Stratified psychiatry: Tomorrow's precision psychiatry?

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial and error between various treatments. An individualized treatment selection is crucial for saving time and financial resources and preventing possible side effects. Because of the complex nature of this problem, a Deep Learning (DL) approach, as a promising method for the precision medicine, was utilized to identify the responders to the treatment using pre-treatment EEG signals. Eighty-three patients with MDD participated in this study to receive treatment using Repetitive Transcranial Magnetic Stimulation (rTMS). A deep hybrid neural network was developed based on three pre-trained convolutional neural networks named DenseNet121, EfficientNetB0, and Xception. The training of each model was performed by feeding three types of EEG representations as the inputs into the models including the Wavelet Transform (WT) images, the connectivity matrix between electrode pairs, and the raw EEG signals. The performance of the proposed models were assessed for the three different input types and achieved the highest accuracy of 94.7% in classifying patients as responders or non-responders when utilizing a sequence of raw EEG images. For the WT and connectivity inputs the best accuracy of model was 94.38% and 94.25% respectively. Therefore, the proposed model can be a step forward towards the clinical implementation of an end-to-end treatment selection framework using raw EEG signals.

Let's Move Towards Precision Suicidology

PURPOSE OF REVIEW

Suicidal behaviour remains a critical public health issue, with limited progress in reducing suicide rates despite various prevention efforts. The introduction of precision psychiatry offers hope by tailoring treatments based on individual genetic, environmental, and lifestyle factors. This approach could enhance the effectiveness of interventions, as current strategies are insufficient-many individuals who die by suicide had recently seen a doctor, but interventions often fail due to rapid progression of suicidal behaviour, reluctance to seek treatment, and poor identification of suicidal ideation.

RECENT FINDINGS

Precision medicine, particularly through the use of machine learning and 'omics' techniques, shows promise in improving suicide prevention by identifying high-risk individuals and developing personalised interventions. Machine learning models can predict suicidal risk more accurately than traditional methods, while genetic markers and environmental factors can create comprehensive risk profiles, allowing for targeted prevention strategies. Stratification in psychiatry, especially concerning depression, is crucial, as treating depression alone does not effectively reduce suicide risk. Pharmacogenomics and emerging research on inflammation, psychological pain, and anhedonia suggest that specific treatments could be more effective for certain subgroups. Ultimately, precision medicine in suicide prevention, though challenging to implement, could revolutionise care by offering more personalised, timely, and effective interventions, potentially reducing suicide rates and improving mental health outcomes. This new approach emphasizes the importance of suicide-specific strategies and research into stratification to better target interventions based on individual patient characteristics.

Inflammatory biomarkers and childhood maltreatment: A cluster analysis in patients with eating disorders

Eating Disorders (EDs) are severe psychiatric disorders, with growing evidence pointing towards the role of childhood maltreatment (CM) influencing their onset, severity, and response to treatment. Preliminary evidence showed that CM could be associated with an elevation of inflammatory biomarkers across the different EDs. The objective of the study was to elucidate the interplay between CM, ED-specific psychopathology, and inflammatory biomarkers. The study involved 198 female participants, comprising 70 patients with anorexia nervosa (AN), 56 patients with bulimia nervosa (BN), and 72 healthy controls (HCs). K-means clustering was used to assess the hypothesis that latent clusters could be described between patients affected by EDs based on serum levels of inflammatory biomarkers alone (CRP, IL-6, suPAR). Additionally, the analysis included a comparison between patients with and without history of childhood maltreatment. Patients with AN exhibited significantly higher suPAR levels than HCs, regardless of the severity of psychopathology. A direct association between CM and elevated levels of inflammatory biomarkers, particularly CRP, IL-6, and suPAR were found. Cluster analysis identified two distinct populations among patients with EDs, with the group showing elevated inflammatory biomarkers likely to report more severe CM. Even though preliminary, the results of the present study support the existence of a biologically grounded "maltreated eco-phenotype" in EDs. The present study also reports results on CRP, IL-6 and suPAR, in patients with EDs. These findings might suggest future potential tailored treatments and interventions designed to target specific subgroups of patients, and potentially improving treatment efficacy.

