# fcon¶

fcon models the functional connectome by extracting an adjacency matrix from a voxelwise time series image. To do this, fcon requires a brain atlas, or a parcellation of the brain’s voxels into regions of interest (network nodes). First, the local mean timeseries within each network node is extracted. The connectivity between time series is subsequently used to define the edges of an adjacency matrix over the parcellation. Currently, static connectivity is estimated using the Pearson correlation but alternative metrics will likely be introduced in the future.

## fcon_atlas¶

Brain atlas or parcellation.

Contains a comma-separated list of the names of the atlases over which the functional connectome should be computed. The atlases should correspond to valid paths in $XCPEDIR/atlas or another appropriate $BRAINATLAS directory. Each atlas will be warped from its coordinate space into the analyte image space, after which the mean time series will be computed across each parcel or atlas label. fcon will execute for all partial string matches.:

# Use the Power 264-sphere parcellation only
fcon_atlas[cxt]=power264

# Use both the Power 264 atlas and the Gordon atlas
fcon_atlas[cxt]=power264,gordon

# Use the 400-node version of the Schaefer atlas
fcon_atlas[cxt]=schaefer400

# Use all available resolutions of the Schaefer atlas
fcon_atlas[cxt]=schaefer

# Use all available atlases
fcon_atlas[cxt]=all


## fcon_metric¶

Connectivity metric.

As of now, you’re stuck with the Pearson correlation, so this effectively does nothing.:

# Use the Pearson correlation
fcon_metric[cxt]=corrcoef


## fcon_rerun¶

Ordinarily, each module will detect whether a particular analysis has run to completion before beginning it. If re-running is disabled, then the module will immediately skip to the next stage of analysis. Otherwise, any completed analyses will be repeated.If you change the run parameters, you should rerun any modules downstream of the change.:

# Skip processing steps if the pipeline detects the expected output
fcon_rerun[cxt]=0

# Repeat all processing steps
fcon_rerun[cxt]=1


## fcon._cleanup¶

Modules often produce numerous intermediate temporary files and images during the course of an analysis. In many cases, these temporary files are undesirable and unnecessarily consume disk space. If cleanup is enabled, any files stamped as temporary will be deleted when a module successfully runs to completion. If a module fails to detect the output that it expects, then temporary files will be retained to facilitate error diagnosis.:

# Remove temporary files
fcon_cleanup[cxt]=1

# Retain temporary files
fcon_cleanup[cxt]=0


## Expected output¶

The main outputs are::
• prefix_{atlas_name}_network.txt # correlation matrix in vector form
• prefix_{atlas_name}.net # Pajek adjacency matrix
• prefix_{atlas_name}_ts.1D # Nodal time series
• prefix_{atlas_name}.nii.gz # atlas in input BOLD signal space
Other outputs depend on the issues such as poor registration of atlas to BOLD image space::
• prefix_missing.txt # index of nodes that bad, out of coverage of bold