Brain regulation of emotional conflict predicts antidepressant treatment response for depression

Association between the functional brain network and antidepressant responsiveness in patients with major depressive disorders: a resting-state EEG study

BACKGROUND

Recent neuroimaging studies have demonstrated that the heterogeneous antidepressant responsiveness in patients with major depressive disorder (MDD) is associated with diverse resting-state functional brain network (rsFBN) topology; however, only limited studies have explored the rsFBN using electroencephalography (EEG). In this study, we aimed to identify EEG-derived rsFBN-based biomarkers to predict pharmacotherapeutic responsiveness.

METHODS

The resting-state EEG signals were acquired for demography-matched three groups: 98 patients with treatment-refractory MDD (trMDD), 269 those with good-responding MDD (grMDD), and 131 healthy controls (HCs). The source-level rsFBN was constructed using 31 sources as nodes and beta-band power envelope correlation (PEC) as edges. The degree centrality (DC) and clustering coefficients (CCs) were calculated for various sparsity levels. Network-based statistic and one-way analysis of variance models were employed for comparing PECs and network indices, respectively. The multiple comparisons were controlled by the false discovery rate.

RESULTS

Patients with trMDD were characterized by the altered dorsal attention network and salience network. Specifically, they exhibited hypoconnection between eye fields and right parietal regions (p = 0.0088), decreased DC in the right supramarginal gyrus (q = 0.0057), and decreased CC in the reward circuit (qs < 0.05). On the other hand, both MDD groups shared increased DC but decreased CC in the posterior cingulate cortex.

CONCLUSIONS

We confirmed that network topology was more severely deteriorated in patients with trMDD, particularly for the attention-regulatory networks. Our findings suggested that the altered rsFBN topologies could serve as potential pathologically interpretable biomarkers for predicting antidepressant responsiveness.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis

BACKGROUND

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems.

METHODS

Through a comprehensive search in PubMed and Scopus databases, we reviewed 5258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps.

RESULTS

No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis in the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3" transcranial magnetic stimulation therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters.

CONCLUSION

Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

Spatiotemporal correlates of emotional conflict processing in typically developing adolescents using magnetoencephalography

Brain networks involved in emotional conflict processing have been extensively studied using functional magnetic resonance imaging in adults. Yet, the temporal correlates of these brain activations are still largely unknown, particularly in a key phase of emotional development, adolescence. Here, we elucidate the spatiotemporal profile of emotional conflict processing in 24 typically developing adolescents (10-18 years; 22 Caucasian) during an emotional face-word Stroop task. Using magnetoencephalography (MEG), we calculated dynamic statistical parametric maps and compared trials with and without emotional conflict whole-brain cluster-based permutation tests, followed by cluster-based ROI time-frequency analyses. Cluster analysis revealed four significant clusters, including early activation of the cingulate and temporal cortices, which may be related to dorsal and ventral streams of processing, respectively. This was followed by late components in the middle frontal and prefrontal cortices, which are likely related to response execution and post-response monitoring. Time-frequency analysis revealed event-related synchronizations and desynchronizations in beta and gamma bands across the cingulate cortex, which highlight the different roles of the cingulate subdivisions. Our findings provide further evidence of the cingulate's key role in emotional conflict processing across time. Improving our understanding of this key cognitive process will guide future work with neuropsychiatric populations, which may aid diagnosis and treatment outcomes.

Psilocybin: From Psychiatric Pariah to Perceived Panacea

OBJECTIVE

The authors critically examine the evidence base for psilocybin administered with psychological support/therapy (PST) in the treatment of psychiatric disorders and offer practical recommendations to guide future research endeavors.

METHODS

PubMed was searched for English-language articles from January 1998 to November 2023, using the search term "psilocybin." A total of 1,449 articles were identified and screened through titles and abstracts. Of these, 21 unique open-label or randomized controlled trials (RCTs) were identified that examine psilocybin for the treatment of obsessive-compulsive and related disorders (N=2), anxiety/depression associated with a cancer diagnosis (N=5), major depressive disorder (MDD; N=8), substance use disorders (N=4), anorexia (N=1), and demoralization (i.e., hopelessness, helplessness, and poor coping) in AIDS survivors (N=1).

