The PLORAS Database: A data repository for Predicting Language Outcome and Recovery After Stroke

Abstract

The PLORAS Database is a relational repository of anatomical and functional imaging data that has primarily been acquired from stroke survivors, along with standardized scores on a wide range of sensory, motor and cognitive abilities, demographic details and medical history. As of January 2015, we have data from 750 patients with an expected accrual rate of 200 patients per year. Expansion will accelerate as we extend our collaborations. The main aim of the database is to Predict Language Outcome and Recovery After Stroke (PLORAS) on the basis of a single structural (anatomical) brain scan that indexes the stereotactic location and extent of brain damage. Predictions are made for individual patients by indicating how other patients with the most similar brain damage, cognitive abilities and demographic details recovered their language skills over time. Predictions are validated by longitudinal follow-ups of patients who initially presented with speech and language difficulties. The PLORAS Database can also be used to predict recovery of other cognitive abilities on the basis of anatomical brain scans. The functional imaging data can be used to understand the neural mechanisms that support recovery from brain damage; and all the data can be used to understand the main sources of inter-subject variability in structure-function mappings in the human brain. Data will be made available for sharing, subject to: funding, ethical approval and patient consent.

Fig. 1

A schematic illustration of the PLORAS system. Currently we have 750 patients who vary with respect to age (range: 19–99 years), gender, handedness, language skills (87% of all patients are native English speakers), time post-stroke (from a few weeks to 40 years after stroke), lesion location and size (lesion size: from < 1 cm³ to > 400 cm³, lesion side: 59% of patients have lesions in the left hemisphere, 22% in the right hemisphere, 18% in both hemispheres), and language deficits. The bespoke relational database links demographic, behavioral and imaging data in a single data repository. An automated lesion identification technique transforms the brain scan of each patient to a high-resolution stereotactic 3D-lesion image (Seghier et al., 2008). Based on the learnt structure–function–recovery rules from all patients in the PLORAS Database, the inference engine generates a probabilistic recovery curve for the new patient (Hope et al., 2013). The curve is a probability distribution through time, with the mean prediction in black, and borders at 2 standard deviations from the mean (i.e. 95% confidence). The white star illustrates the real speech score of the new patient measured at a given time point after stroke.