Latent Atrophy Factors in Alzheimer's Disease
Xiuming Zhang1, Elizabeth C. Mormino2, Reisa A. Sperling2, Mert R. Sabuncu3,4, and B.T. Thomas Yeo1,3,5

1ASTAR-NUS Clinical Imaging Research Centre, Department of Electrical and Computer Engineering, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore, Singapore, 2Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States, 3Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States, 4Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 5Centre for Cognitive Neuroscience, Duke-NUS Graduate Medical School, Singapore, Singapore

Synopsis

Alzheimer's disease (AD) is the most common form of dementia and greatly heterogeneous. Here we develop a model of the heterogeneity of AD-related atrophy, demonstrating that most AD dementia patients and at-risk nondemented participants express multiple latent atrophy factors to varying degrees. Our study also demonstrates that these atrophy factors are associated with distinct cognitive decline trajectories across the preclinical and clinical stages. Our results provide a framework by which biomarker readouts could potentially predict disease progression at the individual level. Our analytic strategy is general and might be utilized to discover subtypes within and across other heterogeneous brain disorders.

Purpose

To discover the underlying factors governing various atrophy patterns among Alzheimer's disease (AD) patients and investigate how these latent factors relate to longitudinal cognitive decline across the clinical spectrum.

Methods

Voxelwise atrophy of 188 AD dementia patients was derived via voxel-based morphometry (VBM)1 from their structural MRI. A Bayesian model2 (Fig. 1) was then applied to estimate the probabilistic atrophy maps of latent atrophy factors and the factor composition of each patient. Subsequent analyses proceeded in three stages. First, the relationship between factors and longitudinal mini mental state examination (MMSE) decline was analyzed for the AD dementia patients with a regression model3. We then repeated similar analyses for 190 amyloid-positive nondemented participants. Finally, the possibility that factor information predicts MCI-to-AD progression was explored.

Results

The model revealed a “temporal” factor associated with extensive atrophy in the temporal lobe and hippocampus (Fig. 2A), a “subcortical” factor associated with atrophy in the cerebellum, striatum, and thalamus (Fig. 2B), and a “cortical” factor associated with atrophy in the frontal and parietal cerebral cortices (Fig. 2C).

The relationship between atrophy factors and cognitive decline was explored by examining change in MMSE while controlling for age, sex, education, and overall brain atrophy. As illustrated in Fig. 3A, among AD dementia patients, MMSE decline rates were markedly different across the three factors (overall p = 1.8e-8) with the cortical factor associated with the fastest MMSE decline. There was no significant difference between temporal and subcortical factors. As illustrated in Fig. 3B, among amyloid-positive nondemented participants, MMSE decline rates were markedly different across the three factors (overall p = 9.8e-6), with the temporal factor associated with the fastest MMSE decline. The cortical factor was associated with faster MMSE decline than the subcortical factor (p = 7.3e-3).

We further explored the possibility that atrophy factor expressions in MCI participants predicted progression to AD dementia within three years with logistic regression while controlling for age, sex, education, and overall brain atrophy. As illustrated in Fig. 4, MCI-to-AD progression rates were significantly different across the three latent factors (overall p = 3.7e-4), with the temporal factor associated with the highest probability of progressing to AD. There was no significant difference between subcortical and cortical factors (p = 0.68).

Discussion

The observed atrophy patterns are consistent with the VBM analyses of pathologically-defined subtypes4 – limbic-predominant and hippocampal-sparing subtypes5. More specifically, the atrophy pattern of the hippocampal-sparing subtype4 is consistent with our cortical atrophy factor. On the other hand, the atrophy pattern of the limbic-predominant subtype4 is consistent with our temporal+subcortical atrophy factor.

Our finding of elevated decline rate among the AD dementia patients expressing the cortical atrophy pattern (Fig. 3A) is consistent with previous neuropathology work5 that showed the fastest longitudinal cognitive decline among AD dementia patients with the hippocampal-sparing subtype. However, analyses of amyloid-positive nondemented participants revealed that the temporal atrophy factor was associated with the fastest longitudinal cognitive decline (Fig. 3B). Overall, this pattern highlights that the associations between cognitive decline and atrophy factors vary across the clinical spectrum and emerge during the preclinical stage of AD (Fig. 5).

One key advantage of our modeling strategy is that individuals can express multiple latent atrophy factors (i.e., mixed membership) rather than belonging to a single subtype. The use of mixed membership modeling provides us with stronger statistical power to dissociate factor-dependent atrophy maps and cognitive decline trajectories. Since each participant expressed his or her own factor composition (e.g., 50% subcortical, 40% cortical, and 10% temporal), the factor composition can be thought of as an individualized subtype diagnosis of the participant, representing a small but crucial step towards precision medicine.

Conclusion

By utilizing a novel Bayesian modeling framework, our study revealed at least three latent AD atrophy factors with distinct cognitive trajectories across the clinical spectrum. Consistent with previous work, the cortical factor was associated with the fastest cognitive decline at the AD dementia stage. However, the temporal factor was associated with the fastest cognitive decline at the nondementia stage and the highest probability of progressing to AD dementia. These results suggest potential implications for prevention and monitoring disease progression. Finally, our methodological framework is general and can be utilized to discover subtypes in other brain disorders.

Acknowledgements

This work was supported by NUS Tier 1, Singapore MOE Tier 2 (MOE2014-T2-2-016), NUS Strategic Research (DPRT/944/09/14), NUS SOM Aspiration Fund (R185000271720), Singapore NMRC (CBRG14nov007, NMRC/CG/013/2013) and NUS YIA. The research also utilized resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896 and instruments supported by 1S10RR023401, 1S10RR019307, and 1S10RR023043 from the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health 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 generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

References

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2. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Machine Learning Res. 2003;3:993-1022.

3. Bernal-Rusiel JL, Greve DN, Reuter M, et al. Statistical analysis of longitudinal neuroimage data with linear mixed effects models. NeuroImage. 2013;66:249-260.

4. Whitwell JL, Dickson DW, Murray ME, et al. Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study. Lancet Neurol. 2012;11(10):868-877.

5. Murray ME, Graff-Radford NR, Ross OA, et al. Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol. 2011;10(9):785-796.

Figures

A Bayesian model of AD dementia patients, latent atrophy factors, and brain structural MRI. Underpinning our approach is the premise that each participant expresses multiple latent atrophy factors to varying degrees (Pr(Factor | Patient)). Each factor is associated with distinct but possibly overlapping patterns of brain atrophy (Pr(Voxel | Factor)).

Distinct atrophy patterns associated with three latent AD atrophy factors. Bright color indicates higher probability of atrophy at that spatial location for a particular atrophy factor (Pr(Voxel | Factor)).

MMSE decline rates were markedly different across the atrophy factors in both (A) AD dementia patients and (B) amyloid-positive nondemented participants. The x-axis corresponds to difference in MMSE decline rate between “pure factors.” The blue dots and red bars represent means and 95% confidence intervals of the difference, respectively.

The temporal factor “T” was associated with the highest probability of progressing from MCI to AD within three years. The x-axis corresponds to the logarithm of the ratio of odds ratios between “pure factors.” Blue dots and red bars represent means and 95% confidence intervals, respectively.

Distinct cognitive decline trajectories of the three atrophy factors. This schematic, derived from real data, summarized the MMSE decline rates from Fig. 3. At the predementia stage, the temporal factor was associated with the fastest decline. However, at the dementia stage, the cortical factor was associated with the fastest decline.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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