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https://www.readbyqxmd.com/read/30496527/recurrent-neural-networks-in-mobile-sampling-and-intervention
#1
Georgia Koppe, Sinan Guloksuz, Ulrich Reininghaus, Daniel Durstewitz
The rapid rise and now widespread distribution of handheld and wearable devices, such as smartphones, fitness trackers, or smartwatches, has opened a new universe of possibilities for monitoring emotion and cognition in everyday-life context, and for applying experience- and context-specific interventions in psychosis. These devices are equipped with multiple sensors, recording channels, and app-based opportunities for assessment using experience sampling methodology (ESM), which enables to collect vast amounts of temporally highly resolved and ecologically valid personal data from various domains in daily life...
November 28, 2018: Schizophrenia Bulletin
https://www.readbyqxmd.com/read/30483163/is-it-possible-to-predict-the-future-in-first-episode-psychosis
#2
REVIEW
Jaana Suvisaari, Outi Mantere, Jaakko Keinänen, Teemu Mäntylä, Eva Rikandi, Maija Lindgren, Tuula Kieseppä, Tuukka T Raij
The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk...
2018: Frontiers in Psychiatry
https://www.readbyqxmd.com/read/30467771/supervised-machine-learning-to-decipher-the-complex-associations-between-neuro-immune-biomarkers-and-quality-of-life-in-schizophrenia
#3
Buranee Kanchanatawan, Sira Sriswasdi, Michael Maes
Stable phase schizophrenia is characterized by altered patterning in tryptophan catabolites (TRYCATs) and memory impairments, which are associated with PHEMN (psychosis, hostility, excitation, mannerism and negative) and DAPS (depression, anxiety and physio-somatic) symptoms. This study was carried out to examine the association between TRYCAT patterning, memory impairments, psychopathological features and health-related quality of life (HR-QoL) in schizophrenia. The World Health Organization (WHO) QoL instrument-Abbreviated version (WHO-QoL-BREF), IgA/IgM responses to TRYCATs, cognitive tests, Scale for the Assessment of Negative Symptoms (SANS), Hamilton and Depression (HAMD) and Anxiety (HAMA) Rating Scales and the Fibromyalgia and Chronic Fatigue Syndrome Rating Scale (FF) were measured in 80 schizophrenia patients and 40 controls...
November 22, 2018: Metabolic Brain Disease
https://www.readbyqxmd.com/read/30459704/the-clinical-picture-of-psychosis-in-manifest-huntington-s-disease-a-comprehensive-analysis-of-the-enroll-hd-database
#4
Natalia P Rocha, Benson Mwangi, Carlos A Gutierrez Candano, Cristina Sampaio, Erin Furr Stimming, Antonio L Teixeira
Background: Psychotic symptoms have been under-investigated in Huntington's disease (HD) and research is needed in order to elucidate the characteristics linked to the unique phenotype of HD patients presenting with psychosis. Objective: To evaluate the frequency and factors associated with psychosis in HD. Methods: Cross-sectional study including manifest individuals with HD from the Enroll-HD database. Both conventional statistical analysis (Stepwise Binary Logistic Regression) and five machine learning algorithms [Least Absolute Shrinkage and Selection Operator (LASSO); Elastic Net; Support Vector Machines (SVM); Random Forest; and class-weighted SVM] were used to describe factors associated with psychosis in manifest HD patients...
2018: Frontiers in Neurology
https://www.readbyqxmd.com/read/30343250/using-fmri-and-machine-learning-to-predict-symptom-improvement-following-cognitive-behavioural-therapy-for-psychosis
#5
Eva Tolmeijer, Veena Kumari, Emmanuelle Peters, Steven C R Williams, Liam Mason
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level...
