Artificial neural network model for the prediction of obsessive-compulsive disorder treatment response

Abnormal spontaneous neural activity in hippocampal-cortical system of patients with obsessive-compulsive disorder and its potential for diagnosis and prediction of early treatment response

Early brain functional changes induced by pharmacotherapy in patients with obsessive-compulsive disorder (OCD) in relation to drugs per se or because of the impact of such drugs on the improvement of OCD remain unclear. Moreover, no neuroimaging biomarkers are available for diagnosis of OCD and prediction of early treatment response. We performed a longitudinal study involving 34 patients with OCD and 36 healthy controls (HCs). Patients with OCD received 5-week treatment with paroxetine (40 mg/d). Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were applied to acquire and analyze the imaging data. Compared with HCs, patients with OCD had higher ReHo values in the right superior temporal gyrus and bilateral hippocampus/parahippocampus/fusiform gyrus/cerebellum at baseline. ReHo values in the left hippocampus and parahippocampus decreased significantly after treatment. The reduction rate (RR) of ReHo values was positively correlated with the RRs of the scores of Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and obsession. Abnormal ReHo values at baseline could serve as potential neuroimaging biomarkers for OCD diagnosis and prediction of early therapeutic response. This study highlighted the important role of the hippocampal-cortical system in the neuropsychological mechanism underlying OCD, pharmacological mechanism underlying OCD treatment, and the possibility of building models for diagnosis and prediction of early treatment response based on spontaneous activity in the hippocampal-cortical system.

Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning

INTRODUCTION

The course of OCD differs widely among OCD patients, varying from chronic symptoms to full remission. No tools for individual prediction of OCD remission are currently available. This study aimed to develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily accessible in the daily clinical routine.

METHODS

Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision trees were used as supervised machine learning technique. The training of the algorithm was performed with 227 predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm was subsequently tested in an independent sample of 215 observations collected in five different centers. Between-center differences were investigated with a bootstrap resampling approach.

RESULTS

The average predictive performance of the algorithm in the test centers resulted in an AUROC of 0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD chronic course, use of psychotropic medications, and better global functioning.

LIMITATIONS

All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover, the sample of the data recruited in some of the test centers was limited in size.

DISCUSSION

The algorithm demonstrated a moderate average predictive performance, and future studies will focus on increasing the stability of the predictive performance across clinical settings.

Genomics of Obsessive-Compulsive Disorder-Toward Personalized Medicine in the Era of Big Data

Pathogenesis of obsessive-compulsive disorder (OCD) mainly involves dysregulation of serotonergic neurotransmission, but a number of other factors are involved. Genetic underprints of OCD fall under the category of "common disease common variant hypothesis," that suggests that if a disease that is heritable is common in the population (a prevalence >1-5%), then the genetic contributors-specific variations in the genetic code-will also be common in the population. Therefore, the genetic contribution in OCD is believed to come from multiple genes simultaneously and it is considered a polygenic disorder. Genomics offers a number of advanced tools to determine causal relationship between the exposure and the outcome of interest. Particularly, methods such as polygenic risk score (PRS) or Mendelian Randomization (MR) enable investigation of new pathways involved in OCD pathogenesis. This premise is also facilitated by the existence of publicly available databases that include vast study samples. Examples include population-based studies such as UK Biobank, China Kadoorie Biobank, Qatar Biobank, All of US Program sponsored by National Institute of Health or Generations launched by Yale University, as well as disease-specific databases, that include patients with OCD and co-existing pathologies, with the following examples: Psychiatric Genomics Consortium (PGC), ENIGMA OCD, The International OCD Foundation Genetics Collaborative (IOCDF-GC) or OCD Collaborative Genetic Association Study. The aim of this review is to present a comprehensive overview of the available Big Data resources for the study of OCD pathogenesis in the context of genomics and demonstrate that OCD should be considered a disorder which requires the approaches offered by personalized medicine.

Olfactory Impairment in Children and Adolescents With Obsessive-Compulsive Disorder

In this study, we aimed to examine the olfactory function of adolescents with obsessive-compulsive disorder (OCD). We investigated olfactory function of 50 adolescents with OCD and 50 healthy controls (min-max, 12-17 years) by the "Sniffin' Sticks" extended test. OCD and depression symptomatology were assessed with the Maudsley Obsessive-Compulsive Inventory (MOCI) and the Children's Depression Inventory (CDI). Adolescents with OCD had lower olfactory performance than healthy controls. The patients who responded positively to the treatment exhibited performance superior to the patients with partial response and those untreated. All olfactory measurements were significantly inversely correlated with MOCI and CDI total scores and OCD duration. Our results show that OCD has a significant impact on all olfactory tests, and olfactory impairment is related to symptom severity, duration, and course of OCD. The decrease in olfactory function may be a noninvasive state marker for OCD. Further investigations in longitudinal studies are required to confirm our results.

