A unique brain fingerprint may help identify the most beneficial therapeutic intervention for individual patients with neurologic disorders such as Alzheimer's disease (AD), potentially sparing millions from undergoing ineffective treatment, new research suggests.
Investigators used computational brain modeling and artificial intelligence techniques to analyze positron emission tomography (PET) and MRI from over 300 patients with the AD and healthy controls.
They found that the personalized therapeutic intervention fingerprint (pTIF) predicted the effectiveness of targeting biological factors, such as brain amyloid/tau deposition, inflammation, and neuronal functional dysregulation, to control the patient's disease course.
Moreover, patients who shared a given pTIF subtype had similar gene expression, which supported the relevance of the pTIF framework in biomarker-driven assisted therapeutic interventions.
"Although needing further validation, the introduced concept is a promising tool for patient stratification," lead author Yasser Iturria-Medina, PhD, assistant professor, Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, and primary investigator, Ludmer Centre for Neuroinformatics & Mental Health, Montreal, Quebec, Canada, told.
"It has broad implications for both the future identification of effective individualized treatments and the selective enrollment of patients in clinical trials and could provide a more effective medical care assisted by individual sophisticated models, undesired secondary effects, and the substantial reduction of pharmaceutical/clinical costs," he said.
Pinpointing Optimal Interventions
Unlike "generalized medicine," personalized medicine (PM) is "based on the optimization of treatment plans for individual patients through consideration of particular characteristics," such as molecular, macroscopic, and medical information, the authors noted.
Previous studies of PM have been limited by misuse of association analyses, incorrect extrapolation of group-based statistical inferences for identifying individual disease biomarkers, and the "paradoxical confidence in broad clinical/cognitive categories for validating new patient subtypes," they add.
Recent advances in brain modeling using network-based approaches have focused on the spreading of normal and pathological functional signals or local interactions among different biological factors, and dynamic network modeling has contributed to understanding dissimilar brain mechanisms.
Additional work has used control theory to predict the functional and cognitive response of the brain under the influence of external experimental interventions.