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Neuroimaging

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Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic res...

Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.

NeuroImage
Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly-controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models deve...

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.

Human brain mapping
Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique t...

Predicting Alzheimer's disease progression using deep recurrent neural networks.

NeuroImage
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more t...

Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

PloS one
The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on subjects at various stage of symptomatic severity due to Alzheimer's disease. As expected, magnetic r...

Cortical surface registration using unsupervised learning.

NeuroImage
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface propert...

Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

Medical & biological engineering & computing
The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most frequent image modality used to evaluate such lesions. Because of the massive amount of data, manual...

Distortion correction of single-shot EPI enabled by deep-learning.

NeuroImage
PURPOSE: A distortion correction method for single-shot EPI was proposed. Point-spread-function encoded EPI (PSF-EPI) images were used as the references to correct traditional EPI images based on deep neural network.

Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI.

International journal of computer assisted radiology and surgery
PURPOSE: Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown th...