Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer's disease

Abstract

Alzheimer's disease (AD) can present with distinct clinical variants. Identifying the earliest neurodegenerative changes associated with each variant has implications for early diagnosis, and for understanding the mechanisms that underlie regional vulnerability and disease progression in AD. We performed voxel-based morphometry to detect atrophy patterns in early clinical stages of four AD phenotypes: Posterior cortical atrophy (PCA, "visual variant," n=93), logopenic variant primary progressive aphasia (lvPPA, "language variant," n=74), and memory-predominant AD categorized as early age-of-onset (EOAD, <65 years, n=114) and late age-of-onset (LOAD, >65 years, n=114). Patients with each syndrome were stratified based on: (1) degree of functional impairment, as measured by the clinical dementia rating (CDR) scale, and (2) overall extent of brain atrophy, as measured by a neuroimaging approach that sums the number of brain voxels showing significantly lower gray matter volume than cognitively normal controls (n=80). Even at the earliest clinical stage (CDR=0.5 or bottom quartile of overall atrophy), patients with each syndrome showed both common and variant-specific atrophy. Common atrophy across variants was found in temporoparietal regions that comprise the posterior default mode network (DMN). Early syndrome-specific atrophy mirrored functional brain networks underlying functions that are uniquely affected in each variant: Language network in lvPPA, posterior cingulate cortex-hippocampal circuit in amnestic EOAD and LOAD, and visual networks in PCA. At more advanced stages, atrophy patterns largely converged across AD variants. These findings support a model in which neurodegeneration selectively targets both the DMN and syndrome-specific vulnerable networks at the earliest clinical stages of AD.

Keywords: Alzheimer's disease; atrophy; default mode network; early-onset dementia; language; logopenic variant primary progressive aphasia; magnetic resonance imaging (MRI); memory; posterior cortical atrophy; vision; voxel-based morphometry.

© 2015 Wiley Periodicals, Inc.

Figures

Figure 1

A neuroimaging approach for staging of disease severity. Smoothed, warped, and modulated gray matter images of healthy controls were used to estimate the effect of nuisance variables on voxel intensities (1). Using multiple regressions in SPM, the beta's and residuals were used to determine the predicted value for each patient given their specific covariate (i.e. age, sex, total intracranial volume, MRI field strength, and center). Based on individual raw values in Alzheimer's disease patients, W‐scores can be calculated using the formula shown in (2). Finally, W‐scores maps were binarized at W 

Figure 2

Atrophy maps of Alzheimer's disease variants compared with controls. Regional differences between healthy controls and EOAD (A), lvPPA (B), PCA (C) and LOAD (D) patients at CDR 0.5 and 1. Voxelwise contrasts were adjusted for age, sex, total intracranial volume, MRI field strength and center, and thresholded at P 

Figure 3

The intersection of distinct Alzheimer's disease phenotypes. Binarized maps of significant voxelwise differences between Alzheimer's disease variants and healthy controls (at P 

Figure 4

Mean W‐score maps across quartiles for distinct Alzheimer's disease variants. Mean W‐score maps show changes of atrophy patterns with increasing quartiles for each of the Alzheimer's disease variants. W‐score maps are superimposed on the study‐specific template obtained with VBM. Q = quartile (1 = lowest extent of whole‐brain atrophy, 4 = highest).

Figure 5

Mean W‐score atrophy values within distinct intrinsic connectivity networks. The y‐axis represents mean W‐scores for brain atrophy extracted within eight functional connectivity network maps for each quartile in EOAD (A), lvPPA (B), PCA (C), and LOAD (D) patients. Statistical comparisons between networks in quartile 1 and within networks across quartiles are shown in Table 3 and Supporting Information Table I.