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Paul M. Matthews

Researcher at Imperial College London

Publications -  641
Citations -  102773

Paul M. Matthews is an academic researcher from Imperial College London. The author has contributed to research in topics: Multiple sclerosis & White matter. The author has an hindex of 140, co-authored 617 publications receiving 88802 citations. Previous affiliations of Paul M. Matthews include John Radcliffe Hospital & King's College London.

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Brain structural and functional connectivity and the progression of neuropathology in Alzheimer's disease.

TL;DR: How functional MRI during both memory encoding and at rest is able to define APOE4 genotype-dependent physiological changes decades before potential development of AD and demonstrate changes distinct from those with healthy aging is reviewed.
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In vivo magnetic resonance spectroscopy of brain and muscle in a type of mitochondrial encephalomyopathy (MERRF).

TL;DR: The relative phosphate metabolite concentrations and intracellular pH in central volumes of the brains of these patients were normal, despite evidence from previous positron emission tomography studies suggesting that there is diffuse impairment of cerebral oxidative metabolism.
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The MSOAC approach to developing performance outcomes to measure and monitor multiple sclerosis disability.

TL;DR: Considerable data exist supporting measures of the functional domains ambulation, manual dexterity, vision, and cognition, and this integrated, standardized dataset is being analyzed to support qualification of disability endpoints by regulatory agencies.
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Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension.

TL;DR: Pathological processes associated with chronically elevated blood pressure are associated with imaging differences suggesting chronic alterations of white matter axonal structure that may affect cognitive functions even with pre-hypertension.
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Learning-Based Quality Control for Cardiac MR Images

TL;DR: In this article, a hybrid decision forest method is used to extract landmarks and probabilistic segmentation maps from both long and short-axis images as a basis to perform the quality checks.