CTE has multiple pathologic variants that might relate to different clinical symptom presentation

Area of Science:

• Neuroscience
• Neuropathology
• Machine Learning in Medicine

Background:

• Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative disease linked to repetitive head injury (RHI).
• Current CTE diagnosis relies on post-mortem identification of hyperphosphorylated tau (pTau) pathology, which is often heterogeneous.
• Understanding CTE heterogeneity is crucial for accurate diagnosis and treatment development.

Purpose of the Study:

• To investigate the heterogeneity of CTE neuropathology using machine learning.
• To identify distinct pathological subtypes and progression patterns of CTE.
• To explore the clinical relevance of identified CTE subtypes.

Main Methods:

• Applied the unsupervised machine learning algorithm subtype and stage inference (SuStaIn).
• Analyzed quantitative pTau density data from 26 brain regions in 207 CTE cases and 75 controls.
• Correlated pathological findings with clinical data, including the Functional Activities Questionnaire (FAQ).

Main Results:

• SuStaIn identified three distinct CTE pathological progression subtypes: cortical predominant, cortical sparing-hippocampal enhanced, and copathology altered.
• Subtype 1 showed rapid cortical pTau progression, while Subtype 2 exhibited slower progression with hippocampal involvement.
• Subtype 3, found in older cases, was enriched for comorbid pathologies and showed enhanced pTau in related regions.
• Functional Activities Questionnaire (FAQ) impairment correlated with pTau burden in Subtype 1, indicating a link to functional decline.

Conclusions:

• CTE neuropathology is heterogeneous, with distinct subtypes characterized by different tau deposition patterns and progression rates.
• These findings validate key pathological variants within CTE.
• Characterizing CTE subtypes is essential for refining diagnostic criteria and advancing in-life diagnosis.