148 research outputs found
Multi view based imaging genetics analysis on Parkinson disease
No full textLongitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinson’s Disease (PD) as a case study. Parkinson's disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases
Opportunities and Barriers for Adoption of a Decision-Support Tool for Alzheimer's Disease
No full textClinical
decision-support tools (DSTs)
represent a valuable resource in healthcare. However, lack of Human Factors considerations and early design research has often limited their successful adoption. To complement previous technically focused work, we studied adoption opportunities of a future DST built on a predictive model of
Alzheimer’s Disease (AD)
progression. Our aim is two-fold: exploring adoption opportunities for DSTs in AD clinical care, and testing a novel combination of methods to support this process. We focused on understanding current clinical needs and practices, and the potential for such a tool to be integrated into the setting, prior to its development. Our user-centred approach was based on field observations and semi-structured interviews, analysed through workflow analysis, user profiles, and a design-reality gap model. The first two are common practice, whilst the latter provided added value in highlighting specific adoption needs. We identified the likely early adopters of the tool as being both psychiatrists and neurologists based in research-oriented clinical settings. We defined ten key requirements for the translation and adoption of DSTs for AD around IT, user, and contextual factors. Future works can use and build on these requirements to stand a greater chance to get adopted in the clinical setting
Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.
No full textNeurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology
[IC‐P‐141]: ANALYSIS OF THE HETEROGENEITY OF POSTERIOR CORTICAL ATROPHY: DATA‐DRIVEN MODEL PREDICTS DISTINCT ATROPHY PATTERNS FOR THREE DIFFERENT COGNITIVE SUBGROUPS
No full textPiloting a novel screening tool for reducing heterogeneity in clinical trials in Alzheimer’s disease
No full textArtificial intelligence for dementia drug discovery and trials optimization
No full textDrug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation
Weakly dense subsets of the measure algebra
No full textPT: J; CR: CARLSON T, THEOREM LIFTING CICHON J, 1985, P AM MATH SOC, V94, P142 FREMLIN D, 1977, 2 THEOREMS MOKOBODZK FREMLIN DH, 1984, MATHEMATIKA, V31, P323 GOFFMAN C, 1953, REAL FUNCTIONS HALMOS PR, 1950, MEASURE THEORY HODEL, 1984, HDB SET THEORETIC TO JECH TJ, 1978, SET THEORY KUNEN K, 1980, SET THEORY MAGIDOR M, 1977, ISRAEL J MATH, V28, P1 MAHARAM D, 1942, P NATL ACAD SCI USA, V28, P108 OXTOBY JC, 1971, MEASURE CATEGORY RUDIN W, 1983, AM MATH MON, V90, P41 SIKORSKI R, 1964, BOOLEAN ALGEBRAS VANDOUWEN E, IN PRESS HDB BOOLEAN; NR: 15; TC: 3; J9: PROC AMER MATH SOC; PG: 9; GA: AR774Source type: Electronic(1
P3‐436: MECHANISTIC PROFILES OF NEURODEGENERATION: A STUDY IN ALZHEIMER'S DISEASE, HEALTHY AGEING AND PRIMARY PROGRESSIVE MULTIPLE SCLEROSIS
No full textmarcellamontagnese/passian-processing: PASSIAN Project Scripts Repository
No full text<p>Welcome to the repository for the PASSIAN Project - aka Piloting A Secure, Scalable, AI-enhanced Infrastructure for Dementia Research On Real-world Data</p>
<p>Our overarching goal is help combat dementia by building research capability for AI in healthcare using real-world data. You can find further info on our project <a href="https://ucl-codec.github.io/pages/projects.html">here</a></p>
Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease
No full textModel-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases
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