Researchers from McGill University suggest that patient’s unique physiology credited by brain fingerprints provides personalized treatments to patients with neurological disease.
Personalized Therapeutic Intervention Fingerprint (pTIF) is a technique that predicts effectiveness by targeting specific biological factors to generate potential intervention responses and assists patients according to their specific treatment needs. The research led by Yasser Iturria-Medina, assistant professor at Department of Neurology and Neurosurgery, used computational brain modeling and artificial intelligence techniques for analysis of neurological disorders. The data of around 300 Alzheimer’s patients and healthy volunteers, included multiple modes of positron emission tomography (PET) and magnetic resonance imaging (MRI). The patients were categorized into their TIF subtypes, depending on the most beneficial factor-specific interventions. The subtypes were checked for their relevance by comparing them to the patients’ individual genetic profiles. It was observed that patients in the same pTIF subtype depicted similar gene expression, which in turn proves that the mechanism in which genes affect their physiology is similar. Drugs prescribed to curb disease progression need to modify gene expression and brain properties at the same time. Hence, drugs tailored to pTIF subtypes are more effective than drugs designed to treat neurological disorders such as Alzheimer’s disease.
The study of pTIF subtypes establishes a direct link between brain dynamics and offers to predict therapeutic responses and molecular and cognitive alterations in patients. These subtypes are expected to be a breakthrough in developing drugs designed for a patient’s unique gene expression profile and phenotypic brain characteristics. Such major advancement in personalized medicines could also improve the effectiveness and reduce the cost of clinical drug trials. The researchers now focus on applying pTIF subtypes to other neurological disorders. Such efforts can extensively validate the process and provides the resulting analytic tools across the world, via open-access platforms. The research was published in the journal Neuroimage on June 14, 2018.