RESULTS

The most developed evidence base is for the treatment of MDD (three double-blind RCTs with positive signals spanning a range of severities). However, the evidence is tempered by threats to internal and external validity, including unsuccessful blinding, small samples, large variability in dosing and PST procedures, limited sample diversity, and possibly large expectancy effects. Knowledge of mechanisms of action and predictors of response is currently limited.

CONCLUSIONS

The evidence is currently insufficient to recommend psilocybin with PST as a psychiatric treatment. Additional rigorously designed clinical trials are needed to definitively establish efficacy in larger and more diverse samples, address dosing considerations, improve blinding, and provide information on mechanisms of action and moderators of clinical response. Head-to-head comparisons with other evidence-based treatments will better inform the potential future role of psilocybin with PST in the treatment of major psychiatric disorders.

HMM for discovering decision-making dynamics using reinforcement learning experiments

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

Changes in P300 amplitude to negative emotional stimuli correlate with treatment responsiveness to sertraline in adolescents with depression

OBJECTIVE

Adolescents with depression is characterized by high rates of recurrence and functional impairment, with a significant association with suicide risk. Antidepressants are commonly prescribed to treat depression, yet few reproducible neurobiological markers for depression and antidepressant treatment response have been identified. Therefore, discovering a stable and reliable neurobiological marker holds significant value for both the clinical diagnosis and treatment of depression in adolescents.

METHODS

One hundred and seven patients with major depressive disorder (MDD group, 30 males, 77 females, mean age: 14.80 years), and 25 healthy subjects (HC group, 13 males, 12 females, mean age: 15.72 years) were recruited to perform a two-choice oddball task related to negative emotional cues. All participants completed a self-administered questionnaire to gather demographic information. A trained psychiatrist administered the Hamilton Depression Scale (HAMD-17) to assess depression severity. Of the 107 adolescents with depression, 61 received antidepressant medication for 8 weeks, and 61 of these patients were followed up. Multichannel EEG was recorded continuously from 64 scalp electrodes using the Curry 8 system. EEG signal preprocessing and analysis was performed offline using the EEGLAB toolbox in MATLAB. The ERP component characteristics associated with emotional processing were extracted from the difference waves and statistically analyzed.

RESULTS

Adolescents with depression exhibited significantly larger P300 amplitudes than healthy controls in response to both neutral and negative emotional cues. Following sertraline treatment, both depression scores and P300 amplitudes decreased significantly in adolescents with depression. Moreover, a strong positive correlation was observed between changes in depression scores and changes in P300 amplitude in response to negative emotional cues before and after treatment.

CONCLUSIONS

Changes in neural reactivity to negative emotional stimuli among adolescents with depression can be selectively modulated by sertraline and are significantly associated with improvements in depressive symptoms.

SIGNIFICANCE

Changes in P300 amplitude to negative emotional stimuli significantly correlate with treatment responsiveness to sertraline in adolescents with depression.

Neuroimaging-based variability in subtyping biomarkers for psychiatric heterogeneity

Neuroimaging-based subtyping is increasingly used to explain heterogeneity in psychiatric disorders. However, the clinical utility of these subtyping efforts remains unclear, and replication has been challenging. Here we examined how the choice of neuroimaging measures influences the derivation of neuro-subtypes and the consequences for clinical delineation. On a clinically heterogeneous dataset (total n = 566) that included controls (n = 268) and cases (n = 298) of psychiatric conditions, including individuals diagnosed with post-traumatic stress disorder (PTSD), traumatic brain injury (TBI), and comorbidity of both (PTSD&TBI), we identified neuro-subtypes among the cases using either structural, resting-state, or task-based measures. The neuro-subtypes for each modality had high internal validity but did not significantly differ in their clinical and cognitive profiles. We further show that the choice of neuroimaging measures for subtyping substantially impacts the identification of neuro-subtypes, leading to low concordance across subtyping solutions. Similar variability in neuro-subtyping was found in an independent dataset (n = 1642) comprised of major depression disorder (MDD, n = 848) and controls (n = 794). Our results suggest that the highly anticipated relationships between neuro-subtypes and clinical features may be difficult to discover.

