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 PTSD. 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 TBI. Investigators at UCSF and Zuckerberg San Francisco General Hospital analyzed an unprecedented array of data, using a machine learning tool called topological data analysis (TDA), which “visualizes” diverse datasets across multiple scales, a technique that has never before been used to study TBI. TDA, which employs mathematics derived from topology, draws on the philosophy that all data has an underlying shape.
Mapping of outcome using TDA revealed that concussion could be stratified into multiple subgroups with diverse prognoses. Among them was a large group of patients who, despite normal brain scans, demonstrated poor recovery and a tendency to get worse, 3-6 months after the injury. These patients were likely to suffer from PTSD. By recognizing these patients as a distinct subgroup, clinicians may be able to anticipate future symptoms and treat them proactively.