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Frank E, Maier D, Pajula J, Suvitaival T, Borgan F, Butz-Ostendorf M, Fischer A, Hietala J, Howes O, Hyötyläinen T, Janssen J, Laurikainen H, Moreno C, Suvisaari J, Van Gils M, Orešič M. Platform for systems medicine research and diagnostic applications in psychotic disorders-The METSY project. Eur Psychiatry 2018; 50:40-46. [PMID: 29361398 DOI: 10.1016/j.eurpsy.2017.12.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 11/30/2017] [Accepted: 12/01/2017] [Indexed: 12/12/2022] Open
Abstract
Psychotic disorders are associated with metabolic abnormalities including alterations in glucose and lipid metabolism. A major challenge in the treatment of psychosis is to identify patients with vulnerable metabolic profiles who may be at risk of developing cardiometabolic co-morbidities. It is established that both central and peripheral metabolic organs use lipids to control energy balance and regulate peripheral insulin sensitivity. The endocannabinoid system, implicated in the regulation of glucose and lipid metabolism, has been shown to be dysregulated in psychosis. It is currently unclear how these endocannabinoid abnormalities relate to metabolic changes in psychosis. Here we review recent research in the field of metabolic co-morbidities in psychotic disorders as well as the methods to study them and potential links to the endocannabinoid system. We also describe the bioinformatics platforms developed in the EU project METSY for the investigations of the biological etiology in patients at risk of psychosis and in first episode psychosis patients. The METSY project was established with the aim to identify and evaluate multi-modal peripheral and neuroimaging markers that may be able to predict the onset and prognosis of psychiatric and metabolic symptoms in patients at risk of developing psychosis and first episode psychosis patients. Given the intrinsic complexity and widespread role of lipid metabolism, a systems biology approach which combines molecular, structural and functional neuroimaging methods with detailed metabolic characterisation and multi-variate network analysis is essential in order to identify how lipid dysregulation may contribute to psychotic disorders. A decision support system, integrating clinical, neuropsychological and neuroimaging data, was also developed in order to aid clinical decision making in psychosis. Knowledge of common and specific mechanisms may aid the etiopathogenic understanding of psychotic and metabolic disorders, facilitate early disease detection, aid treatment selection and elucidate new targets for pharmacological treatments.
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Affiliation(s)
| | | | - Juha Pajula
- VTT Technical Research Centre of Finland Ltd., FI-33720 Tampere, Finland
| | | | - Faith Borgan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London WC2R 2LS, UK; Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London W12 0HS, UK
| | | | | | - Jarmo Hietala
- Department of Psychiatry, University of Turku, FI-20520 Turku, Finland; Turku PET Centre, Turku University Hospital, FI-20521 Turku, Finland
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London WC2R 2LS, UK; Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, Imperial College London, London W12 0HS, UK
| | | | - Joost Janssen
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - Heikki Laurikainen
- Department of Psychiatry, University of Turku, FI-20520 Turku, Finland; Turku PET Centre, Turku University Hospital, FI-20521 Turku, Finland
| | - Carmen Moreno
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - Jaana Suvisaari
- National Institute for Health and Welfare (THL), FI-00300 Helsinki, Finland
| | - Mark Van Gils
- VTT Technical Research Centre of Finland Ltd., FI-33720 Tampere, Finland
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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Bohland JW, Myers EM, Kim E. An informatics approach to integrating genetic and neurological data in speech and language neuroscience. Neuroinformatics 2014; 12:39-62. [PMID: 23949335 DOI: 10.1007/s12021-013-9201-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
A number of heritable disorders impair the normal development of speech and language processes and occur in large numbers within the general population. While candidate genes and loci have been identified, the gap between genotype and phenotype is vast, limiting current understanding of the biology of normal and disordered processes. This gap exists not only in our scientific knowledge, but also in our research communities, where genetics researchers and speech, language, and cognitive scientists tend to operate independently. Here we describe a web-based, domain-specific, curated database that represents information about genotype-phenotype relations specific to speech and language disorders, as well as neuroimaging results demonstrating focal brain differences in relevant patients versus controls. Bringing these two distinct data types into a common database ( http://neurospeech.org/sldb ) is a first step toward bringing molecular level information into cognitive and computational theories of speech and language function. One bridge between these data types is provided by densely sampled profiles of gene expression in the brain, such as those provided by the Allen Brain Atlases. Here we present results from exploratory analyses of human brain gene expression profiles for genes implicated in speech and language disorders, which are annotated in our database. We then discuss how such datasets can be useful in the development of computational models that bridge levels of analysis, necessary to provide a mechanistic understanding of heritable language disorders. We further describe our general approach to information integration, discuss important caveats and considerations, and offer a specific but speculative example based on genes implicated in stuttering and basal ganglia function in speech motor control.
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Affiliation(s)
- Jason W Bohland
- Departments of Health Sciences and Speech, Language, and Hearing Sciences, Boston University, 635 Commonwealth Ave, Room 403, Boston, MA, 02215, USA,
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Arbib MA, Bonaiuto JJ, Bornkessel-Schlesewsky I, Kemmerer D, MacWhinney B, Nielsen FÅ, Oztop E. Action and language mechanisms in the brain: data, models and neuroinformatics. Neuroinformatics 2014; 12:209-25. [PMID: 24234916 PMCID: PMC4101894 DOI: 10.1007/s12021-013-9210-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We assess the challenges of studying action and language mechanisms in the brain, both singly and in relation to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding – separately or together – neurocomputational models and empirical data that serve systems and cognitive neuroscience.
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Affiliation(s)
- Michael A. Arbib
- Computer Science and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - James J. Bonaiuto
- Division of Biology, California Institute of Technology, Pasadena, CA, USA
| | | | - David Kemmerer
- Speech, Language, & Hearing Sciences and Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Brian MacWhinney
- Psychology, Computational Linguistics, and Modern Languages, Carnegie Mellon University, Pittsburgh, PA, USA
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