June 18th, 2013 OHBM morning workshop 1Data-driven brain parcellations:A statistical perspectiveBertrand ThirionINRIA Sacl...
June 18th, 2013 OHBM morning workshop 2Rationale for parcel-based data analysisK parcels rather than 105voxels− Multiple ...
June 18th, 2013 OHBM morning workshop 3Atlases or data-driven parcellations ?Atlases (AAL, Harvard-Oxford...) can beused ...
June 18th, 2013 OHBM morning workshop 4Data-driven parcellations: how ?Any kind of data...Cyto-architectureSulco-gyral a...
June 18th, 2013 OHBM morning workshop 5Model selection for brain parcellationsLow K: parcelsrepresentfunctionalsignals poo...
June 18th, 2013 OHBM morning workshop 6Criteria for model evaluation(Penalized) goodness of fit− BIC criterion: penalized...
June 18th, 2013 OHBM morning workshop 7Impact of changing K on the variance001.6Variance (a.u.)σ12σ22The allocation of va...
June 18th, 2013 OHBM morning workshop 8Results: goodness of fitKopt~ 4000 to 7000Wards > K-means > spectral clusteringFo...
June 18th, 2013 OHBM morning workshop 9Results: reproducibilityKopt~ 200Spectral clustering > Wards > K-meansTo reproduc...
June 18th, 2013 OHBM morning workshop 10Hints from simulationsPoor between subject registration might artificiallyinflate...
June 18th, 2013 OHBM morning workshop 11DiscussionCurrent atlases are too coarse to yield reliableaverages of fMRI dataG...
June 18th, 2013 OHBM morning workshop 12What about resting stateConsider linear decompositions and clusteringThe signal ...
June 18th, 2013 OHBM morning workshop 13Resting-state parcellations[Abraham et al MICCAI 2013]
June 18th, 2013 OHBM morning workshop 14ConclusionUsefulness of brain parcellations− A good model depends on the context−...
June 18th, 2013 OHBM morning workshop 15AcknowledgementsGaël Varoquaux, Alexandre Abraham, AlanTucholka, Benoit da Mota, ...
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Hbm parcellations

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Hbm parcellations

  1. 1. June 18th, 2013 OHBM morning workshop 1Data-driven brain parcellations:A statistical perspectiveBertrand ThirionINRIA Saclay–Ile de France, PARIETAL team,Neurospinbertrand.thirion@inria.fr
  2. 2. June 18th, 2013 OHBM morning workshop 2Rationale for parcel-based data analysisK parcels rather than 105voxels− Multiple comparisons− Connectivity studies− Brain-level MVPALocal physiological parameters [Chaari etal MICCAI 2012][Thirion et al HBM 2006, Compstat 2010][Craddock et al. HBM 2012][Varoquaux et al. Nimg 2013][Yeo et al. J. neurosphys. 2011]parcel voxel cluster
  3. 3. June 18th, 2013 OHBM morning workshop 3Atlases or data-driven parcellations ?Atlases (AAL, Harvard-Oxford...) can beused to define ROIs: A priori definition and labels Limited resolutionData-driven Parcellations: Flexible description, better data fit Do not fit a priori with current knowledgeLack of consistency: [Bohland et al. Plos One2009]
  4. 4. June 18th, 2013 OHBM morning workshop 4Data-driven parcellations: how ?Any kind of data...Cyto-architectureSulco-gyral anatomyAnatomical connectivityFunctional data:− Resting-state fMRI− Activation fMRI− Meta-analysis / co-activation)… many possible methodsK-means, mixture modelsSpectral clusteringAgglomerative clusteringDecompositions approaches:− ICA, sparse PCA and variants
  5. 5. June 18th, 2013 OHBM morning workshop 5Model selection for brain parcellationsLow K: parcelsrepresentfunctionalsignals poorlyLarge K:parcels are notreproducibleSee also [Craddock et al. HBM 2012]Model selection is an ill-posed problem- A model is not good in itself, but in view of a given objective- the data dot not conform well to models?
  6. 6. June 18th, 2013 OHBM morning workshop 6Criteria for model evaluation(Penalized) goodness of fit− BIC criterion: penalized log-likelihood− Cross validation: log-likelihood on left-out data (CVLL)Reproducibility across bootstrap samples− Estimate parcellation on different subgroups and compareco-labelling statistics (mutual information, rand index)VoxelsignalParcelmeansignalrandomsubject effectnoise
  7. 7. June 18th, 2013 OHBM morning workshop 7Impact of changing K on the variance001.6Variance (a.u.)σ12σ22The allocation of variance into inter- andintra-subject components depends on K− σ12= within subject variance− σ22= between subject variance
  8. 8. June 18th, 2013 OHBM morning workshop 8Results: goodness of fitKopt~ 4000 to 7000Wards > K-means > spectral clusteringFor a good summary of the activation values, use a (very)large number of parcelsBIC CV-LL
  9. 9. June 18th, 2013 OHBM morning workshop 9Results: reproducibilityKopt~ 200Spectral clustering > Wards > K-meansTo reproduce well the parcels, use ~ 200 parcelsAccuracyReproducibility
  10. 10. June 18th, 2013 OHBM morning workshop 10Hints from simulationsPoor between subject registration might artificiallyinflate the number of parcels required to fit thesignal Functional registration should improve the estimation[Sabuncu et al. Cerb. Cortex 2009, Robinson et al. IPMI2013 ]Smoothing also inflates the number of parcels
  11. 11. June 18th, 2013 OHBM morning workshop 11DiscussionCurrent atlases are too coarse to yield reliableaverages of fMRI dataGoodness of fit is different from stability /reproducibility [Strother et al. 2002]Wards methods better suited than alternatives
  12. 12. June 18th, 2013 OHBM morning workshop 12What about resting stateConsider linear decompositions and clusteringThe signal cannot be easily modeled probabilisticallyCross-validation of the R2of resting-state signals, AMISmaller number of regions (~80) [Abraham et al MICCAI 2013]Accuracy Reproducibility
  13. 13. June 18th, 2013 OHBM morning workshop 13Resting-state parcellations[Abraham et al MICCAI 2013]
  14. 14. June 18th, 2013 OHBM morning workshop 14ConclusionUsefulness of brain parcellations− A good model depends on the context− Reproducibility and accuracy yields different responsesNeed (more) multi-modal data to properly define regionsWinners:− Wards clustering (large K)− Dictionary learning (small K)Might be worth combining results from different parcellations[Varoquaux et al. ICML 2012, da Mota et al. MICCAI 2013, Poster#1275]
  15. 15. June 18th, 2013 OHBM morning workshop 15AcknowledgementsGaël Varoquaux, Alexandre Abraham, AlanTucholka, Benoit da Mota, Virgile Fritsch, VincentMichelJB Poline, Guillaume Flandin, Philippe Pinel

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