scholarly journals Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

2021 ◽  
Author(s):  
Helena Pelin ◽  
Marcus Ising ◽  
Frederike Stein ◽  
Susanne Meinert ◽  
Tina Meller ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Psychotic Disorders ◽  
Characteristic Curve ◽  
Well Being ◽  
Healthy Controls ◽  
Discovery Sample ◽  
Polygenic Scores ◽  
Genetic Features ◽  
Molecular Etiology ◽  
Disease Stages

AbstractPsychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1–3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

scholarly journals Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

2021 ◽  
Author(s):  
Helena Pelin ◽  
Marcus Ising ◽  
Frederike Stein ◽  
Susanne Meinert ◽  
Tina Meller ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Psychotic Disorders ◽  
Clinical Manifestations ◽  
Well Being ◽  
Classification Systems ◽  
Therapeutic Interventions ◽  
Healthy Controls ◽  
Discovery Sample ◽  
Polygenic Scores ◽  
Genetic Features

AbstractPsychiatric disorders show heterogeneous clinical manifestations and disease trajectories, with current classification systems not accurately reflecting their molecular etiology. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify diagnostically mixed psychiatric patient clusters that share clinical and genetic features and may profit from similar therapeutic interventions. We used unsupervised high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N=1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, was characterized by general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. MDD patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N=622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction AUC=81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatment regimes.

scholarly journals Polygenic scores for schizophrenia and general cognitive ability: associations with six cognitive domains, premorbid intelligence, and cognitive composite score in individuals with a psychotic disorder and in healthy controls

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Magnus Johan Engen ◽  
Siv Hege Lyngstad ◽  
Torill Ueland ◽  
Carmen Elisabeth Simonsen ◽  
Anja Vaskinn ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Ability ◽  
Psychotic Disorder ◽  
Psychotic Disorders ◽  
Cognitive Impairments ◽  
Genetic Correlations ◽  
Healthy Controls ◽  
General Cognitive Ability ◽  
Cognitive Domains ◽  
Polygenic Scores

AbstractCognitive impairments are considered core features in schizophrenia and other psychotic disorders. Cognitive impairments are, to a lesser degree, also documented in healthy first-degree relatives. Although recent studies have shown (negative) genetic correlations between schizophrenia and general cognitive ability, the association between polygenic risk for schizophrenia and individual cognitive phenotypes remains unclear. We here investigated the association between a polygenic score for schizophrenia (SCZPGS) and six well-defined cognitive domains, in addition to a composite measure of cognitive ability and a measure of premorbid intellectual ability in 731 participants with a psychotic disorder and 851 healthy controls. We also investigated the association between a PGS for general cognitive ability (COGPGS) and the same cognitive domains in the same sample. We found no significant associations between the SCZPGS and any cognitive phenotypes, in either patients with a psychotic disorder or healthy controls. For COGPGS we observed stronger associations with cognitive phenotypes in healthy controls than in participants with psychotic disorders. In healthy controls, the association between COGPGS (at the p value threshold of ≥0.01) and working memory remained significant after Bonferroni correction (β = 0.12, p = 8.6 × 10−5). Altogether, the lack of associations between SCZPGS and COGPGS with cognitive performance in participants with psychotic disorders suggests that either environmental factors or unassessed genetic factors play a role in the development of cognitive impairments in psychotic disorders. Working memory should be further studied as an important cognitive phenotype.

scholarly journals Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with neurodevelopment

2019 ◽  
Author(s):  
Sarah E Morgan ◽  
Jonathan Young ◽  
Ameera X Patel ◽  
Kirstie J Whitaker ◽  
Cristina Scarpazza ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Psychotic Disorders ◽  
Aerobic Glycolysis ◽  
Characteristic Curve ◽  
Structural Connectivity ◽  
Mean Diffusivity ◽  
Case Control ◽  
Independent Study ◽  
Healthy Controls ◽  
Case Control Studies

AbstractBackgroundMachine learning (ML) can distinguish cases with psychotic disorder from healthy controls based on magnetic resonance imaging (MRI) data, with reported accuracy in the range 60-100%. It is not yet clear which MRI metrics are the most informative for case-control ML.MethodsWe analysed multi-modal MRI data from two independent case-control studies of patients with psychotic disorders (cases, N = 65, 28; controls, N = 59, 80) and compared ML accuracy across 5 MRI metrics. Cortical thickness, mean diffusivity and fractional anisotropy were estimated at each of 308 cortical regions, as well as functional and structural connectivity between each pair of regions. Functional connectivity data were also used to classify non-psychotic siblings of cases (N=64) and to distinguish cases from controls in a third independent study (cases, N=67; controls, N = 81).ResultsIn both principal studies, the most diagnostic metric was fMRI connectivity: the areas under the receiver operating characteristic curve were 92% and 77%, respectively. The cortical map of diagnostic connectivity features was replicable between studies (r = 0.31, P < 0.001); correlated with replicable case-control differences in fMRI degree centrality, and with prior cortical maps of aerobic glycolysis and adolescent development of functional connectivity; predicted intermediate probabilities of psychosis in siblings; and replicated in the third case-control study.ConclusionsML most accurately distinguished cases from controls by a replicable pattern of fMRI connectivity features, highlighting abnormal hubness of cortical nodes in an anatomical pattern consistent with the concept of psychosis as a disorder of network development.

scholarly journals Cortical thickness distinguishes between major depression and schizophrenia in adolescents

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zheyi Zhou ◽  
Kangcheng Wang ◽  
Jinxiang Tang ◽  
Dongtao Wei ◽  
Li Song ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Cortical Thickness ◽  
Psychotic Disorders ◽  
Support Vector ◽  
Healthy Controls ◽  
Classification Feature ◽  
Highest Weight ◽  
Temporal Pole ◽  
The Right ◽  
Leave One Out

Abstract Background Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. Methods We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. Results We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. Conclusions Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

Polygenic Scores Predict the Development of Alcohol and Nicotine Use Problems from Adolescence through Young Adulthood

2020 ◽  
Author(s):  
Joseph D. Deak ◽  
D. Angus Clark ◽  
Mengzhen Liu ◽  
C. Emily Durbin ◽  
William G. Iacono ◽  
...  
Keyword(s):  
Substance Use ◽  
Alcohol Use ◽  
Young Adulthood ◽  
Externalizing Behavior ◽  
Molecular Genetic ◽  
Early Adulthood ◽  
Predictive Utility ◽  
Problematic Use ◽  
Discovery Sample ◽  
Polygenic Scores

Objective: Molecular genetic studies of alcohol and nicotine have identified many genome-wide loci. We examined the predictive utility of drinking and smoking polygenic scores (PGS) for alcohol and nicotine use from late childhood to early adulthood, substance-specific versus broader-liability PGS effects, and if PGS performance varied between consumption versus pathological use. Methods: Latent growth curve models with structured residuals were used to assess the predictive utility of drinks per week and regular smoking PGS for measures of alcohol and nicotine consumption and problematic use from age 14 to 34. PGSs were generated from the largest discovery sample for alcohol and nicotine use to date (i.e., GSCAN), and examined for associations with alcohol and nicotine use in the Minnesota Twin Family Study (N=3225).Results: The drinking PGS was a significant predictor of age 14 problematic alcohol use and increases in problematic use during young adulthood. The smoking PGS was a significant predictor for all nicotine use outcomes. After adjusting for the effects of both PGSs, the smoking PGS demonstrated incremental predictive utility for most alcohol use outcomes and remained a significant predictor of nicotine use trajectories. Conclusions: Higher PGS for drinking and smoking were associated with more problematic levels of substance use longitudinally. The smoking PGS seems to capture both nicotine-specific and non-specific genetic liability for substance use, and may index genetic risk for broader externalizing behavior. Validation of PGS within longitudinal designs may have important clinical implications should future studies support the clinical utility of PGS for substance use disorders.