How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review

BACKGROUND

New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example.

OBJECTIVE

The impact of new technologies on health professionals' practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD.

METHODS

We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video].

RESULTS

We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention.

CONCLUSIONS

The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

The revolution of personalized psychiatry: will technology make it happen sooner?

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive-compulsive disorder

OBJECTIVE

About 30% of obsessive-compulsive disorder (OCD) patients exhibit an inadequate response to pharmacotherapy. The detection of clinical variables associated with treatment response may result in achievement of remission in shorter period, preventing illness development and reducing socioeconomic costs.

METHODS

In total, 330 subjects with OCD diagnosis underwent 12-week pharmacotherapy with fluvoxamine (150-300 mg). Treatment response was ≥25% reduction in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) score. In total, 36 clinical attributes of 151 subjects who had completed their treatment course were analyzed. Data mining algorithms included missing value handling, feature selection, and new analytical method based on ensemble classification. The results were compared with those of other traditional classification algorithms such as decision tree, support vector machines, k-nearest neighbor, and random forest.

RESULTS

Sexual and contamination obsessions are high-ranked predictors of resistance to fluvoxamine pharmacotherapy as well as high Y-BOCS obsessive score. Our results showed that the proposed analysis strategy has good ability to distinguish responder and nonresponder patients according to their clinical features with 86% accuracy, 79% sensitivity, and 89% specificity.

CONCLUSION

This study proposed an analytical approach which is an accurate and a sensitive method for the analysis of high-dimensional medical data sets containing more number of missing values. The treatment of OCD could be improved by better understanding of the predictors of pharmacotherapy, which may lead to more effective treatment of patients with OCD.

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder.

Studying depression using imaging and machine learning methods

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

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Past studies successfully studied depression using machine learning and imaging.

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Past studies have limitations in their methods.

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Methods for future studies can be improved.

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Future studies could yield more robust models to diagnosis and treat depression.

Neuropsychological and neurophysiological insights into hoarding disorder

Hoarding disorder (HD) is associated with significant personal impairment in function and constitutes a severe public health burden. Individuals who hoard experience intense distress in discarding a large number of objects, which results in extreme clutter. Research and theory suggest that hoarding may be associated with specific deficits in information processing, particularly in the areas of attention, memory, and executive functioning. There is also growing interest in the neural underpinnings of hoarding behavior. Thus, the primary aim of this review is to summarize the current state of evidence regarding neuropsychological deficits associated with hoarding and review research on its neurophysiological underpinnings. We also outline the prominent theoretical model of hoarding and provide an up-to-date description of empirically based psychological and medical treatment approaches for HD. Finally, we discuss important future avenues for elaborating our model of HD and improving treatment access and outcomes for this disabling disorder.

Meta-analysis: hoarding symptoms associated with poor treatment outcome in obsessive-compulsive disorder

DSM-5 recognizes hoarding disorder as distinct from obsessive-compulsive disorder (OCD), codifying a new consensus. Hoarding disorder was previously classified as a symptom of OCD and patients received treatments designed for OCD. We conducted a meta-analysis to determine whether OCD patients with hoarding symptoms responded differently to traditional OCD treatments compared with OCD patients without hoarding symptoms. An electronic search was conducted for eligible studies in PubMed. A trial was eligible for inclusion if it (1) was a randomized controlled trial, cohort or case-control study; (2) compared treatment response between OCD patients with and those without hoarding symptoms, or examined response to treatment between OCD symptom dimensions (which typically include hoarding) and (3) examined treatment response to pharmacotherapy, behavioral therapy or their combination. Our primary outcome was differential treatment response between OCD patients with and those without hoarding symptoms, expressed as an odds ratio (OR). Twenty-one studies involving 3039 total participants including 304 with hoarding symptoms were included. Patients with OCD and hoarding symptoms were significantly less likely to respond to traditional OCD treatments than OCD patients without hoarding symptoms (OR=0.50 (95% confidence interval 0.42-0.60), z=-7.5, P<0.0001). This finding was consistent across treatment modalities. OCD patients with hoarding symptoms represent a population in need of further treatment research. OCD patients with hoarding symptoms may benefit more from interventions specifically targeting their hoarding symptoms.

