analysis reflecting the complexity of ongoing resting-state EEG activity.

Discussion

ECT and MST are known to influence cortical inhibition associated with depression, suicidal ideation severity, and clinical outcome. Therefore, evaluating cortical inhibition and brain temporal dynamics will help understand the pathophysiology of depression and suicidal ideation and define new biological targets that could aid clinicians in diagnosing and selecting treatments. Resting-state EEG complexity was previously associated with the degree of cognitive side effects after a seizure therapy. This neurophysiological metric may help clinicians assess the risk for adverse effects caused by these useful and effective treatments.

Trial registration

ClinicalTrials.govNCT03191058. Registered on June 19, 2017.

Magnetic Seizure Therapy Compared to Electroconvulsive Therapy for Schizophrenia: A Randomized Controlled Trial

Background: Magnetic seizure therapy (MST) is a potential alternative to electroconvulsive therapy (ECT). However, reports on the use of MST for patients with schizophrenia, particularly in developing countries, which is a main indication for ECT, are limited. Methods: From February 2017 to July 2018, 79 inpatients who met the DSM-5 criteria for schizophrenia were randomized to receive 10 sessions of MST (43 inpatients) or ECT (36 inpatients) over the course of 4 weeks. At baseline and 4-week follow-up, the Positive and Negative Syndrome Scale (PANSS) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) were used to assess symptom severity and cognitive functions, respectively. Results: Seventy-one patients who completed at least half of the treatment protocol were included in the per-protocol analysis. MST generated a non-significant larger antipsychotic effect in terms of a reduction in PANSS total score [g = 0.17, 95% confidence interval (CI) = -0.30, 0.63] and response rate [relative risk (RR) = 1.41, 95% CI = 0.83-2.39]. Twenty-four participants failed to complete the cognitive assessment as a result of severe psychotic symptoms. MST showed significant less cognitive impairment over ECT in terms of immediate memory (g = 1.26, 95% CI = 0.63-1.89), language function (g =1.14, 95% CI = 0.52-1.76), delayed memory (g = 0.75, 95% CI = 0.16-1.35), and global cognitive function (g = 1.07, 95% CI = 0.45-1.68). The intention-to-treat analysis generated similar results except for the differences in delayed memory became statistically insignificant. Better baseline cognitive performance predicted MST and ECT response. Conclusions: Compared to bitemporal ECT with brief pulses and age-dose method, MST had similar antipsychotic efficacy with fewer cognitive impairments, indicating that MST is a promising alternative to ECT as an add-on treatment for schizophrenia. Clinical Trial Registration: ClinicalTrials.gov, identifier: NCT02746965.

Signal complexity indicators of health status in clinical EEG

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual's health status and is a promising avenue for clinical biomarker development.

The brain and its time: intrinsic neural timescales are key for input processing

We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

Magnetic seizure therapy for treatment-resistant depression

BACKGROUND

Magnetic seizure therapy (MST) is a potential alternative to electroconvulsive therapy (ECT). Reports to date on use of MST for patients with treatment-resistant depression (TRD) are limited.

OBJECTIVES

To evaluate the effects of MST in comparison with sham-MST, antidepressant, and other forms of electric or magnetic treatment for adults with TRD.

SEARCH METHODS

In March 2020, we searched a wide range of international electronic sources for published, unpublished, and ongoing studies. We handsearched the reference lists of all included studies and relevant systematic reviews and conference proceedings of the Annual Meeting of the American College of Neuropsychopharmacology (ACNP), the Annual Scientific Convention and Meeting, and the Annual Meeting of the European College of Neuropsychopharmacology (ECNP) to identify additional studies.

SELECTION CRITERIA

All randomised clinical trials (RCTs) focused on MST for adults with TRD.

DATA COLLECTION AND ANALYSIS

Two review authors extracted data independently. For binary outcomes, we calculated risk ratios (RRs) and 95% confidence intervals (CIs). For continuous data, we estimated mean differences (MDs) between groups and 95% CIs. We employed a random-effects model for analyses. We assessed risk of bias for included studies and created a 'Summary of findings' table using the GRADE approach. Our main outcomes of interest were symptom severity, cognitive function, suicide, quality of life, social functioning, dropout for any reason, serious adverse events, and adverse events that led to discontinuation of treatment.

