Introduction to machine learning in fmri analysis. Miika Koskinen, D.Sc. BRU, O.V. Lounasmaa Laboratory Aalto University

Transcription

1 Introduction to machine learning in fmri analysis Miika Koskinen, D.Sc. BRU, O.V. Lounasmaa Laboratory Aalto University

2 Machine learning Focus on pattern recognition, i.e. to learn automatically to discriminate complex patterns in data and make reasonable decisions with the data Statistical dependencies and consistencies in data Involves modeling Classification, clustering, regression, sequence labeling,

3 Encoding Input Model Output " Encoding: how brain activity is affected by variation in outside world Activity in voxels is predicted given the stimulus, mental or task conditions Example: general linear model (GLM) predicting single voxel activity May provide physiological information of stimulusresponse relationship

4 decoding Input Model Output " Decoding: how much can be learned about the world (sensory stimuli, cognitive state, movement) from brain activity e.g. linear classifier, decoder, or predicting picture properties from fmri responses

5 Multivariate analysis Multivariate analysis utilize full spatial pattern of brain activity in contrast to univariate approach Haynes and Rees (2006) 1) Takes advantage of weak information across multiple locations 2) Separate voxels may jointly carry information about cognitive state even when these regions, analyzed separately, do not 3) Fine-grained information may be removed in preprocessing steps (e.g. spatial smoothing) in conventional analysis where it is assumed that voxels in a cluster behave similarly 4) Paradigm shift from comparing average activity between conditions and across subjects to better handle single-trial responses and events

6 Multivariate analysis " A key question for decoding cognitive states is how well the brain activity of these states is separable " Ideally, cognitive states are encoded in spatially distinct locations, modules, e.g. fusiform face area (FFA) respond to faces and parahippocampal place area (PPA) responses to houses and visual scenes " Spatially distributed patterns of brain activity " Pattern recognition and machine learning techniques

7 Pattern classification Special case of decoding model Classification may help to answer Does fmri data carry information about a given variable? Does the pattern of activity in a region carry enough information to distinguish between conditions? Where or when is class information represented? How is class information encoded? Data from All voxels Scan through voxels few adjacent voxels at a time, i.e. searchlight.

8 Terminology Pereira & Botvinick 2011

9 Terminology Pereira & Botvinick 2011

10 Classification pipeline 1. Feature generation (e.g. voxel activity in the simplest case) 2. Feature selection to reduce the number of features Mask anatomically, select voxels showing some activity, searchlight, PCA, ICA, anova Reduces noise, prevent overlearning 3. Choose the classifier model Generative model: Bayes rule based, models the joint distribution (e.g. Gaussian naïve bayes, linear discriminant analysis) Discriminative model: decision boundary directly from the features (e.g. logistic regression, support vector machine)

11 Pattern vector (in pattern classification literature feature vector) Pattern classifier Classifier testing Haynes & Rees 2006

12 Classification pipeline 4. Statistical testing of accuracy Generate accuracy distribution for H0 hypothesis Analytically (e.g. binomial test) Permutation test (more conservative and tolerates better dependencies between examples in the test set) Use separate test data (e.g. cross-validation)

13 Example: What object subject was looking at? Cox & Savoy 2003 " 10 picture categories: baskets, birds, butterflies, chairs, tropical fish, garden gnomes, horses, cows, African masks, teapots " 20 s blocks for each category consisting of 2 s exemplars " Selection of responding voxels with ANOVA out of 27,000 voxels " Find voxels that vary significantly at least one of the categories " 3 different classifiers: linear support vector machine (SVM) the best one

14 Example: Cox & Savoy 2003

15 References and further reading " Cox and Savoy (2003): Functional magnetic resonance imaging (fmri) brain reading : detecting and classifying distributed patterns of fmri activity in human visual cortex. NeuroImage 19: " Haynes and Rees (2006): Decoding mental states from brain activity in humans. Nature Rev Neurosci 7: " Kay, Naselaris, Prenger, Gallant (2008): Identifying natural images from human brain activity. Nature doi: /nature06713 " Lemm, Blankertz, Dickhaus, Müller (2011): Introduction to machine learning for brain imaging. NeuroImage 56: " Naselaris, Kay, Nishimoto, Gallant (2011): Encoding and decoding in fmri. NeuroImage, doi: /j.neuroimage " Pereira, Mitchell, Botvinick (2009): Machine learning classifiers and fmri: A tutorial overview. NeuroImage 45: " Pereira and Botvinick (2010): Information mapping with pattern classifiers: a comparative study. Neuroimage :