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Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

Il Bin Kim, Seon-Cheol Park
2021 Diagnostics  
The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes.  ...  Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability.  ...  Conflicts of Interest: The authors have no conflict of interest relevant to this article.  ... 
doi:10.3390/diagnostics11091631 pmid:34573974 fatcat:6olkyfttffdo3bxccv37ragloe

Addressing heterogeneity (and homogeneity) in treatment mechanisms in depression and the potential to develop diagnostic and predictive biomarkers

Cynthia H.Y. Fu, Yong Fan, Christos Davatzikos
2019 NeuroImage: Clinical  
It has been 10 years since machine learning was first applied to neuroimaging data in psychiatric disorders to identify diagnostic and prognostic markers at the level of the individual.  ...  Proof of concept findings in major depression have since been extended in international samples and are beginning to include hundreds of samples from multisite data.  ...  Acknowledgements YF, CD are partially supported by National Institute of Health, USA grants: EB022573, MH112070, and AG054409.  ... 
doi:10.1016/j.nicl.2019.101997 pmid:31525565 pmcid:PMC6807387 fatcat:edmxf7sazbag7bxdm3cawa7yeu

Classification of Social Anxiety Disorder With Support Vector Machine Analysis Using Neural Correlates of Social Signals of Threat

Mengqi Xing, Jacklynn M. Fitzgerald, Heide Klumpp
2020 Frontiers in Psychiatry  
Previous studies of indirect/implicit processing of threatening faces have shown that support vector machine (SVM) pattern recognition significantly differentiates individuals with SAD from healthy participants  ...  Recursive feature elimination (RFE) was used for feature selection and to rank the contribution of regions in predicting SAD diagnosis.  ...  Feature Selection and Support Vector Machine Performance For the primary contrast of interest, threat (vs. happy) faces, no regions were excluded based on RFE.  ... 
doi:10.3389/fpsyt.2020.00144 pmid:32231598 pmcid:PMC7082922 fatcat:in54v4eec5fqldgx2x2cz3g64u

Differential impact of transdiagnostic, dimensional psychopathology on multiple scales of functional connectivity [article]

Darsol Seok, Joanne Beer, Marc Jaskir, Nathan Smyk, Adna Jaganjac, Walid Makhoul, Philip Cook, Mark Elliott, Russell Shinohara, Yvette I Sheline
2021 bioRxiv   pre-print
patterns, were used to associate connectivity patterns with six different dimensions of psychopathology: anxiety sensitivity, anxious arousal, rumination, anhedonia, insomnia and negative affect.  ...  Three modeling approaches (seed-based correlation analysis [SCA], support vector regression [SVR] and Brain Basis Set Modeling [BSS]), each relying on increasingly dense representations of functional connectivity  ...  Modeling Approach 2: Support vector regression (SVR) Support vector machines (SVMs) are a class of supervised machine learning techniques that attempt to learn a hyperplane that maximizes the margin between  ... 
doi:10.1101/2021.03.05.434151 fatcat:7ayhosx7q5gvdh5eeq4nufdjje

Integrating Neurobiological Markers of Depression

Tim Hahn, Andre F. Marquand, Ann-Christine Ehlis, Thomas Dresler, Sarah Kittel-Schneider, Tomasz A. Jarczok, Klaus-Peter Lesch, Peter M. Jakob, Janaina Mourao-Miranda, Michael J. Brammer, Andreas J. Fallgatter
2010 Archives of General Psychiatry  
The predictive model identifies a combination of neural responses to neutral faces, large rewards, and safety cues as nonredundant predictors of depression.  ...  Design: Two groups of participants underwent functional magnetic resonance imaging during 3 tasks probing neural processes relevant to depression.  ...  To benchmark the single-GP classifiers, we compared GP classifier accuracy ratios to the performance of the linear support vector machine (SVM) classifiers, 12,14 which constitute the most widely used  ... 
doi:10.1001/archgenpsychiatry.2010.178 pmid:21135315 fatcat:r3h3ufiajjfajirh3jem27xdnq

Plasma Metabolites Predict Severity of Depression and Suicidal Ideation in Psychiatric Patients-A Multicenter Pilot Analysis

Daiki Setoyama, Takahiro A. Kato, Ryota Hashimoto, Hiroshi Kunugi, Kotaro Hattori, Kohei Hayakawa, Mina Sato-Kasai, Norihiro Shimokawa, Sachie Kaneko, Sumiko Yoshida, Yu-ichi Goto, Yuka Yasuda (+7 others)
2016 PLoS ONE  
We successfully created a classification model to discriminate depressive patients with or without SI by machine learning technique.  ...  Evaluating the severity of depression (SOD), especially suicidal ideation (SI), is crucial in the treatment of not only patients with mood disorders but also psychiatric patients in general.  ...  Acknowledgments The authors would like to thank Dr. Koji Tanaka  ... 
doi:10.1371/journal.pone.0165267 pmid:27984586 pmcid:PMC5161310 fatcat:m7kexbgknrgctpogkonfji7isa

Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders

Andreas Menke
2018 Pharmacogenomics and Personalized Medicine  
In conclusion, precision medicine uses measurable health parameters to identify individuals at risk of a mental disorder, to improve the diagnostic process and to deliver a patient-tailored treatment.  ...  However, in the last decades, the understanding of biological mechanisms underlying mental disorders has grown and can be used for the development of precision medicine, that is, to deliver a patient-tailored  ...  and support vector machines to identify outcome predictors; and unsupervised methods, such as algorithms for dimensionality reduction and data clustering to reveal biological subgroups in patients. 162  ... 
doi:10.2147/pgpm.s146110 pmid:30510440 pmcid:PMC6250105 fatcat:ptge7tscgrh53dnsk3katktkiq