Does 18 Hz deep TMS benefit a different subgroup of depressed patients relative to 10 Hz rTMS? The role of the individual alpha frequency

Both 10 Hz repetitive transcranial magnetic stimulation (rTMS) as well as 18 Hz deep TMS (dTMS) constitute effective, FDA-approved TMS treatment protocols for depression. However, not all patients experience sufficient symptom relief after either of these protocols. Biomarker-guided treatment stratification could aid in personalizing treatment and thereby enhancing improvement. An individual alpha frequency (iAF)-based EEG-biomarker, Brainmarker-I, can differentially stratify patients to depression treatments. For instance, an iAF close to 10 Hz was associated with better improvement to 10 Hz rTMS, possibly reflecting entrainment of endogenous oscillations to the stimulation frequency. Accordingly, we examined whether 18 Hz dTMS would result in better improvement in individuals whose iAF lies around 9 Hz, a harmonic frequency of 18 Hz. Curve fitting and regression analyses were conducted to assess the relation between iAF and improvement. For treatment stratification purposes, correlations with iAF-distance to 10 Hz compared 18 Hz dTMS (N = 114) to 10 Hz rTMS (N = 72). We found a robust quadratic effect, indicating that patients with an iAF around 9 Hz exhibited least symptom improvement (r2=0.126, p<.001). Improvement correlated positively with iAF-distance to 10 Hz (p=.003). A secondary analysis in 20 Hz figure-of-eight data confirmed this direction. A significant interaction of iAF-distance and stimulation frequency between 10 and 18 Hz datasets emerged (p=.026). These results question entrainment of endogenous oscillations by their harmonic frequency for 18 Hz, and suggest that 10 Hz and 18 Hz TMS target different subgroups of depression patients. This study adds to iAF stratification, augmenting Brainmarker-I with alternative TMS protocols (18 Hz/20 Hz) for patients with a slower iAF, thereby broadening clinical applicability and relevance of the biomarker.

Continuous theta burst stimulation for bipolar depression: A multicenter, double-blind randomized controlled study exploring treatment efficacy and predictive potential of kynurenine metabolites

BACKGROUND

While theta burst stimulation (TBS) shows promise in Major Depressive Disorder (MDD), its effectiveness in bipolar depression (BD-D) remains uncertain. Optimizing treatment parameters is crucial in the pursuit of rapid symptom relief. Moreover, aligning with personalized treatment strategies and increased interest in immunopsychiatry, biomarker-based stratification of patients most likely to benefit from TBS might improve remission rates. We investigated treatment effectiveness of continuous TBS (cTBS) compared to sham in BD-D, and assessed the capacity of plasma kynurenine pathway metabolites to predict treatment outcome.

METHODS

Thirty-seven patients with BD-D underwent accelerated active or sham cTBS treatment in a multicenter, double-blind, randomized controlled trial. Depressive symptoms were measured with the 17-item Hamilton Depression Rating Scale (HDRS-17) before treatment (T0), 3-4 days posttreatment (T1) and 10-11 days posttreatment (T2). Plasma tryptophan, kynurenine, kynurenic acid and quinolinic acid concentrations were quantified with ELISA. Linear mixed models were used for statistical analyses.

RESULTS

Although the total sample showed depressive symptom improvement, active cTBS did not demonstrate greater symptom alleviation compared to sham. However, higher baseline quinolinic acid significantly predicted symptom improvement in the active treatment group, not in sham-stimulated patients.

LIMITATIONS

The modest sample size limited the power to detect significant differences with regard to treatment effect. Also, the follow-up period was 10-11 days, whereas similar studies usually follow up for at least one month.

CONCLUSION

More research is required to optimize cTBS for BD-D and explore the involvement of quinolinic acid in treatment outcome.

Should rTMS be considered a first-line treatment for major depressive episodes in adults?

Treatment-resistant depression (TRD) is an epidemic with rising social, economic, and political costs. In a patient whose major depressive episode (MDE) persists through an adequate antidepressant trial, insurance companies often cover alternative treatments which may include repetitive transcranial magnetic stimulation (rTMS). RTMS is an FDA-cleared neuromodulation technique for TRD which is safe, efficacious, noninvasive, and well-tolerated. Recent developments in the optimization of rTMS algorithms and targeting have increased the efficacy of rTMS in treating depression, improved the clinical convenience of these treatments, and decreased the cost of a course of rTMS. In this opinion paper, we make a case for why conventional FDA-cleared rTMS should be considered as a first-line treatment for all adult MDEs. RTMS is compared to other first-line treatments including psychotherapy and SSRIs. These observations suggest that rTMS has similar efficacy, fewer side-effects, lower risk of serious adverse events, comparable compliance, the potential for more rapid relief, and cost-effectiveness. This suggestion, however, would be strengthened by further research with an emphasis on treatment-naive subjects in their first depressive episode, and trials directly contrasting rTMS with SSRIs or psychotherapy.

Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients-a randomized rater-blinded trial

BACKGROUND

Brain-derived neurotrophic factor (BDNF) is essential for antidepressant treatment of major depressive disorder (MDD). Our repeated studies suggest that DNA methylation of a specific CpG site in the promoter region of exon IV of the BDNF gene (CpG -87) might be predictive of the efficacy of monoaminergic antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and others. This trial aims to evaluate whether knowing the biomarker is non-inferior to treatment-as-usual (TAU) regarding remission rates while exhibiting significantly fewer adverse events (AE).

METHODS

The BDNF trial is a prospective, randomized, rater-blinded diagnostic study conducted at five university hospitals in Germany. The study's main hypothesis is that {1} knowing the methylation status of CpG -87 is non-inferior to not knowing it with respect to the remission rate while it significantly reduces the AE rate in patients experiencing at least one AE. The baseline assessment will occur upon hospitalization and a follow-up assessment on day 49 (± 3). A telephone follow-up will be conducted on day 70 (± 3). A total of 256 patients will be recruited, and methylation will be evaluated in all participants. They will be randomly assigned to either the marker or the TAU group. In the marker group, the methylation results will be shared with both the patient and their treating physician. In the TAU group, neither the patients nor their treating physicians will receive the marker status. The primary endpoints include the rate of patients achieving remission on day 49 (± 3), defined as a score of ≤ 10 on the Hamilton Depression Rating Scale (HDRS-24), and the occurrence of AE.

ETHICS AND DISSEMINATION

The trial protocol has received approval from the Institutional Review Boards at the five participating universities. This trial holds significance in generating valuable data on a predictive biomarker for antidepressant treatment in patients with MDD. The findings will be shared with study participants, disseminated through professional society meetings, and published in peer-reviewed journals.

TRIAL REGISTRATION

German Clinical Trial Register DRKS00032503. Registered on 17 August 2023.

Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors

Introduction

Individuals with depression who do not respond to two or more courses of serotonergic antidepressants tend to have greater deficits in reward processing and greater internalizing symptoms, yet there is no validated self-report method to determine the likelihood of treatment resistance based on these symptom dimensions.

Methods

This online case-control study leverages machine learning techniques to identify differences in self-reported anhedonia and internalizing symptom profiles of antidepressant non-responders compared to responders and healthy controls, as an initial proof-of-concept for relating these indicators to medication responsiveness. Random forest classifiers were used to identify a subset from a set of 24 reward predictors that distinguished among serotonergic medication resistant, non-resistant, and non-depressed individuals recruited online (N = 393). Feature selection was implemented to refine model prediction and improve interpretability.

Results

Accuracies for full predictor models ranged from .54 to .71, while feature selected models retained 3-5 predictors and generated accuracies of .42 to .70. Several models performed significantly above chance. Sensitivity for non-responders was greatest after feature selection when compared to only responders, reaching .82 with 3 predictors. The predictors retained from feature selection were then explored using factor analysis at the item level and cluster analysis of the full data to determine empirically driven data structures.

Discussion

Non-responders displayed 3 distinct symptom profiles along internalizing dimensions of anxiety, anhedonia, motivation, and cognitive function. Results should be replicated in a prospective cohort sample for predictive validity; however, this study demonstrates validity for using a limited anhedonia and internalizing self-report instrument for distinguishing between antidepressant resistant and responsive depression profiles.

Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies

Noninvasive brain stimulation (NIBS) treatments have gained considerable attention as potential therapeutic intervention for psychiatric disorders. The identification of reliable biomarkers for predicting clinical response to NIBS has been a major focus of research in recent years. Neuroimaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (MRI), have been used to identify potential biomarkers that could predict response to NIBS. However, identifying clinically actionable brain biomarkers requires robustness. In this systematic review, we aimed to summarize the current state of brain biomarker research for NIBS in depression, focusing only on well-powered studies (N ≥ 88) and/or studies that aimed at independently replicating previous findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies met the inclusion criteria. All focused on repetitive transcranial magnetic stimulation treatment in depression. After reviewing the included studies, we found the following MRI and EEG biomarkers to be most robust: 1) functional MRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) functional MRI-based network connectivity, 3) task-induced EEG frontal-midline theta, and 4) EEG individual alpha frequency. Future prospective studies should further investigate the clinical actionability of these specific EEG and MRI biomarkers to bring biomarkers closer to clinical reality.

