Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis

Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis

The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.

Transcranial Magnetic Stimulation-Based Machine Learning Prediction of Tumor Grading in Motor-Eloquent Gliomas

BACKGROUND

Navigated transcranial magnetic stimulation (nTMS) is a well-established preoperative mapping tool for motor-eloquent glioma surgery. Machine learning (ML) and nTMS may improve clinical outcome prediction and histological correlation.

METHODS

This was a retrospective cohort study of patients who underwent surgery for motor-eloquent gliomas between 2018 and 2022. Ten healthy subjects were included. Preoperative nTMS-derived variables were collected: resting motor threshold (RMT), interhemispheric RMT ratio (iRMTr)-abnormal if above 10%-and cortical excitability score-number of abnormal iRMTrs. World Health Organization (WHO) grade and molecular profile were collected to characterize each tumor. ML models were fitted to the data after statistical feature selection to predict tumor grade.

RESULTS

A total of 177 patients were recruited: WHO grade 2-32 patients, WHO grade 3-65 patients, and WHO grade 4-80 patients. For the upper limb, abnormal iRMTr were identified in 22.7% of WHO grade 2, 62.5% of WHO grade 3, and 75.4% of WHO grade 4 patients. For the lower limb, iRMTr was abnormal in 23.1% of WHO grade 2, 67.6% of WHO grade 3%, and 63.6% of WHO grade 4 patients. Cortical excitability score (P = .04) was statistically significantly related with WHO grading. Using these variables as predictors, the ML model had an accuracy of 0.57 to predict WHO grade 4 lesions. In subgroup analysis of high-grade gliomas vs low-grade gliomas, the accuracy for high-grade gliomas prediction increased to 0.83. The inclusion of molecular data into the model-IDH mutation and 1p19q codeletion status-increases the accuracy of the model in predicting tumor grading (0.95 and 0.74, respectively).

CONCLUSION

ML algorithms based on nTMS-derived interhemispheric excitability assessment provide accurate predictions of HGGs affecting the motor pathway. Their accuracy is further increased when molecular data are fitted onto the model paving the way for a joint preoperative approach with radiogenomics.

The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach

BACKGROUND

Prediction of treatment resistance in schizophrenia (TRS) would be helpful to reduce the duration of ineffective treatment and avoid delays in clozapine initiation. We applied machine learning to identify clinical, sociodemographic, familial, and environmental variables that are associated with TRS and could potentially predict TRS in the future.

STUDY DESIGN

Baseline and follow-up data on trait(-like) variables from the Genetic Risk and Outcome of Psychosis (GROUP) study were used. For the main analysis, we selected patients with non-affective psychotic disorders who met TRS (n = 200) or antipsychotic-responsive criteria (n = 423) throughout the study. For a sensitivity analysis, we only selected patients who met TRS (n = 76) or antipsychotic-responsive criteria (n = 123) at follow-up but not at baseline. Random forest models were trained to predict TRS in both datasets. SHapley Additive exPlanation values were used to examine the variables' contributions to the prediction.

STUDY RESULTS

Premorbid functioning, age at onset, and educational degree were most consistently associated with TRS across both analyses. Marital status, current household, intelligence quotient, number of moves, and family loading score for substance abuse also consistently contributed to the prediction of TRS in the main or sensitivity analysis. The diagnostic performance of our models was modest (area under the curve: 0.66-0.69).

CONCLUSIONS

We demonstrate that various clinical, sociodemographic, familial, and environmental variables are associated with TRS. Our models only showed modest performance in predicting TRS. Prospective large multi-centre studies are needed to validate our findings and investigate whether the model's performance can be improved by adding data from different modalities.

Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability

Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries.

Stability of specific personality network features corresponding to openness trait across different adult age periods: A machine learning analysis

The functional connectivity patterns of the brain during resting state are closely related to an individual's cognition, emotion, behavior, and social interactions, making it an important research method to measure personality traits in an unbiased way, replacing traditional paper-and-pencil tests. However, due to the dynamic nature of the brain, whether the changes in functional connectivity caused by age can stably map onto personality traits has not been previously investigated. This study focuses on whether network features that are significantly related to personality traits can effectively distinguish subjects with different personality traits, and whether these network features vary across different periods of adulthood. The study included 343 healthy adult participants, divided into early adulthood and middle adulthood groups according to the age threshold of 35. Resting-state functional magnetic resonance imaging (fMRI) and the Big Five personality questionnaire were collected. we investigated the relationship between personality traits and intrinsic whole-brain functional connectome. We then used support vector machine (SVM) to evaluate the performance of personality network features in distinguishing subjects with high and low scores in the early-adulthood sample, and cross-validated in the mid-adulthood sample. Additionally, edge-based analysis (NBS) was used to explore the stability of personality networks across the two age samples. Our results show that the network features corresponding to openness personality trait are stable and can effectively differentiate subjects with different scores in both age samples. Furthermore, this study found that these network features vary to some extent across different periods of adulthood. These findings provide new evidence and insights into the application of resting-state functional connectivity patterns in measuring personality traits and help us better understand the dynamic characteristics of the human brain.

Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.

One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry

Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.

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.

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

Objective

To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.

Methods

The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.