Psychiatric Neural Networks and Precision Therapeutics by Machine Learning

Hidetoshi Komatsu, Emi Watanabe, Mamoru Fukuchi
2021 Biomedicines  
In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for  ...  Learning and environmental adaptation increase the likelihood of survival and improve the quality of life.  ...  Acknowledgments: We would like to thank Abul K. Azad and Wei-hsuan Yu for reviewing this manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/biomedicines9040403 pmid:33917863 fatcat:qnptg73avbc5bdurwcjvf472p4

Recall bias and major depression lifetime prevalence

Scott B. Patten
2003 Social Psychiatry and Psychiatric Epidemiology  
K E Y W O R D S diffusion MRI, magnetic resonance imaging, major depressive disorder, structural MRI, support vector machine  ...  and support vector machine (SVM)-were used.  ...  TABLE 3 3 Predictive accuracy of binary classification MDD = major depressive disorder; HC = healthy controls; PLR = penalized logistic regression model with elastic net penalty; SVM = support vector machine  ... 
doi:10.1007/s00127-003-0649-9 pmid:12799778 fatcat:novbkmotqrc2pfcercqfryzsce

Neurostructural Heterogeneity in Youth with Internalizing Symptoms [article]

Antonia N Kaczkurkin, Aristeidis Sotiras, Erica B Baller, Monica E Calkins, Ganesh B Chand, Zaixu Cui, Guray Erus, Yong Fan, Raquel E Gur, Ruben C Gur, Tyler M Moore, David R Roalf (+7 others)
2019 bioRxiv   pre-print
We used a recently developed semi-supervised machine learning method (HYDRA) to delineate patterns of neurobiological heterogeneity within youth with internalizing symptoms using structural imaging data  ...  An alternative to classifying psychopathology based on clinical symptoms is to identify neurobiologically-informed subtypes based on brain imaging data.  ...  A) Schematic illustrating the use of a linear support vector machine (SVM) to separate cases from controls with a separating hyperplane, shown here as a gray line.  ... 
doi:10.1101/614438 fatcat:cmhxcvnsfzgbdmhu6coni2y7ga

Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models

Amita Sharma, Willem J. M. I. Verbeke, Zezhi Li
2021 PLoS ONE  
We used several machine learning models and extracted the variable importance contributing to a type of anxiety disorder.  ...  In this study, we apply machine learning (ML) techniques to understand the importance of a set of biomarkers with four types of anxiety disorders—Generalized Anxiety Disorder (GAD), Agoraphobia (AP), Social  ...  The authors wish to acknowledge all participants of the Lifelines Cohort Study, the contributing research centers delivering data to Lifelines, and everybody involved in the set up and implementation of  ... 
doi:10.1371/journal.pone.0251365 pmid:33970950 pmcid:PMC8109802 fatcat:nbcrvmhwpngmtox46xc6e4yhla

Statistical analysis plan for stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) study

Eva Petkova, R. Todd Ogden, Thaddeus Tarpey, Adam Ciarleglio, Bei Jiang, Zhe Su, Thomas Carmody, Philip Adams, Helena C. Kraemer, Bruce D. Grannemann, Maria A. Oquendo, Ramin Parsey (+4 others)
2017 Contemporary Clinical Trials Communications  
Antidepressant medications are commonly used to treat depression, but only about 30% of patients reach remission with any single first-step antidepressant.  ...  The lack of evidence-based knowledge to guide treatment decisions for patients with depression has lead to the recognition that specially designed studies with the primary objective being to discover biosignatures  ...  Acknowledgement The authors would like to thank the Editors and reviewers for their constructive suggestions and comments for improving this paper. This work is supported by NIMH/NIH under awards  ... 
doi:10.1016/j.conctc.2017.02.007 pmid:28670629 pmcid:PMC5485858 fatcat:d5pahra7mjge5ltajt527nzifm

Identifying motor functional neurological disorder using resting-state functional connectivity

Jennifer Wegrzyk, Valeria Kebets, Jonas Richiardi, Silvio Galli, Dimitri Van de Ville, Selma Aybek
2018 NeuroImage: Clinical  
Methods: We classified 23 mFND patients and 25 age-and gender-matched healthy controls based on wholebrain RS functional connectivity (FC) data, using a support vector machine classifier and the standard  ...  Conclusions: The good accuracy to discriminate patients from controls suggests that RS FC could be used as a biomarker with high diagnostic value in future clinical practice to identify mFND patients at  ...  We used a linear Support Vector Machine (SVM) classifier with L2 regularization to learn a discriminant function that would optimally separate the two groups.  ... 
doi:10.1016/j.nicl.2017.10.012 pmid:29071210 pmcid:PMC5651543 fatcat:aoziim6jovgl5a4vxcnahea224

Machine Learning Techniques for Anxiety Disorder

Elif ALTINTAŞ, Zeyneb UYLAŞ AKSU, Zeynep GÜMÜŞ DEMİR
2021 European Journal of Science and Technology  
In recent years, artificial intelligence based applications have been improved and used to improve the timing, sensitivity and quality of diagnosis of psychiatric diseases.  ...  We aim to review the existing literature on the use of artificial intelligence techniques in the assessment of subjects with anxiety disorder.  ...  Biomarker, one of the scanning and pattern methods, has been used in the diagnosis of the brain image. Wolfers et al.  ... 
doi:10.31590/ejosat.999914 fatcat:apaj6rt4y5f3pm5u3nn2epkbi4

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

Thalia Richter, Barak Fishbain, Gal Richter-Levin, Hadas Okon-Singer
2021 Journal of Personalized Medicine  
The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders.  ...  In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jpm11100957 pmid:34683098 fatcat:fms7g6ryw5e2jmkpnlc5bpc34y
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