Abstract
Neuroimaging evidence has suggested white matter microstructure are heavily affected in Alzheimer's disease (AD). However, whether white matter dysfunction is localized at the specific regions of fiber tracts and whether they would be a potential biomarker for AD remain unclear. By automated fiber quantification (AFQ), we applied diffusion tensor images from 25 healthy controls (HC), 24 amnestic mild cognitive impairment (aMCI) patients and 18 AD patients to create tract profiles along 16 major white matter fibers. We compared diffusion metrics [Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA), and radial diffusivity (DR)] between groups. To assess the diagnostic value, we applied a random forest (RF) classifier, a type of machine learning method. In the global tract level, we found that aMCI and AD patients showed higher MD, DA, and DR values in some fiber tracts mostly in the left hemisphere compared to HC. In the point-wise level, widespread disruption were distributed on specific locations of different tracts. The point-wise MD measurements presented the best classification performance with respect to differentiating AD from HC. The two most important variables were localized in the prefrontal potion of left uncinate fasciculus and anterior thalamic radiation. In addition, the point-wise DA in the posterior component of the left cingulum cingulate displayed the most robust discriminative ability to identify AD from aMCI. Our findings provide evidence that white matter abnormalities based on the AFQ method could be as a diagnostic biomarker in AD.
Highlights
Alzheimer’s disease (AD), the most common type of dementia in the elderly, is a growing global public health concern with enormous implications for individuals, families and society (GBD 2016 Dementia Collaborators, 2019)
The amnestic mild cognitive impairment (aMCI) patients included in this study were diagnosed according to the recommendations of Petersen and described as follows (Petersen, 2004): (1) memory complaint confirmed by the subject and/or an informant; (2) objective cognitive performance documented by an auditory verbal learning test-delayed recall (AVLT-DR) scores below or equal to 1.5 standard deviation (SD) of education- and age-adjusted norms; (3) clinical dementia rating (CDR) score = 0.5; (4) the scores for the Mini-Mental State Examination (MMSE) ≥ 24; and (5) not sufficient to dementia according to NINCDS-ADRDA and DSM-IV
For the altered fiber tracts and point-wise location, we found no significant correlation between diffusion metrics and cognitive performance in aMCI or AD
Summary
Alzheimer’s disease (AD), the most common type of dementia in the elderly, is a growing global public health concern with enormous implications for individuals, families and society (GBD 2016 Dementia Collaborators, 2019). White Matter Microstructure in AD regarded as a disease of the brain’s gray matter (Pini et al, 2016). In recent years, neuroimaging studies have suggested that in addition to the features of neuronal loss, white matter alterations may be the important pathophysiological characteristics of AD (Nasrabady et al, 2018). Our knowledge about white matter degeneration in AD is still limited compared to what we know about gray matter atrophy (Nasrabady et al, 2018). Whether the patterns of white matter changes are different across fiber tracts and whether they would be a promising biomarker for AD remain largely unknown. Diffusion tensor imaging (DTI) has been a widely-used tool to detect microstructural integrity of white matter. Several DTI analytical approaches, including regions of interest (ROI)-based analysis, voxel-based morphometry (VBM), and tract-based spatial statistics (TBSS) have been used in AD-related studies
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