Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease
Authors:
- 15 December 2023
- journal article
- research article
- Published by Wiley in Journal of Magnetic Resonance Imaging
- Vol. 60 (4) , 1458-1469
Abstract
Background: Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).Purpose: To evaluate the sensitivity of FW‐DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW‐DTI indices to predict amyloid‐beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.Study Type: Retrospective.Population: Thirty‐eight Aβ‐negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ‐negative MCI patients (MCI‐n) (68.87 ± 8.83 years old, 60% female), 29 Aβ‐positive MCI patients (MCI‐p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ‐positive AD patients (72.93 ± 9.11 years old, 55% female).Field Strength/Sequence: 3.0T; DTI, T1‐weighted, T2‐weighted, T2 star‐weighted angiography, and Aβ PET (18F‐florbetaben or 11C‐PIB).Assessment: FW‐corrected and standard diffusion indices were analyzed using trace‐based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).Statistical Tests: Chi‐squared test, one‐way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value P = 0.114). There was a significant association between FW‐DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW‐DTI was all over 89.89%.Data Conclusion: FW‐DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW‐DTI indices has good accuracy and could help early diagnosis.Evidence Level: 4Technical Efficacy: Stage 2Keywords
This publication has 34 references indexed in Scilit:
- Beta-amyloid imaging with florbetabenClinical and Translational Imaging, 2015
- Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion‐Tensor and Magnetic Resonance Imaging DataJournal of Neuroimaging, 2015
- The extent of diffusion MRI markers of neuroinflammation and white matter deterioration in chronic schizophreniaSchizophrenia Research, 2015
- Widespread white matter degeneration preceding the onset of dementiaAlzheimer's & Dementia, 2014
- Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical reviewNeuroscience & Biobehavioral Reviews, 2012
- Free water elimination and mapping from diffusion MRIMagnetic Resonance in Medicine, 2009
- Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inferenceNeuroImage, 2009
- 11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairmentBrain, 2008
- The Clinical Dementia Rating (CDR)Neurology, 1993
- MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal agingAmerican Journal of Roentgenology, 1987