Clinical features in co-occuring obsessive-compulsive disorder and bipolar disorder: A systematic review and meta-analysis

Obsessive-compulsive disorder (OCD) frequently co-occurs with various psychiatric conditions and may impact as many as one-fifth of individuals diagnosed with bipolar disorder (BD). Despite the expanding body of literature on the coexistence of OCD and BD, there is a notable lack of comprehensive data pertaining to the distinct features of obsessive-compulsive symptoms that define this comorbidity. To bridge this knowledge gap, we conducted a systematic search of PubMed/MEDLINE, Scopus, EMBASE, and PsycINFO until August 7th, 2023. We performed random-effects meta-analyses to compare individuals with both OCD and BD to those with OCD in terms of OCD symptomatology as well as the specific categories of obsessions and compulsions. Out of the 10,393 records initially screened, 17 studies were ultimately incorporated into the qualitative assessment, with 15 of them being included in the quantitative analysis. Individuals with OCD and BD experienced fewer lifetime contamination obsessions (OR=0.71; 95 %CI=0.53, 0.95; p = 0.021) and more sexual obsessions (OR=1.77; 95 %CI=1.03, 3.04; p = 0.04) compared to individuals with OCD without BD. No significant difference was observed for other types of obsessions or compulsions or for the severity of OCD symptoms, although BD type may play a role according to meta-regression analyses. The detection of the presence of sexual or contamination obsessions through a detailed interview may be the focus of clinical attention when assessing OCD in the context of comorbid BD. Sub-phenotyping complex clinical presentation of comorbid psychiatric disorders can aid in making more informed decisions when choosing an appropriate treatment approach.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder

Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from baseline of >33% on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 h after infusion. EEG recordings were obtained pre-, start-, end- and 24 h post- infusion, and the resting EEG was automatically scored using the Vigilance Algorithm Leipzig (VIGALL). Relative to placebo (sodium chloride 0.9%), ketamine increased the amount of low-vigilance stage B1 at end-infusion. This increase in B1 was positively related to serum concentrations of ketamine, but not to norketamine, and was independent of clinical response. In contrast, treatment responders showed a distinct EEG pattern characterized by a decrease in high-vigilance stage A1 and an increase in low-vigilance B2/3, regardless of whether placebo or ketamine had been given. Furthermore, pretreatment EEG differed between responders and non-responders with responders showing a higher percentage of stage A1 (53% vs. 21%). The logistic regression fitted on the percent of A1 stages was able to predict treatment outcomes in the testing dataset with an area under the ROC curve of 0.7. Ketamine affects EEG vigilance in a distinct pattern observed only in responders. Consequently, the percentage of pretreatment stage A1 shows significant potential as a predictive biomarker of treatment response.Clinical Trials Registration: https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-000952-17/CZ Registration number: EudraCT Number: 2013-000952-17.

A Retrospective Case-Control Study on the Differences in the Effectiveness of Theta-Burst Stimulation Therapy for Depression with and without Antidepressant Medication

Transcranial magnetic stimulation (TMS) therapy has few side effects and comparable therapeutic effects to antidepressant treatment, but few studies have introduced TMS therapy as an initial treatment for MDD. The objective of this study was to retrospectively compare the clinical outcomes between 50 MDD patients without antidepressants (i.e., TMS monotherapy) and 50 MDD patients with antidepressants plus TMS therapy, matched for age, sex, and depression severity. The presence or absence of antidepressant therapy in first-line treatment was determined via a detailed interview by psychiatrists. The study design was a retrospective observational case-control study using the TMS registry data. The key inclusion criteria were adult patients who met the diagnosis of MDD and received 20-30 sessions of intermittent theta-burst stimulation (iTBS) therapy to the left dorsolateral prefrontal cortex (DLPFC). In this study, the Montgomery-Åsberg Depression Rating Scale (MADRS) was used as the primary outcome measure. No significant group differences existed in the baseline MADRS total score between the unmedicated and medicated patient groups. Following TMS therapy, no significant group differences in response rate, remission rate, or relative total score change in the MADRS were observed. The main limitations were the retrospective design and the use of registry data as a source. Our findings suggest that TMS monotherapy may be as effective as TMS add-on therapy to antidepressants when used as the first-line therapy for MDD, but randomized controlled trials are needed.

