This python package implements algorithms introduced in the paper:
Shadi K, Bakhshi S, Gutman DA, Mayberg HS, Dovrolis C. A Symmetry-Based Method to Infer Structural Brain Networks from Probabilistic Tractography Data. Frontiers in Neuroinformatics. 2016.
For technical details, please consult to the paper above.
Install the latest release with
-> pip install pymania
There are no hard dependencies other than the Python standard library and numpy. MANIA runs with Python 2.7.
-> import pymania
The package exposes four functions listed below.
- Running mania for a single subject's probtrackx results:
-> mania_on_subject(study_path, number_of_streamlines_per_seed)
Note: <study_path> must point to a subject folder. Inside the subject folder, there should be a subfolder called "probtrackx" in which the results of probtrackx resides - see the sample_subject folder as an example.
The ouput is a binary numpy 2D array saved in a subfolder called "MANIA" with .net extension.
- Calculating the confidence metric for the edges of a subject network:
-> conf(study_path)
The ouput is a float numpy 2D array saved in a subfolder called "MANIA" with .conf extension.
- Running mania at group level:
-> group_mania(subject_list,output_folder)
The ouput is a binary numpy 2D array saved in the output_folder with agg.net name.
subject_list is the list of study folders. each element of the list is a study folder for a subject.
- Generating synthetic data (see the mania paper):
-> P = synth_probabilistic_anatomy(Number_of_nodes,density,mu1,mu2)
The ouput P is a float numpy 2D array simulating anatomical probablistic connectome by maximum entropy model.