MAIN RESULTS

We included three studies (65 participants) comparing MST with ECT. Two studies reported depressive symptoms with the Hamilton Rating Scale for Depression (HAMD). However, in one study, the data were skewed and there was an imbalance in baseline characteristics. Analysis of these two studies showed no clear differences in depressive symptoms between treatment groups (MD 0.71, 95% CI -2.23 to 3.65; 2 studies, 40 participants; very low-certainty evidence). Two studies investigated multiple domains of cognitive function. However most of the outcomes were not measured by validated neuropsychological tests, and many of the data suffered from unbalanced baseline and skewed distribution. Analysis of immediate memory performance measured by the Wechsler Memory Scale showed no clear differences between treatment groups (MD 0.40, 95% CI -4.16 to 4.96; 1 study, 20 participants; very low-certainty evidence). Analysis of delayed memory performance measured by the Wechsler Memory Scale also showed no clear differences between treatment groups (MD 2.57, 95% CI -2.39 to 7.53; 1 study, 20 participants; very low-certainty evidence). Only one study reported quality of life, but the data were skewed and baseline data were unbalanced across groups. Analysis of quality of life showed no clear differences between treatment groups (MD 14.86, 95% CI -42.26 to 71.98; 1 study, 20 participants; very low-certainty evidence). Only one study reported dropout and adverse events that led to discontinuation of treatment. Analysis of reported data showed no clear differences between treatment groups for this outcome (RR 1.38, 95% CI 0.28 to 6.91; 1 study, 25 participants; very low-certainty evidence). Adverse events occurred in only two participants who received ECT (worsening of preexisting coronary heart disease and a cognitive adverse effect). None of the included studies reported outcomes on suicide and social functioning. No RCTs comparing MST with other treatments were identified.

AUTHORS' CONCLUSIONS

Evidence regarding effects of MST on patients with TRD is currently insufficient. Our analyses of available data did not reveal clearly different effects between MST and ECT. We are uncertain about these findings because of risk of bias and imprecision of estimates. Large, long, well-designed, and well-reported trials are needed to further examine the effects of MST.

Resting-state electroencephalographic functional network alterations in major depressive disorder following magnetic seizure therapy

Magnetic seizure therapy (MST) is emerging as a safe and well-tolerated experimental intervention for major depressive disorder (MDD), with very minimal cognitive side-effects. However, the underlying mechanism of action of MST remains uncertain. Here, we used resting-state electroencephalography (RS-EEG) to characterise the physiological effects of MST for treatment resistant MDD. We recorded RS-EEG in 21 patients before and after an open label trial of MST applied over the prefrontal cortex using a bilateral twin coil. RS-EEG was analysed for changes in functional connectivity, network topology, and spectral power. We also ran further baseline comparisons between the MDD patients and a cohort of healthy controls (n = 22). Network-based connectivity analysis revealed a functional subnetwork of significantly increased theta connectivity spanning frontal and parieto-occipital channels following MST. The change in theta connectivity was further found to predict clinical response to treatment. An additional widespread subnetwork of reduced beta connectivity was also elucidated. Graph-based topological analyses showed an increase in functional network segregation and reduction in integration in the theta band, with a decline in segregation in the beta band. Finally, delta and theta power were significantly elevated following treatment, while gamma power declined. No baseline differences between MDD patients and healthy subjects were observed. These results highlight widespread changes in resting-state brain dynamics following a course of MST in MDD patients, with changes in theta connectivity providing a potential physiological marker of treatment response. Future prospective studies are required to confirm these initial findings.

Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting-state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6-min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo-parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro-temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain's intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity's intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain.

Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis

Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient’s final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach

OBJECTIVE

To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach.

METHODS

Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation.

RESULTS

Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder.

