Hbm parcellations

June 18th, 2013 OHBM morning workshop 1

Data-driven brain parcellations: A statistical perspective

Bertrand ThirionINRIA Saclay–Ile de France, PARIETAL team,

[email protected]

mailto:[email protected]


Rationale for parcel-based data analysis K parcels rather than 105 voxels

− Multiple comparisons

− Connectivity studies

− Brain-level MVPA Local physiological parameters [Chaari et

al 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


Atlases or data-driven parcellations ?

Atlases (AAL, Harvard-Oxford...) can be used 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 One

2009]


Data-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


Model selection for brain parcellations

Low K: parcels represent functional signals poorly

Large K: parcels are not reproducible

See 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

?


Criteria 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 compare

co-labelling statistics (mutual information, rand index)

Voxel signal

Parcel mean signal

random subject effect

noise


Impact of changing K on the variance

00

1.6

Variance (a.u.)

σ12

σ22

The allocation of variance into inter- and intra-subject components depends on K

− σ1

2 = within subject variance

− σ2

2 = between subject variance


Results: goodness of fit Kopt ~ 4000 to 7000

Wards > K-means > spectral clustering

For a good summary of the activation values, use a (very) large number of parcels

BIC CV-LL


Results: reproducibility Kopt ~ 200

Spectral clustering > Wards > K-means

To reproduce well the parcels, use ~ 200 parcels

Accuracy

Reproducibility


Hints from simulations

Poor between subject registration might artificially inflate the number of parcels required to fit the signal

Functional registration should improve the estimation [Sabuncu et al. Cerb. Cortex 2009, Robinson et al. IPMI 2013 ]

Smoothing also inflates the number of parcels


Discussion

Current atlases are too coarse to yield reliable averages of fMRI data

Goodness of fit is different from stability / reproducibility [Strother et al. 2002]

Wards' methods better suited than alternatives


What about resting state Consider linear decompositions and clustering

The signal cannot be easily modeled probabilistically

Cross-validation of the R2 of resting-state signals, AMI

Smaller number of regions (~80) [Abraham et al MICCAI 2013]

Accuracy Reproducibility


Resting-state parcellations

[Abraham et al MICCAI 2013]


Conclusion

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:

− Ward's 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]


Acknowledgements

Gaël Varoquaux, Alexandre Abraham, Alan Tucholka, Benoit da Mota, Virgile Fritsch, Vincent Michel

JB Poline, Guillaume Flandin, Philippe Pinel

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Hbm parcellations