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Year : 2020  |  Volume : 68  |  Issue : 2  |  Page : 427--434

Radial Diffusivity is the Best Global Biomarker Able to Discriminate Healthy Elders, Mild Cognitive Impairment, and Alzheimer's Disease: A Diagnostic Study of DTI-Derived Data

1 Deputy Director of Academic Affairs and Education and Geriatrics Unit, Medica Sur Clinic and Foundation, Mexico City, Mexico
2 Magnetic Resonance Unit, Medica Sur Clinic and Foundation, Mexico City, Mexico
3 Hospital General de Mexico “Dr. Eduardo Liceaga”, Mexico City, Mexico; I.M. Sechenov First Moscow State Medical University (Sechenov University), Department of Radiology, Moscow, Russia

Correspondence Address:
Ernesto Roldan-Valadez
Hospital General de Mexico gDr. Eduardo Liceagah, Dr. Balmis 148 Street, Col. Doctores, Del. Cuauhtemoc, 06726. Mexico City

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.284376

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Introduction: For the past two decades, diffusion tensor imaging (DTI)-derived metrics allowed the characterization of Alzheimer's disease (AzD). Previous studies reported only a few parameters (most commonly fractional anisotropy, mean diffusivity, and axial and radial diffusivities measured at selected regions). We aimed to assess the diagnostic performance of 11 DTI-derived tensor metrics by using a global approach. Materials and Methods: A prospective study performed in 34 subjects: 12 healthy elders, 11 mild cognitive impairment (MCI) patients, and 11 patients with AzD. Postprocessing of DTI magnetic resonance imaging allowed the calculation of 11 tensor metrics. Anisotropies included fractional (FA), and relative (RA). Diffusivities considered simple isotropic diffusion (p), simple anisotropic diffusion (q), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Tensors included the diffusion tensor total magnitude (L); and the linear (Cl), planar (Cp), and spherical tensors (Cs). We performed a multivariate discriminant analysis and diagnostic tests assessment. Results: RD was the only variable selected to assemble a predictive model: Wilks' λ = 0.581, χ2 (2) = 14.673, P = 0.001. The model's overall accuracy was 64.5%, with areas under the curve of 0.81, 0.73 and 0.66 to diagnose AzD, MCI, and healthy brains, respectively. Conclusions: Global DTI-derived RD alone can discriminate between healthy elders, MCI, and AzD patients. Although this study proves evidence of a potential biomarker, it does not provide clinical guidance yet. Additional studies comparing DTI metrics might determine their usefulness to monitor disease progression, measure outcome in drug trials, and even perform the screening of pre-AzD subjects.


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