Accelerated TMS - moving quickly into the future of depression treatment

Accelerated TMS is an emerging application of Transcranial Magnetic Stimulation (TMS) aimed to reduce treatment length and improve response time. Extant literature generally shows similar efficacy and safety profiles compared to the FDA-cleared protocols for TMS to treat major depressive disorder (MDD), yet accelerated TMS research remains at a very early stage in development. The few applied protocols have not been standardized and vary significantly across a set of core elements. In this review, we consider nine elements that include treatment parameters (i.e., frequency and inter-stimulation interval), cumulative exposure (i.e., number of treatment days, sessions per day, and pulses per session), individualized parameters (i.e., treatment target and dose), and brain state (i.e., context and concurrent treatments). Precisely which of these elements is critical and what parameters are most optimal for the treatment of MDD remains unclear. Other important considerations for accelerated TMS include durability of effect, safety profiles as doses increase over time, the possibility and advantage of individualized functional neuronavigation, use of biological readouts, and accessibility for patients most in need of the treatment. Overall, accelerated TMS appears to hold promise to reduce treatment time and achieve rapid reduction in depressive symptoms, but at this time significant work remains to be done. Rigorous clinical trials combining clinical outcomes and neuroscientific measures such as electroencephalogram, magnetic resonance imaging and e-field modeling are needed to define the future of accelerated TMS for MDD.

Electronic health records and stratified psychiatry: bridge to precision treatment?

The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data's power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.

Comparing clinical decision-making of AI technology to a multi-professional care team in an electronic cognitive behavioural therapy program for depression: protocol

Introduction

Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debilitating effects, only one-third of patients newly diagnosed with depression initiate treatment. Electronic cognitive behavioural therapy (e-CBT) is an effective treatment for depression and is a feasible solution to make mental health care more accessible. Due to its online format, e-CBT can be combined with variable therapist engagement to address different care needs. Typically, a multi-professional care team determines which combination therapy most benefits the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) has been proposed to offset these costs.

Methods

This study is a double-blinded randomized controlled trial recruiting individuals experiencing depression. The degree of care intensity a participant will receive will be randomly decided by either: (1) a machine learning algorithm, or (2) an assessment made by a group of healthcare professionals. Subsequently, participants will receive depression-specific e-CBT treatment through the secure online platform. There will be three available intensities of therapist interaction: (1) e-CBT; (2) e-CBT with a 15-20-min phone/video call; and (3) e-CBT with pharmacotherapy. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources.

Discussion

Artificial intelligence and providing patients with varying intensities of care can increase the efficiency of mental health care services. This study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to online psychotherapy by allocating the correct intensity of therapist care for individuals diagnosed with depression. This will be done by comparing a decision-making machine learning algorithm to a multi-professional care team. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources with the convergence of technologies and healthcare.

Ethics

The study received ethics approval and began participant recruitment in December 2022. Participant recruitment has been conducted through targeted advertisements and physician referrals. Complete data collection and analysis are expected to conclude by August 2024.

Clinical trial registration

ClinicalTrials.Gov, identifier NCT04747873.

Remote neurocognitive interventions for attention-deficit/hyperactivity disorder - Opportunities and challenges

Improving neurocognitive functions through remote interventions has been a promising approach to developing new treatments for attention-deficit/hyperactivity disorder (AD/HD). Remote neurocognitive interventions may address the shortcomings of the current prevailing pharmacological therapies for AD/HD, e.g., side effects and access barriers. Here we review the current options for remote neurocognitive interventions to reduce AD/HD symptoms, including cognitive training, EEG neurofeedback training, transcranial electrical stimulation, and external cranial nerve stimulation. We begin with an overview of the neurocognitive deficits in AD/HD to identify the targets for developing interventions. The role of neuroplasticity in each intervention is then highlighted due to its essential role in facilitating neuropsychological adaptations. Following this, each intervention type is discussed in terms of the critical details of the intervention protocols, the role of neuroplasticity, and the available evidence. Finally, we offer suggestions for future directions in terms of optimizing the existing intervention protocols and developing novel protocols.

EEG source functional connectivity in patients after a recent suicide attempt

OBJECTIVE

Electroencephalogram (EEG) based frequency measures within the alpha frequency range (AFR), including functional connectivity, show potential in assessing the underlying pathophysiology of depression and suicide-related outcomes. We investigated the association between AFR connectivity, suicidal thoughts and behaviors, and depression in a transdiagnostic sample of patients after a recent suicide attempt (SA).

METHODS

Lagged source-based measures of linear and nonlinear whole-brain connectivity within the standard AFR ([sAFR], 8-12 Hz) and the individually referenced AFR (iAFR) were applied to 70 15-minute resting-state EEGs from patients after a SA and 70 age- and gender-matched healthy controls (HC). Hypotheses were tested using network-based statistics and multiple regression models.