RepPer: Perception of Psychiatric Disorders on Twitter in French (Preprint)

2020 ◽  
Author(s):  
Sarah Delanys ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
François Olivier ◽  
Josiane Mothe
Keyword(s):  
Social Media ◽  
Psychiatric Disorders ◽  
Digital Technology ◽  
Psychotic Disorders ◽  
Negative Polarity ◽  
Machine Learning Algorithms ◽  
Annotation Scheme ◽  
Word Use ◽  
Wide Range ◽  
Initial Dataset

BACKGROUND With the advent of digital technology and specifically user generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. OBJECTIVE Our aim is to study how generic, nosographic and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has three complementary goals: (1) to analyze the types of psychiatric word use namely medical, misuse, irrelevant, (2) to analyze the polarity conveyed in the tweets that use these terms (positive/negative/neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English ). METHODS Our study has been conducted on a corpus of tweets in French posted between 01/01/2016 to 12/31/2018 and collected using dedicated keywords. The corpus has been manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. Two analysis have been performed. First a qualitative analysis to measure the reliability of the produced manual annotation, then a quantitative analysis considering mainly term frequency in each layer and exploring the interactions between them. RESULTS One of the first result is a resource as an annotated dataset . The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3,040 tweets that corresponds to the resource resulting from our work. The second result is the analysis of the annotations; it shows that terms are misused in 45.3% of the tweets and that their associated polarity is negative in 86.2% of the cases. When considering the three types of term use, 59.5% of the tweets are associated to a negative polarity. Misused terms related to psychotic disorders (55.5%) are more frequent to those related to mood disorders (26.5%). CONCLUSIONS Some psychiatric terms are misused in the corpora we studied; which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks which are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced; so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.

scholarly journals Psychiatric disorders in China: strengths and challenges of contemporary research and clinical services

2021 ◽  
pp. 1-14
Author(s):  
Xiao Chang ◽  
Qiyong Gong ◽  
Chunbo Li ◽  
Weihua Yue ◽  
Xin Yu ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Disease Burden ◽  
Large Scale ◽  
Well Being ◽  
Research Quality ◽  
Psychiatric Research ◽  
Clinical Services ◽  
Contributing Factors ◽  
Brain Functions ◽  
Neuroscience Research

Abstract China accounts for 17% of the global disease burden attributable to mental, neurological and substance use disorders. As a country undergoing profound societal change, China faces growing challenges to reduce the disease burden caused by psychiatric disorders. In this review, we aim to present an overview of progress in neuroscience research and clinical services for psychiatric disorders in China during the past three decades, analysing contributing factors and potential challenges to the field development. We first review studies in the epidemiological, genetic and neuroimaging fields as examples to illustrate a growing contribution of studies from China to the neuroscience research. Next, we introduce large-scale, open-access imaging genetic cohorts and recently initiated brain banks in China as platforms to study healthy brain functions and brain disorders. Then, we show progress in clinical services, including an integration of hospital and community-based healthcare systems and early intervention schemes. We finally discuss opportunities and existing challenges: achievements in research and clinical services are indispensable to the growing funding investment and continued engagement in international collaborations. The unique aspect of traditional Chinese medicine may provide insights to develop a novel treatment for psychiatric disorders. Yet obstacles still remain to promote research quality and to provide ubiquitous clinical services to vulnerable populations. Taken together, we expect to see a sustained advancement in psychiatric research and healthcare system in China. These achievements will contribute to the global efforts to realize good physical, mental and social well-being for all individuals.

scholarly journals Optimal Utility of H-Reflex RDD as a Biomarker of Spinal Disinhibition in Painful and Painless Diabetic Neuropathy

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1247
Author(s):  
Anne Worthington ◽  
Alise Kalteniece ◽  
Maryam Ferdousi ◽  
Luca Donofrio ◽  
Shaishav Dhage ◽  
...  
Keyword(s):  
Diabetic Neuropathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Initial Response ◽  
Healthy Controls ◽  
Painful Diabetic Neuropathy ◽  
Stimulus Response ◽  
Rate Dependent ◽  
Potential Biomarker ◽  
Spinal Inhibition

Impaired rate-dependent depression of the Hoffman reflex (HRDD) is a potential biomarker of impaired spinal inhibition in patients with painful diabetic neuropathy. However, the optimum stimulus-response parameters that identify patients with spinal disinhibition are currently unknown. We systematically compared HRDD, performed using trains of 10 stimuli at five stimulation frequencies (0.3, 0.5, 1, 2 and 3 Hz), in 42 subjects with painful and 62 subjects with painless diabetic neuropathy with comparable neuropathy severity, and 34 healthy controls. HRDD was calculated using individual and mean responses compared to the initial response. At stimulation frequencies of 1, 2 and 3 Hz, HRDD was significantly impaired in patients with painful diabetic neuropathy compared to patients with painless diabetic neuropathy for all parameters and for most parameters when compared to healthy controls. HRDD was significantly enhanced in patients with painless diabetic neuropathy compared to controls for responses towards the end of the 1 Hz stimulation train. Receiver operating characteristic curve analysis in patients with and without pain showed that the area under the curve was greatest for response averages of stimuli 2–4 and 2–5 at 1 Hz, AUC = 0.84 (95%CI 0.76–0.92). Trains of 5 stimuli delivered at 1 Hz can segregate patients with painful diabetic neuropathy and spinal disinhibition, whereas longer stimulus trains are required to segregate patients with painless diabetic neuropathy and enhanced spinal inhibition.