The role of biomarkers in clinical development of drugs for neuropsychiatric disorders - A pragmatic guide

The failure rate of drugs being developed for neuropsychiatric indications remains high. Optimizing drug discovery and development requires not only a better neurobiological understanding of disease aetiology and development, but also the means by which we can measure relevant biological and clinical processes related to disease progression, drug target engagement, and sensitivity to treatment. Here we address the role and key considerations for the selection of biomarkers in clinical drug development for neuropsychiatric disorders. We do not provide an exhaustive list of biomarkers; rather we lay out a pragmatic, well-defined biomarker selection strategy that addresses the main goals for each of the phases in the drug development cycle. We discuss the key questions and issues that concern biomarker selection and implementation in each phase of development. For the better development of biomarkers, we emphasize the need to focus on discrete biological dysfunction and/or symptom domains rather than diagnoses. We also advocate the use of biomarker-based patient stratification in phase 2 and 3 to increase sensitivity and power and reduce costs. Our aim is to enhance precision and chances of success for these complex and heterogeneous brain disorders with a high unmet medical need.

Development of a Fluorescent Probe with High Selectivity based on Thiol-ene Click Nucleophilic Cascade Reactions for Delving into the Action Mechanism of Serotonin in Depression

The intrinsic correlation between depression and serotonin (5-HT) is a highly debated topic, with significant implications for the diagnosis, treatment, and advancement of drugs targeting neurological disorders. To address this important question, it is of utmost priority to understand the action mechanism of serotonin in depression through fluorescence imaging studies. However, the development of efficient molecular probes for serotonin is hindered by the lack of responsive sites with high selectivity for serotonin at the present time. Herein, we developed the first highly selective serotonin responsive site, 3-mercaptopropionate, utilizing thiol-ene click cascade nucleophilic reactions. The novel responsive site was then employed to construct the powerful molecular probe SJ-5-HT for imaging the serotonin level changes in the depression cells and brain tissues. Importantly, the imaging studies reveal that the level of serotonin in patients with depression may not be the primary factor, while the ability of neurons in patients with depression to release serotonin appears to be more critical. Additionally, this serotonin release capability correlates strongly with the levels of mTOR (intracellular mammalian target of rapamycin). These discoveries could offer valuable insights into the molecular mechanisms underpinning depression and furnish mTOR as a novel direction for the advancement of antidepressant therapies.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis

Background

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems.

Methods

Through a comprehensive search in PubMed and Scopus databases, we reviewed 5,258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of consistent antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation (TMS) therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps.

Results

No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis across the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3" TMS therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters.

Conclusion

Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

Weight gained during treatment predicts 6-month body mass index in a large sample of patients with anorexia nervosa using ensemble machine learning

OBJECTIVE

This study used machine learning methods to analyze data on treatment outcomes from individuals with anorexia nervosa admitted to a specialized eating disorders treatment program.

METHODS

Of 368 individuals with anorexia nervosa (209 adolescents and 159 adults), 160 individuals had data available for a 6-month follow-up analysis. Participants were treated in a 6-day-per-week partial-hospital program. Participants were assessed for eating disorder-specific and non-specific psychopathology. The analyses used established machine learning procedures combined in an ensemble model from support vector machine learning, random forest prediction, and the elastic net regularized regression with an exploration (training; 75%) and confirmation (test; 25%) split of the data.

RESULTS

The models predicting body mass index (BMI) at 6-month follow-up explained a 28.6% variance in the training set (n = 120). The model had good performance in predicting 6-month BMI in the test dataset (n = 40), with predicted BMI significantly correlating with actual BMI (r = .51, p = 0.01). The change in BMI from admission to discharge was the most important predictor, strongly correlating with reported BMI at 6-month follow-up (r = .55). Behavioral variables were much less predictive of BMI outcome. Results were similar for z-transformed BMI in the adolescent-only group. Length of stay was most predictive of weight gain in treatment (r = .56) but did not predict longer-term BMI.