2018: NeuroImage: Clinical
https://www.readbyqxmd.com/read/30267047/prediction-models-of-functional-outcomes-for-individuals-in-the-clinical-high-risk-state-for-psychosis-or-with-recent-onset-depression-a-multimodal-multisite-machine-learning-analysis
#6
Nikolaos Koutsouleris, Lana Kambeitz-Ilankovic, Stephan Ruhrmann, Marlene Rosen, Anne Ruef, Dominic B Dwyer, Marco Paolini, Katharine Chisholm, Joseph Kambeitz, Theresa Haidl, André Schmidt, John Gillam, Frauke Schultze-Lutter, Peter Falkai, Maximilian Reiser, Anita Riecher-Rössler, Rachel Upthegrove, Jarmo Hietala, Raimo K R Salokangas, Christos Pantelis, Eva Meisenzahl, Stephen J Wood, Dirk Beque, Paolo Brambilla, Stefan Borgwardt
Importance: Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. Objective: To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning...
November 1, 2018: JAMA Psychiatry
https://www.readbyqxmd.com/read/30242828/identifying-a-neuroanatomical-signature-of-schizophrenia-reproducible-across-sites-and-stages-using-machine-learning-with-structured-sparsity
#7
A de Pierrefeu, T Löfstedt, C Laidi, F Hadj-Selem, J Bourgin, T Hajek, F Spaniel, M Kolenic, P Ciuciu, N Hamdani, M Leboyer, T Fovet, R Jardri, J Houenou, E Duchesnay
OBJECTIVE: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect...
December 2018: Acta Psychiatrica Scandinavica
https://www.readbyqxmd.com/read/30242810/is-it-ethical-to-use-prognostic-estimates-from-machine-learning-to-treat-psychosis
#8
Nicole Martinez-Martin, Laura B Dunn, Laura Weiss Roberts
Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary examines the following ethical questions about machine learning in a case of a patient with new onset psychosis: (1) When is clinical innovation ethically acceptable? (2) How should clinicians communicate with patients about the ethical issues raised by a machine learning predictive model?
September 1, 2018: AMA Journal of Ethics
https://www.readbyqxmd.com/read/30054176/is-there-a-symptomatic-distinction-between-the-affective-psychoses-and-schizophrenia-a-machine-learning-approach
#9
S Jauhar, R Krishnadas, M M Nour, D Cunningham-Owens, E C Johnstone, S M Lawrie
Dubiety exists over whether clinical symptoms of schizophrenia can be distinguished from affective psychosis, the assumption being that absence of a "point of rarity" indicates lack of nosological distinction, based on prior group-level analyses. Advanced machine learning techniques, using unsupervised (hierarchical clustering) and supervised (regularized logistic regression algorithm and nested-cross-validation) were applied to a dataset of 202 patients with functional psychosis (schizophrenia n = 120, affective psychosis, n = 82)...
July 24, 2018: Schizophrenia Research
https://www.readbyqxmd.com/read/29971330/use-of-machine-learning-to-determine-deviance-in-neuroanatomical-maturity-associated-with-future-psychosis-in-youths-at-clinically-high-risk
#10
Yoonho Chung, Jean Addington, Carrie E Bearden, Kristin Cadenhead, Barbara Cornblatt, Daniel H Mathalon, Thomas McGlashan, Diana Perkins, Larry J Seidman, Ming Tsuang, Elaine Walker, Scott W Woods, Sarah McEwen, Theo G M van Erp, Tyrone D Cannon
Importance: Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but whether neural indicators of these trajectories are associated with future psychosis is unclear. Objective: To investigate distinct neuroanatomical markers that can differentiate aberrant neurodevelopmental trajectories among clinically high-risk (CHR) individuals. Design, Setting, and Participants: In this prospective longitudinal multicenter study, a neuroanatomical-based age prediction model was developed using a supervised machine learning technique with T1-weighted magnetic resonance imaging scans of 953 healthy controls 3 to 21 years of age from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and then applied to scans of 275 CHR individuals (including 39 who developed psychosis) and 109 healthy controls 12 to 21 years of age from the North American Prodrome Longitudinal Study 2 (NAPLS 2) for external validation and clinical application...
