Abstract

Current treatments for Alzheimer’s disease are only symptomatic and limited to reduce the progression rate of the mental deterioration. Mild Cognitive Impairment, a transitional stage in which the patient is not cognitively normal but do not meet the criteria for specific dementia, is associated with high risk for development of Alzheimer’s disease. Thus, non-invasive techniques to predict the individual’s risk to develop Alzheimer’s disease can be very helpful, considering the possibility of early treatment. Diffusion Tensor Imaging, as an indicator of cerebral white matter integrity, may detect and track earlier evidence of white matter abnormalities in patients developing Alzheimer’s disease. Here we performed a voxel-based analysis of fractional anisotropy in three classes of subjects: Alzheimer’s disease patients, Mild Cognitive Impairment patients, and healthy controls. We performed Support Vector Machine classification between the three groups, using Fisher Score feature selection and Leave-one-out cross-validation. Bilateral intersection of hippocampal cingulum and parahippocampal gyrus (referred as parahippocampal cingulum) is the region that best discriminates Alzheimer’s disease fractional anisotropy values, resulting in an accuracy of 93% for discriminating between Alzheimer’s disease and controls, and 90% between Alzheimer’s disease and Mild Cognitive Impairment. These results suggest that pattern classification of Diffusion Tensor Imaging can help diagnosis of Alzheimer’s disease, specially when focusing on the parahippocampal cingulum.

Highlights

  • Alzheimer’s disease (AD) is a neurodegenerative disease and the most frequent type of dementia in the elderly

  • Linear Support Vector Machine (SVM) classification based on all voxels inside the brain achieved an accuracy of 60% between AD and healthy controls, 57% between AD and Mild cognitive impairment (MCI) patients, and 47% between MCI and controls

  • The set of voxels whose Fisher Score were higher than 1.0 reached the highest accuracy between AD patients and healthy controls at 80%, between AD and MCI at 77%, and between MCI and controls at 60%

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Summary

Introduction

Alzheimer’s disease (AD) is a neurodegenerative disease and the most frequent type of dementia in the elderly. Anatomical, physiological and biochemical biomarkers that reflect specific features of AD have become relevant candidates to be incorporated in the diagnostic criteria[2] These biomarkers include extracellular deposits of amyloid-ß protein[3], stages of intraneuronal neurofibrillary tangles of tau protein[4], and neuritic plaque score[5]. Amyloid-ß concentrations in CSF already changes 5–10 years before the onset of clinical AD9 Invasive techniques, such as lumbar puncture, have shown efficacy in identifying the individual risk of future development of AD10, but the safety of the procedure is controversial[11]. In terms of imaging-based diagnosis, hippocampal volumetry has been proposed as a biomarker for AD13, as significant atrophy of the hippocampal formation demonstrated by MRI has identified preclinical stages of AD with 80% accuracy[14]. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) added DTI among several other imaging techniques in an effort to identify reliable biomarkers of AD18

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