collection
https://read.qxmd.com/read/28678984/association-of-neural-and-emotional-impacts-of-reward-prediction-errors-with-major-depression
#1
JOURNAL ARTICLE
Robb B Rutledge, Michael Moutoussis, Peter Smittenaar, Peter Zeidman, Tanja Taylor, Louise Hrynkiewicz, Jordan Lam, Nikolina Skandali, Jenifer Z Siegel, Olga T Ousdal, Gita Prabhu, Peter Dayan, Peter Fonagy, Raymond J Dolan
IMPORTANCE: Major depressive disorder (MDD) is associated with deficits in representing reward prediction errors (RPEs), which are the difference between experienced and predicted reward. Reward prediction errors underlie learning of values in reinforcement learning models, are represented by phasic dopamine release, and are known to affect momentary mood. OBJECTIVE: To combine functional neuroimaging, computational modeling, and smartphone-based large-scale data collection to test, in the absence of learning-related concerns, the hypothesis that depression attenuates the impact of RPEs...
August 1, 2017: JAMA Psychiatry
https://read.qxmd.com/read/32979849/biotyping-in-psychosis-using-multiple-computational-approaches-with-one-data-set
#2
REVIEW
Carol A Tamminga, Brett A Clementz, Godfrey Pearlson, Macheri Keshavan, Elliot S Gershon, Elena I Ivleva, Jennifer McDowell, Shashwath A Meda, Sarah Keedy, Vince D Calhoun, Paulo Lizano, Jeffrey R Bishop, Matthew Hudgens-Haney, Ney Alliey-Rodriguez, Huma Asif, Robert Gibbons
Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis...
January 2021: Neuropsychopharmacology
https://read.qxmd.com/read/28973224/digital-phenotyping-technology-for-a-new-science-of-behavior
#3
JOURNAL ARTICLE
Thomas R Insel
No abstract text is available yet for this article.
October 3, 2017: JAMA
https://read.qxmd.com/read/32620005/advances-in-the-computational-understanding-of-mental-illness
#4
REVIEW
Quentin J M Huys, Michael Browning, Martin P Paulus, Michael J Frank
Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning...
January 2021: Neuropsychopharmacology
https://read.qxmd.com/read/31508498/toward-clinical-digital-phenotyping-a-timely-opportunity-to-consider-purpose-quality-and-safety
#5
REVIEW
Kit Huckvale, Svetha Venkatesh, Helen Christensen
The use of data generated passively by personal electronic devices, such as smartphones, to measure human function in health and disease has generated significant research interest. Particularly in psychiatry, objective, continuous quantitation using patients' own devices may result in clinically useful markers that can be used to refine diagnostic processes, tailor treatment choices, improve condition monitoring for actionable outcomes, such as early signs of relapse, and develop new intervention models. If a principal goal for digital phenotyping is clinical improvement, research needs to attend now to factors that will help or hinder future clinical adoption...
2019: NPJ Digital Medicine
https://read.qxmd.com/read/31650093/digital-phenotyping-for-psychiatry-accommodating-data-and-theory-with-network-science-methodologies
#6
JOURNAL ARTICLE
D M Lydon-Staley, I Barnett, T D Satterthwaite, D S Bassett
Digital phenotyping is the moment-by-moment quantification of our interactions with digital devices. With appropriate tools, digital phenotyping data afford unprecedented insight into our transactions with the world and hold promise for developing novel signatures of psychopathology that will aid in diagnosis, prognosis, and treatment selection of psychiatric disorders. In this review, we highlight empirical work merging digital phenotyping data, and particularly experience-sampling data collected via smartphone, with network theories of psychopathology and network science methodologies...
