Affiliations: [a] Department of Biostatistics, University of Washington, Seattle, WA, USA
| [b] National Alzheimer’s Coordinating Center, Seattle, WA, USA
| [c] Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| [d] Department of Radiology, University of Washington, Seattle, WA, USA
| [e] Department of Statistics, University of California Irvine, CA, USA
Correspondence:
[*]
Correspondence to: David R. Haynor, MD, PhD, Department of Radiology, University of Washington, Seattle, WA, USA. Tel.: +1 206 543-3320; E-mail: haynor@uw.edu.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://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.
Abstract: Background:Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer’s disease (AD) through the assessment of brain atrophy. Objective:Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker. Methods:We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2. Results:The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aβ42 values, demonstrated a tendency toward a more rapid increase of atrophy. Conclusions:The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.
