The many roads to psychosis: recent advances in understanding risk and mechanisms.

Schizophrenia is a chronic and severe mental illness which frequently leads to substantial lifelong disability. The past five years have seen major progress in our understanding of the complex genetic architecture of this disorder. Two major barriers to understanding the core biological processes that underlie schizophrenia and developing better interventions are (1) the absence of etiologically defined biomarkers and (2) the clinical and genetic heterogeneity of the disorder. Here, we review recent advances that have led to changes in our understanding of risk factors and mechanisms involved in the development of schizophrenia. In particular, mechanistic and clinically oriented approaches have now converged on a focus on disruptions in early neurodevelopment and synaptic plasticity as being critical for both understanding trajectories and intervening to change them. Translating these new findings into treatments that substantively change the lives of patients is the next major challenge for the field

The MNI data-sharing and processing ecosystem

AbstractNeuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing

A cost model for ontology engineering

In this report we propose a methodology for cost estimation for ontologies and analyze cost factors implied in the engineering process. We examine the appropriateness of a COCOMO-like parametric approach to ontology cost estimation and propose a non-calibrated ontology cost model, which is to be continuously refined along with the collection of empiric data on person month efforts invested in developing ontologies in real-world projects. We further describe the human-driven evaluation of the cost drivers described in the parametric model on the basis of the cost models’ quality framework by Boehm[5

Ontology based software engineering - software engineering 2.0

This paper describes the use of ontologies in different aspects of software engineering. This use of ontologies varies from support for software developers at multiple sites to the use of an ontology to provide semantics in different categories ofsoftware, particularly on the web. The world's first and only software engineering ontology and a project management ontology in conjunction with a domain ontology are used to provide support for software development that is taking place at multiple sites. Ontologies are used to provide semantics to deal with heterogeneity in the representation of multiple information sources, enable the selection and composition of web services and grid resources, provide the shared knowledge base for multiagent systems, provide semantics and structure for trust and reputation systems and privacy based systems and codification of shared knawledge within different domains in business, science, manufacturing, engineering and utilities. They, therefore, bring a new paradigm to software engineering through the use of semantics as a central mechanism which will revolutionize the way software is developed and consumed in the future leading to the development of software as a service bringing about the dawn of software engineering 2.0

pSCANNER: Patient-centered scalable national network for effectiveness research

pre-printThis article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses

Doctor of Philosophy

dissertationClinical research plays a vital role in producing knowledge valuable for understanding human disease and improving healthcare quality. Human subject protection is an obligation essential to the clinical research endeavor, much of which is governed by federal regulations and rules. Institutional Review Boards (IRBs) are responsible for overseeing human subject research to protect individuals from harm and to preserve their rights. Researchers are required to submit and maintain an IRB application, which is an important component in the clinical research process that can significantly affect the timeliness and ethical quality of the study. As clinical research has expanded in both volume and scope over recent years, IRBs are facing increasing challenges in providing efficient and effective oversight. The Clinical Research Informatics (CRI) domain has made significant efforts to support various aspects of clinical research through developing information systems and standards. However, information technology use by IRBs has not received much attention from the CRI community. This dissertation project analyzed over 100 IRB application systems currently used at major academic institutions in the United States. The varieties of system types and lack of standardized application forms across institutions are discussed in detail. The need for building an IRB domain analysis model is identified. . iv In this dissertation, I developed an IRB domain analysis model with a special focus on promoting interoperability among CRI systems to streamline the clinical research workflow. The model was evaluated by a comparison with five real-world IRB application systems. Finally, a prototype implementation of the model was demonstrated by the integration of an electronic IRB system with a health data query system. This dissertation project fills a gap in the research of information technology use for the IRB oversight domain. Adoption of the IRB domain analysis model has potential to enhance efficient and high-quality ethics oversight and to streamline the clinical research workflow