Cerebellar Probabilistic Parcellation project using HierarchBayesParcel and FunctionalFusion
Diedrichsenlab/HierarchicalBayesParcel Diedrichsenlab/FunctionalFusion Diedrichsenlab/DCBC Diedrichsenlab/cortico_cereb_connectivity
Other dependencies:
see requirements.txt
Atlas was estimated using:
scripts/atlas_paper/fit_atlas.py
Single dataset parcellations (Fig 1a)
single_parcellations.ipynb
Symmetric & asymmetric atlas (Fig 2a, 2b & 2c)
atlas_flatmaps.ipynb
Plotting MDS plots for different single dataset parcellations (Fig 1B & 1C & 1D) & Statistics for between-dataset ARI (similarity; normalized to within-dataset ARI) between all datasets and the task-general (MDTB) and rest-based (HCP) data:
notebooks/evaluate_mds.ipynb
Plotting DCBC & Statistics DCBC (Fig 1E & 1F):
notebooks/evaluate_dcbc.ipynb
Comparison of existing atlasses to fusion
notebooks/evaluate_existing.ipynb
Plotting symmetry (boundary and functional symmetry, i.e. functional lateralization) and comparing asymmetric and symmetric atlas versions:
notebooks/symmetry.ipynb
Statistics on DCBC of 68 regions vs 40 regions in individual DCBC (Fig 1F):
notebooks/evaluate_dcbc.ipynb
Dendrogram (Fig 2D):
scripts/atlas_paper/dendrogram.py
Some manual adjustments had to be made on the produced figure, because the internal ordering of the leaves within a domain is randomly generated by the dendrogram plotting algorithm (for example M4a and M4b are ordered M4b, M4a).
The parcels are reordered according to their functional profiles using the reorder_model function in hierarchical_clustering. Clustering of the 68 subregions into 32 regions (medium) and 4 domains (coarse) is then performed on the reordered model using the function cluster_parcel in hierarchical_clustering.py. Both functions are called by the function reorder_models in scripts/atlas_paper/parcel_hierarchy.py.
The order of regions went through several rounds of revisions, with all rounds documented in the reorder_models function.
Characterizing regions based on functional responses in the MDTB dataset over and above motor responses is done by estimating a linear model using ridge regression (L2 regularization) with the motor features (left hand presses, right hand presses & saccades) and task condition indicators as the design matrix. The feature model is run using:
scripts/atlas_paper/feature_model.py
which calls the ridgeFit function in:
scripts/atlas_paper/ridge_reg.py
The motor, action, demand and social-linguistic-spatial regions are described in individual notebooks using all datasets and the MDTB feature model:
Motor regions
notebooks/atlas_paper/motor.ipynb (Fig S1)
Action regions
notebooks/atlas_paper/action.ipynb
Demand regions
notebooks/atlas_paper/demand.ipynb
Sociolinguistic regions
notebooks/atlas_paper/social.ipynb
Probabilistic maps:
function export_all_probmaps in export_atlas.py (Fig S2a)
Region sizes:
notebooks/atlas_paper/size_comparison.ipynb (Fig S2b & S2)
Volumetric atlas view:
notebooks/atlas_paper/atlas_volume.ipynb (Fig S3)
Localizing individual regions
notebooks/atlas_paper/task_differences.ipynb (Fig S4)
Cortical connectivity models are estimated and evaluated in the repository
diedrichsenlab/cortico_cereb_connectivity denoted ccc for short.
Models were trained evaluated ccc.run_model, which is called from ccc.scripts.script_train_eval_models.py
Models are then fused (i.e. simply averaged) using ccc.scripts.script_fuse_models.py
- Model 4: Demand, HCP and MDTB
- Model 5: all datasets including HCP
- Model 6: all datasets excluding HCP
- Model 7: all datasets excluding HCP and Somatotopic
The final model evaluation results reported in the paper can be found in ccc.notebooks.Evaluate_model_int.ipynb.
To summarize the connectivity pattern by cerebellar regions:
import cortico_cereb_connectivity.scripts.script_summarize_weights as csw
csw.make_weight_map('Fusion','06',method='L2Regression')
To summarize further by cortical ROI:
T = csw.make_weight_table(dataset="Fusion",extension="06",cortical_roi="")
Summary figures (by MSHBM_Prior_15_fsLR32)
notebooks/atlas_paper/cortical_connectivity.ipynb
Full connectivity maps:
notebooks/atlas_paper/connectivity_weights.ipynb (Fig S5 & Fig S6)
Size comparison of left and right regions (voxel-wise) of the asymmetric atlas (Fig S8)
notebooks/atlas_paper/size_comparison.ipynb
XX
XX add the figure names to plots in jupyter notebook
To get individual parcellations (previously saved as pytorch tensor to save
time) run the following script:
scripts.individual_variability.export_uhats(model_name)
Which Calls:
evaluate.get_individual_parcellation(model_name)
Plotting the individual parcellations: (Fig 5A):
notebooks/individual_parcellation.ipynb
Calculating and plotting individual variability (Fig 5B):
notebooks/individual_variability.ipynb
which calls:
scripts.individual_variability.calc_variability(Data, Info, subject_wise=False):
Plotting probability maps for parcels (Fig 5C):
notebooks/individual_group.ipynb
plot.plot_parcel_prob
Comparing group and individual parcellations with varying length of data (Fig 5D & 5E):
notebooks/individual_group.ipynb
export_atlas.export_map takes a marginal probabilitiy of a arrangement model and generates the probseg and dseg to save in the ProbabilisticParcellationModel/Atlases directory.
The final step is to take the 2mm atlas in MNISymC2 space and resample it to 1mm isotropic in SUIT, MNIAsym and MNISym spaces defined in the FunctionalFusion/Atlases directory. The reslicing is done on the probseg.nii files and then a new dseg.nii file is computed.
scripts.export_atlas_script.py
which calls
export_atlas.resample_atlas('NettekovenSym32','MNISymC2','MNI152NLin2009cSymC')
The files are then copied for inclusion into the cerebellar_atlases repository by hand.
The spatial subdivision of the atlas is then produced in each atlas space seperately - in the final 1mm space.
subdivde_atlas_spatial(fname='NettekovenSym32',atlas='SUIT',outname='NettekovenSym128')