September 1, 2018: JAMA Psychiatry
https://www.readbyqxmd.com/read/29790237/towards-a-new-classification-of-stable-phase-schizophrenia-into-major-and-simple-neuro-cognitive-psychosis-results-of-unsupervised-machine-learning-analysis
#11
Buranee Kanchanatawan, Sira Sriswasdi, Supaksorn Thika, Drozdstoy Stoyanov, Sunee Sirivichayakul, André F Carvalho, Michel Geffard, Michael Maes
RATIONALE: Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate classifications should be based on supervised and unsupervised learning rather than on consensus criteria. METHODS: This study used machine learning as means to provide a more accurate classification of patients with stable phase schizophrenia...
August 2018: Journal of Evaluation in Clinical Practice
https://www.readbyqxmd.com/read/29726413/a-new-machine-learning-framework-for-understanding-the-link-between-cannabis-use-and-first-episode-psychosis
#12
Wajdi Alghamdi, Daniel Stamate, Daniel Stahl, Alexander Zamyatin, Robin Murray, Marta Di Forti
Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes' predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the prediction performance of the models with respect to models built on data without these specific attributes...
2018: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/29614390/can-neuropsychological-testing-facilitate-differential-diagnosis-between-at-risk-mental-state-arms-for-psychosis-and-adult-attention-deficit-hyperactivity-disorder-adhd
#13
Erich Studerus, Salvatore Corbisiero, Nadine Mazzariello, Sarah Ittig, Letizia Leanza, Laura Egloff, Katharina Beck, Ulrike Heitz, Christina Andreou, Rolf-Dieter Stieglitz, Anita Riecher-Rössler
BACKGROUND: Patients with an at-risk mental state (ARMS) for psychosis and patients with attention-deficit/hyperactivity disorder (ADHD) have many overlapping signs and symptoms and hence can be difficult to differentiate clinically. The aim of this study was to investigate whether the differential diagnosis between ARMS and adult ADHD could be improved by neuropsychological testing. METHODS: 168 ARMS patients, 123 adult ADHD patients and 109 healthy controls (HC) were recruited via specialized clinics of the University of Basel Psychiatric Hospital...
August 2018: European Psychiatry: the Journal of the Association of European Psychiatrists
https://www.readbyqxmd.com/read/29548527/investigating-brain-structural-patterns-in-first-episode-psychosis-and-schizophrenia-using-mri-and-a-machine-learning-approach
#14
Adriana Miyazaki de Moura, Walter Hugo Lopez Pinaya, Ary Gadelha, André Zugman, Cristiano Noto, Quirino Cordeiro, Sintia Iole Belangero, Andrea P Jackowski, Rodrigo A Bressan, João Ricardo Sato
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) or healthy controls (HC), from a schizophrenia model perspective. Brain regions volumetric data were estimated by using MRI images of SCZ and FEP patients and HC. First, we evaluated the MLDA performance in discriminating SCZ from controls, which provided a score based on a model for changes in brain structure in SCZ...
May 30, 2018: Psychiatry research. Neuroimaging
https://www.readbyqxmd.com/read/29454222/obesity-dyslipidemia-and-brain-age-in-first-episode-psychosis
#15
Marian Kolenic, Katja Franke, Jaroslav Hlinka, Martin Matejka, Jana Capkova, Zdenka Pausova, Rudolf Uher, Martin Alda, Filip Spaniel, Tomas Hajek
INTRODUCTION: Obesity and dyslipidemia may negatively affect brain health and are frequent medical comorbidities of schizophrenia and related disorders. Despite the high burden of metabolic disorders, little is known about their effects on brain structure in psychosis. We investigated, whether obesity or dyslipidemia contributed to brain alterations in first-episode psychosis (FEP). METHODS: 120 participants with FEP, who were undergoing their first psychiatric hospitalization, had <24 months of untreated psychosis and were 18-35 years old and 114 controls within the same age range participated in the study...