CONCLUSIONS

This study, using an agnostic ensemble machine learning approach in the largest to-date sample of individuals with anorexia nervosa, suggests that achieving weight gain goals in treatment predicts longer-term weight-related outcomes. Other potential predictors, personality, mood, or eating disorder-specific symptoms were relatively much less predictive.

PUBLIC SIGNIFICANCE

The results from this study indicate that the amount of weight gained during treatment predicts BMI 6 months after discharge from a high level of care. This suggests that patients require sufficient time in a higher level of care treatment to meet their specific weight goals and be able to maintain normal weight.

Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial

BACKGROUND

Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices.

OBJECTIVE

This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses.

METHODS

This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response.

RESULTS

The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations.

CONCLUSIONS

The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42547.

Brain Source Localization and Functional Connectivity in Group Identity Regulation of Overbidding in Contest

Contests may be highly effective in eliciting high levels of effort, but they also carry the risk of inefficient resource allocation due to excessive effort (overbidding), squandering valuable social resources. While a growing body of research has focused on how group identity exacerbates out-group conflict, its influence on in-group conflict remains relatively unexplored. This study endeavors to explore the impact of group identity on conflicts within and between groups in competitive environments, thereby addressing gaps in the current research landscape and dissecting the involved neurobiological mechanisms. By employing source localization and functional connectivity techniques, our research aims to identify the brain regions involved in competitive decision-making and group identity processes, as well as the functional connectivities between social brain areas. The results of our investigation revealed that participants exhibited activation in the bilateral frontal and prefrontal lobes during the bidding behavior before the group identity task. Subsequently, after the task, additional activation was observed in the right temporal lobe. Results from functional connectivity studies indicated that group identity tasks modify decision-making processes by promoting group norms, empathy, and blurred self-other boundaries for in-group decisions, while out-group decisions after the group identity task see heightened cognitive control, an increased dependence on rational judgment, introspection of self-environment relationships, and a greater focus on anticipating others' behaviors. This study reveals the widespread occurrence of overbidding behavior and demonstrates the role of group identity in mitigating this phenomenon, concurrently providing a comprehensive analysis of the underlying neural mechanisms.

Neural and Cognitive Predictors of Stimulant Treatment Efficacy in Medication-Naïve ADHD Adults: A Pilot Diffusion Tensor Imaging Study

OBJECTIVE

Stimulant medications are the main treatment for Attention Deficit Hyperactivity Disorder (ADHD), but overall treatment efficacy in adults has less than a 60% response rate. This study aimed to identify neural and cognitive markers predictive of longitudinal improvement in response to stimulant treatment in drug-naïve adults with ADHD.

METHOD

We used diffusion tensor imaging (DTI) and executive function measures with 36 drug-naïve adult ADHD patients in a prospective study design.

RESULTS

Structural connectivity (measured by fractional anisotropy, FA) in striatal regions correlated with ADHD clinical symptom improvement following stimulant treatment (amphetamine or methylphenidate) in better medication responders. A significant positive correlation was also found between working memory performance and stimulant-related symptom improvement. Higher pre-treatment working memory scores correlated with greater response.

CONCLUSION

These findings provide evidence of pre-treatment neural and behavioral markers predictive of longitudinal treatment response to stimulant medications in adults with ADHD.

