CBPtools: A Python package for regional connectivity-based parcellation

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2 3 4 5 Clusters 0.2 0.0 0.2 0.4 0.6 0.8 1.0

Adjusted Rand Index

rsfMRI dMRI rsfMRI dMRI 0.0 0.1 0.2 0.3 0.4 0.5 Silhouette Index k 2 3 4 5

Niels Reuter

1,2

, Sarah Genon

2

, Shahrzad Kharabian

2

, Felix Hoffstaedter

1,2

, Tobias

Kalenscher

3

, Kaustubh. R. Patil

1,2

& Simon B. Eickhoff

1,2

1_{Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany;}

2Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany;

3_{Department of Comparative Psychology, Institute of Experimental Psychology, Heinrich-Heine University, Düsseldorf, Düsseldorf, Germany} n.reuter@fz-juelich.de

CBPtools: A Python package for

regional connectivity-based parcellation

 Here we have outlined and demonstrated the effectiveness of CBPtools to procure rsfMRI and

dMRI connectivity-derived parcellations.

 By unifying methodological choices behind the rCBP procedure, we offer a fast and stable means

to effortlessly and efficiently conduct reproducible and data-driven parcellation analysis.

 Computational demands highlighted by complex algorithms and large datasets are mitigated by

efficient parallel execution of the procedure using the Snakemake [6] software.

Acknowledgments: This study was supported by the European Union‘s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 (HBP SGA2) References:

[1] Eickhoff SB, Thirion B, Varoquaux G, Bzdok D (2015) Connectivity-based parcellation: Critique and implications. Hum Brain Mapp 36:4771–4792. doi: 10.1002/hbm.22933;

[2] Ruan J, Bludau S, Palomero-Gallagher N, et al (2018) Cytoarchitecture, probability maps, and functions of the human supplementary and pre-supplementary motor areas. Brain Struct Funct 223:4169–4186. doi: 10.1007/s00429-018-1738-6; [3] Glasser MF, Sotiropoulos SN, Wilson JA, et al (2013) The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80:105–124. doi: 10.1016/j.neuroimage.2013.04.127;

[4] Van Essen DC, Smith SM, Barch DM, et al (2013) The WU-Minn Human Connectome Project: an overview. Neuroimage 80:62–79. doi: 10.1016/j.neuroimage.2013.05.041 [5] Generated with Mango (Multi-image analysis GUI; http://ric.uthscsa.edu/mango/)

[6] Köster J, Rahmann S (2012) Snakemake--a scalable bioinformatics workflow engine. Bioinformatics 28:2520–2522. doi: 10.1093/bioinformatics/bts480

Regional connectivity-based parcellation (rCBP) is a widely used

procedure for revealing the underlying brain organization of a region-of-interest (ROI) by mapping its whole-brain connectivity profiles [1].

 No standardized guidelines currently exist for applying rCBP  No standardized software currently exist for applying rCBP  Most studies utilizing it are based on in-house software.

Here, we outline and demonstrate CBPtools, an open-source plug-and-play yet customizable rCBP software package made in Python (3.5+). By introducing CBPtools we provide researchers the means to conduct reproducible, data-driven rCBP analyses on multiple neuroimaging modalities and large amounts of subject data.

 Group-clustering labels are mapped on

seed image

 2-cluster solution shown to best fit input

data (Fig. 7)

 ARI between cytoarchitectonically defined

subdivision and both dMRI and rsfMRI > .7

1. Connectivity-Based Parcellation Workflow

2. Connectivity Computation

3. Clustering

6. NIfTI Images

Get CBPtools @ https://github.com/inm7/cbptools

7. Conclusion

k-means clustering used to cluster

connectivity matrices per subject and per k

 Randomly chooses k seed voxels as

cluster centers (Fig. 3, circles)

 Assigns each seed voxel to closest

centroid using Euclidean distance of its connectivity profile

 Move centroid to the mean of all

assigned voxels and repeat

Subject clustering results are used to construct a representative group clustering.

 Hierarchical clustering

creates reference clustering of all subject clusterings

 Subject clusterings are

relabeled to match

reference clustering

 Mode of all relabeled subject

clusterings taken as group clustering

5. Grouping

A F D B C G E

Fig. 3 k-means clustering with k = 3. Voxels

(squares) group around the 3 centroids (circles)

X

Connectivity matrices are calculated for each subject using linear

correlations for resting-state functional magnetic resonance imaging (MRI) and probabilistic tractography for diffusion MRI data

A B C F G D E Target Voxels S e e d V o xe ls

1

2

3

1

2

3

2

1

3

2

1

3

2

3

1

3

1

2

Fig. 5 Relabeling through permutations.Here is a recursion tree for

permutations of the labels “1, 2, 3“

Fig. 2 Computation of a seed-voxel by target-voxel connectivity matrix

Fig. 1 CBPtools workflow for applying rCBP to diffusion MRI (dMRI) or resting-state functional MRI (rsfMRI) data

S im ila ri ty A B C D E F G

Fig. 4 Hierarchical clustering with a 3-cluster cut-off.

Voxels (squares) grouped at each step

Fig. 9 3D representation [5] of volumetric 2-cluster combined preSMA and

SMA region-of-interest solution

4. Validity & Similarity

Fig. 8 Adjusted rand index scores between subject clusterings and group-clusterings

for rsfMRI and dMRI at k = 2, 3, 4, and 5

Fig. 7 Silhouette index for rsfMRI and dMRI at k =

2, 3, 4, and 5

 Example data: 300 unrelated healthy subjects from the

Human connectome Project [3, 4]

 Example region: combined preSMA-SMA mask from the

Juelich Cytoarchitectonic Atlas [2]

 Cluster quality metrics computed for each subject- and

group-clustering at all values of clustering granularity k

Mask Preprocessing

Mask Preprocessing Mask Preprocessing_{Mask Preprocessing}

Input

Subsample

Remove every second voxel

Subsample

Remove every second voxel

Seed extraction

Remove seed voxels from target space

Seed extraction

Remove seed voxels from target space

Upsample

Decrease voxel size of mask

Upsample

Decrease voxel size of mask

Connectivity

Connectivity ConnectivityConnectivity probtrackx2 Probabilistic tractography to derive connectivity probtrackx2 Probabilistic tractography to derive connectivity Apply Masks

Mask input time-series with seed- and target-masks

Apply Masks

Mask input time-series with seed- and target-masks

Compute Connectivity

Linear correlations between seed- and target-masked time-series

Compute Connectivity

Linear correlations between seed- and target-masked time-series Grouping Grouping Clustering Clustering Connectivity Connectivity Transform

Fisher Z and/or PCA transform connectivity

Transform

Fisher Z and/or PCA transform connectivity Similarity Similarity Validity Validity dMRI BedpostX output Report Report rsfMRI Resting-state time-series Seed Mask

ROI NIfTI image

Target Mask

Feature NIfTI image

Median Filter

Apply a median filter to the ROI image

Median Filter

Apply a median filter to the

ROI image MasksMasks

Downsample

Increase voxel size of mask

Downsample

Increase voxel size of mask

Median Filter

Target Mask Seed Mask

Seed Mask Target Mask

Signal Cleaning

Smoothing, confound regression, band- pass filtering

Signal Cleaning

Smoothing, confound regression, band- pass filtering A B C D E F G