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machine learning psychosis

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...
September 26, 2018: JAMA Psychiatry
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...
September 21, 2018: Acta Psychiatrica Scandinavica
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
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
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
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
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
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
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
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
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
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
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)
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
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
Raymond Salvador, Joaquim Radua, Erick J Canales-Rodríguez, Aleix Solanes, Salvador Sarró, José M Goikolea, Alicia Valiente, Gemma C Monté, María Del Carmen Natividad, Amalia Guerrero-Pedraza, Noemí Moro, Paloma Fernández-Corcuera, Benedikt L Amann, Teresa Maristany, Eduard Vieta, Peter J McKenna, Edith Pomarol-Clotet
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps...
2017: PloS One
Matcheri S Keshavan, Mukund Sudarshan
Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machine learning approaches can potentially help us understand how the brain normally interprets sensory inputs. Artificial neural networks (ANN) progressively extract higher and higher-level features of sensory input and identify the nature of an object based on a priori information. However, some ANNs which use algorithms such as the "deep-dreaming" developed by Google, allow the network to over-emphasize some objects it "thinks" it recognizes in those areas, and iteratively enhance such outputs leading to representations that appear farther and farther from "reality"...
October 2017: Schizophrenia Research
Walter H L Pinaya, Ary Gadelha, Orla M Doyle, Cristiano Noto, André Zugman, Quirino Cordeiro, Andrea P Jackowski, Rodrigo A Bressan, João R Sato
Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143)...
December 12, 2016: Scientific Reports
Andrea Mechelli, Ashleigh Lin, Stephen Wood, Patrick McGorry, Paul Amminger, Stefania Tognin, Philip McGuire, Jonathan Young, Barnaby Nelson, Alison Yung
Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR...
June 2017: Schizophrenia Research
Paolo Fusar-Poli, Grazia Rutigliano, Daniel Stahl, André Schmidt, Valentina Ramella-Cravaro, Shetty Hitesh, Philip McGuire
Importance: Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific determinants of pretest risk of psychosis onset in individuals undergoing clinical high risk (CHR) assessment are unknown. Objectives: To investigate the characteristics and determinants of pretest risk of psychosis onset in individuals undergoing CHR assessment and to develop and externally validate a pretest risk stratification model...
December 1, 2016: JAMA Psychiatry
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