Matlab code for generating randomized networks with the same degree sequence and edge weight distribution as a target network and approximately the same edge length distribution and length-weight relationship.

fcn_match_length_degree_distribution.m

Q: When should I used this function?

A: If your application requires a rewiring-based null model that preserves basic spatial and structural properties.

References:

  1. Betzel & Bassett (2017). The specificity and robustness of long-distance connections in weighted, inter-areal connectomes.


Matlab code for generating consensus communities (requires the genlouvain package) from a set of pre-computed partitions.

fcn_consensus_communities.m

Q: When should I used this function?

A: When you've generated many partitions using modularity maximization or infomap and want a semi-principled, semi-supervised approach for getting an "average" partition.

Note: There are other nice software packages for generating consensus networks, e.g. work from Santo Fortunato & Olaf Sporns.

References:

  1. Betzel & Bassett (2017). The specificity and robustness of long-distance connections in weighted, inter-areal connectomes

  2. Betzel et al (2017). The modular organization of human anatomical brain networks: Accounting for the cost of wiring [link].

  3. Betzel et al (2017). Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression [link].


Matlab code for estimating time-varying connectivity with tapered window.

fcn_taper.m

Q: When should I used this function?

A: It's just another way of estimating windowed functional connectivity. The difference is that it discounts distant (in the past) fluctuations.

References:

  1. Betzel et al (2016). Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks [link]

note: Eq. 3 and Eq. 4 in the above paper have errors. The variable N in Eq. 3 should be L. Eq. 4 should not have a square root.