ConnectMatrix
The files denoted as "ConnectMatrix" are structures including:
- a field ConMat (matrix ROIxROI=154x154) storing the brain structural connectivity matrix computed
from the raw data,
- a field Structures that stores the region names for each node
- a field NPoints that gives the region size (voxel number).
For each subject, two ConnectMatrix files are given: one was used as baseline and is indicated by the
final subscript "C" and the other was used for the lesion simulation and is indicated by a final
subscript "L".
Lesion files
Different types of simulated lesions were created and stored.
The file names indicate the different lesion types:
- Hubs lesions: lesions to the nodes (10) with higher degree
- Random lesions: lesions to random nodes (selected for all subjects (sameTarget) or for the
individual subjects (individualTarget)).
- Edges lesions: lesion to random connections in the networks.
In each case, the specific edges attacked can be found by considering the degree or the strength
evolution at each step.
Hubs lesions:
For this type of lesions we stored (Nodes, Evar, Deg, S, BC, WBC, CmB, CmW, CBCb, CBCc).
Nodes has dimensions N x Sub indicating respectively the number of attacks and the number of
subjects.
Evar stores how the efficiency changes and has dimension N+1 x 1 given by the number of attacks.
All the network metrics (degree (Deg), strenght (S), betweenness centrality (BC), weighted
betweenness centrality (WBC), communicability (CmB), weighted communicability (CmW), centrality
communicability (CBCb), weighted centrality communicability (CBCw)) have size N x s x sub indicating
respectively the number of nodes, steps and number of subjects.
Random lesions:
For this type of lesions we stored (Nodes, EffLoss, Deg, S, BC, WBC, CmB,CmW,CBCb,CBCc).
Nodes has dimensions N x rep x sub indicating respectively the number of attacks, the repetitions
and the number of subjects.
EffLoss stores how the efficiency changes and has dimension N+1 x rep given by the number of
attacks and repetitions.
All the network metrics (degree (Deg), strenght (S), betweenness centrality (BC), weighted
betweenness centrality (WBC), communicability (CmB), weighted communicability (CmW), centrality
communicability (CBCb), weighted centrality communicability (CBCw)) have size N x s x Rep x sub
indicating respectively the number of nodes, steps, number of repetitions and number of subjects.
Random lesions:
For this type of lesions we stored (Edges, EffLoss, Deg, S, BC, WBC, CmB, CmW, CBCb, CBCc).
Edges has dimensions N x 2 x rep x sub indicating respectively the number of attacks, the connected
nodes, the repetitions and the number of subjects, i.e. Edges(1,:,r,s) stores the first removed
connection.
EffLoss stores how the efficiency changes and has dimension N+1 x rep for the number of attacks and
repetitions.
All the network metrics (degree (Deg), strenght (S), betweenness centrality (BC), weighted
betweenness centrality (WBC), communicability (CmB), weighted communicability (CmW), centrality
communicability (CBCb), weighted centrality communicability (CBCw)) have size N x s x Rep x sub
indicating respectively the number of nodes, steps, number of repetitions and number of subjects.
Strategy evaluation:
The file StrategyEvaluation.mat stores the variables LesionB, LesionW, ResBhubs, ResWhubs that
show how the binary (B) and weighted (W) efficiency change if the targets of the lesions are selected
according to different strategies, namely: randomly, high degree, high strength, high betweennes
centrality, high weighted betweenness centrality, high communicability, high weighted
communicability, high centrality communicability, high weighted centrality communicabiltiy.
LesionB and LesionW show the case were the network properties are recomputed after each attack,
while ResBhubs and ResWhubs show the case where the lesion sites are selected at the beginning
without recomputation step by step.