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Classifying Instantaneous Cognitive States from fMRI Data

Classifying Instantaneous Cognitive States from fMRI Data. Tom Mitchell, Rebecca Hutchinson, Marcel Just, Stefan Niculescu, Francisco Pereira, Xuerui Wang Carnegie Mellon University November, 2003. …. Does fMRI contain enough information?

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Classifying Instantaneous Cognitive States from fMRI Data

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  1. Classifying Instantaneous Cognitive States from fMRI Data Tom Mitchell, Rebecca Hutchinson, Marcel Just, Stefan Niculescu, Francisco Pereira, Xuerui Wang Carnegie Mellon University November, 2003

  2. …

  3. Does fMRI contain enough information? • Can we devise learning algorithms to construct such “virtual sensors”? Cognitive state sequence COGNITIVE TASK “Virtual sensors” of cognitive state

  4. Learning Virtual Sensors • Learn fMRI(t,t+k)  CognitiveState • Classifiers: • Gaussian Naïve Bayes, SVM, kNN • Trained per subject, per experiment • Feature selection/abstraction • Select subset of voxels (by signal, by anatomy) • Select subinterval of time • Average activities over space, time • Normalize voxel activities

  5. Trial: read sentence, view picture, answer whether sentence describes picture Picture presented first in half of trials, sentence first in other half Three possible objects: star, dollar, plus Collected by Just et al. Study 1: Pictures and Sentences

  6. It is true that the star is above the plus?

  7. + --- *

  8. Is Subject Viewing Picture or Sentence? • Learn fMRI(t,t+8)  {Picture, Sentence} • Leave two out cross-validation was used to assess the performance of the classifiers • SVMs and GNB worked better than kNN • Some Details: • 12 subjects, 40 pictures, 40 sentences • 1397 - 2864 voxels per subject, 7 ROIs • fMRI snapshot taken every half second

  9. Error for Single-Subject Classifiers • Error computed by averaging over all subjects • 95% confidence intervals per subject are ~ 10% large • Error of default classifier is 50%

  10. Can We Train Subject-Indep Classifiers? • Approach: define supervoxels based on anatomically defined regions of interest • Normalize per voxel activity for each subject • Each value scaled now in [0,1] • Abstract to seven brain region supervoxels • 16 snapshots for each supervoxel • Train on n-1 subjects, test on nth • Leave one subject out cross validation

  11. Error for Cross Subject Classifiers • NO Feature Selection used in this experiment • 95% confidence intervals approximately 5% large • Error of default classifier is 50%

  12. Family members Occupations Tools Kitchen items Dwellings Building parts Study 2: Word Categories • 4 legged animals • Fish • Trees • Flowers • Fruits • Vegetables

  13. Word Categories Study • Stimulus: • 12 blocks of words: • Category name (2 sec) • Word (400 msec), Blank screen (1200 msec); answer • Word (400 msec), Blank screen (1200 msec); answer • … • Subject answers whether each word in category • 20 words per block, nearly all in category

  14. Training Classifier for Word Categories • Learn fMRI(t)  Word Category • Training methods: kNN, GNB • Leave one example out from each class used to assess performance • Some Details: • 10 subjects, 20 examples per class • 8470 - 11,136 voxels per subject, 30 ROIs • fMRI snapshot taken every second

  15. Dataset \ Classifier GNB 1NN 3NN 5NN Words 0.08 0.30 0.20 0.16 Study 2: Results Classifier outputs ranked list of classes Evaluate by the fraction of classes ranked ahead of true class • 0=perfect, 0.5=random, 1.0 unbelievably poor

  16. Study 3: Syntactic Ambiguity • Is subject reading ambiguous or unambiguous sentence? • “The experienced soldiers warned about the dangers conducted the midnight raid.” • “The experienced soldiers spoke about the dangers before the midnight raid.” • Almost random results if no feature selection used • With feature selection: • SVM - 77% accuracy • GNB - 75% accuracy • 5NN – 72% accuracy

  17. Feature Selection • Four feature selection methods: • Active (n most active available voxels compared to baseline fixation activity, according to a t-test) • RoiActive (n most active voxels in each ROI) • RoiActiveAvg (average of the n most active voxels in each ROI) • Disc (n most discriminating voxels according to a trained classifier) • Active works best

  18. Feature Selection Dataset Feature Selection GNB SVM 1NN 3NN 5NN PictureSent No 0.29 0.32 0.43 0.41 0.37 Active 0.16 0.09 0.20 0.18 0.19 Words No 0.10 N/A 0.40 0.40 0.40 Active 0.08 N/A 0.30 0.20 0.16 Synt Amb No 0.43 0.38 0.50 0.46 0.47 Active 0.25 0.23 0.29 0.29 0.28

  19. Summary • Proved that there is enough information in the fMRI signal to allow decoding of Cognitive States • Successful training of classifiers for instantaneous cognitive state in three studies • Cross subject classifiers trained by abstracting to anatomically defined ROIs • Feature selection and abstraction are essential

  20. Research Opportunities • Learning temporal models • HMM’s, Temporal Bayes Nets • Learn to discriminate whether a subject has certain mental disease • Discovering useful data abstractions • ICA, PCA, hidden layers in Neural Nets • Merging data from multiple sources • fMRI, ERP, reaction times

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