Venlafaxine extended-release treatment of hoarding disorder

Hoarding disorder, classified as a separate disorder in Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5), is a common, chronic, and potentially disabling syndrome that can be difficult to treat. Only one previous study prospectively measured response to pharmacotherapy in compulsive hoarders, finding that hoarders responded as well to paroxetine as did nonhoarding obsessive-compulsive disorder patients. However, paroxetine was not tolerated well in that study, and the overall response was moderate. Therefore, we conducted an open-label trial of venlafaxine extended-release for hoarding disorder. Twenty-four patients fulfilling the DSM-5 criteria for hoarding disorder were treated with venlafaxine extended-release for 12 weeks. All patients were free of psychotropic medications for at least 6 weeks before the study. No other psychotropic medications, cognitive-behavioral therapy, organizers, or cleaning crews were permitted during the study. To measure the severity of hoarding, the Saving Inventory-Revised (SI-R) and the UCLA Hoarding Severity Scale (UHSS) were administered before and after treatment. Twenty-three of the 24 patients completed treatment. Hoarding symptoms improved significantly, with a mean 36% decrease in UHSS scores and a mean 32% decrease in SI-R scores. Sixteen of the 23 completers (70%) were classified as responders to venlafaxine extended-release. These results suggest that venlafaxine extended-release may be effective for the treatment of hoarding disorder.

Self-reported quality of life and clinician-rated functioning in mood and anxiety disorders: relationships and neuropsychological correlates

This study aimed to investigate 1) the relationship between subjective perception of quality of life (QoL) and clinician-rated levels of psychosocial functioning and 2) the relationship of these indicators with neuropsychological performances, in a sample of 117 subjects with mood and anxiety disorders hospitalized for a 4-week psychiatric rehabilitation program. At the beginning of the hospitalization, QoL and clinician-rated functioning were respectively measured by the World Health Organization Quality of Life Assessment-Brief Form (WHOQOL-BREF) and the Global Assessment of Functioning (GAF) scale, and subjects were administered a neuropsychological battery evaluating verbal and visual memory, working memory, attention, visual-constructive ability, language fluency and comprehension. We did not find any association between WHOQOL-BREF and GAF scores and between cognitive impairment and lower QoL or clinician-rated functioning. Our results suggest that 1) the individuals' condition encompasses different dimensions that are not fully captured by using only clinician-rated or self-administered evaluations; 2) the GAF scale seems unable to indicate the cognitive impairments of our subjects and the WHOQOL-BREF does not appear to be influenced by these deficits. Overall, our findings suggest the need of simultaneously use of multiple assessment tools, including objective evaluations of functioning and different measures of QoL, in order to obtain a more complete clinical picture of the patients. This may allow to identify more specific targets of therapeutic interventions and more reliable measures of outcome.

Risk classification of cancer survival using ANN with gene expression data from multiple laboratories

Numerous cancer studies have combined gene expression experiments and clinical survival data to predict the prognosis of patients of specific gene types. However, most results of these studies were data dependent and were not suitable for other data sets. This study performed cross-laboratory validations for the cancer patient data from 4 hospitals. We investigated the feasibility of survival risk predictions using high-throughput gene expression data and clinical data. We analyzed multiple data sets for prognostic applications in lung cancer diagnosis. After building tens of thousands of various ANN architectures using the training data, five survival-time correlated genes were identified from 4 microarray gene expression data sets by examining the correlation between gene signatures and patient survival time. The experimental results showed that gene expression data can be used for valid predictions of cancer patient survival classification with an overall accuracy of 83.0% based on survival time trusted data. The results show the prediction model yielded excellent predictions given that patients in the high-risk group obtained a lower median overall survival compared with low-risk patients (log-rank test P-value<0.00001). This study provides a foundation for further clinical studies and research into other types of cancer. We hope these findings will improve the prognostic methods of cancer patients.

Pharmacogenetics of obsessive-compulsive disorders

Genetic factors have been shown to influence occurrence and severity of several psychiatric disorders and also to modulate outcome to drug treatment. Obsessive-compulsive disorder (OCD) is a severe psychiatric condition with clear genetic roots; there is also some evidence to suggest that genetic factors may impact on response to drug treatment. Typically between 40 and 60% of patients are deemed nonresponders to antidepressant medication and clinical factors have only been modestly correlated with treatment response. Thus, identification of biological factors which may relate to treatment response could be extremely valuable in improving clinical outcome. In this article, we briefly review previous work regarding clinical and demographical factors associated with drug response in OCD, then focus on recent findings regarding candidate genes which may influence drug response, including those in the serotonin system, brain-derived neurotrophic factor and the glutamate transporter gene. The cytochrome system may also be highly relevant to drug response. Thus far, relatively few studies regarding the pharmacogenetics of OCD have been published, and therefore further investigation with functional analyses and consideration of environmental factors are warranted to facilitate clinical use of pharmacogenetic findings in the future.