Major depressive disorder associated alterations in the effective connectivity of the face processing network: a systematic review

Major depressive disorder (MDD) is marked by altered processing of emotional stimuli, including facial expressions. Recent neuroimaging research has attempted to investigate how these stimuli alter the directional interactions between brain regions in those with MDD; however, methodological heterogeneity has made identifying consistent effects difficult. To address this, we systematically examined studies investigating MDD-associated differences present in effective connectivity during the processing of emotional facial expressions. We searched five databases: PsycINFO, EMBASE, PubMed, Scopus, and Web of Science, using a preregistered protocol (registration number: CRD42021271586). Of the 510 unique studies screened, 17 met our inclusion criteria. These studies identified that compared with healthy controls, participants with MDD demonstrated (1) reduced connectivity from the dorsolateral prefrontal cortex to the amygdala during the processing of negatively valenced expressions, and (2) increased inhibitory connectivity from the ventromedial prefrontal cortex to amygdala during the processing of happy facial expressions. Most studies investigating the amygdala and anterior cingulate cortex noted differences in their connectivity; however, the precise nature of these differences was inconsistent between studies. As such, commonalities observed across neuroimaging modalities warrant careful investigation to determine the specificity of these effects to particular subregions and emotional expressions. Future research examining longitudinal connectivity changes associated with treatment response may provide important insights into mechanisms underpinning therapeutic interventions, thus enabling more targeted treatment strategies.

Altered task-related decoupling of the rostral anterior cingulate cortex in depression

Dysfunctional activity of the rostral anterior cingulate cortex (rACC) - an extensively connected hub region of the default mode network - has been broadly linked to cognitive and affective impairments in depression. However, the nature of aberrant task-related rACC suppression in depression is incompletely understood. In this study, we sought to characterize functional connectivity of rACC activity suppression ('deactivation') - an essential feature of rACC function - during external task engagement in depression. Specifically, we aimed to explore neural patterns of functional decoupling and coupling with the rACC during its task-driven suppression. We enrolled 81 15- to 25-year-old young people with moderate-to-severe major depressive disorder (MDD) before they commenced a 12-week clinical trial that assessed the effectiveness of cognitive behavioral therapy plus either fluoxetine or placebo. Ninety-four matched healthy controls were also recruited. Participants completed a functional magnetic resonance imaging face matching task known to elicit rACC suppression. To identify brain regions associated with the rACC during its task-driven suppression, we employed a seed-based functional connectivity analysis. We found MDD participants, compared to controls, showed significantly reduced 'decoupling' of the rACC with extended task-specific regions during task performance. Specifically, less decoupling was observed in the occipital and fusiform gyrus, dorsal ACC, medial prefrontal cortex, cuneus, amygdala, thalamus, and hippocampus. Notably, impaired decoupling was apparent in participants who did not remit to treatment, but not treatment remitters. Further, we found MDD participants showed significant increased coupling with the anterior insula cortex during task engagement. Our findings indicate that aberrant task-related rACC suppression is associated with disruptions in adaptive neural communication and dynamic switching between internal and external cognitive modes that may underpin maladaptive cognitions and biased emotional processing in depression.

Multivariate classification based on large-scale brain networks during early abstinence predicted lapse among male detoxified alcohol-dependent patients

Identifying biomarkers to predict lapse of alcohol-dependence (AD) is essential for treatment and prevention strategies, but remains remarkably challenging. With an aim to identify neuroimaging features for predicting AD lapse, 66 male AD patients during early-abstinence (baseline) after hospitalized detoxification underwent resting-state functional magnetic resonance imaging and were then followed-up for 6 months. The relevance-vector-machine (RVM) analysis on baseline large-scale brain networks yielded an elegant model for differentiating relapsing patients (n = 38) from abstainers, with the area under the curve of 0.912 and the accuracy by leave-one-out cross-validation of 0.833. This model captured key information about neuro-connectome biomarkers for predicting AD lapse.

