Structured Outlier Detection in Neuroimaging Studies with Minimal Convex Polytopes

Area of Science:

• Neuroimaging
• Computational Anatomy
• Biomedical Data Analysis

Background:

• Computer-assisted imaging analysis is challenged by normal neuroanatomical variability.
• Defining normative ranges and identifying outliers is crucial for disease characterization.
• Understanding deviations in individuals may reveal prodromal disease states.

Purpose of the Study:

• Propose a novel geometric concept, the minimal convex polytope (MCP), for neuroimaging analysis.
• Simultaneously capture high-probability regions in normal subjects and delineate outliers.
• Characterize deviations from the normative range in brain structure.

Main Methods:

• Developed and validated the minimal convex polytope (MCP) geometric concept.
• Applied MCP to simulated datasets for method verification.
• Utilized MCP on an imaging study of 177 controls, 123 Alzheimer's disease (AD) patients, and 285 mild cognitive impairment (MCI) patients.

Main Results:

• Identified cerebellar degeneration as a significant deviation pattern within the control group.
• Demonstrated that a subset of Alzheimer's disease patients exhibit an accelerated deviation pattern.
• Showcased MCP's ability to capture normative data and identify outlier deviations.

Conclusions:

• The minimal convex polytope (MCP) method effectively characterizes normative ranges and deviations in neuroimaging data.
• MCP can aid in identifying early pathological changes, potentially distinguishing accelerated disease progression.
• Findings suggest cerebellar degeneration is a key variation in healthy aging and a potential marker in neurodegenerative diseases.