CONCLUSIONS

Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

Neurocognitive Effects of Combined Electroconvulsive Therapy (ECT) and Venlafaxine in Geriatric Depression: Phase 1 of the PRIDE Study

OBJECTIVE

There is limited information regarding the tolerability of electroconvulsive therapy (ECT) combined with pharmacotherapy in elderly adults with major depressive disorder (MDD). Addressing this gap, we report acute neurocognitive outcomes from Phase 1 of the Prolonging Remission in Depressed Elderly (PRIDE) study.

METHODS

Elderly adults (age ≥60) with MDD received an acute course of 6 times seizure threshold right unilateral ultrabrief pulse (RUL-UB) ECT. Venlafaxine was initiated during the first treatment week and continued throughout the study. A comprehensive neurocognitive battery was administered at baseline and 72 hours following the last ECT session. Statistical significance was defined as a two-sided p-value of less than 0.05.

RESULTS

A total of 240 elderly adults were enrolled. Neurocognitive performance acutely declined post ECT on measures of psychomotor and verbal processing speed, autobiographical memory consistency, short-term verbal recall and recognition of learned words, phonemic fluency, and complex visual scanning/cognitive flexibility. The magnitude of change from baseline to end for most neurocognitive measures was modest.

CONCLUSION

This is the first study to characterize the neurocognitive effects of combined RUL-UB ECT and venlafaxine in elderly adults with MDD and provides new evidence for the tolerability of RUL-UB ECT in an elderly sample. Of the cognitive domains assessed, only phonemic fluency, complex visual scanning, and cognitive flexibility qualitatively declined from low average to mildly impaired. While some acute changes in neurocognitive performance were statistically significant, the majority of the indices as based on the effect sizes remained relatively stable.

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.

Correlation of EEG spectra, connectivity, and information theoretical biomarkers with psychological states in the epilepsy monitoring unit - A pilot study

At the level of individual experience, the relation between electroencephalographic (EEG) phenomena and subjective ratings of psychological states is poorly examined. This study investigated the correlation of quantitative EEG markers with systematic high-frequency monitoring of psychological states in patients admitted to the epilepsy monitoring unit (EMU). We used a digital questionnaire, including eight standardized items about stress, energy level, mood, ward atmosphere, seizure likelihood, hopefulness/frustration, boredom, and self-efficacy. Self-assessments were collected four times per day, in total 15 times during the stay in the EMU. We extracted brainrate, Hjorth parameters, Hurst exponent, Wackermann parameters, and power spectral density from the EEG. We performed correlation between these quantitative EEG measures and responses to the 8 items and evaluated their significance on single subject and on group level. Twenty-one consecutive patients (12 women/9 men, median age: 29 years, range: 18-74 years) were recruited. On group level, no significant correlations were found whereas on single-subject level, we found significant correlations for 6 out of 21 patients. Most significant correlations were found between Hjorth parameters and items that reflect changes in mood or stress. This study supports the feasibility of correlating quantitative EEG measures with psychological states in routine EMU settings and emphasizes the need for single-subject statistics when assessing aspects with high interindividual variance. Future studies should select samples with high within-subject variability of psychological states and examine a subsample with patients encountering a critical number of seizures needed in order to relate the psychological states to the ultimate question: Are psychological states potential indicators for seizure likelihood?

Magnetic Seizure Therapy: Towards Personalized Seizure Therapy for Major Depression

Magnetic seizure therapy (MST) is a noninvasive neuromodulation therapy under investigation for the treatment of severe neuropsychiatric disorders. MST involves inducing a therapeutic seizure under anesthesia in a setting similar to electroconvulsive therapy (ECT). To date, randomized controlled trials suggest that MST has similar antidepressant efficacy as ECT, but without significant cognitive adverse effects. Large scale clinical trials are currently underway to confirm these preliminary findings. So far, there has only been one study evaluating the clinical predictors of response to MST and more research is needed. This study found that patients with fewer episodes of depression and a positive family history of depression had a better response to MST. Overall, the ability of MST to focus the delivery of the electric field and the resultant seizure makes targeting seizure therapy to specific brain regions possible, and further research will be helpful in identifying personalized targets to maximize clinical benefit. In this review, we describe MST methodology and how it could be individualized to each patient. We also summarize the clinical and cognitive effects of MST and provide indications of which patients may be most likely to benefit. Finally, we summarize the studied neurophysiological predictors of response.