April 2018: Journal of Psychiatric Research
https://www.readbyqxmd.com/read/29380275/in-schizophrenia-depression-anxiety-and-physiosomatic-symptoms-are-strongly-related-to-psychotic-symptoms-and-excitation-impairments-in-episodic-memory-and-increased-production-of-neurotoxic-tryptophan-catabolites-a-multivariate-and-machine-learning-study
#16
Buranee Kanchanatawan, Supaksorn Thika, Sunee Sirivichayakul, André F Carvalho, Michel Geffard, Michael Maes
The depression, anxiety and physiosomatic symptoms (DAPS) of schizophrenia are associated with negative symptoms and changes in tryptophan catabolite (TRYCAT) patterning. The aim of this study is to delineate the associations between DAPS and psychosis, hostility, excitation, and mannerism (PHEM) symptoms, cognitive tests as measured using the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) and IgA/IgM responses to TRYCATs. We included 40 healthy controls and 80 participants with schizophrenia...
April 2018: Neurotoxicity Research
https://www.readbyqxmd.com/read/29358019/the-early-psychosis-screener-eps-quantitative-validation-against-the-sips-using-machine-learning
#17
B B Brodey, R R Girgis, O V Favorov, J Addington, D O Perkins, C E Bearden, S W Woods, E F Walker, B A Cornblatt, G Brucato, B Walsh, K A Elkin, I S Brodey
Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire-Brief Version (PQ-B) and 148 additional items were administered to 229 individuals being screened with the SIPS at 7 North American Prodrome Longitudinal Study sites and at Columbia University. Fifty individuals were found to have SIPS scores of 0, 1, or 2, making them clinically low risk (CLR) controls; 144 were classified as clinically high risk (CHR) (SIPS 3-5) and 35 were found to have first episode psychosis (FEP) (SIPS 6)...
January 18, 2018: Schizophrenia Research
https://www.readbyqxmd.com/read/29352548/prediction-of-psychosis-across-protocols-and-risk-cohorts-using-automated-language-analysis
#18
Cheryl M Corcoran, Facundo Carrillo, Diego Fernández-Slezak, Gillinder Bedi, Casimir Klim, Daniel C Javitt, Carrie E Bearden, Guillermo A Cecchi
Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e...
February 2018: World Psychiatry: Official Journal of the World Psychiatric Association (WPA)
https://www.readbyqxmd.com/read/29175503/non-literal-language-comprehension-in-a-large-sample-of-first-episode-psychosis-patients-in-adulthood
#19
Cinzia Perlini, Marcella Bellani, Livio Finos, Antonio Lasalvia, Chiara Bonetto, Paolo Scocco, Armando D'Agostino, Stefano Torresani, Massimiliano Imbesi, Francesca Bellini, Angela Konze, Angela Veronese, Mirella Ruggeri, Paolo Brambilla
To date no data still exist on the comprehension of figurative language in the early phases of psychosis. The aim of this study is to investigate for the first time the comprehension of metaphors and idioms at the onset of the illness. Two-hundred-twenty eight (228) first episode psychosis (FEP) patients (168 NAP, non-affective psychosis; 60 AP, affective psychosis) and 70 healthy controls (HC) were assessed. Groups were contrasted on: a) type of stimulus (metaphors vs idioms) and b) type of response (OPEN = spontaneous explanations vs CLOSED = multiple choice answer)...
February 2018: Psychiatry Research
https://www.readbyqxmd.com/read/28882707/functional-connectivity-of-large-scale-brain-networks-in-patients-with-anti-nmda-receptor-encephalitis-an-observational-study
#20
Michael Peer, Harald Prüss, Inbal Ben-Dayan, Friedemann Paul, Shahar Arzy, Carsten Finke
BACKGROUND: In anti-NMDA receptor (NMDAR) encephalitis, antibody-mediated dysfunction of NMDARs causes severe neuropsychiatric symptoms, including psychosis, memory deficits, and movement disorders. However, it remains elusive how antibody-mediated NMDAR dysfunction leads to these symptoms, and whether the symptoms arise from impairment in specific brain regions and the interactions between impaired regions. METHODS: In this observational study, we recruited 43 patients with anti-NMDAR encephalitis from a tertiary university hospital and 43 age-matched and sex-matched healthy controls without a history of neurological or psychiatric disorders, who were recruited from the general population of Berlin...
October 2017: Lancet Psychiatry
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