Peak width of skeletonized mean diffusivity and cognitive performance in cerebral amyloid angiopathy.

cerebral amyloid angiopathy (CAA) cerebral small vessel disease cognitive functions diffusion-weighted imaging peak width of skeletonized mean diffusivity (PSMD)

Journal

Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481

Informations de publication

Date de publication:
2023
Historique:
received: 09 01 2023
accepted: 20 03 2023
medline: 20 4 2023
pubmed: 20 4 2023
entrez: 20 04 2023
Statut: epublish

Résumé

Cerebral Amyloid Angiopathy (CAA) is a cerebral small vessel disease that can lead to microstructural disruption of white matter (WM), which can be measured by the Peak Width of Skeletonized Mean Diffusivity (PSMD). We hypothesized that PSMD measures would be increased in patients with CAA compared to healthy controls (HC), and increased PSMD is associated with lower cognitive scores in patients with CAA. Eighty-one probable CAA patients without cognitive impairment who were diagnosed with Boston criteria and 23 HCs were included. All subjects underwent an advanced brain MRI with high-resolution diffusion-weighted imaging (DWI). PSMD scores were quantified from a probabilistic skeleton of the WM tracts in the mean diffusivity (MD) image using a combination of fractional anisotropy (FA) and the FSL Tract-Based Spatial Statistics (TBSS) algorithm (www.psmd-marker.com). Within CAA cohort, standardized z-scores of processing speed, executive functioning and memory were obtained. The mean of age and sex were similar between CAA patients (69.6 ± 7.3, 59.3% male) and HCs (70.6 ± 8.5, 56.5% male) ( Peak Width of Skeletonized Mean Diffusivity is increased in CAA, and it is associated with worse cognitive scores supporting the view that disruption of white matter has a significant role in cognitive impairment in CAA. As a robust marker, PSMD can be used in clinical trials or practice.

Sections du résumé

Background UNASSIGNED
Cerebral Amyloid Angiopathy (CAA) is a cerebral small vessel disease that can lead to microstructural disruption of white matter (WM), which can be measured by the Peak Width of Skeletonized Mean Diffusivity (PSMD). We hypothesized that PSMD measures would be increased in patients with CAA compared to healthy controls (HC), and increased PSMD is associated with lower cognitive scores in patients with CAA.
Methods UNASSIGNED
Eighty-one probable CAA patients without cognitive impairment who were diagnosed with Boston criteria and 23 HCs were included. All subjects underwent an advanced brain MRI with high-resolution diffusion-weighted imaging (DWI). PSMD scores were quantified from a probabilistic skeleton of the WM tracts in the mean diffusivity (MD) image using a combination of fractional anisotropy (FA) and the FSL Tract-Based Spatial Statistics (TBSS) algorithm (www.psmd-marker.com). Within CAA cohort, standardized z-scores of processing speed, executive functioning and memory were obtained.
Results UNASSIGNED
The mean of age and sex were similar between CAA patients (69.6 ± 7.3, 59.3% male) and HCs (70.6 ± 8.5, 56.5% male) (
Discussion UNASSIGNED
Peak Width of Skeletonized Mean Diffusivity is increased in CAA, and it is associated with worse cognitive scores supporting the view that disruption of white matter has a significant role in cognitive impairment in CAA. As a robust marker, PSMD can be used in clinical trials or practice.

Identifiants

pubmed: 37077322
doi: 10.3389/fnins.2023.1141007
pmc: PMC10106761
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1141007

Informations de copyright

Copyright © 2023 Horn, Gokcal, Becker, Das, Schwab, Zanon Zotin, Goldstein, Rosand, Viswanathan, Polimeni, Duering, Greenberg and Gurol.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Mitchell J Horn (MJ)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.

Elif Gokcal (E)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.

J Alex Becker (JA)

Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.

Alvin S Das (AS)

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

Kristin Schwab (K)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.

Maria Clara Zanon Zotin (MC)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, Center for Imaging Sciences and Medical Physics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.

Joshua N Goldstein (JN)

Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.

Jonathan Rosand (J)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.

Anand Viswanathan (A)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.

Jonathan R Polimeni (JR)

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.

Marco Duering (M)

Medical Image Analysis Center (MIAC), Department of Biomedical Engineering, University of Basel, Basel, Switzerland.

Steven M Greenberg (SM)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.

M Edip Gurol (ME)

Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.

Classifications MeSH