Ucsf–led study may lead to precision medicine treatment for traumatic brain injury.
Scientists have used a unique computational technique that sifts through big data to identify a subset of concussion patients with normal brain scans, who may deteriorate months after diagnosis and develop confusion, personality changes and differences in vision and hearing, as well as post–traumatic stress disorder. This finding, which is corroborated by the identification of molecular biomarkers, is paving the way to a precision medicine approach to the diagnosis and treatment of patients with traumatic brain injury.

Investigators headed by scientists at UC San Francisco and its partner institution Zuckerberg San Francisco General Hospital and Trauma Center (ZSFG) analyzed an unprecedented array of data, using a machine learning technology called topological data analysis (TDA), which “visualizes” diverse datasets across multiple scales, a technique that has never before been used to study traumatic brain injury.

TDA, which employs mathematics derived from topology, draws on the philosophy that all data has an underlying shape. It creates a summary or compressed representation of all the data points using algorithms that map patient data into a multidimensional space. The new research relied on a TDA platform developed by Ayasdi, an advanced analytics company based in Menlo Park, Calif.

“TDA is a type of machine intelligence that provides a way to easily visualize patient differences across the full spectrum of traumatic brain injury from concussion to coma,” said senior co–author, Adam Ferguson, PhD, associate professor in the Department of Neurological Surgery and a member of the UCSF Weill Institute for Neurosciences. “This has potential to transform diagnosis and predict outcome by providing a new level of precision.”

The study, publishing in the journal PLOS ONE on March 1, 2017, is part of a government–funded multisite initiative called TRACK–TBI, Transforming Research and Clinical Knowledge in Traumatic Brain Injury, which was established to identify new diagnostic and prognostic markers, and to refine outcome assessments.