Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and alzheimer's disease patients

the Alzheimer's Disease Neuroimaging Initiative (ADNI)

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)767-779
Number of pages13
JournalJournal of Magnetic Resonance Imaging
Volume48
Issue number3
DOIs
Publication statusPublished - Sep 1 2018

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Diffusion Tensor Imaging
Alzheimer Disease
Brain
ROC Curve
Healthy Volunteers
Neuroimaging
Anisotropy
Area Under Curve
Cognitive Dysfunction
White Matter
Fourier Analysis
Masks
Dementia
Disease Progression
Analysis of Variance
Biomarkers
Prospective Studies
Students

Keywords

  • Alzheimer's disease
  • cognitive dysfunction
  • diffusion tensor imaging
  • magnetic resonance imaging
  • white matter

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and alzheimer's disease patients. / the Alzheimer's Disease Neuroimaging Initiative (ADNI).

In: Journal of Magnetic Resonance Imaging, Vol. 48, No. 3, 01.09.2018, p. 767-779.

Research output: Contribution to journalArticle

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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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author = "{the Alzheimer's Disease Neuroimaging Initiative (ADNI)} and Giovanni Giulietti and Mario Torso and Laura Serra and Barbara Span{\`o} and Camillo Marra and Carlo Caltagirone and Mara Cercignani and Marco Bozzali",
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AU - Torso, Mario

AU - Serra, Laura

AU - Spanò, Barbara

AU - Marra, Camillo

AU - Caltagirone, Carlo

AU - Cercignani, Mara

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