NeuroImage
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Neurofibrillary tau tangles are a hallmark pathology of Alzheimer's disease (AD) and are more closely associated with AD-related cortical atrophy and symptom severity than amyloid-beta (Aβ). However, studies regarding the effect of tau on longitudinal cortical thinning, particularly in healthy aging and preclinical AD, have been limited in number due to the relatively recent introduction of in vivo PET tracers for imaging tau pathology. Here, we investigate [18F]-flortaucipir (FTP, a marker of paired helical filament tau) PET as a predictor of atrophy in healthy aging and preclinical AD. ⋯ Within the high PiB group, significant differences between prospective and retrospective rates of thinning were similarly observed. However, no consistent pattern of tau-related change in cortical thickness within the low PiB group was discerned. These results provide support for the hypothesis that tau pathology is a driver of future atrophy as well as provide additional evidence for tau-PET as an effective AD biomarker for interventional clinical trials.
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Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. ⋯ The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 b = 0 images and 90 DWIs measures around 1-1.5 mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.
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Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. ⋯ The maximum standard deviation in mean MWF in different regions of interest between subjects was smaller for the proposed method (0.0193) compared to the regularized NNLS algorithm (0.0266). In conclusion, the proposed method for MWF estimation is less computationally expensive and less susceptible to noise compared to state of the art methods. These improvements might be an important step towards clinical translation of MWF measurements.