Judged by your neighbors: A novel framework for personalized assessment of brain structural aging effects in diverse...

Area of Science:

• Clinical decision making
• Precision medicine
• Medical imaging analysis

Background:

• Accurate detection of norm deviations is crucial for effective clinical decision-making, diagnosis, and treatment.
• Current population-average-based normative modeling methods lack personalization and can obscure critical health information.
• Patient heterogeneity necessitates advanced approaches beyond generic comparisons for personalized healthcare.

Purpose of the Study:

• To introduce Nearest Neighbor Normativity (N³), a novel strategy to refine normativity evaluations in heterogeneous clinical populations.
• To address the limitations of traditional methods in capturing individual health nuances and supporting precision medicine.
• To improve the detection of pathological alterations through personalized normativity assessments.

Main Methods:

• Developed the Nearest Neighbor Normativity (N³) framework, which utilizes multiple normative prototypes and tailored control groups.
• Employs multi-perspective comparisons to accommodate individual variations within diverse patient cohorts.
• Applied the N³ framework to analyze brain structure data from a large cohort of 36,896 individuals.

Main Results:

• The N³ framework demonstrated significant outperformance compared to traditional methods in identifying pathological alterations.
• Empirical evidence supports the utility of N³ in refining normativity evaluations for diverse and heterogeneous populations.
• The study successfully applied N³ to large-scale brain structure data, highlighting its scalability.

Conclusions:

• Nearest Neighbor Normativity (N³) offers a superior approach for individual health assessments in clinical practice.
• N³ supports the paradigm shift towards precision medicine by providing a more personalized metric for norm deviations.
• This framework has the potential to significantly enhance personalized patient care by moving beyond generic benchmarks.