Results

The area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877-0.926), 0.901 (95% CI: 0.874-0.923), 0.902 (95% CI: 0.876-0.924), and 0.955 (95% CI: 0.935-0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).

Conclusion

Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.

Recent advances in psychoradiology

Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.

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.

Predicting the Effects of Repetitive Transcranial Magnetic Stimulation on Cognitive Functions in Patients With Alzheimer's Disease by Automated EEG Analysis

Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that generally affects the elderly. Today, after the limited benefit of the pharmacological treatment strategies, numerous noninvasive brain stimulation techniques have been developed. Transcranial magnetic stimulation (TMS), based on electromagnetic stimulation, is one of the most widely used methods. The main problem in the use of TMS is the existence of large individual variability in the results. This causes a waste of money, time, and more importantly, a burden for delicate patients. Hence, it is a necessity to form an efficient and personalized TMS application protocol. In this paper, we performed a machine-learning analysis to see whether it is possible to predict the responses of patients with AD to TMS by analyzing their electroencephalography (EEG) signals. For that purpose, we analyzed both the EEG signals collected before and after the TMS application (EEG1 and EEG2, respectively). Through correlating EEG1 and repetitive transcranial magnetic stimulation (rTMS) outcomes, we tried to see whether it is possible to predict patients' responses before the treatment application. On the other hand, by EEG2 analysis, we investigated TMS impacts on EEG, more importantly if this impact is correlated with patients' response to the treatment. We used the support vector machine (SVM) classifier due to its multiple advantages for the current task with feature selection processes by stepwise linear discriminant analysis (SWLDA) and SVM. However, to justify our numerical analysis framework, we examined and compared the performances of different feature selection and classification techniques. Since we have a limited sample number, we used the leave-one-out method for the validation with the Monte Carlo technique to eliminate bias by a small sample size. In the conclusion, we observed that the correlation between rTMS outcomes and EEG2 is stronger than EEG1, since we observed, respectively, 93 and 79% of accuracies during our data analysis. Besides the informative features of EEG2 are focused on theta band, it indicates that TMS is characterizing the theta band signals in patients with AD in direct relation to patients' response to rTMS. This shows that it is more possible to determine patients' benefit from the TMS at the early stages of the treatment, which would increase the efficiency of rTMS applications in patients with Alzheimer's disease.

From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation

People with schizophrenia spectrum disorders (SSDs) often experience persistent social cognitive impairments, associated with poor functional outcome. There are currently no approved treatment options for these debilitating symptoms, highlighting the need for novel therapeutic strategies. Work to date has elucidated differential social processes and underlying neural circuitry affected in SSDs, which may be amenable to modulation using neurostimulation. Further, advances in functional connectivity mapping and electric field modeling may be used to identify individualized treatment targets to maximize the impact of brain stimulation on social cognitive networks. Here, we review literature supporting a roadmap for translating functional connectivity biomarker discovery to individualized treatment development for social cognitive impairments in SSDs. First, we outline the relevance of social cognitive impairments in SSDs. We review machine learning approaches for dimensional brain-behavior biomarker discovery, emphasizing the importance of individual differences. We synthesize research showing that brain stimulation techniques, such as repetitive transcranial magnetic stimulation, can be used to target relevant networks. Further, functional connectivity-based individualized targeting may enhance treatment response. We then outline recent approaches to account for neuroanatomical variability and optimize coil positioning to individually maximize target engagement. Overall, the synthesized literature provides support for the utility and feasibility of this translational approach to precision treatment. The proposed roadmap to translate biomarkers of social cognitive impairments to individualized treatment is currently under evaluation in precision-guided trials. Such a translational approach may also be applicable across conditions and generalizable for the development of individualized neurostimulation targeting other behavioral deficits.

Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression

BACKGROUND

Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.

AIMS

We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.

METHOD

Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).

RESULTS

Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.

CONCLUSIONS

Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.

Rationale and study design of a trial to assess rTMS add-on value for the amelioration of negative symptoms of schizophrenia (RADOVAN)

Background

Methods

Discussion

Trial registration number

Data dissemination

Concept of the Munich/Augsburg Consortium Precision in Mental Health for the German Center of Mental Health

The Federal Ministry of Education and Research (BMBF) issued a call for a new nationwide research network on mental disorders, the German Center of Mental Health (DZPG). The Munich/Augsburg consortium was selected to participate as one of six partner sites with its concept "Precision in Mental Health (PriMe): Understanding, predicting, and preventing chronicity." PriMe bundles interdisciplinary research from the Ludwig-Maximilians-University (LMU), Technical University of Munich (TUM), University of Augsburg (UniA), Helmholtz Center Munich (HMGU), and Max Planck Institute of Psychiatry (MPIP) and has a focus on schizophrenia (SZ), bipolar disorder (BPD), and major depressive disorder (MDD). PriMe takes a longitudinal perspective on these three disorders from the at-risk stage to the first-episode, relapsing, and chronic stages. These disorders pose a major health burden because in up to 50% of patients they cause untreatable residual symptoms, which lead to early social and vocational disability, comorbidities, and excess mortality. PriMe aims at reducing mortality on different levels, e.g., reducing death by psychiatric and somatic comorbidities, and will approach this goal by addressing interdisciplinary and cross-sector approaches across the lifespan. PriMe aims to add a precision medicine framework to the DZPG that will propel deeper understanding, more accurate prediction, and personalized prevention to prevent disease chronicity and mortality across mental illnesses. This framework is structured along the translational chain and will be used by PriMe to innovate the preventive and therapeutic management of SZ, BPD, and MDD from rural to urban areas and from patients in early disease stages to patients with long-term disease courses. Research will build on platforms that include one on model systems, one on the identification and validation of predictive markers, one on the development of novel multimodal treatments, one on the regulation and strengthening of the uptake and dissemination of personalized treatments, and finally one on testing of the clinical effectiveness, utility, and scalability of such personalized treatments. In accordance with the translational chain, PriMe's expertise includes the ability to integrate understanding of bio-behavioral processes based on innovative models, to translate this knowledge into clinical practice and to promote user participation in mental health research and care.