scholarly journals Psychometric properties of the EQ-5D-5L for aboriginal Australians: a multi-method study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Pedro Henrique Ribeiro Santiago ◽  
Dandara Haag ◽  
Davi Manzini Macedo ◽  
Gail Garvey ◽  
Megan Smith ◽  
...  
Keyword(s):  
Torres Strait Islander ◽  
Discriminant Validity ◽  
Reference Group ◽  
Characteristic Curve ◽  
Well Being ◽  
Two Dimensions ◽  
Future Research ◽  
Partial Credit Model ◽  
Validity And Reliability ◽  
Torres Strait

Abstract Introduction In Australia, health-related quality of life (HRQoL) instruments have been adopted in national population surveys to inform policy decisions that affect the health of Aboriginal and Torres Strait Islanders. However, Western-developed HRQoL instruments should not be assumed to capture Indigenous conceptualization of health and well-being. In our study, following recommendations for cultural adaptation, an Indigenous Reference Group indicated the EQ-5D-5L as a potentially valid instrument to measure aspects of HRQoL and endorsed further psychometric evaluation. Thus, this study aimed to investigate the construct validity and reliability of the EQ-5D-5L in an Aboriginal Australian population. Methods The EQ-5D-5L was applied in a sample of 1012 Aboriginal adults. Dimensionality was evaluated using Exploratory Graph Analysis. The Partial Credit Model was employed to evaluate item performance and adequacy of response categories. Area under the receiver operating characteristic curve (AUROC) was used to investigate discriminant validity regarding chronic pain, general health and experiences of discrimination. Results The EQ-5D-5L comprised two dimensions, Physiological and Psychological, and reliability was adequate. Performance at an item level was excellent and the EQ-5D-5L individual items displayed good discriminant validity. Conclusions The EQ-5D-5L is a suitable instrument to measure five specific aspects (Mobility, Self-Care, Usual activities, Pain/Discomfort, Anxiety/Depression) of Aboriginal and Torres Strait Islander HRQoL. A future research agenda comprises the investigation of other domains of Aboriginal and Torres Strait Islander HRQoL and potential expansions to the instrument.

scholarly journals A microRNA panel compared to environmental and polygenic scores for colorectal cancer risk prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Janhavi R. Raut ◽  
Ben Schöttker ◽  
Bernd Holleczek ◽  
Feng Guo ◽  
Megha Bhardwaj ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Prediction ◽  
Risk Score ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Population Based ◽  
Polygenic Risk Score ◽  
Nucleotide Polymorphisms ◽  
Polygenic Scores

AbstractCirculating microRNAs (miRNAs) could improve colorectal cancer (CRC) risk prediction. Here, we derive a blood-based miRNA panel and evaluate its ability to predict CRC occurrence in a population-based cohort of adults aged 50–75 years. Forty-one miRNAs are preselected from independent studies and measured by quantitative-real-time-polymerase-chain-reaction in serum collected at baseline of 198 participants who develop CRC during 14 years of follow-up and 178 randomly selected controls. A 7-miRNA score is derived by logistic regression. Its predictive ability, quantified by the optimism-corrected area-under-the-receiver-operating-characteristic-curve (AUC) using .632+ bootstrap is 0.794. Predictive ability is compared to that of an environmental risk score (ERS) based on known risk factors and a polygenic risk score (PRS) based on 140 previously identified single-nucleotide-polymorphisms. In participants with all scores available, optimism-corrected-AUC is 0.802 for the 7-miRNA score, while AUC (95% CI) is 0.557 (0.498–0.616) for the ERS and 0.622 (0.564–0.681) for the PRS.

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