RESULTS

Results showed no significant differences between patients after a SA and HC in any of the assessed connectivity modalities. However, a subgroup analysis revealed significantly increased nonlinear connectivity within the sAFR for patients after a SA with a depressive disorder or episode ([DD], n = 53) compared to matched HC. Furthermore, a multiple regression model, including significant main effects for group and global nonlinear connectivity within the sAFR outperformed all other models in explaining variance in depressive symptom severity.

CONCLUSIONS

Our study further supports the importance of the AFR in pathomechanisms of suicidality and depression. The iAFR does not seem to improve validity of phase-based connectivity.

SIGNIFICANCE

Our results implicate distinct neurophysiological patterns in suicidal subgroups. Exploring the potential of these patterns for treatment stratification might advance targeted interventions for suicidal thoughts and behaviors.

Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers

BACKGROUND

Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation.

METHODS

In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software.

RESULTS

Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset.

DISCUSSION

Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Network perturbation-based biomarkers of depression and treatment response

Using concurrent TMS-EEG, Han et al.¹ identified temporal and spectral signatures of depression in a prefrontal-orbitofrontal-hippocampal network, which renormalized after rTMS. This highlights the relevance of causal network perturbation for the assessment of disease-related network states and their therapeutic modulation.

Electrophysiological and Clinical Predictors of Methylphenidate, Guanfacine, and Combined Treatment Outcomes in Children With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

The combination of d-methylphenidate and guanfacine (an α-2A agonist) has emerged as a potential alternative to either monotherapy in children with attention-deficit/hyperactivity disorder (ADHD), but it is unclear what predicts response to these treatments. This study is the first to investigate pretreatment clinical and electroencephalography (EEG) profiles as predictors of treatment outcome in children randomized to these different medications.

METHOD

A total of 181 children with ADHD (aged 7-14 years; 123 boys) completed an 8-week randomized, double-blind, comparative study with d-methylphenidate, guanfacine, or combined treatments. Pretreatment assessments included ratings on ADHD, anxiety, and oppositional behavior. EEG activity from cortical sources localized within midfrontal and midoccipital regions was measured during a spatial working memory task with encoding, maintenance, and retrieval phases. Analyses tested whether pretreatment clinical and EEG measures predicted treatment-related change in ADHD severity.

RESULTS

Higher pretreatment hyperactivity-impulsivity and oppositional symptoms and lower anxiety predicted greater ADHD improvements across all medication groups. Pretreatment event-related midfrontal beta power predicted treatment outcome with combined and monotherapy treatments, albeit in different directions. Weaker beta modulations predicted improvements with combined treatment, whereas stronger modulation during encoding and retrieval predicted improvements with d-methylphenidate and guanfacine, respectively. A multivariate model including EEG and clinical measures explained twice as much variance in ADHD improvement with guanfacine and combined treatment (R2= 0.34-0.41) as clinical measures alone (R2 = 0.14-.21).

CONCLUSION

We identified treatment-specific and shared predictors of response to different pharmacotherapies in children with ADHD. If replicated, these findings would suggest that aggregating information from clinical and brain measures may aid personalized treatment decisions in ADHD.

CLINICAL TRIAL REGISTRATION INFORMATION

Single Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder; https://clinicaltrials.gov; NCT00429273.

A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression

Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.

The pathophysiology of Post SSRI Sexual Dysfunction - Lessons from a case study

BACKGROUND

Although Post-SSRI Sexual Dysfunction (PSSD) has finally been recognized by the European Medicines Agency as a medical condition that can outlast discontinuation of SSRI and SNRI antidepressants, this condition is still largely unknown by patients, doctors, and researchers, and hence, poorly understood, underdiagnosed, and undertreated.

OBJECTIVE

Becoming familiar with the symptomatology of PSSD and understanding the underlying mechanisms and treatment options.

METHOD

We applied a design thinking approach to innovation to 1) provide insights into the medical condition as well as the personal needs and pains of a targeted patient; and 2) generate ideas for new solutions from the perspective of this particular patient. These insights and ideas informed a literature search on the potential pathophysiological mechanisms that could underlie the patient's symptoms.

RESULTS

The 55-year-old male patient developed symptoms of low libido, delayed ejaculation, erectile dysfunction, 'brain zaps', overactive bladder and urinary inconsistency after discontinuation of the SNRI venlafaxine. In many of these symptoms a dysregulation in serotonergic activity has been implicated, with an important role of 5-HT_1A receptor downregulation and possible downstream effects on neurosteroid and oxytocin systems.