Developments in Biological Mechanisms and Treatments for Negative Symptoms and Cognitive Dysfunction of Schizophrenia

The causal mechanisms and treatment for the negative symptoms and cognitive dysfunction in schizophrenia are the main issues attracting the attention of psychiatrists over the last decade. The first part of this review summarizes the pathogenesis of schizophrenia, especially the negative symptoms and cognitive dysfunction from the perspectives of genetics and epigenetics. The second part describes the novel medications and several advanced physical therapies (e.g., transcranial magnetic stimulation and transcranial direct current stimulation) for the negative symptoms and cognitive dysfunction that will optimize the therapeutic strategy for patients with schizophrenia in future.

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.

Triple network hypothesis-related disrupted connections in schizophrenia: A spectral dynamic causal modeling analysis with functional magnetic resonance imaging

OBJECTIVE

The symptom-related neurobiology characteristic of schizophrenia in the brain from a network perspective is still poorly understood, leading to a lack of potential biologically-based markers and difficulty identifying therapeutic targets. We aim to test the dysregulated cross-network interactions among the Salience Network (SN), Central Executive Network (CEN) and Default Mode Network (DMN) and how they contributed to different symptoms in schizophrenia patients.

METHODS

We examined network interactions among the SN, CEN and DMN in 76 patients with schizophrenia vs. 80 well-matched controls using dynamic causal modeling (DCM). We further analyzed the relation between network dynamics and Positive and Negative Syndrome Scale (PANSS).

RESULTS

We observed that the DMN, CEN and SN across healthy controls and schizophrenia patients showed several similarities within or between-network pattern in the resting state. Comparing schizophrenia to controls, SN-centered cross-network interactions were most significantly reduced. Crucially, the strength of connections from CEN subnetwork 1 to DMN subnetwork 1 was positively correlated with the Positive Score of PANSS. The connection from the DMN subnetwork 2 to CEN subnetwork 2 was negatively correlated with the Negative Score of PANSS.

CONCLUSIONS

Our study provides strong evidence for the dysregulation among SN, CEN and DMN in a triple-network perspective in schizophrenia. The connection between DMN and CEN could be clinically-relevant neurobiological signature of schizophrenia symptoms. Our study indicated that the description of brain triple network hypothesis could be a novel and possible bio-marker for schizophrenia.

Accelerated aging signatures in subjects with schizophrenia and their unaffected siblings

Schizophrenia (SZ) is a chronic debilitating disease. Subjects with SZ have significant shorter life expectancy. Growing evidence suggests that a process of pathological accelerated aging occurs in SZ, leading to early development of severe clinical diseases and worse morbimortality. Furthermore, unaffected relatives can share certain endophenotypes with subjects with SZ. We aim to characterize accelerated aging as a possible endophenotype of schizophrenia by using a machine learning (ML) model of peripheral biomarkers to accurately differentiate subjects with SZ (n = 35), their unaffected siblings (SB, n = 36) and healthy controls (HC, n = 47). We used a random forest algorithm that included biomarkers related to aging: eotaxins CCL-11 and CCL-24; the oxidative stress markers thiobarbituric acid-reactive substances (TBARS), protein carbonyl content (PCC), glutathione peroxidase (GPx); and telomere length (TL). The ML algorithm of biomarkers was able to distinguish individuals with SZ from HC with prediction accuracy of 79.7%, SZ from SB with 62.5% accuracy and SB from HC with 75.5% accuracy. These results support the hypothesis that a pathological accelerated aging might occur in SZ, and this pathological aging could be an endophenotype of the disease, once this profile was also observed in SB, suggesting that SB might suffer from an accelerated aging in some level.

