Different patterns of white matter degeneration using multiple diffusion indices and volumetric data in mild cognitive impairment and Alzheimer patients

Alzheimeŕs disease (AD) represents the most prevalent neurodegenerative disorder that causes cognitive decline in old age. In its early stages, AD is associated with microstructural abnormalities in white matter (WM). In the current study, multiple indices of diffusion tensor imaging (DTI) and brain volumetric measurements were employed to comprehensively investigate the landscape of AD pathology. The sample comprised 58 individuals including cognitively normal subjects (controls), amnestic mild cognitive impairment (MCI) and AD patients. Relative to controls, both MCI and AD subjects showed widespread changes of anisotropic fraction (FA) in the corpus callosum, cingulate and uncinate fasciculus. Mean diffusivity and radial changes were also observed in AD patients in comparison with controls. After controlling for the gray matter atrophy the number of regions of significantly lower FA in AD patients relative to controls was decreased; nonetheless, unique areas of microstructural damage remained, e.g., the corpus callosum and uncinate fasciculus. Despite sample size limitations, the current results suggest that a combination of secondary and primary degeneration occurrs in MCI and AD, although the secondary degeneration appears to have a more critical role during the stages of disease involving dementia

Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI

We develop a general analytical and numerical framework for estimating intra- and extra-neurite water fractions and diffusion coefficients, as well as neurite orientational dispersion, in each imaging voxel. By employing a set of rotational invariants and their expansion in the powers of diffusion weighting, we analytically uncover the nontrivial topology of the parameter estimation landscape, showing that multiple branches of parameters describe the measurement almost equally well, with only one of them corresponding to the biophysical reality. A comprehensive acquisition shows that the branch choice varies across the brain. Our framework reveals hidden degeneracies in MRI parameter estimation for neuronal tissue, provides microstructural and orientational maps in the whole brain without constraints or priors, and connects modern biophysical modeling with clinical MRI.Comment: 25 pages, 12 figures, elsarticle two-colum

Rates of lobar atrophy in asymptomatic MAPT mutation carriers.

IntroductionThe aim of this study was to investigate the rates of lobar atrophy in the asymptomatic microtubule-associated protein tau (MAPT) mutation carriers.MethodsMAPT mutation carriers (n = 14; 10 asymptomatic, 4 converters from asymptomatic to symptomatic) and noncarriers (n = 13) underwent structural magnetic resonance imaging and were followed annually with a median of 9.2 years. Longitudinal changes in lobar atrophy were analyzed using the tensor-based morphometry with symmetric normalization algorithm.ResultsThe rate of temporal lobe atrophy in asymptomatic MAPT mutation carriers was faster than that in noncarriers. Although the greatest rate of atrophy was observed in the temporal lobe in converters, they also had increased atrophy rates in the frontal and parietal lobes compared to noncarriers.DiscussionAccelerated decline in temporal lobe volume occurs in asymptomatic MAPT mutation carriers followed by the frontal and parietal lobe in those who have become symptomatic. The findings have implications for monitoring the progression of neurodegeneration during clinical trials in asymptomatic MAPT mutation carriers

On the primacy and irreducible nature of first-person versus third-person information

In\ua0this\ua0essay,\ua0we\ua0will\ua0support\ua0the\ua0claim\ua0that\ua0at\ua0the\ua0current\ua0level\ua0of\ua0scientific advancement\ua0a)\ua0some\ua0first-person\ua0accounts\ua0cannot\ua0be\ua0reduced\ua0to\ua0their third-person\ua0neural\ua0and\ua0psychophysiological\ua0correlates\ua0and\ua0b)\ua0that\ua0these first-person\ua0accounts\ua0are\ua0the\ua0only\ua0information\ua0to\ua0reckon\ua0when\ua0it\ua0is\ua0necessary\ua0to analyse\ua0qualia\ua0contents. Consequently,\ua0for\ua0many\ua0phenomena,\ua0first-person\ua0accounts\ua0are\ua0the\ua0only\ua0reliable source\ua0of\ua0information\ua0available\ua0and\ua0the\ua0knowledge\ua0of\ua0their\ua0neural\ua0and psychophysical\ua0correlates\ua0don\u2019t\ua0offer\ua0any\ua0additional\ua0information\ua0about\ua0them

An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients

Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be widely studied. While there is no consensus on whether MCIs actually "convert" to AD, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline image brain scan, of whether an MCI individual will convert to AD within a multi-year period following the initial clinical visit. This is in fact not a traditional supervised learning problem since, in ADNI, there are no definitive labeled examples of MCI conversion. Prior works have defined MCI subclasses based on whether or not clinical/cognitive scores such as CDR significantly change from baseline. There are concerns with these definitions, however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative conversion definition, wherein an MCI patient is declared to be a converter if any of the patient's brain scans (at follow-up visits) are classified "AD" by an (accurately-designed) Control-AD classifier. This novel definition bootstraps the design of a second classifier, specifically trained to predict whether or not MCIs will convert. This second classifier thus predicts whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate this new definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations much more consistent with known AD brain region biomarkers. We also identify key prognostic region biomarkers, essential for accurately discriminating the converter and nonconverter groups