Slides from a talk given at SfN 2016. The task of dividing the human brain into regions has been captivating scientists for many years. In the following work we revisit this challenge and introduce a new evaluation technique that works for both cortical and subcortical parcellations. Our approach is based on data from a diverse set of cognitive experiments that employs nonparametric methods to account for smoothness and parcel size biases. As reported before parcel variance was a function of parcel size in that smaller parcels were more likely to be homogenous (even in random data). However, when we used map-specific null distributions to account for both smoothness of statistical maps as well as number of parcels in atlases, unbiased estimates become apparent. Both Yeo et al. and Collins et al. parcellations produce scores for random data similar to those derived from real data. In contrast, Shen et al., AAL, and Gordon et al. show lower within parcel variance when applied to real data than when applied to random data (but no distinction can be made between them). In addition to looking at within parcel variance we also applied a novel metric based on the intuition that different parts of the brain should not only be homogenous, but also different from each other. To quantify this we calculated a ratio of between and within parcel variances (standardized using individual null models). This approach indirectly penalizes parcellations with too many unnecessary parcels. Using this measure we show that Yeo et al. parcellation fits data better (Figure 1) than Collins et al. atlas despite having fewer parcels (7 vs 10). We present a novel approach to evaluating atlases and parcellations of the human brain that captures diverse patterns observed across many cognitive studies. Our testing methodology overcomes biases introduced by the size of the parcels and smoothness of input data, but also, in contrast to previous methods, can be applied to whole brain volumetric data. We have found that in contrast to previous reports based on resting state cortico cortical connectivity Shen et al. and AAL atlases can delineate brain regions with above average accuracy.

1. EVALUATION OF FULL BRAIN
PARCELLATION SCHEMES
USINGTHE NEUROVAULT DATABASEOF STATISTICAL MAPS
Krzysztof J. Gorgolewski,ArielleTambini, Joke Durnez,
VanessaV. Sochat, Joe Wexler, RussellA. Poldrack

4. Motivation
• There are many different ways to divide the brain into regions.
• What makes one parcellation better than other?

5. Evaluate different parcellation
scheme using task fMRI
activation patterns
Goal

6. Methods: Included atlases
Name Number of parcels Coverage Properties
Gordon et al. 333 cortical asymmetrical
AAL 116 cortical + subcortical asymmetrical
Collins et al. 9 cortical + subcortical symmetrical
Yeo et al. 7 or 17 cortical symmetrical networks
Glasser et al. 180 or 360 cortical symmetrical or
asymmetrical
Harvard-Oxford 58 cortical + subcortical symmetrical
Brainnetome 246 cortical + subcortical asymmetrical

7. Methods: Metrics
• A good parcellation should accurately delineate
activation patterns from task fMRI
• Variance ofT/Z values within parcels should be
minimized
• Homogenous brain areas should be covered by minimal
number of parcels

8. Methods: Procedure
1. Gather a large collection of statistical maps
2. For each map:
1. Calculate mean within parcel variance
2. Calculate between parcel variance
3. Between/within parcel variance ratio should be
higher for better parcellations

9. Methods: Statistical maps
• Inclusion criteria:
• From a published study
• In MNI space
• Unthresholded
• Task fMRI
Final collection:
• 625 statistical maps from 79 papers
• Representing 87 different tasks

10. Methods: Included statistical
maps

11. Methods: Confounds and how to
deal with them
Confounds:
1. Parcellation with more regions have smaller parcels and thus
lower within parcel variance
2. Each input map has different smoothness
Instead of asking:
How well does this parcellation represent this activation pattern?
Ask:
How this parcellation represents this activation pattern in
contrast to a random pattern?

12. Methods: Null distribution
For each statistical map:
1. Estimate smoothness
2. Shuffle values
3. Smooth
4. Repeat x1000
Example statistical map
Randomized statistical map with
matched smoothness

13. Within parcel variance
big parcels small parcels

14. Normalization
before after

15. Normalized between/within parcel
variance

16. Top symmetrical
Name Between/within parcel
variance [z-score]
Within parcel variance
[z-score]
Yeo et al. (17) 25.84 -5.43
Yeo et al. (7) 24.19 -2.77
Glasser et al. (180) 22.62 -7.89
Harvard-Oxford 14.80 -5.04

17. Top asymmetrical
Name Between/within parcel variance
[z-score]
Within parcel variance
[z-score]
Glasser et al. (360) 14.91 -6.82
Shen et al. (100) 14.34 -8.40
Brainnetome 13.92 -7.38
Shen et al. (50) 13.88 -7.37

18. Conclusions
• Most parcellations perform well above
chance
• Symmetrical parcellation do better
•Yeo networks perform particularly well
• There is no clear winner

19. Future directions
• Surface based comparison
• Subcortical only comparison
• Improve manual quality control