Temporal characteristics of emotional conflict processing: the modulation role of attachment styles

Theoretical account of attachment proposed that individual differences in adult attachment styles play a key role in adjusting balance between affective evaluation and cognitive control. Yet, little is known about the temporal characteristics of emotional conflict processing modulated by attachment styles. Accordingly, the present study used event-related potentials (ERP) and multivariate pattern analysis (MVPA) combined with an emotional face-word Stroop task to investigate the temporal dynamics of attachment-related cognitive-affective patterns in emotional conflict processing. The ERP results demonstrated multiple-process of emotional conflict modulated by attachment styles. In early sensory processing, positive faces captured avoidant attachment individuals' attention as reflected in greater P1, while the same situation led to greater N170 in secure and anxious individuals. Crucially, impairment in conflict-monitoring function was found in anxious individuals as reflected by the absence of interference effect on N450, leading to impaired ability of inhibitory control as indicated by decreased slow potential. In contrast, avoidant individuals showed greater slow potential for inhibiting emotional interference. Furthermore, MVPA revealed that the corresponding time window for conflict monitoring was found for emotional distractors decoding rather than congruency decoding in the anxious attachment group. Convergent results from ERPs and MVPA indicated that the deficits in emotional conflict monitoring and resolution among anxious individuals might be due to the excessive approach to emotional distractors, as they habitually use emotional evaluation rather than cognitive control. In summary, the present study provides electrophysiological evidence that attachment styles modulated emotional conflict processing, which highlights the contribution of attachment to social information processing.

Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression

Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Improving EEG Decoding via Clustering-Based Multitask Feature Learning

Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution and, hence, can only yield a suboptimal decoding accuracy. To uncover the intrinsic distribution structure of EEG data, we propose a clustering-based multitask feature learning algorithm for improved EEG pattern decoding. Specifically, we perform affinity propagation-based clustering to explore the subclasses (i.e., clusters) in each of the original classes and then assign each subclass a unique label based on a one-versus-all encoding strategy. With the encoded label matrix, we devise a novel multitask learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses. We then train a linear support vector machine with the optimized features for EEG pattern decoding. Extensive experimental studies are conducted on three EEG data sets to validate the effectiveness of our algorithm in comparison with other state-of-the-art approaches. The improved experimental results demonstrate the outstanding superiority of our algorithm, suggesting its prominent performance for EEG pattern decoding in BCI applications.

Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial

BACKGROUND

The brain circuitry of depression and anxiety/fear is well-established, involving regions such as the limbic system and prefrontal cortex. We expand prior literature by examining the extent to which four discrete factors of anxiety (immediate state anxiety, physiological/panic, neuroticism/worry, and agitation/restlessness) among depressed outpatients are associated with differential responses during reactivity to and regulation of emotional conflict.

METHODS

A total of 172 subjects diagnosed with major depressive disorder underwent functional magnetic resonance imaging while performing an Emotional Stroop Task. Two main contrasts were examined using whole brain voxel wise analyses: emotional reactivity and emotion regulation. We also evaluated the association of these contrasts with the four aforementioned anxiety factors.

RESULTS

During emotional reactivity, participants with higher immediate state anxiety showed potentiated activation in the rolandic operculum and insula, while individuals with higher levels of physiological/panic demonstrated decreased activation in the posterior cingulate. No significant results emerged for any of the four factors on emotion regulation. When re-analyzing these statistically-significant brain regions through analyses of a subsample with (n = 92) and without (n = 80) a current anxiety disorder, no significant associations occurred among those without an anxiety disorder. Among those with an anxiety disorder, results were similar to the full sample, except the posterior cingulate was associated with the neuroticism/worry factor.

CONCLUSIONS

Divergent patterns of task-related brain activation across four discrete anxiety factors could be used to inform treatment decisions and target specific aspects of anxiety that involve intrinsic processing to attenuate overactive responses to emotional stimuli.

Dorsolateral prefrontal cortex and amygdala function during cognitive reappraisal predicts weight restoration and emotion regulation impairment in anorexia nervosa

BACKGROUND

Although deficits in affective processing are a core component of anorexia nervosa (AN), we lack a detailed characterization of the neurobiological underpinnings of emotion regulation impairment in AN. Moreover, it remains unclear whether these neural correlates scale with clinical outcomes.

METHODS

We investigated the neural correlates of negative emotion regulation in a sample of young women receiving day-hospital treatment for AN (n = 21) and healthy controls (n = 21). We aimed to determine whether aberrant brain activation patterns during emotion regulation predicted weight gain following treatment in AN patients and were linked to AN severity. To achieve this, participants completed a cognitive reappraisal paradigm during functional magnetic resonance imaging. Skin conductance response, as well as subjective distress ratings, were recorded to corroborate task engagement.