Sex Matters: A Multivariate Pattern Analysis of Sex- and Gender-Related Neuroanatomical Differences in Cis- and Transgender Individuals Using Structural Magnetic Resonance Imaging

Univariate analyses of structural neuroimaging data have produced heterogeneous results regarding anatomical sex- and gender-related differences. The current study aimed at delineating and cross-validating brain volumetric surrogates of sex and gender by comparing the structural magnetic resonance imaging data of cis- and transgender subjects using multivariate pattern analysis. Gray matter (GM) tissue maps of 29 transgender men, 23 transgender women, 35 cisgender women, and 34 cisgender men were created using voxel-based morphometry and analyzed using support vector classification. Generalizability of the models was estimated using repeated nested cross-validation. For external validation, significant models were applied to hormone-treated transgender subjects (n = 32) and individuals diagnosed with depression (n = 27). Sex was identified with a balanced accuracy (BAC) of 82.6% (false discovery rate [pFDR] < 0.001) in cisgender, but only with 67.5% (pFDR = 0.04) in transgender participants indicating differences in the neuroanatomical patterns associated with sex in transgender despite the major effect of sex on GM volume irrespective of the self-identification as a woman or man. Gender identity and gender incongruence could not be reliably identified (all pFDR > 0.05). The neuroanatomical signature of sex in cisgender did not interact with depressive features (BAC = 74.7%) but was affected by hormone therapy when applied in transgender women (P < 0.001).

Prediction of response to repetitive transcranial magnetic stimulation in phantom sounds based on individual brain anatomy

Non-invasive brain stimulation can reduce the severity of tinnitus phantom sounds beyond the time of stimulation by inducing regional neuroplastic changes. However, there are no good clinical predictors for treatment outcome. We used machine learning to investigate whether brain anatomy can predict therapeutic outcome. Sixty-one chronic tinnitus patients received repetitive transcranial magnetic stimulation of left dorsolateral prefrontal and temporal cortex. Before repetitive transcranial magnetic stimulation, a structural magnetic resonance image was obtained from all patients. To predict individual treatment response in new subjects, we employed a support vector machine ensemble for individual out-of-sample prediction. In the cross-validation, the support vector machine ensemble based on stratified sub-sampling and feature selection yielded an area under the curve of 0.87 for prediction of therapy success in new, previously unseen subjects. This corresponded to a balanced accuracy of 83.5%, sensitivity of 77.2% and specificity of 87.2%. Investigating the most selected features showed the involvement of the auditory cortex but also revealed a network of non-auditory brain areas. These findings suggest that idiosyncratic brain patterns accurately predict individual responses to repetitive transcranial magnetic stimulation treatment for tinnitus. Our findings may hence pave the way for future investigations into the precision treatment of tinnitus, involving automatic identification of the appropriate treatment method for the individual patient.

Repetitive Transcranial Magnetic Stimulation as a Therapeutic and Probe in Schizophrenia: Examining the Role of Neuroimaging and Future Directions

Schizophrenia is a complex condition associated with perceptual disturbances, decreased motivation and affect, and disrupted cognition. Individuals living with schizophrenia may experience myriad poor outcomes, including impairment in independent living and function as well as decreased life expectancy. Though existing treatments may offer benefit, many individuals still experience treatment resistant and disabling symptoms. In light of the negative outcomes associated with schizophrenia and the limitations in currently available treatments, there is a significant need for novel therapeutic interventions. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique that can modulate the activity of discrete cortical regions, allowing direct manipulation of local brain activation and indirect manipulation of the target's associated neural networks. rTMS has been studied in schizophrenia for the treatment of auditory hallucinations, negative symptoms, and cognitive deficits, with mixed results. The field's inability to arrive at a consensus on the use rTMS in schizophrenia has stemmed from a variety of issues, perhaps most notably the significant heterogeneity amongst existing trials. In addition, it is likely that factors specific to schizophrenia, rather than the rTMS itself, have presented barriers to the interpretation of existing results. However, advances in approaches to rTMS as a biologic probe and therapeutic, many of which include the integration of neuroimaging with rTMS, offer hope that this technology may still play a role in improving the understanding and treatment of schizophrenia.

The intervention, the patient and the illness - Personalizing non-invasive brain stimulation in psychiatry

Current hypotheses on the therapeutic action of non-invasive brain stimulation (NIBS) in psychiatric disorders build on the abundant data from neuroimaging studies. This makes NIBS a very promising tool for developing personalized interventions within a precision medicine framework. NIBS methods fundamentally vary in their neurophysiological properties. They comprise repetitive transcranial magnetic stimulation (rTMS) and its variants (e.g. theta burst stimulation - TBS) as well as different types of transcranial electrical stimulation (tES), with the largest body of evidence for transcranial direct current stimulation (tDCS). In the last two decades, significant conceptual progress has been made in terms of NIBS targets, i.e. from single brain regions to neural circuits and to functional connectivity as well as their states, recently leading to brain state modulating closed-loop approaches. Regarding structural and functional brain anatomy, NIBS meets an individually unique constellation, which varies across normal and pathophysiological states. Thus, individual constitutions and signatures of disorders may be indistinguishable at a given time point, but can theoretically be parsed along course- and treatment-related trajectories. We address precision interventions on three levels: 1) the NIBS intervention, 2) the constitutional factors of a single patient, and 3) the phenotypes and pathophysiology of illness. With examples from research on depressive disorders, we propose solutions and discuss future perspectives, e.g. individual MRI-based electrical field strength as a proxy for NIBS dosage, and also symptoms, their clusters, or biotypes instead of disorder focused NIBS. In conclusion, we propose interleaved research on these three levels along a general track of reverse and forward translation including both clinically directed research in preclinical model systems, and biomarker guided controlled clinical trials. Besides driving the development of safe and efficacious interventions, this framework could also deepen our understanding of psychiatric disorders at their neurophysiological underpinnings.

Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

Background

Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance.

Methods

Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities.

Results

The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03).

Conclusion

Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-021-00552-3.

Repetitive transcranial magnetic stimulation (rTMS) for schizophrenia patients treated with clozapine

OBJECTIVES

Biological strategies to improve treatment efficacy in clozapine-treated patients are urgently needed. Repetitive transcranial magnetic stimulation (rTMS) merits consideration as intervention for patients with persistent auditory hallucinations (AH) or negative symptoms (NS) not responding sufficiently to clozapine treatment.

METHODS

Data from 10 international RCTs of rTMS for patients being treated with clozapine were pooled. Two levels of symptomatic response were defined: improvement of ≥20% and ≥50% on study-specific primary endpoint scales. Changes in the positive and negative syndrome scale (PANSS) from baseline to endpoint assessment were also analysed.

RESULTS

Analyses of 131 patients did not reveal a significant difference for ≥20% and ≥50% response thresholds for improvement of AH, negative or total symptoms between active and sham rTMS groups. The number needed to treat (NNT) for an improvement in persistent AH was nine following active rTMS. PANSS scores did not improve significantly from baseline to endpoint between active and sham groups in studies investigating NS and AH.

CONCLUSIONS

rTMS as a treatment for persistent symptoms in clozapine-treated patients did not show a beneficial effect of active compared to sham treatment. For AH, the size of the NNTs indicates a possible beneficial effect of rTMS.

Bifrontal high-frequency transcranial random noise stimulation is not effective as an add-on treatment in depression

BACKGROUND

Depressive disorders are linked to dysfunction in prefrontal cortical areas. Hence, non-invasive neurostimulation of the prefrontal cortex has demonstrated antidepressant efficacy. In the present study, we investigated the efficacy of high frequency transcranial random noise stimulation (hf-tRNS) as an add-on treatment for depression in a sham-controlled randomized trial.

METHODS

Forty in-patients with depression were randomized and treated with real or sham hf-tRNS (100-650 Hz) with 0 mA offset. The electrodes were mounted over the left and right dorsolateral prefrontal cortex. The Hamilton Depression Rating Scale (primary outcome), the Major Depression Inventory, the Clinical Global Impression scale and the Global Assessment of Functioning scale were used for assessment at baseline, after 3 weeks of intervention (end of treatment), and 9 weeks after intervention. Safety parameters included cognitive functioning and reported side-effects.

RESULTS

Comparison of real and sham treatment at the planned interim analysis showed an amelioration of symptoms in both groups for all outcomes with numeric but not statistically significant superiority of the sham arm for the primary outcome. Thus, the study was terminated prematurely after an interim analysis. There were no systematic differences with respect to safety parameters.

LIMITATIONS

The negative finding might be related to the specific stimulation parameters used in this study.

CONCLUSIONS

Our study suggests that prefrontal hf-tRNS is safe but not effective as an add-on treatment of depression. The challenge for future studies employing transcranial electric stimulation remains to identify effective stimulation parameters for the treatment of depression.

Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non-responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter-hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non-responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI-derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies.

Effect Of Cognitive And Exercise Rehabilitation On Gait In Male Schizophrenic Patients Suffering From Depression Disorder

Objective ― The purpose of this study was to investigate the effect of cognitive and exercise rehabilitation training, and non-invasive brain stimulation on the gait of schizophrenic patients suffering from depression disorder. Methods ― Fifty-five male schizophrenic patients suffering from depression disorder aged 24 to 66 years were assigned to either a control group (n=10), cognitive rehabilitation group (n=10), exercise rehabilitation training group (n=13), transcranial direct current stimulation (tDCS) group (n=11) or multimodal intervention group (n=11). Gait was measured using a 10-meter gait speed test under normal, in dual-task cognitive-gait, and in dual task motor-gait conditions. Results ― Gait speed during the 10-meter walk test under normal conditions was significantly (P≤0.05) different from pre- to post-test in the exercise rehabilitation training, and cognitive rehabilitation multimodal intervention. Gait speed during the dual task motor-gait condition was significantly different from pre- to post-test in the exercise rehabilitation training, cognitive rehabilitation and, multimodal intervention groups (P≤0.05). Gait speed during the dual task cognitive-gait condition significantly changed from pre- to post-test following exercise rehabilitation training, cognitive rehabilitation, tDCS and the multimodal intervention group (P≤0.05). Conclusion ― This study’s findings demonstrate that exercise rehabilitation training, cognitive rehabilitation, and multimodal interventions could assist with preventing falls within Schizophrenic patients suffering from depression and thereby improve their functional independence.

Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern

'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Incisionless MR-guided focused ultrasound: technical considerations and current therapeutic approaches in psychiatric disorders

INTRODUCTION

MR-guided focused ultrasound operating at higher intensities have been reported to effectively and precisely ablate deeper brain structures like the basal ganglia or the thalamic nuclei for the treatment of refractory movement disorders, neuropathic pain and most recently neuropsychiatric disorders, while low-intensity focused ultrasound represents an approach promoting mechanical blood-brain-barrier opening and neuromodulation. This narrative review summarizes the technical development and the therapeutic potential of incisionless MRgFUS in order to treat neuropsychiatric disorders.

