Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia.

ABSTRACT: Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of significance which are characteristic for conventional analysis techniques, and to provide predictive value, we applied a machine learning technique--support vector machine--to differentiate between subjects with and without dyslexia. In a sample of 22 students with dyslexia (20 women) and 27 students without dyslexia (25 women) (18-21 years), a classification performance of 80% (p < 0.001; d-prime = 1.67) was achieved on the basis of differences in gray matter (sensitivity 82%, specificity 78%). The voxels that were most reliable for classification w

SUBMITTER: Tamboer P 

PROVIDER: S-EPMC4832088 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature