Researchers recently identified patterns of amyloid plaque deposits on PET imaging that could be indicative of how Alzheimer's disease will progress in asymptomatic patients.
By applying an AI algorithm known as the Subtype and Stage Inference (SuStaIn) model (developed in 2018) to amyloid-PET data, experts were able link two patterns of amyloid deposits with the emergence of clinical characteristics of the disease. They found that while the patterns began in different regions (subcortical and cingulate regions), they both progressed to the same region—the cerebellum—via different pathways.
The findings were published on Feb. 2 in Translational Psychiatry, where experts involved in the new study suggested that their work could provide insight into who might be eligible for clinical trials relative to AD.
“In sum, our findings suggest that the spatiotemporal variants of amyloid depositions are in close association with disease trajectories; these findings may provide insight into the disease monitoring and enrollment of therapeutic trials in AD,” lead researcher Yuqing Sun, PhD, of Beijing Normal University, and co-authors explained.
For their study, Sun and colleagues used PET imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Individuals included in the study were either deemed cognitively normal (CN) or had mild cognitive impairment (MCI).
Researchers referred to the first pattern as the "subcortex priority subtype. This pattern originated in subcortical regions, before advancing to the cingulate region and then the insula.
The second pattern, referred to as the “cortex priority subtype,” first appeared in the cingulate region. In this subtype, patterns originated in the cingulate region then progressed to the cortical regions, followed by the insula and the subcortical regions. The thalamus and basal ganglia also showed abnormalities earlier than other regions in this subtype. Patients with this pattern were more likely to show typical clinical signs and symptoms of AD.
“The amyloid subtypes showed distinct regional progression patterns and AD profiles,” the group wrote. “Furthermore, the regional progression patterns were associated with clinical and biomarker characteristics.”
The authors noted that identifying these spatiotemporal variations could play an important role in clinical research and precision medicine.
The study is available here.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She joined Innovate Healthcare in 2021 and has since put her unique expertise to use in her editorial role with Health Imaging.