RESULTS

Compared to controls, patients with AN showed reduced activation in the dorsolateral prefrontal cortex (dlPFC) during cognitive reappraisal [pFWE<0.05, threshold-free cluster enhancement (TFCE) corrected]. Importantly, psycho-physiological interaction analysis revealed reduced functional connectivity between the dlPFC and the amygdala in AN patients during emotion regulation (pFWE<0.05, TFCE corrected), and dlPFC-amygdala uncoupling was associated with emotion regulation deficits (r = -0.511, p = 0.018) and eating disorder severity (r = -0.565, p = .008) in the AN group. Finally, dlPFC activity positively correlated with increases in body mass index (r = 0.471, p = 0.042) and in body fat mass percentage (r = 0.605, p = 0.008) following 12 weeks of treatment.

CONCLUSIONS

Taken together, our findings indicate that individuals with AN present altered fronto-amygdalar response during cognitive reappraisal and that this response may serve as a predictor of response to treatment and be linked to clinical severity.

Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial

BACKGROUND

The lack of biomarkers to inform antidepressant selection is a key challenge in personalized depression treatment. This work identifies candidate biomarkers by building deep learning predictors of individual treatment outcomes using reward processing measures from functional magnetic resonance imaging, clinical assessments, and demographics.

METHODS

Participants in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study (n = 222) underwent reward processing task-based functional magnetic resonance imaging at baseline and were randomized to 8 weeks of sertraline (n = 106) or placebo (n = 116). Subsequently, sertraline nonresponders (n = 37) switched to 8 weeks of bupropion. The change in Hamilton Depression Rating Scale was measured after treatment. Reward processing, clinical measurements, and demographics were used to train treatment-specific deep learning models.

RESULTS

The predictive model for sertraline achieved R² of 48% (95% CI, 33%-61%; p < 10^-3) in predicting the change in Hamilton Depression Rating Scale and number-needed-to-treat (NNT) of 4.86 participants in predicting response. The placebo model achieved R² of 28% (95% CI, 15%-42%; p < 10^-3) and NNT of 2.95 in predicting response. The bupropion model achieved R² of 34% (95% CI, 10%-59%, p < 10^-3) and NNT of 1.68 in predicting response. Brain regions where reward processing activity was predictive included the prefrontal cortex and cerebellar crus 1 for sertraline and the cingulate cortex, caudate, orbitofrontal cortex, and crus 1 for bupropion.

CONCLUSIONS

These findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD

The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.

Functional connectivity between the ventral anterior cingulate and amygdala during implicit emotional conflict regulation and daily-life emotion dysregulation

Emotional conflict adaptation involving ventral anterior cingulate cortex (ACC) suppression of the amygdala is thought to be important in emotion regulation, with evidence of impaired implicit emotion regulation in emotional distress disorders. However, it is unclear how this impairment is associated with daily-life emotion dysregulation in emotional distress disorders. In the current study, female participants with an emotional distress disorder (N = 27) were scanned with MRI while completing an implicit emotion conflict regulation task that involved identifying the facial expression of an image while ignoring an overlaid congruent or incongruent affect label. Participants then completed two weeks of ambulatory assessment of daily-life emotion dysregulation. Consistent with previous research on comorbid emotional distress disorders (Etkin and Schatzberg, 2011), there was no behavioral effect of emotional conflict adaptation (p = .701) but a significant effect of congruent adaptation (p = .006), suggesting impairment is specific to implicit emotional conflict regulation. Additionally, there was no neural evidence of emotional conflict adaptation in the ventral ACC and amygdala (ps > .766). Further, in our primary psychophysiological interactions analyses, we examined ventral ACC-amygdala functional connectivity. As hypothesized, increased ventral ACC-amygdala functional connectivity for emotional conflict adaptation was associated with increased daily-life affective instability (p = .022), but not mean daily-life negative affect (p = .372). Overall, results provide behavioral and neural evidence of impaired implicit emotional conflict adaptation in individuals with emotional distress disorders and suggests that this impairment is related to daily-life affective instability in these disorders.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Depression patient-derived cortical neurons reveal potential biomarkers for antidepressant response

