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README.md

cepy

Implementation of the connectome embedding (CE) framework.

Embedding of brain graph or connectome embedding (CE) involves finding a compact vectorized representation of nodes that captures their higher-order topological attributes. CE are obtained using the node2vec algorithm fitted on random walk on a brain graph. The current framework includes a novel approach to align separately learned embeddings to the same latent space. Cepy is tested for Python 3.6, 3.7 and 3.8.

Installation

pip install cepy

Usage

import cepy as ce
import numpy as np

# Load an adjacency matrix (structural connectivity matrix)
sc_group = ce.get_example('sc_group_matrix')

# Initiate and fit the connectome embedding model
ce_group = ce.CE(permutations = 1, seed=1)  
ce_group.fit(sc_group)

# Extract the cosine similarity matrix among all pairwise nodes
cosine_sim = ce_group.similarity()

# Save and load the model
ce_group.save_model('group_ce.json') 
ce_loaded = ce.load_model('group_ce.json') # load it

# Load two existing CE models  
ce_subject1 = ce.get_example('ce_subject1')
ce_subject2 = ce.get_example('ce_subject2')

# Align the two to the space of the [ce_group]:
ce_subject1_aligned = ce.align(ce_group, ce_subject1)
ce_subject2_aligned = ce.align(ce_group, ce_subject2)

# Extract the node vectorized representations (normalized) for subsequent use - prediction, for example 
w_sbject1 = ce_subject1_aligned.weights.get_w_mean(norm = True)
w_sbject2 = ce_subject2_aligned.weights.get_w_mean(norm = True)
 

A set of example interactive Jupyter notebooks are also available here.

Citing

If you find cepy useful for your research, please consider citing the following paper:

Levakov, G., Faskowitz, J., Avidan, G. & Sporns, O. (2021). Mapping structure to function
 and behavior with individual-level connectome embedding. bioRxiv. doi: https://doi.org/10.1101/2021.01.13.426513 

Contributing

Cepy is an open-source software project, and we welcome contributions from anyone. We suggest raising an issue prior to working on a new feature.

Reference

  • The node2vec implementation is modeified from the node2vec package by Elior Cohen and the connectome_embedding code by Gideon Rosenthal.
  • Rosenthal, G., Váša, F., Griffa, A., Hagmann, P., Amico, E., Goñi, J., ... & Sporns, O. (2018). Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes. Nature communications, 9(1), 1-12. ;

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