Electroconvulsive therapy "corrects" the neural architecture of visuospatial memory: Implications for typical cognitive-affective functioning

Although electroconvulsive therapy (ECT) is a widely used and effective treatment for refractory depression, the neural underpinnings of its therapeutic effects remain poorly understood. To address this issue, here, we focused on a core cognitive deficit associated with depression, which tends to be reliably ameliorated through ECT, specifically, the ability to learn visuospatial information. Thus, we pursued three goals. First, we tested whether ECT can “normalize” the functional brain organization patterns associated with visuospatial memory and whether such corrections would predict post-ECT improvements in learning visuospatial information. Second, we investigated whether, among healthy individuals, stronger expression of the neural pattern, susceptible to adjustments through ECT, would predict reduced incidence of depression-relevant cognition and affect. Third, we sought to quantify the heritability of the ECT-correctable neural profile. Thus, in a task fMRI study with a clinical and a healthy comparison sample, we characterized two functional connectome patterns: one that typifies trait depression (i.e., differentiates patients from healthy individuals) and another that is susceptible to “normalization” through ECT. Both before and after ECT, greater expression of the trait depression neural profile was associated with more frequent repetitive thinking about past personal events (affective persistence), a hallmark of depressogenic cognition. Complementarily, post-treatment, stronger expression of the ECT-corrected neural profile was linked to improvements in visuospatial learning, a mental ability which is markedly impaired in depression. Subsequently, using data from the Human Connectome Project (HCP) (N = 333), we demonstrated that the functional brain organization of healthy participants with greater levels of subclinical depression and higher incidence of its associated cognitive deficits (affective persistence, impaired learning) shows greater similarity to the trait depression neural profile and reduced similarity to the ECT-correctable neural profile, as identified in the patient sample. These results tended to be specific to learning-relevant task contexts (working memory, perceptual relational processing). Genetic analyses based on HCP twin data (N = 128 pairs) suggested that, among healthy individuals, a functional brain organization similar to the one normalized by ECT in the patient sample is endogenous to cognitive contexts that require visuospatial processing that extends beyond the here-and-now. Broadly, the present findings supported our hypothesis that some of the therapeutic effects of ECT may be due to its correcting the expression of a naturally occurring pattern of functional brain organization that facilitates integration of internal and external cognition beyond the immediate present. Given their substantial susceptibility to both genetic and environmental effects, such mechanisms may be useful both for identifying at risk individuals and for monitoring progress of interventions targeting mood-related pathology.

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Trait depression and ECT-correctable neural profiles were described in patients.

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The former was related to rumination and the latter to improved learning after ECT.

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Their relative expression was linked to subclinical depression in a healthy sample.

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Twin analyses implied that both profiles are endogenous to working memory contexts.

Spatial complexity of brain signal is altered in patients with generalized anxiety disorder

BACKGROUND

Is it healthy to be chaotic? Recent studies have argued that mental disorders are associated with more orderly neural activities, corresponding to a less flexible functional system. These conclusions were derived from altered temporal complexity. However, the relationship between spatial complexity and health is unknown, although spatial configuration is of importance for normal brain function.

METHODS

Based on resting-state functional magnetic resonance imaging data, we used Sample entropy (SampEn) to evaluate the altered spatial complexity in patients with generalized anxiety disorder (GAD; n = 47) compared to healthy controls (HCs; n = 38) and the relationship between spatial complexity and anxiety level.

RESULTS

Converging results showed increased spatial complexity in patients with GAD, indicating more chaotic spatial configuration. Interestingly, inverted-U relationship was revealed between spatial complexity and anxiety level, suggesting complex relationship between health and the chaos of human brain.

LIMITATIONS

Anxiety-related alteration of spatial complexity should be verified at voxel level in a larger sample and compared with results of other indices to clarify the mechanism of spatial chaotic of anxiety.

CONCLUSIONS

Altered spatial complexity in the brain of GAD patients mirrors inverted-U relationship between anxiety and behavioral performance, which may reflect an important characteristic of anxiety. These results indicate that SampEn is a good reflection of human health or trait mental characteristic.

