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Neurodegenerative disorders, such as Alzheimer's disease, are associated with changes in multiple neuroimaging and biological measures. These may provide complementary information for diagnosis and prognosis. We present a multi-modality classification framework in which manifolds are constructed based on pairwise similarity measures derived from random(More)
This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline(More)
Imaging biomarkers for Alzheimer's disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and(More)
New diagnostic criteria for Alzheimer's disease (AD) treat different biomarkers of neuronal injury as equivalent. Here, we quantified the degree of agreement between hippocampal volume on structural magnetic resonance imaging, regional glucose metabolism on positron emission tomography, and levels of phosphorylated tau in cerebrospinal fluid (CSF) in 585(More)
We describe a computationally efficient biomarker discovery approach, based on a combination of penalised regression and a resampling method, for the identification of lo-calised brain regions that are highly discriminative between two groups of brain images. The proposed procedure has been applied for classification of brain images in subjects with(More)
BACKGROUND Mobile learning (ML) is an emerging educational method with success dependent on many factors including the ML device, physical infrastructure and user characteristics. At Gippsland Medical School (GMS), students are given a laptop at the commencement of their four-year degree. We evaluated the educational impact of the ML program from students'(More)
This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative database. T1-weighted images (1.5 T and 3 T) have(More)
Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method(More)