Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to...

Area of Science:

• Neuroscience and Medical Imaging
• Personalized Medicine
• Biomarker Discovery

Background:

• Personalized Medicine (PM) aims to individualize patient treatment but faces significant challenges in neurology.
• Predicting brain responses to interventions and understanding causal disease mechanisms are key limitations.
• Current clinical categories may not accurately reflect individual molecular alterations or therapeutic needs.

Purpose of the Study:

• To introduce and validate the personalized Therapeutic Intervention Fingerprint (pTIF) for predicting intervention effectiveness in neurological patients.
• To assess the ability of pTIF to predict individual gene expression (GE) profiles and identify distinct patient subgroups.
• To demonstrate the potential of pTIF for guiding personalized therapeutic interventions and clinical trial enrollment.

Main Methods:

• Inferred pTIF from multimodal longitudinal neuroimaging data (amyloid-β, metabolic, tau PET; vascular, functional, structural MRI).
• Studied an aging population (N=331) including cognitively normal and neurodegenerative individuals.
• Compared pTIF predictions with cognitive/clinical evaluations for predicting gene expression variability.

Main Results:

• The pTIF framework significantly outperformed cognitive and clinical evaluations in predicting individual gene expression profiles.
• Regrouping patients by predicted interventions revealed distinct molecular pathway signatures corresponding to different therapeutic needs.
• Demonstrated the identification of dissimilar pathological stages and varying therapeutic requirements across the population.

Conclusions:

• The pTIF framework offers a novel approach to predict individual treatment responses in neurological disorders.
• pTIF links multifactorial brain dynamics, predicted treatment responses, and molecular alterations at the patient level.
• This biomarker-driven framework advances personalized therapeutic interventions and selective clinical trial enrollment.