Electroconvulsive Therapy Modulates Resting-State EEG Oscillatory Pattern and Phase Synchronization in Nodes of the Default Mode Network in Patients With Depressive Disorder

Introduction: Electroconvulsive therapy (ECT) has antidepressant effects, but it also has possible cognitive side effects. The effects of ECT on neuronal oscillatory pattern and phase synchronization, and the relationship between clinical response or cognitive change and electroencephalogram (EEG) measurements remain elusive. Methods: Individuals with unipolar depressive disorder receiving bilateral ECT were recruited. Five minutes of resting, eyes-closed, 19-lead EEG recordings were obtained before and after a course of ECT. Non-overlapping 60 artifact-free epocs of 2-s duration were used for the analyses. We used exact low resolution electromagnetic tomography (eLORETA) to compute the whole-brain three-dimensional intracortical distribution of current source density (CSD) and phase synchronization among 28 regions-of-interest (ROIs). Paired t-tests were used to identify cortical voxels and connectivities showing changes after ECT. Montgomery Asberg Depression Rating Scale (MADRS) and Mini-Mental State Examination (MMSE) were used to evaluate the severity of depression and the global cognitive function. Correlation analyses were conducted to identify the relationship between changes in the EEG measurements and changes in MADRS or MMSE. Results: Thirteen depressed patients (five females, mean age: 58.4 years old) were included. ECT increased theta CSD in the anterior cingulate cortex (ACC), and decreased beta CSD in the frontal pole (FP), and gamma CSD in the inferior parietal lobule (IPL). ECT increased theta phase synchronization between the posterior cingulate cortex (PCC) and the anterior frontal cortex, and decreased beta phase synchronization between the PCC and temporal regions. A decline in beta synchronization in the left hemisphere was associated with cognitive changes after ECT. Conclusion: ECT modulated resting-state EEG oscillatory patterns and phase synchronization in central nodes of the default mode network (DMN). Changes in beta synchronization in the left hemisphere might explain the ECT-related cognitive side effects.

A novel approach for assessing neuromodulation using phase-locked information measured with TMS-EEG

Neuromodulation therapies such as electroconvulsive therapy (ECT) are used to treat several neuropsychiatric disorders, including major depressive disorder (MDD). Recent work has highlighted the use of combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) to evaluate the therapeutic effects of neuromodulation; particularly, the phase locking factor (PLF) and phase locking value (PLV) can reportedly assess neuromodulation-induced functional changes in cortical networks. To examine changes in TMS-induced PLV and PLF before and after ECT, and their relationship with depression severity in patients with MDD, TMS-EEG and the Montgomery–Åsberg Depression Rating Scale (MADRS; depression severity) were implemented before and after ECT in 10 patients with MDD. Single-pulse TMS was applied to the visual and motor areas to induce phase propagation in the visuo-motor network at rest. Functional changes were assessed using PLF and PLV data. Pre-ECT TMS-induced alpha band (9–12 Hz) PLV was negatively correlated with depression severity, and increments of post-ECT from pre-ECT TMS-induced alpha band PLV were positively correlated with the reduction in depression severity. Moreover, we found a negative correlation between pre-ECT TMS-induced PLF at TMS-destination and depression severity. Finally, differences in post-ECT TMS-induced PLF peak latencies between visual and motor areas were positively correlated with depression severity. TMS-EEG-based PLV and PLF may be used to assess the therapeutic effects of neuromodulation and depressive states, respectively. Furthermore, our results provide new insights about the neural mechanisms of ECT and depression.

Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices.

Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods.

Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures.

Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.

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.

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The (electric or magnetic) induction of seizures is effective for severe depression but its mechanism of action is unclear.

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We investigated whether the modulation of brain network dynamics underlies the therapeutic efficacy of seizure therapy.

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Global brain-network dynamics were studied using EEG microstate analysis.

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Significant changes in microstate characteristics were specific to responders of electroconvulsive therapy (ECT).

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Relative change in the duration of microstates C and D was shown to be a strong predictor of response to ECT.

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.