AREAS COVERED

A narrative review of clinical trials assessing the safety and efficacy of MRgFUS. A literature review was performed using the following search terms: MR-guided focused ultrasound, psychiatric disorders, noninvasive and invasive brain modulation/stimulation techniques.

EXPERT OPINION

MRgFUS ablation is under clinical investigation (unblinded study design) for obsessive-compulsive disorders (OCDs) [capsulotomy; ALIC] and depression/anxiety disorders [capsulotomy] and has demonstrated an improvement in OCD and depression, although of preliminary character. Low-intensity ultrasound applications have been explored in Alzheimer´s disease (phase 1 study) and healthy subjects. Currently, limited evidence hinders comparison and selection between MRgFUS and noninvasive/invasive brain modulation therapies. However, comparative, sham-controlled trials are needed to reexamine the preliminary findings for the treatment of psychiatric disorders.

A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects

BACKGROUND

Schizophrenia risk is associated with both genetic and environmental risk factors. Furthermore, cognitive abnormalities are established core characteristics of schizophrenia. We aim to assess whether a classification approach encompassing risk factors, cognition, and their associations can discriminate patients with schizophrenia (SCZs) from healthy control subjects (HCs). We hypothesized that cognition would demonstrate greater HC-SCZ classification accuracy and that combined gene-environment stratification would improve the discrimination performance of cognition.

METHODS

Genome-wide association study-based genetic, environmental, and neurocognitive classifiers were trained to separate 337 HCs from 103 SCZs using support vector classification and repeated nested cross-validation. We validated classifiers on independent datasets using within-diagnostic (SCZ) and cross-diagnostic (clinically isolated syndrome for multiple sclerosis, another condition with cognitive abnormalities) approaches. Then, we tested whether gene-environment multivariate stratification modulated the discrimination performance of the cognitive classifier in iterative subsamples.

RESULTS

The cognitive classifier discriminated SCZs from HCs with a balanced accuracy (BAC) of 88.7%, followed by environmental (BAC = 65.1%) and genetic (BAC = 55.5%) classifiers. Similar classification performance was measured in the within-diagnosis validation sample (HC-SCZ BACs, cognition = 70.5%; environment = 65.8%; genetics = 49.9%). The cognitive classifier was relatively specific to schizophrenia (HC-clinically isolated syndrome for multiple sclerosis BAC = 56.7%). Combined gene-environment stratification allowed cognitive features to classify HCs from SCZs with 89.4% BAC.

CONCLUSIONS

Consistent with cognitive deficits being core features of the phenotype of SCZs, our results suggest that cognitive features alone bear the greatest amount of information for classification of SCZs. Consistent with genes and environment being risk factors, gene-environment stratification modulates HC-SCZ classification performance of cognition, perhaps providing another target for refining early identification and intervention strategies.

Efficacy and Safety of Transcranial Direct Current Stimulation for Treating Negative Symptoms in Schizophrenia: A Randomized Clinical Trial

IMPORTANCE

Negative symptoms represent a substantial burden in schizophrenia. Although preliminary studies have suggested that transcranial direct current stimulation (tDCS) is effective for some clusters of symptoms, the clinical benefits for negative symptoms are unclear.

OBJECTIVE

To determine the efficacy and safety of tDCS vs sham as an add-on treatment for patients with schizophrenia and predominant negative symptoms.

DESIGN, SETTING, AND PARTICIPANTS

The double-blind Schizophrenia Treatment With Electric Transcranial Stimulation (STARTS) randomized clinical trial was conducted from September 2014 to March 2018 in 2 outpatient clinics in the state of São Paulo, Brazil. Patients with schizophrenia with stable negative and positive symptoms and a minimum score of 20 points in the negative symptoms subscale of the Positive and Negative Syndrome Scale (PANSS) were included.

INTERVENTIONS

Ten sessions of tDCS performed twice a day for 5 days or a sham procedure. The anode and the cathode were positioned over the left prefrontal cortex and the left temporoparietal junction, respectively.

MAIN OUTCOMES AND MEASURES

Change in the PANSS negative symptoms subscale score at week 6 was the primary outcome. Patients were followed-up for an additional 6 weeks.

RESULTS

Of the 100 included patients, 20 (20.0%) were female, and the mean (SD) age was 35.3 (9.3) years. A total of 95 patients (95.0%) finished the trial. In the intention-to-treat analysis, patients receiving active tDCS showed a significantly greater improvement in PANSS score compared with those receiving the sham procedure (difference, 2.65; 95% CI, 1.51-3.79; number needed to treat, 3.18; 95% CI, 2.12-6.99; P < .001). Response rates for negative symptoms (20% improvement or greater) were also higher in the active group (20 of 50 [40%]) vs the sham group (2 of 50 [4%]) (P < .001). These effects persisted at follow-up. Transcranial direct current stimulation was well tolerated, and adverse effects did not differ between groups, except for burning sensation over the scalp in the active group (43.8%) vs the sham group (14.3%) (P = .003).

CONCLUSIONS AND RELEVANCE

Transcranial direct current stimulation was effective and safe in ameliorating negative symptoms in patients with schizophrenia.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02535676.

