2,295 research outputs found
Alzheimer's Disease Prediction Using Longitudinal and Heterogeneous Magnetic Resonance Imaging
Recent evidence has shown that structural magnetic resonance imaging (MRI) is
an effective tool for Alzheimer's disease (AD) prediction and diagnosis. While
traditional MRI-based diagnosis uses images acquired at a single time point, a
longitudinal study is more sensitive and accurate in detecting early
pathological changes of the AD. Two main difficulties arise in longitudinal
MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects
(i.e., different scanning time and different total number of scans); (2) the
heterogeneous progressions of high-dimensional regions of interest (ROIs) in
MRI. In this work, we propose a novel feature selection and estimation method
which can be applied to extract features from the heterogeneous longitudinal
MRI. A key ingredient of our method is the combination of smoothing splines and
the -penalty. We perform experiments on the Alzheimer's Disease
Neuroimaging Initiative (ADNI) database. The results corroborate the advantages
of the proposed method for AD prediction in longitudinal studies
Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps.
Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small
Ticket to Talk: Supporting Conversation between Young People and People with Dementia through Digital Media
We explore the role of digital media in supporting intergenerational interactions between people with dementia and young people. Though meaningful social interaction is integral to quality of life in dementia, initiating conversation with a person with dementia can be challenging, especially for younger people who may lack knowledge of someone’s life history. This can be further compounded without a nuanced understanding of the nature of dementia, along with an unfamiliarity in leading and maintaining conversation. We designed a mobile application - Ticket to Talk - to support intergenerational interactions by encouraging young people to collect media relevant to individuals with dementia to use in conversations with people with dementia. We evaluated Ticket to Talk through trials with two families, a care home, and groups of older people. We highlight difficulties in using technologies such as this as a conversational tool, the value of digital media in supporting intergenerational interactions, and the potential to positively shape people with dementia’s agency in social settings
Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression
We present a new method for the detection of gene pathways associated with a
multivariate quantitative trait, and use it to identify causal pathways
associated with an imaging endophenotype characteristic of longitudinal
structural change in the brains of patients with Alzheimer's disease (AD). Our
method, known as pathways sparse reduced-rank regression (PsRRR), uses group
lasso penalised regression to jointly model the effects of genome-wide single
nucleotide polymorphisms (SNPs), grouped into functional pathways using prior
knowledge of gene-gene interactions. Pathways are ranked in order of importance
using a resampling strategy that exploits finite sample variability. Our
application study uses whole genome scans and MR images from 464 subjects in
the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs
are mapped to 185 gene pathways from the KEGG pathways database. Voxel-wise
imaging signatures characteristic of AD are obtained by analysing 3D patterns
of structural change at 6, 12 and 24 months relative to baseline. High-ranking,
AD endophenotype-associated pathways in our study include those describing
chemokine, Jak-stat and insulin signalling pathways, and tight junction
interactions. All of these have been previously implicated in AD biology. In a
secondary analysis, we investigate SNPs and genes that may be driving pathway
selection, and identify a number of previously validated AD genes including
CR1, APOE and TOMM40
Applications of neuroimaging to disease-modification trials in Alzheimer's disease.
Critical to development of new therapies for Alzheimer's disease (AD) is the ability to detect clinical or pathological change over time. Clinical outcome measures typically used in therapeutic trials have unfortunately proven to be relatively variable and somewhat insensitive to change in this slowly progressive disease. For this reason, development of surrogate biomarkers that identify significant disease-associated brain changes are necessary to expedite treatment development in AD. Since AD pathology is present in the brain many years prior to clinical manifestation, ideally we want to develop biomarkers of disease that identify abnormal brain structure or function even prior to cognitive decline. Magnetic resonance imaging, fluorodeoxyglucose positron emission tomography, new amyloid imaging techniques, and spinal fluid markers of AD all have great potential to provide surrogate endpoint measures for AD pathology. The Alzheimer's disease neuroimaging initiative (ADNI) was developed for the distinct purpose of evaluating surrogate biomarkers for drug development in AD. Recent evidence from ADNI demonstrates that imaging may provide more sensitive, and earlier, measures of disease progression than traditional clinical measures for powering clinical drug trials in Alzheimer's disease. This review discusses recently presented data from the ADNI dataset, and the importance of imaging in the future of drug development in AD
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