Could Alzheimer’s variability be explained by different brain atrophy patterns?

Written by Lauren Pulling

Researchers at Massachusetts General Hospital (MA, USA) and the National University of Singapore (Singapore) have demonstrated that variation in the symptoms of Alzheimer’s disease (AD) could be explained by differences in brain atrophy patterns.  Utilizing mathematical modeling, the team identified specific atrophy patterns that appear to be related to the loss of particular cognitive abilities, indicating that different atrophy patterns may explain how AD can manifest in individual patients.
“The symptom severity and neurodegeneration can vary widely across patients in AD,” explained Thomas Yeo (Maschusetts General Hospital). “Our work shows that participants in this study exhibit at least three atrophy patterns – cortical, temporal or subcortical – that are associated with variability in cognitive decline not only in patients diagnosed with Alzheimer’s but also in individuals with mild cognitive impairment or those who are cognitively normal but are at risk for Alzheimer’s.”

The study, published in Proceedings of the National Academy of Sciences, analyzed data from 378 participants collected as part of the Alzheimer’s Disease Neuroimaging Initiative, a collaborative project to develop biomarkers for AD diagnosis and tracking. Of the participants, 188 had been diagnosed with AD, and the others, 147 of whom had mild cognitive impairment and 43 were cognitively normal, had an increased risk of AD based on their brain levels of Aβ plaques.

First, the researchers analyzed data from baseline structural MRI scans with a mathematical model that estimated the probability of an association between particular details on the MRI and differences in brain atrophy in specific brain regions. Based on the locations of these atrophy factors, the team identified three atrophy factor patterns: cortical, temporal and subcortical.

Follow-up scans after 2 years indicated that atrophy factor patterns were consistent in individuals and did not reflect different stages of AD. Additionally, most participants exhibited more than more factor.

Behavioral and cognitive tests at 6-month intervals also indicated links between particular atrophy patterns and specific cognitive defects. For example, participants who exhibited temporal atrophy predominantly suffered from greater deficits in memory.

“Most previous studies focused on patients already diagnosed, but we were able to establish distinct atrophy patterns not only in diagnosed patients but also in at-risk participants who had mild impairment or were cognitively normal at the outset of the study,” commented Yeo. “That is important because the neurodegenerative cascade that leads to Alzheimer’s starts years, possibly decades, before diagnosis. So understanding different atrophy patterns among at-risk individuals is quite valuable.

“Previous studies assumed that an individual can only express a single neurodegenerative pattern, which is highly restrictive since in any aged person there could be multiple pathological factors going on at the same time – such as vascular impairment along with the amyloid plaques and tau tangles that are directly associated with Alzheimer’s. So individuals who are affected by multiple, co-existing pathologies might be expected to exhibit multiple atrophy patterns.”

Further research is now required into the mechanisms by which brain atrophy patterns may affect specific cognitive abilities, as well as the link between atrophy patterns and amyloid and tau distribution.

Sources: Zhang X, Mormino EC, Sun N, Sperling RA, Sabuncu MR, Thomas BT. Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease. PNAS doi:10.1073/pnas.1611073113 (2016) (Epub ahead of print); www.massgeneral.org/about/pressrelease.aspx?id=2002