CONCLUSIONS

The clinical presentation and development of symptoms are suggestive of PSSD but need further clinical elaboration. Further knowledge of post-treatment changes in serotonergic - and possibly noradrenergic - mechanisms is required to improve our understanding of the clinical complaints and to inform appropriate treatment regimes.

Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review

The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.

Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study

BACKGROUND

Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.

METHODS

The biomarker in this study was developed in a heterogeneous clinical sample (N = 4249) and first applied to two large transfer datasets, a priori stratifying young males (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored.

RESULTS

Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.

CONCLUSIONS

This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

Apathy in depression: An arterial spin labeling perfusion MRI study

INTRODUCTION

Apathy, as defined as a deficit in goal-directed behaviors, is a critical clinical dimension in depression associated with chronic impairment. Little is known about its cerebral perfusion specificities in depression. To explore neurovascular mechanisms underpinning apathy in depression by pseudo-continuous arterial spin labeling (pCASL) magnetic resonance imaging (MRI).

METHODS

Perfusion imaging analysis was performed on 90 depressed patients included in a prospective study between November 2014 and February 2017. Imaging data included anatomical 3D T1-weighted and perfusion pCASL sequences. A multiple regression analysis relating the quantified cerebral blood flow (CBF) in different regions of interest defined from the FreeSurfer atlas, to the Apathy Evaluation Scale (AES) total score was conducted.

RESULTS

After confound adjustment (demographics, disease and clinical characteristics) and correction for multiple comparisons, we observed a strong negative relationship between the CBF in the left anterior cingulate cortex (ACC) and the AES score (standardized beta = -0.74, corrected p value = 0.0008).

CONCLUSION

Our results emphasized the left ACC as a key region involved in apathy severity in a population of depressed participants. Perfusion correlates of apathy in depression evidenced in this study may contribute to characterize different phenotypes of depression.

Statins in depression: a repurposed medical treatment can provide novel insights in mental health

Depression has a large burden, but the development of new drugs for its treatment has proved difficult. Progresses in neuroscience have highlighted several physiopathological pathways, notably inflammatory and metabolic ones, likely involved in the genesis of depressive symptoms. A novel strategy proposes to repurpose established medical treatments of known safety and to investigate their potential antidepressant activity. Among numerous candidates, growing evidence suggests that statins may have a positive role in the treatment of depressive disorders, although some have raised concerns about possible depressogenic effects of these widely prescribed medications. This narrative review summarises relevant findings from translational studies implicating many interconnected neurobiological and neuropsychological, cardiovascular, endocrine-metabolic, and immunological mechanisms by which statins could influence mood. Also, the most recent clinical investigations on the effects of statins in depression are presented. Overall, the use of statins for the treatment of depressive symptoms cannot be recommended based on the available literature, though this might change as several larger, methodologically robust studies are being conducted. Nevertheless, statins can already be acknowledged as a driver of innovation in mental health, as they provide a novel perspective to the physical health of people with depression and for the development of more precise antidepressant treatments.

Treatment biomarkers for ADHD: Taking stock and moving forward

The development of treatment biomarkers for psychiatric disorders has been challenging, particularly for heterogeneous neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). Promising findings are also rarely translated into clinical practice, especially with regard to treatment decisions and development of novel treatments. Despite this slow progress, the available neuroimaging, electrophysiological (EEG) and genetic literature provides a solid foundation for biomarker discovery. This article gives an updated review of promising treatment biomarkers for ADHD which may enhance personalized medicine and novel treatment development. The available literature points to promising pre-treatment profiles predicting efficacy of various pharmacological and non-pharmacological treatments for ADHD. These candidate predictive biomarkers, particularly those based on low-cost and non-invasive EEG assessments, show promise for the future stratification of patients to specific treatments. Studies with repeated biomarker assessments further show that different treatments produce distinct changes in brain profiles, which track treatment-related clinical improvements. These candidate monitoring/response biomarkers may aid future monitoring of treatment effects and point to mechanistic targets for novel treatments, such as neurotherapies. Nevertheless, existing research does not support any immediate clinical applications of treatment biomarkers for ADHD. Key barriers are the paucity of replications and external validations, the use of small and homogeneous samples of predominantly White children, and practical limitations, including the cost and technical requirements of biomarker assessments and their unknown feasibility and acceptability for people with ADHD. We conclude with a discussion of future directions and methodological changes to promote clinical translation and enhance personalized treatment decisions for diverse groups of individuals with ADHD.

Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.

