This technology is an artificial intelligence-based method to derive age-specific changes in brain functional connectivity that can be used to study age-dependent changes in an individual patient.
To better understand and detect brain abnormalities, we must first have a concrete understanding of baseline healthy neurological function and development. Previous studies in this area have assumed a linear trend for development of functional connectivity in the brain, which may not be an accurate assumption and therefore may skew interpretations of brain scans and result in inaccurate diagnosis. Although MRI technology is highly adept to study the brain’s functional connectivity, methods and data are lacking to study time-dependent functional connectivity changes during brain development within a unique patient.
This technology consists of a sliding-window based clustering method to characterize brain development over time, independent of pre-built characterizations. This technology allows for measured changes in brain patterns over time and can also leverage details in an individual patient’s medical history to elucidate disease progression or response to treatment. By characterizing brain development over time, physicians may be empowered to develop individualized, custom treatment plans. The introduction of this technology eliminates reliance on synthetic reference datasets, forgoes the unlikely assumption of linear progression, and allows for individual-level rather than population-level comparisons of disease states.
IR CU23122
Licensing Contact: Sara Gusik