Background: Amnestic mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD). However, the clinical conversion from MCI to AD is unpredictable. Hence, identification of noninvasive biomarkers able to detect early changes induced by dementia is a pressing need. Purpose: To explore the added value of histogram analysis applied to measures derived from diffusion tensor imaging (DTI) for detecting brain tissue differences between AD, MCI, and healthy subjects (HS). Study Type: Prospective. Population/Subjects: A local cohort (57 AD, 28 MCI, 23 HS), and an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (41 AD, 58 MCI, 41 HS). Field Strength: 3T. Dual-echo turbo spin echo (TSE); fluid-attenuated inversion recovery (FLAIR); modified-driven-equilibrium-Fourier-transform (MDEFT); inversion-recovery spoiled gradient recalled (IR-SPGR); diffusion tensor imaging (DTI). Assessment: Normal-appearing white matter (NAWM) masks were obtained using the T1-weighted volumes for tissue segmentation and T2-weighted images for removal of hyperintensities/lesions. From DTI images, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AXD), and radial diffusivity (RD) were obtained. NAWM histograms of FA, MD, AXD, and RD were derived and characterized estimating: peak height, peak location, mean value (MV), and quartiles (C25, C50, C75), which were compared between groups. Receiver operating characteristic (ROC) and area under ROC curves (AUC) were calculated. To confirm our results, the same analysis was repeated on the ADNI dataset. Statistical Tests: One-way analysis of variance (ANOVA), post-hoc Student's t-test, multiclass ROC analysis. Results: For the local cohort, C25 of AXD had the maximum capability of group discrimination with AUC of 0.80 for “HS vs. patients” comparison and 0.74 for “AD vs. others” comparison. For the ADNI cohort, MV of AXD revealed the maximum group discrimination capability with AUC of 0.75 for “HS vs. patients” comparison and 0.75 for “AD vs. others” comparison. Data Conclusion: AXD of NAWM might be an early marker of microstructural brain tissue changes occurring during the AD course and might be useful for assessing disease progression. Level of Evidence: 1. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;48:767–779.
Giulietti, G., Torso, M., Serra, L., Spano, B., Marra, C., Caltagirone, C., Cercignani, M., Bozzali, M., Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and alzheimer's disease patients, <<JOURNAL OF MAGNETIC RESONANCE IMAGING>>, 2018; 48 (3): 767-779. [doi:10.1002/jmri.25947] [http://hdl.handle.net/10807/206294]
Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and alzheimer's disease patients
Marra, Camillo;
2018
Abstract
Background: Amnestic mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD). However, the clinical conversion from MCI to AD is unpredictable. Hence, identification of noninvasive biomarkers able to detect early changes induced by dementia is a pressing need. Purpose: To explore the added value of histogram analysis applied to measures derived from diffusion tensor imaging (DTI) for detecting brain tissue differences between AD, MCI, and healthy subjects (HS). Study Type: Prospective. Population/Subjects: A local cohort (57 AD, 28 MCI, 23 HS), and an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (41 AD, 58 MCI, 41 HS). Field Strength: 3T. Dual-echo turbo spin echo (TSE); fluid-attenuated inversion recovery (FLAIR); modified-driven-equilibrium-Fourier-transform (MDEFT); inversion-recovery spoiled gradient recalled (IR-SPGR); diffusion tensor imaging (DTI). Assessment: Normal-appearing white matter (NAWM) masks were obtained using the T1-weighted volumes for tissue segmentation and T2-weighted images for removal of hyperintensities/lesions. From DTI images, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AXD), and radial diffusivity (RD) were obtained. NAWM histograms of FA, MD, AXD, and RD were derived and characterized estimating: peak height, peak location, mean value (MV), and quartiles (C25, C50, C75), which were compared between groups. Receiver operating characteristic (ROC) and area under ROC curves (AUC) were calculated. To confirm our results, the same analysis was repeated on the ADNI dataset. Statistical Tests: One-way analysis of variance (ANOVA), post-hoc Student's t-test, multiclass ROC analysis. Results: For the local cohort, C25 of AXD had the maximum capability of group discrimination with AUC of 0.80 for “HS vs. patients” comparison and 0.74 for “AD vs. others” comparison. For the ADNI cohort, MV of AXD revealed the maximum group discrimination capability with AUC of 0.75 for “HS vs. patients” comparison and 0.75 for “AD vs. others” comparison. Data Conclusion: AXD of NAWM might be an early marker of microstructural brain tissue changes occurring during the AD course and might be useful for assessing disease progression. Level of Evidence: 1. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;48:767–779.
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