From Genes to Therapy in Autism Spectrum Disorder

In recent years, findings from genetic and other biological studies are starting to reveal the role of various molecular mechanisms that contribute to the etiology of ASD [...]

A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder

Psychotic symptoms are rarely concurrent with the clinical manifestations of depression. Additionally, whether psychotic major depression is a subtype of major depression or a clinical syndrome distinct from non-psychotic major depression remains controversial. Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, we developed a machine-learning-algorithm-based prediction model for concurrent psychotic symptoms in patients with depressive disorders. The advantages of machine learning algorithms include the easy identification of trends and patterns, handling of multi-dimensional and multi-faceted data, and wide application. Among 1171 patients with depressive disorders, those with psychotic symptoms were characterized by significantly higher rates of depressed mood, loss of interest and enjoyment, reduced energy and diminished activity, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, psychomotor agitation or retardation, disturbed sleep, diminished appetite, and greater proportions of moderate and severe degrees of depression compared to patients without psychotic symptoms. The area under the curve was 0.823. The overall accuracy was 0.931 (95% confidence interval: 0.897-0.956). Severe depression (degree of depression) was the most important variable in the prediction model, followed by diminished appetite, subthreshold (degree of depression), ideas or acts of self-harm or suicide, outpatient status, age, psychomotor retardation or agitation, and others. In conclusion, the machine-learning-based model predicted concurrent psychotic symptoms in patients with major depression in connection with the "severity psychosis" hypothesis.

TMS Database Registry Consortium Research Project in Japan (TReC-J) for Future Personalized Psychiatry

The registry project led by the Japanese Society for Clinical Transcranial Magnetic Stimulation (TMS) Research aims to establish a centralized database of epidemiological, clinical, and biological data on TMS therapy for refractory psychiatric disorders, including treatment-resistant depression, as well as to contribute to the elucidation of the therapeutic mechanism of TMS therapy and to the validation of its efficacy by analyzing and evaluating these data in a systematic approach. The objective of this registry project is to collect a wide range of complex data linked to patients with various neuropsychiatric disorders who received TMS therapy throughout Japan, and to make effective use of these data to promote cross-sectional and longitudinal exploratory observational studies. Research utilizing this registry project will be conducted in a multicenter, non-invasive, retrospective, and prospective observational research study design, regardless of the framework of insurance medical care, private practice, or clinical research. Through the establishment of the registry, which aims to make use of data, we will advance the elucidation of treatment mechanisms and identification of predictors of therapeutic response to TMS therapy for refractory psychiatric disorders on a more real-world research basis. Furthermore, as a future vision, we aim to develop novel neuromodulation medical devices, algorithms for predicting treatment efficacy, and digital therapeutics based on the knowledge generated from this TMS registry database.

Precision psychiatry: The complexity of personalizing antipsychotic dosing

Recently, Salagre and Vieta commented on the complexity of implementing precision medicine in psychiatry. For 25 years, this author has focused on a circumscribed type of precision medicine: personalized dosing using pharmacokinetic mechanisms to stratified patients. This short communication focuses on personalized dosing of three oral antipsychotics (clozapine, risperidone and paliperidone) and presents their maintenance dosing in a table which provides dose-correction factors generated by pharmacokinetic studies. Inhibitors need dose-correction factors < 1 and inducers need correction factors >1. Clozapine maintenance dosing is based on the dose needed to reach 350 ng/ml (the minimum plasma therapeutic concentration in treatment-resistant schizophrenia). Clozapine maintenance dosing is influenced by 3 levels of complexity: 1) ancestry groups (Asians/Native Americans; Europeans and Blacks), 2) sex-smoking subgroups (lowest dose in female non-smokers and highest in male smokers) and 3) presence/absence of poor metabolizer status (due to genetic and non-genetic causes including co-prescription of inhibitors, obesity or inflammation). Risperidone and paliperidone maintenance dosing are based on the dose needed to reach plasma concentrations of 20-60 ng/ml. Risperidone PMs need approximately half the dose, which can be explained by genetics (CYP2D6 PMs) or co-prescription of CYP2D6 inhibitors. Fluoxetine co-prescription may require one fourth the risperidone maintenance dose. Carbamazepine co-prescription may require twice the risperidone maintenance dose. Although not well studied, two groups may need higher doses of oral paliperidone: Koreans may need 1.5 times higher doses while those taking carbamazepine may need 3 times higher paliperidone maintenance doses. Precision dosing in psychiatry requires using blood levels of individuals.

The potential of precision psychiatry: what is in reach?

Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person's own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.