Pattern classification - Brain Research Imaging Centre Edinburgh

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Overview • Introduction – Univariate & multivariate approaches – Data representation • <strong>Pattern</strong> Recognition – Machine learning – Validation & inference – Weight maps & feature selection – fMRI application – Multiclass problem • Conclusion & PRoNTo
Conclusions Key points: • More sensitivity (~like omnibus test with SPM) • NO local (voxel/blob) inference CANNOT report coordinates nor thresholded weight map • Require cross-validation (split in train/test sets) report accuracy/PPV (or MSE) • MUST assess significance of accuracy permutation approach
Page 1 and 2: Pattern Recognition for Neuroimagin
Page 3 and 4: Overview • Introduction - Univari
Page 5 and 6: BOLD signal Univariate vs. multivar
Page 7 and 8: Neuroimaging data “feature vector
Page 9 and 10: Pattern recognition framework Input
Page 11 and 12: voxel 2 Classification example L R
Page 13 and 14: Kernel approaches • Mathematical
Page 15 and 16: Linear classifier • hyperplanes t
Page 17 and 18: Support Vector Machine SVM = “max
Page 19 and 20: SVM vs. GP SVM Hard binary classif
Page 21 and 22: Training set Test set … … …
Page 23 and 24: Confusion matrix & accuracy Confusi
Page 25 and 26: Regression MSE • LOO error in one
Page 29 and 30: Example of masks Linear machine Wei
Page 33 and 34: Design Between subjects •2 groups
Page 35 and 36: Design: Within subject • Block or
Page 37 and 38: Design: Within subject • Block or
Page 39: Multiclass problem Multiclass machi
Page 44: Thank you for your attention! Any q

Pattern classification - Brain Research Imaging Centre Edinburgh

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