Physical reserve and its underpinning functional neural networks moderate the relationship between white matter hyperintensity and postural balance in older adults with subcortical ischemic vascular cognitive impairment.

White matter hyperintensities (WMH) are markers of subcortical ischemic vascular cognitive impairment (SIVCI) associated with impaired postural balance. Physical reserve (PR) is a recently established construct that reflects one's capacity to maintain physical function despite brain pathology. This cross-sectional study aims to map functional networks associated with PR, and examining the relationship between PR, WMH, and postural balance.

Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.

Introduction: Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model's predictions.

Myelin Content and Gait Impairment in Older Adults with Cerebral Small Vessel Disease and Mild Cognitive Impairment.

We investigated whether myelin is associated with gait parameters in older adults with cerebral small vessel disease (cSVD). Cross-sectional data from sixty-four participants with cSVD and mild cognitive impairment were analyzed. Myelin was assessed via MRI multi-echo gradient and spin echo T relaxation sequence, indexed as myelin water fraction (MWF).

Myelin and Physical Activity in Older Adults With Cerebral Small Vessel Disease and Mild Cognitive Impairment.

Background: Myelin loss is a feature of cerebral small vessel disease (cSVD). Although physical activity levels may exert protective effects over cSVD pathology, its specific relationship with myelin content in people living with the cSVD is unknown. Thus, we investigated whether physical activity levels are associated with myelin in community-dwelling older adults with cSVD and mild cognitive impairment.

Reshaping the path of vascular cognitive impairment with resistance training: a study protocol for a randomized controlled trial.

Background: Subcortical ischemic vascular cognitive impairment (SIVCI) is the most common form of vascular cognitive impairment. Importantly, SIVCI is considered the most treatable form of cognitive impairment in older adults, due to its modifiable risk factors such as hypertension, diabetes mellitus, and hypercholesterolemia. Exercise training is a promising intervention to delay the progression of SIVCI, as it actively targets these cardiometabolic risk factors.

Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT.

Background: Chronic obstructive pulmonary disease (COPD) is underdiagnosed in the community. Thoracic CT scans are widely used for diagnostic and screening purposes for lung cancer. In this proof-of-concept study, we aimed to evaluate a software pipeline for the automated detection of COPD, based on deep learning and a dataset of low-dose CTs that were performed for early detection of lung cancer.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e.

Fast computation of myelin maps from MRI T₂ relaxation data using multicore CPU and graphics card parallelization.

Purpose: To develop a fast algorithm for computing myelin maps from multiecho T2 relaxation data using parallel computation with multicore CPUs and graphics processing units (GPUs).

Materials And Methods: Using an existing MATLAB (MathWorks, Natick, MA) implementation with basic (nonalgorithm-specific) parallelism as a guide, we developed a new version to perform the same computations but using C++ to optimize the hybrid utilization of multicore CPUs and GPUs, based on experimentation to determine which algorithmic components would benefit from CPU versus GPU parallelization. Using 32-echo T2 data of dimensions 256 × 256 × 7 from 17 multiple sclerosis patients and 18 healthy subjects, we compared the two methods in terms of speed, myelin values, and the ability to distinguish between the two patient groups using Student's t-tests.

Improving the clinical correlation of multiple sclerosis black hole volume change by paired-scan analysis.

The change in T 1-hypointense lesion ("black hole") volume is an important marker of pathological progression in multiple sclerosis (MS). Black hole boundaries often have low contrast and are difficult to determine accurately and most (semi-)automated segmentation methods first compute the T 2-hyperintense lesions, which are a superset of the black holes and are typically more distinct, to form a search space for the T 1w lesions. Two main potential sources of measurement noise in longitudinal black hole volume computation are partial volume and variability in the T 2w lesion segmentation.

Optimizing the use of radiologist seed points for improved multiple sclerosis lesion segmentation.

Many current methods for multiple sclerosis (MS) lesion segmentation require radiologist seed points as input, but do not necessarily allow the expert to work in an intuitive or efficient way. Ironically, most methods also assume that the points are placed optimally. This paper examines how seed points can be processed with intuitive heuristics, which provide improved segmentation accuracy while facilitating quick and natural point placement.

Detection and measurement of coverage loss in interleaved multi-acquisition brain MRIs due to motion-induced inter-slice misalignment.

In MRI scans that are acquired in a slice-by-slice manner, patient motion during scanning can cause adjacent slices to overlap, resulting in duplicate coverage in some areas and missing coverage in others. Scans in which multiple slices are acquired simultaneously and interleaved with other sets of slices are particularly vulnerable because a single movement can result in the misalignment and overlap of many slices. Despite the fact that considerable data losses can occur even with few visible artifacts, this problem has received very little attention from MRI researchers.