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

Personalized Medicine (PM) seeks to assist the patients according to their specific treatment needs and potential intervention responses. However, in the neurological context, this approach is limited by crucial methodological challenges, such as the requirement for an understanding of the causal disease mechanisms and the inability to predict the brain's response to therapeutic interventions. Here, we introduce and validate the concept of the personalized Therapeutic Intervention Fingerprint (pTIF), which predicts the effectiveness of potential interventions for controlling a patient's disease evolution. Each subject's pTIF can be inferred from multimodal longitudinal imaging (e.g. amyloid-β, metabolic and tau PET; vascular, functional and structural MRI). We studied an aging population (N = 331) comprising cognitively normal and neurodegenerative patients, longitudinally scanned using six different neuroimaging modalities. We found that the resulting pTIF vastly outperforms cognitive and clinical evaluations on predicting individual variability in gene expression (GE) profiles. Furthermore, after regrouping the patients according to their predicted primary single-target interventions, we observed that these pTIF-based subgroups present distinctively altered molecular pathway signatures, supporting the across-population identification of dissimilar pathological stages, in active correspondence with different therapeutic needs. The results further evidence the imprecision of using broad clinical categories for understanding individual molecular alterations and selecting appropriate therapeutic needs. To our knowledge, this is the first study highlighting the direct link between multifactorial brain dynamics, predicted treatment responses, and molecular alterations at the patient level. Inspired by the principles of PM, the proposed pTIF framework is a promising step towards biomarker-driven assisted therapeutic interventions, with additional important implications for selective enrollment of patients in clinical trials.