White matter hyperintensities and their relationship to cognition: Effects of segmentation algorithm.

Neuroimage

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, 90292, USA. Electronic address:

Published: February 2020


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

White matter hyperintensities (WMHs) are brain white matter lesions that are hyperintense on fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) scans. Larger WMH volumes have been associated with Alzheimer's disease (AD) and with cognitive decline. However, the relationship between WMH volumes and cross-sectional cognitive measures has been inconsistent. We hypothesize that this inconsistency may arise from 1) the presence of AD-specific neuropathology that may obscure any WMH effects on cognition, and 2) varying criteria for creating a WMH segmentation. Manual and automated programs are typically used to determine segmentation boundaries, but criteria for those boundaries can differ. It remains unclear whether WMH volumes are associated with cognitive deficits, and which segmentation criteria influence the relationships between WMH volumes and clinical outcomes. In a sample of 260 non-demented participants (ages 55-90, 141 males, 119 females) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we compared the performance of five WMH segmentation methods, by relating the WMH volumes derived using each method to both clinical diagnosis and composite measures of executive function and memory. To separate WMH effects on cognition from effects related to AD-specific processes, we performed analyses separately in people with and without abnormal cerebrospinal fluid amyloid levels. WMH volume estimates that excluded more diffuse, lower-intensity lesions were more strongly correlated with clinical diagnosis and cognitive performance, and only in those without abnormal amyloid levels. These findings may inform best practices for WMH segmentation, and suggest that AD neuropathology may mask WMH effects on clinical diagnosis and cognition.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981030PMC
http://dx.doi.org/10.1016/j.neuroimage.2019.116327DOI Listing

Publication Analysis

Top Keywords

wmh volumes
20
white matter
12
wmh
12
wmh effects
12
wmh segmentation
12
clinical diagnosis
12
matter hyperintensities
8
cognition effects
8
volumes associated
8
alzheimer's disease
8

Similar Publications

Background And Objectives: The relationship between insomnia and cognitive decline is poorly understood. We investigated associations between chronic insomnia, longitudinal cognitive outcomes, and brain health in older adults.

Methods: From the population-based Mayo Clinic Study of Aging, we identified cognitively unimpaired older adults with or without a diagnosis of chronic insomnia who underwent annual neuropsychological assessments (z-scored global cognitive scores and cognitive status) and had quantified serial imaging outcomes (amyloid-PET burden [centiloid] and white matter hyperintensities from MRI [WMH, % of intracranial volume]).

View Article and Find Full Text PDF

Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.

View Article and Find Full Text PDF

Background: The aim of this study was to investigate the associations between pulse pressure (PP) and age-related structural brain changes including brain volumes, white matter hyperintensities (WMH), fractional anisotropy, silent brain lesions, microbleeds, cerebral blood flow and metabolism, and beta-amyloid accumulation.

Methods: Systematic review of PubMed (MEDLINE), Scopus, and Ovid Embase (from inception to January 2023) and references of included studies among adult populations was conducted. Findings were summarized narratively and by performing a fixed-effects meta-analysis.

View Article and Find Full Text PDF

BackgroundDisruptions of deep medullary veins (DMV) have been associated with the radiological severity and cognitive impairment observed in cerebral small vessel disease (SVD). Glymphatic dysfunction may serve as a potential mechanism underlying these associations.ObjectiveWe aimed to clarify the associations between DMV disruptions, MRI indices previously hypothesized as related to glymphatic function, white matter hyperintensities (WMH), and cognitive impairment in SVD.

View Article and Find Full Text PDF

White matter hyperintensities (WMH) are commonly assessed using the Fazekas scale, a subjective visual grading system. Despite the emergence of deep learning models for automatic WMH grading, their application in stroke patients remains limited. This study aimed to develop and validate an automatic segmentation and grading model for WMH in stroke patients, utilizing spatial-probabilistic methods.

View Article and Find Full Text PDF