March 2019: Current Opinion in Biomedical Engineering
https://read.qxmd.com/read/31445619/digital-phenotyping-with-mobile-and-wearable-devices-advanced-symptom-measurement-in-child-and-adolescent-depression
#7
EDITORIAL
Lydia Sequeira, Marco Battaglia, Steve Perrotta, Kathleen Merikangas, John Strauss
With an estimated 75% of all mental disorders beginning in the first two decades of life,1 childhood and adolescence are crucial developmental periods to identify and intercept the unfolding of mental health problems, their relationships with physical health, and the multiple, interwoven connections to the surrounding environment.2 Because an individual's mental health is best conceptualized, captured, and treated by taking into account the network of physiological and social functions that constitute the context of individual experience, accessing and analyzing data on multiple health indicators simultaneously can accelerate prediction of disease progression...
September 2019: Journal of the American Academy of Child and Adolescent Psychiatry
https://read.qxmd.com/read/30999271/machine-learning-and-big-data-in-psychiatry-toward-clinical-applications
#8
REVIEW
Robb B Rutledge, Adam M Chekroud, Quentin Jm Huys
Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis...
April 2019: Current Opinion in Neurobiology
https://read.qxmd.com/read/31230144/modeling-subjective-belief-states-in-computational-psychiatry-interoceptive-inference-as-a-candidate-framework
#9
JOURNAL ARTICLE
Xiaosi Gu, Thomas H B FitzGerald, Karl J Friston
The nascent field computational psychiatry has undergone exponential growth since its inception. To date, much of the published work has focused on choice behaviors, which are primarily modeled within a reinforcement learning framework. While this initial normative effort represents a milestone in psychiatry research, the reality is that many psychiatric disorders are defined by disturbances in subjective states (e.g., depression, anxiety) and associated beliefs (e.g., dysmorphophobia, paranoid ideation), which are not considered in normative models...
August 2019: Psychopharmacology
https://read.qxmd.com/read/30810713/severity-and-variability-of-depression-symptoms-predicting-suicide-attempt-in-high-risk-individuals
#10
JOURNAL ARTICLE
Nadine M Melhem, Giovanna Porta, Maria A Oquendo, Jamie Zelazny, John G Keilp, Satish Iyengar, Ainsley Burke, Boris Birmaher, Barbara Stanley, J John Mann, David A Brent
IMPORTANCE: Predicting suicidal behavior continues to be among the most challenging tasks in psychiatry. OBJECTIVES: To examine the trajectories of clinical predictors of suicide attempt (specifically, depression symptoms, hopelessness, impulsivity, aggression, impulsive aggression, and irritability) for their ability to predict suicide attempt and to compute a risk score for suicide attempts. DESIGN, SETTING, AND PARTICIPANTS: This is a longitudinal study of the offspring of parents (or probands) with mood disorders who were recruited from inpatient units at Western Psychiatric Institute and Clinic (Pittsburgh) and New York State Psychiatric Institute...
June 1, 2019: JAMA Psychiatry
https://read.qxmd.com/read/30770893/deep-neural-networks-in-psychiatry
#11
REVIEW
Daniel Durstewitz, Georgia Koppe, Andreas Meyer-Lindenberg
Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them...
November 2019: Molecular Psychiatry
https://read.qxmd.com/read/30718455/brain-connectivity-alterations-in-early-psychosis-from-clinical-to-neuroimaging-staging
#12
JOURNAL ARTICLE
Alessandra Griffa, Philipp S Baumann, Paul Klauser, Emeline Mullier, Martine Cleusix, Raoul Jenni, Martijn P van den Heuvel, Kim Q Do, Philippe Conus, Patric Hagmann
Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode...
February 4, 2019: Translational Psychiatry
https://read.qxmd.com/read/30737014/predicting-polygenic-risk-of-psychiatric-disorders
#13
REVIEW
Alicia R Martin, Mark J Daly, Elise B Robinson, Steven E Hyman, Benjamin M Neale
Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms...
July 15, 2019: Biological Psychiatry
https://read.qxmd.com/read/30686485/mapping-the-delirium-literature-through-probabilistic-topic-modeling-and-network-analysis-a-computational-scoping-review
#14
REVIEW
Thomas H McCoy
BACKGROUND: Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically-ill populations. Delirium is recognized, through both professional competencies and instructional materials, as a core topic in consultation psychiatry. OBJECTIVE: Conduct a computational scoping review of the delirium literature to identify the overall contours of this literature and evolution of the delirium literature over time. METHODS: Algorithmic analysis of all research articles on delirium indexed in MEDLINE between 1995 and 2015 using network analysis of citation Medical Subject Headings (MeSH) tags and probabilistic topic modeling of article abstracts...
