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Notes:
UPDATES GraphVar beta 0.62
1. Added two NEW "dynamic" graph measures!
- nodal flexibility and nodal promiscuity coefficient which are
based on changing community assignments in an ordered multislice
matrix (as in Braun et al., 2015: Dynamic reconfiguration of
frontal brain networks during executive cognition in humans)
2. Added new "regular" graph metrics:
- global cost-efficiency (as in Bassett et al.,2009): Cognitive
fitness of cost-efficient brain functional networks, PNAS.
- small-world propensity (unbiased assessment of small-world
structure in networks of varying densities) -> developed by
Muldoon, Bridgeford and Bassett (http://arxiv.org/abs/1505.02194)
3. Finally, you can do directed analyses (graph metrics and raw
matrix calculations) if you input e.g. directed granger causality
matrices!
4. Added "CheckFrag": will check if network fragmentation with
respect to the settings in your network construction occur
5. GraphVar now saves more output files when doing sliding window
analyses (not only the dynamic summary measure as before) and will
also save all results when only "calculate and Export" for further
usage.
Files in your interim results folder are now (depending on what
computations you do here with clustering_coeff as example):
- clustering_coef_bu_4.9_1.mat: dynamic summary measure (e.g.
variance) of clustering_coef_bu across windows for each node on
threshold 0.49 for all subjects
- clustering_coef_bu_4.9_1per_SW.mat: the (normalized)
clustering_coef_bu for each node in each of the sliding windows on
threshold 0.49 for all subjects
- clustering_coef_bu_4.9_1-rand1.mat: dynamic summary measure (e.g.
variance) of clustering_coef_bu across windows for each node in the
first random network on threshold 0.49 for all subjects
- clustering_coef_bu_4.9_1-rand_per_SW.mat: the clustering_coef_bu
for each node in each random network in each of the sliding windows
on threshold 0.49 for all subjects (i.e., cell comprised of:
subjects x random networks x sliding windows)
6. Changed the normalization procedure for dynamic summary measures
(this does not include "nodal flexibility/promiscuity":
- OLD normalization procedure: the dynamic summary measure of the
orig. data was devided by the mean of the dynamic summary measure
of the random data (there was a lot of information loss)
- NOW: first, per sliding window graph metrics are normalized as
usual by division of the mean of the same graph metric derived in
random networks in the same sliding window. Second, the dynamic
summary measure is calculated across sliding windows of the
beforehand normalized graph metrics.
Changes:
UPDATES GraphVar beta 0.62
1. Added two NEW "dynamic" graph measures!
- nodal flexibility and nodal promiscuity coefficient which are
based on changing community assignments in an ordered multislice
matrix (as in Braun et al., 2015: Dynamic reconfiguration of
frontal brain networks during executive cognition in humans)
2. Added new "regular" graph metrics:
- global cost-efficiency (as in Bassett et al.,2009): Cognitive
fitness of cost-efficient brain functional networks, PNAS.
- small-world propensity (unbiased assessment of small-world
structure in networks of varying densities) -> developed by
Muldoon, Bridgeford and Bassett (http://arxiv.org/abs/1505.02194)
3. Finally, you can do directed analyses (graph metrics and raw
matrix calculations) if you input e.g. directed granger causality
matrices!
4. Added "CheckFrag": will check if network fragmentation with
respect to the settings in your network construction occur
5. GraphVar now saves more output files when doing sliding window
analyses (not only the dynamic summary measure as before) and will
also save all results when only "calculate and Export" for further
usage.
Files in your interim results folder are now (depending on what
computations you do here with clustering_coeff as example):
- clustering_coef_bu_4.9_1.mat: dynamic summary measure (e.g.
variance) of clustering_coef_bu across windows for each node on
threshold 0.49 for all subjects
- clustering_coef_bu_4.9_1per_SW.mat: the (normalized)
clustering_coef_bu for each node in each of the sliding windows on
threshold 0.49 for all subjects
- clustering_coef_bu_4.9_1-rand1.mat: dynamic summary measure (e.g.
variance) of clustering_coef_bu across windows for each node in the
first random network on threshold 0.49 for all subjects
- clustering_coef_bu_4.9_1-rand_per_SW.mat: the clustering_coef_bu
for each node in each random network in each of the sliding windows
on threshold 0.49 for all subjects (i.e., cell comprised of:
subjects x random networks x sliding windows)
6. Changed the normalization procedure for dynamic summary measures
(this does not include "nodal flexibility/promiscuity":
- OLD normalization procedure: the dynamic summary measure of the
orig. data was devided by the mean of the dynamic summary measure
of the random data (there was a lot of information loss)
- NOW: first, per sliding window graph metrics are normalized as
usual by division of the mean of the same graph metric derived in
random networks in the same sliding window. Second, the dynamic
summary measure is calculated across sliding windows of the
beforehand normalized graph metrics.
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