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Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia - Machine learning and dyslexia_mmc1

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Legends for figures: Figure 1:

Classification Scheme Figure 2:

Regions with voxels involved in discriminating between subjects with and without dyslexia. Post hoc analysis revealed that the region in blue (LIPL) is smaller in subjects with dyslexia and that the regions in red (LOFG and ROFG) are larger in subjects with dyslexia.

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Supplementary Information Sample 1

Group differences on school grades, intelligence and five elements of dyslexia

___________________________________________________________________________

Dyslexia No dyslexia

(N=22) (N=27)

Mean SD Mean SD T p

___________________________________________________________________________ Final School Grades

Dutch 6.50 0.89 7.16 0.75 2.71 0.010 English 6.71 1.01 6.92 1.32 0.59 0.562 Other Languages 6.61 0.70 7.15 0.58 2.77 0.008 Mathematics 6.19 1.66 6.41 0.73 0.55 0.587 Other Courses 6.95 0.59 7.00 0.55 0.27 0.788 __________________________________________________________________________ Intelligence (factor scores, normalized to a student population)

Non-verbal -0.28 0.86 -0.06 0.98 0.74 0.466 intelligence Speed of numeric -0.65 0.62 -0.30 0.82 1.52 0.136 processing Vocabulary -0.54 1.02 -0.03 1.00 1.62 0.113 ___________________________________________________________________________ Elements of Dyslexia (Z-scores)

Spelling -1.60 0.65 0.54 0.79 10.23 < 0.001 Phonology -1.23 1.02 0.33 0.86 5.77 < 0.001 Short-term -1.32 0.78 0.42 0.99 6.71 < 0.001 memory Visual/attentional -0.62 0.66 0.49 0.82 5.12 < 0.001 Confusion Whole-word -1.23 0.71 0.43 0.78 7.70 < 0.001 reading ___________________________________________________________________________

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