Prediction of individual responses to electroconvulsive therapy in patients with schizophrenia: Machine learning analysis of resting-state electroencephalography

BACKGROUND

Electroconvulsive therapy (ECT) has strong efficacy in patients with treatment refractory schizophrenia. However, access to ECT has been limited by high costs, professional labor, treatment duration, and significant adverse effects. To provide support for the decision to perform ECT, we aimed to predict individual responses to ECT among patients with schizophrenia using machine learning analysis of resting-state electroencephalography (EEG).

METHODS

Forty-seven patients diagnosed with schizophrenia or schizoaffective disorder with EEG recordings before the course of ECT were treated at Seoul National University Hospital. Among these patients, 29 were responders who showed scores of 3 or less on the Clinical Global Impression Severity scale after the course of ECT. Transfer entropy (TE), which represents information flow, was extracted from baseline EEG data and used as a feature. Feature selection was performed with four methods, including Random Subset Feature Selection (RSFS). The random forest classifier was used to predict individual ECT responses.

RESULTS

The averaged TE, especially in frontal regions, was higher in ECT responders than in nonresponders. A predictive model using the RSFS method classified ECT responders and nonresponders with 85.3% balanced accuracy, 85.2% accuracy, 88.7% sensitivity, and 81.8% specificity. The positive predictive value was 82.6%, and the negative predictive value was 88.2%.

CONCLUSIONS

The results of the current study suggest that higher effective connectivity in frontal areas may be associated with a favorable ECT response. Furthermore, personalized decisions to perform ECT in clinical practice could be augmented by resting-state EEG biomarkers of the ECT response in schizophrenia patients.

Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy

Combining advanced neuroimaging with novel computational methods in network science and machine learning has led to increasingly meaningful descriptions of structure and function in both the normal and the abnormal brain, thereby contributing significantly to our understanding of psychiatric disorders as circuit dysfunctions. Despite its marked potential for psychiatric care, this approach has not yet extended beyond the research setting to any clinically useful applications. Here we review current developments in the study of neuroimaging data using network models and machine learning methods, with a focus on their promise in offering a framework for clinical translation. We discuss 3 potential contributions of these methods to psychiatric care: 1) a better understanding of psychopathology beyond current diagnostic boundaries; 2) individualized prediction of treatment response and prognosis; and 3) formal theories to guide the development of novel interventions. Finally, we highlight current obstacles and sketch a forward-looking perspective of how the application of machine learning and network modeling methods should proceed to accelerate their potential transformation of clinically useful tools.

[Brain imaging in schizophrenia : A review of current trends and developments]

Imaging methods have become the main approach for identifying dysfunctional neuronal networks in schizophrenia. This review article presents recent results of disorders of neuronal networks at structural and functional levels and summarizes the current developments. Large multicenter analyses have further established patterns of regional brain alterations, while novel methods in magnetic resonance (MR) morphometry have contributed to differentiating early from delayed brain structural changes. The use of machine learning approaches has not only enabled the establishment of classification models using biological data for future differential diagnostic use, it has also facilitated multivariate models for outcome prediction following therapeutic interventions. Novel methods, such as BrainAGE, a surrogate marker of accelerated brain aging processes, have added to longitudinal studies to gain insights into the brain structural dynamics from early brain developmental alterations to progressive structural brain changes after disease onset.

Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014-2018)

A group of European experts reappraised the guidelines on the therapeutic efficacy of repetitive transcranial magnetic stimulation (rTMS) previously published in 2014 [Lefaucheur et al., Clin Neurophysiol 2014;125:2150-206]. These updated recommendations take into account all rTMS publications, including data prior to 2014, as well as currently reviewed literature until the end of 2018. Level A evidence (definite efficacy) was reached for: high-frequency (HF) rTMS of the primary motor cortex (M1) contralateral to the painful side for neuropathic pain; HF-rTMS of the left dorsolateral prefrontal cortex (DLPFC) using a figure-of-8 or a H1-coil for depression; low-frequency (LF) rTMS of contralesional M1 for hand motor recovery in the post-acute stage of stroke. Level B evidence (probable efficacy) was reached for: HF-rTMS of the left M1 or DLPFC for improving quality of life or pain, respectively, in fibromyalgia; HF-rTMS of bilateral M1 regions or the left DLPFC for improving motor impairment or depression, respectively, in Parkinson's disease; HF-rTMS of ipsilesional M1 for promoting motor recovery at the post-acute stage of stroke; intermittent theta burst stimulation targeted to the leg motor cortex for lower limb spasticity in multiple sclerosis; HF-rTMS of the right DLPFC in posttraumatic stress disorder; LF-rTMS of the right inferior frontal gyrus in chronic post-stroke non-fluent aphasia; LF-rTMS of the right DLPFC in depression; and bihemispheric stimulation of the DLPFC combining right-sided LF-rTMS (or continuous theta burst stimulation) and left-sided HF-rTMS (or intermittent theta burst stimulation) in depression. Level A/B evidence is not reached concerning efficacy of rTMS in any other condition. The current recommendations are based on the differences reached in therapeutic efficacy of real vs. sham rTMS protocols, replicated in a sufficient number of independent studies. This does not mean that the benefit produced by rTMS inevitably reaches a level of clinical relevance.

