Spatial Patterns of Cerebral Blood Flow in Alzheimer's Disease Identified by the Subtype and Stage Inference...

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Alzheimer's disease (AD) research often models amyloid and tau protein deposition with a singular progression pattern.
• Cerebral blood flow (CBF) serves as an indirect biomarker for AD pathology.
• Understanding diverse progression patterns is crucial for effective AD management.

Purpose of the Study:

• To investigate changing patterns of cerebral blood flow (CBF) in Alzheimer's disease (AD).
• To differentiate subtypes of CBF changes in AD using advanced algorithms.
• To explore the potential clinical utility of CBF subtyping in AD treatment.

Main Methods:

• Utilized data from 341 participants with memory loss, including 115 with AD, 176 with mild cognitive impairment, and 50 with subjective cognitive decline.
• Employed 99mTc-ethyl cysteinate dimer single-photon emission computed tomography scans to assess CBF.
• Applied the Subtype and Stage Inference (SuStaIn) machine-learning algorithm to differentiate CBF patterns.

Main Results:

• The SuStaIn algorithm identified two distinct CBF subtypes in the AD cohort.
• These subtypes included a typical AD pattern and a cortical pattern with hippocampal sparing.
• The identified CBF patterns showed high similarity to previous findings from other neuroimaging modalities.

Conclusions:

• Two distinct patterns of CBF change were observed in individuals with AD.
• CBF subtyping may offer clinical utility for tailoring AD treatments.
• Further research into CBF patterns can refine our understanding of AD progression.