Regular articleTwo distinct classes of degenerative change are independently linked to clinical progression in mild cognitive impairment
Introduction
The conclusive diagnosis of Alzheimer's disease (AD) is currently determined based on the presence of AD pathology, such as beta-amyloid plaques and neurofibrillary tangles, which guides models of the pathophysiology of the disease (Hyman et al., 2012, Jack et al., 2010). However, there is a diversity of additional changes that occur in the brain throughout the course of the disease which are typically highly prevalent across patients yet considered either secondary or independent to the primary diagnostic pathologies. In a recent study (Coutu et al., 2016), we found 2 statistically distinct classes of imaging markers (factors) indicative of degenerative processes that were affected by AD. One factor was strongly linked to imaging measures of cortical atrophy that are presumed to be related to the neurodegenerative changes in AD and to plaque and tangle accumulation. This factor was therefore interpreted to be “neurodegenerative” (neurodegenerative factor [NDF]). The other factor was statistically independent from the NDF, was highly weighted by white matter lesions of presumed vascular origin (Gottesman et al., 2010, Gouw et al., 2011, Jeerakathil et al., 2004, Pantoni, 2010, Rostrup et al., 2012, Wardlaw et al., 2013), and was strongly associated with age. This factor was therefore interpreted to represent “age- and vascular-related” tissue damage (age- and vascular-related factor [AVF]). Of particular interest, both factors were independently weighted by hippocampal volume demonstrating the multiple sources of variance contributing to this often used imaging marker of AD neurodegeneration (Atiya et al., 2003). Both factors were also related to Mini–Mental State Examination (MMSE) scores cross-sectionally. The main goals of this follow-up work were to replicate our previous factor analysis in a distinct data set and determine the longitudinal associations between these degenerative factors and cognitive decline in older adults with mild cognitive impairment (MCI). Secondary exploratory goals included investigating associations between those classes of degenerative change and CSF biomarkers, and distinguishing converters from older adults with MCI who have not converted.
Section snippets
Participants and MRI acquisition
The cross-sectional data set used to replicate the factor analysis came from the Alzheimer's Disease Neuroimaging Initiative GO/2 (ADNI, http://adni.loni.usc.edu) and included 113 controls, 159 participants with MCI, and 92 participants with AD who underwent whole-brain MRI scanning on a 3-Tesla Siemens scanner as described in ADNI Core MRI protocols (Jack et al., 2008) and had sagittal T1-weighted images and pulsed arterial spin labeling images available at the time of download. The specific
Replication of the factor analysis in a distinct cross-sectional data set
The cross-sectional data set used for replication was completely separate from the data set used in the original factor analysis (Coutu et al., 2016) but had comparable demographics with no significant differences within or across groups (Supplementary Table 1). The replication of the factor analysis yielded 2 significant factors (AVF′ and NDF′) with very similar loadings as AVF and NDF from our previous study (Coutu et al., 2016; Supplementary Table 2). Both factors showed a high loading from
Discussion
The current work demonstrates that 2 statistically distinct classes of degenerative change indexed by structural MRI are important independent predictors of longitudinal cognitive decline in individuals with mild cognitive impairment (MCI). To demonstrate this, we first replicated the factor analysis we recently published in a distinct data set (Coutu et al., 2016), showing 2 distinct classes of degenerative changes both involving hippocampal changes: one class interpreted as representing “age-
Disclosure statement
The authors have no actual or potential conflicts of interest.
Acknowledgements
This study was supported by the National Institutes of Health grants R01NR010827, P41RR14075, S10RR021110, S10RR023401, S10RR019307, S10RR019254, and S10RR023043.
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; NIH grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through
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Cited by (0)
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Biospective Inc, Montreal, Canada.
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Computer Vision Laboratory, ETH Zurich, Switzerland.
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Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.