March 2019: Psychosomatics
https://read.qxmd.com/read/30715542/from-computation-to-the-first-person-auditory-verbal-hallucinations-and-delusions-of-thought-interference-in-schizophrenia-spectrum-psychoses
#15
JOURNAL ARTICLE
Clara S Humpston, Rick A Adams, David Benrimoh, Matthew R Broome, Philip R Corlett, Philip Gerrans, Guillermo Horga, Thomas Parr, Elizabeth Pienkos, Albert R Powers, Andrea Raballo, Cherise Rosen, David E J Linden
Schizophrenia-spectrum psychoses are highly complex and heterogeneous disorders that necessitate multiple lines of scientific inquiry and levels of explanation. In recent years, both computational and phenomenological approaches to the understanding of mental illness have received much interest, and significant progress has been made in both fields. However, there has been relatively little progress bridging investigations in these seemingly disparate fields. In this conceptual review and collaborative project from the 4th Meeting of the International Consortium on Hallucination Research, we aim to facilitate the beginning of such dialogue between fields and put forward the argument that computational psychiatry and phenomenology can in fact inform each other, rather than being viewed as isolated or even incompatible approaches...
February 1, 2019: Schizophrenia Bulletin
https://read.qxmd.com/read/30575457/mapping-symptoms-to-brain-networks-with-the-human-connectome
#16
REVIEW
Michael D Fox
New England Journal of Medicine, Volume 379, Issue 23, Page 2237-2245, December 2018.
December 6, 2018: New England Journal of Medicine
https://read.qxmd.com/read/30510440/precision-pharmacotherapy-psychiatry-s-future-direction-in-preventing-diagnosing-and-treating-mental-disorders
#17
REVIEW
Andreas Menke
Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems...
2018: Pharmacogenomics and Personalized Medicine
https://read.qxmd.com/read/30306886/machine-learning-multivariate-pattern-analysis-predicts-classification-of-posttraumatic-stress-disorder-and-its-dissociative-subtype-a-multimodal-neuroimaging-approach
#18
JOURNAL ARTICLE
Andrew A Nicholson, Maria Densmore, Margaret C McKinnon, Richard W J Neufeld, Paul A Frewen, Jean Théberge, Rakesh Jetly, J Donald Richardson, Ruth A Lanius
BACKGROUND: The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition methods have been applied recently to predict many psychiatric disorders, these techniques have not been utilized to predict subtypes of posttraumatic stress disorder (PTSD), including the dissociative subtype of PTSD (PTSD + DS). METHODS: Using Multiclass Gaussian Process Classification within PRoNTo, we examined the classification accuracy of: (i) the mean amplitude of low-frequency fluctuations (mALFF; reflecting spontaneous neural activity during rest); and (ii) seed-based amygdala complex functional connectivity within 181 participants [PTSD (n = 81); PTSD + DS (n = 49); and age-matched healthy trauma-unexposed controls (n = 51)]...
September 2019: Psychological Medicine
https://read.qxmd.com/read/30419537/ensemble-machine-learning-prediction-of-posttraumatic-stress-disorder-screening-status-after-emergency-room-hospitalization
#19
JOURNAL ARTICLE
Santiago Papini, Derek Pisner, Jason Shumake, Mark B Powers, Christopher G Beevers, Evan E Rainey, Jasper A J Smits, Ann Marie Warren
Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support...
December 2018: Journal of Anxiety Disorders
https://read.qxmd.com/read/30386225/applying-big-data-methods-to-understanding-human-behavior-and-health
#20
JOURNAL ARTICLE
Ahmed A Moustafa, Thierno M O Diallo, Nicola Amoroso, Nazar Zaki, Mubashir Hassan, Hany Alashwal
No abstract text is available yet for this article.
2018: Frontiers in Computational Neuroscience
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