Major depressive disorder is highly prevalent worldwide and has been affecting an increasing number of people each year. Current first line antidepressants show merely 37% remission, and physicians are forced to use a trial-and-error approach when choosing a single antidepressant out of dozens of available medications. We sought to identify a method of testing that would provide patient-specific information on whether a patient will respond to a medication using in vitro modeling. Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression study were used to rapidly generate cortical neurons and screen them for bupropion effects, for which the donor patients showed remission or non-remission. We provide evidence for biomarkers specific for bupropion response, including synaptic connectivity and morphology changes as well as specific gene expression alterations. These biomarkers support the concept of personalized antidepressant treatment based on in vitro platforms and could be utilized as predictors to patient response in the clinic.

Multi-View Multi-Scale Optimization of Feature Representation for EEG Classification Improvement

Effectively extracting common space pattern (CSP) features from motor imagery (MI) EEG signals is often highly dependent on the filter band selection. At the same time, optimizing the EEG channel combinations is another key issue that substantially affects the SMR feature representations. Although numerous algorithms have been developed to find channels that record important characteristics of MI, most of them select channels in a cumbersome way with low computational efficiency, thereby limiting the practicality of MI-based BCI systems. In this study, we propose the multi-scale optimization (MSO) of spatial patterns, optimizing filter bands over multiple channel sets within CSPs to further improve the performance of MI-based BCI. Specifically, several channel subsets are first heuristically predefined, and then raw EEG data specific to each of these subsets bandpass-filtered at the overlap between a set of filter bands. Further, instead of solving learning problems for each channel subset independently, we propose a multi-view learning based sparse optimization to jointly extract robust CSP features with L_2,1 -norm regularization, aiming to capture the shared salient information across multiple related spatial patterns for enhanced classification performance. A support vector machine (SVM) classifier is then trained on these optimized EEG features for accurate recognition of MI tasks. Experimental results on three public EEG datasets validate the effectiveness of MSO compared to several other competing methods and their variants. These superior experimental results demonstrate that the proposed MSO method has promising potential in MI-based BCIs.

Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45-0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.

Psychological mechanisms and functions of 5-HT and SSRIs in potential therapeutic change: Lessons from the serotonergic modulation of action selection, learning, affect, and social cognition

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans - which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

An electroencephalographic signature predicts antidepressant response in major depression

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Early score fluctuation and placebo response in a study of major depressive disorder

Early score fluctuation in double-blind, placebo-controlled studies may affect the reliability of the baseline measurement and adversely affect the eventual study outcome. We examined the effect of early score fluctuation during a 2-week double-blind placebo lead-in period in a phase II, double-blind, placebo-controlled trial of adjunctive s-adenosyl methionine (MSI-195) in MDD subjects who had had an inadequate response to ongoing antidepressant treatment. The overall study failed to meet its specified endpoints. We examined the score trajectories of all placebo-assigned subjects during the double-blind placebo lead-in period and subsequent 6-week treatment period. Placebo-assigned subjects with ≥20% HamD₁₇ or MADRS score fluctuations (improvement or worsening) during the double-blind placebo lead-in period (prior to randomization) had significantly higher rates of placebo response and remission at week 8 compared to subjects with <20% response. A post-hoc analysis of evaluable subjects taken from the ITT population that excluded subjects with ≥20% early score response yielded higher effect sizes for both the HamD₁₇ and MADRS sub-groups and statistical significance for MSI-195 over placebo in the MADRS sub-group (p = 0.012) with an effect size of 0.404. A reliable baseline measure is an asset for signal detection. These post-hoc findings suggest that study designs that anticipate and attempt to manage early response prior to randomization may yield more meaningful outcome data for trials of MDD and possibly other disorders as well.