[Brain stimulation for the selective treatment of schizophrenia symptom domains : Non-invasive and invasive concepts]

Given that one third of patients with schizophrenia (SZ) only show limited response to established treatments, alternative therapeutic strategies such as non-invasive/invasive brain stimulation approaches have emerged as an adjunctive treatment option for distinct SZ symptom domains (e.g. acoustic hallucinations, negative/positive symptoms and cognitive impairment). Taking comparative interventional studies and standardized technical parameters into consideration, current meta-analyses indicate that adjunctive electroconvulsive therapy, repetitive transcranial magnetic stimulation and transcranial direct current stimulation have a positive effect. Invasive deep brain stimulation and MR-guided ultrasound brain ablation procedures represent treatment modalities that are currently being clinically tested. Complementary pre-interventional screening approaches (e.g. electrophysiology, neuroimaging and molecular inflammatory profiling) have been recommended in order to identify symptom-tailored predictive measures for diagnosis and treatment.

Functional neuroimaging in treatment resistant schizophrenia: A systematic review

Despite the availability of several drugs, about 30% of patients with schizophrenia still fail to respond properly to a course of appropriate antipsychotic treatment. Functional neuroimaging studies have shown widespread patterns of altered activation and functional connectivity in treatment-resistant schizophrenia (TRS). The aim of the present study was to examine the available functional magnetic resonance imaging studies investigating TRS and to identify common patterns of altered brain function that could predict the lack of response to antipsychotic treatment in this disorder. Alterations of activation and functional connectivity in fronto-temporal, cortico-striatal, default mode network and salience networks, and of their interplay, were associated with TRS. Our findings support the notion that large-scale network alterations present in schizophrenia lie in a continuum within treatment response with the most severe dysfunction in TRS. Few studies with small sample size and without adequate control group limit the generalizability of current literature. Future controlled longitudinal studies are needed to identify neuroimaging biomarkers of pharmacotherapy response to inform individual treatment selection and facilitate early clinical response.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Translational machine learning for psychiatric neuroimaging

Despite its initial promise, neuroimaging has not been widely translated into clinical psychiatry to assist in the prediction of diagnoses, prognoses, and optimal therapeutic strategies. Machine learning approaches may enhance the translational potential of neuroimaging because they specifically focus on overcoming biases by optimizing the generalizability of pipelines that measure complex brain patterns to predict targets at a single-subject level. This article introduces some fundamentals of a translational machine learning approach before selectively reviewing literature to-date. Promising initial results are then balanced by the description of limitations that should be considered in order to interpret existing research and maximize the possibility of future translation. Future directions are then presented in order to inspire further research and progress the field towards clinical translation.

Forty years of structural brain imaging in mental disorders: is it clinically useful or not?

Structural brain imaging was introduced into routine clinical practice more than 40 years ago with the hope that it would support the diagnosis and treatment of mental disorders. It is now widely used to exclude organic brain disease (eg, brain tumors, cardiovascular, and inflammatory processes) in mental disorders. However, questions have been raised about whether structural brain imaging is still needed today and whether it could also be clinically useful to apply new biostatistical methods, such as machine learning. Therefore, the current paper not only reviews structural findings in Alzheimer disease, depression, bipolar disorder, and schizophrenia but also discusses the role of structural imaging in supporting diagnostic, prognostic, and therapeutic processes in mental disorders. Thus, it attempts to answer the questions whether, after four decades of use, structural brain imaging is clinically useful in mental disorders or whether it will become so in the future.

Structural similarity networks predict clinical outcome in early-phase psychosis

Despite recent advances, there is still a major need for prediction of treatment success in schizophrenia, a condition long considered a disorder of dysconnectivity in the brain. Graph theory provides a means to characterize the connectivity in both healthy and abnormal brains. We calculated structural similarity networks in each participant and hypothesized that the "hubness", i.e., the number of edges connecting a node to the rest of the network, would be associated with clinical outcome. This prospective controlled study took place at an academic research center and included 82 early-phase psychosis patients (23 females; mean age [SD] = 21.6 [5.5] years) and 58 healthy controls. Medications were administered in a double-blind randomized manner, and patients were scanned at baseline prior to treatment with second-generation antipsychotics. Symptoms were assessed with the Brief Psychiatric Rating Scale at baseline and over the course of 12 weeks. Nodal degree of structural similarity networks was computed for each subject and entered as a predictor of individual treatment response into a partial least squares (PLS) regression. The model fit was significant in a permutation test with 1000 permutations (P = 0.006), and the first two PLS regression components explained 29% (95% CI: 27; 30) of the variance in treatment response after cross-validation. Nodes loading strongly on the first PLS component were primarily located in the orbito- and prefrontal cortex, whereas nodes loading strongly on the second PLS component were primarily located in the superior temporal, precentral, and middle cingulate cortex. These data suggest a link between brain network morphology and clinical outcome in early-phase psychosis.

Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study

INTRODUCTION

Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response.

OBJECTS

We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia.

METHODS

Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFF_ratio , was evaluated based on diagnostic tests and clinical correlates.

RESULTS

In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFF_ratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFF_ratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset.

CONCLUSIONS

Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.

Development of Neuroimaging-Based Biomarkers in Psychiatry

This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.