Novel data analysis for SCI research

Zur Übersicht

In scientific experiments (from basic to clinical research) many different values are generated. For example after spinal cord injury, researchers are interested in tissue alterations (histology), whether the blood pressure is altered (physiology), how the locomotion pattern is altered (behavior), and many more. Important for the evaluation of these results, is also their confirmation in different models (reproducibility).

Big Data
With this, many more data with further variables accrue. Even if they are already stored within certain databases, e.g. the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI), they are still difficult to overlook, to structure, and to value regarding their importance.

Discovering new connections
Adam Ferguson, who is amongst others funded by Wings for Life, now presents the application of a data-analysis tool (topological data analysis, TDA) that allows to structure data in clusters to so demonstrate their relation. With this, researchers are able to generate new and important insights out of already existing data. This quick and user-friendly recognition of connections among heterogenous data sets is very important for preclinical and clinical researchers. Novel hypothesis can be set up and confirmed, e.g. also sub-groups can be identified, for whom a certain treatment might be effective.

Progress in many areas
The work of Adam Ferguson translates the application of TDA to the field of spinal cord injury research. Using TDA, he could demonstrate that an increased blood pressure during an operation is associated with a worsened long term outcome of motoric function as well as bladder function. Hence, blood pressure presents as an important risk factor for acute care of spinal cord injured patients. This connection hasn’t been found previously and implies a thorough monitoring of the blood pressure.