Network Atlases

Overview

A network atlas is a data file describing networks of the brain, each containing a number of related regions of interest. It also contains metadata such as network colors and names, ROI spatial coordinates (with associtated mesh/space), and optionally, a surface parcellation.

class NetworkAtlas

Network atlas (also known as infomap) Defines ROI positions/information and networks

Parameters:

• name – The name of the atlas

• net_names – N_nets x 1 matrix. The names of the networks

• ROI_key – N_ROIs x 2 matrix. First column is ROI (Region Of Interest) indexes, second column is the network they belong to

• ROI_order – N_ROIs x 1 vector. Functional Connectivity data indexes corresponding to ROIs

• ROI_pos – N_ROIs x 3 matrix. Centroid positions for each ROI.

• net_colors – N_nets x 3 matrix. The color of each network when plotted.

• parcels – (Optional) MATLAB struct field for surface parcellations. Contains two sub-fields ctx_l and ctx_r. N_vertsx 1 vectors. Each element of a vector corresponds to a vertex within the spatial mesh and contains the index of the ROI for that vertex.

• space – (Optional) The mesh that the atlas` ROI locations/parcels are in. Two options - Talairach (TT) or Montreal Neurological Institute (MNI)

numNets()

Returns:

The number of networks

numNetPairs()

Returns:

The number of network pairs

numROIs()

Returns:

The number of Regions Of Interest (ROI)

numROIPairs()

Returns:

The number of ROI pairs

Provided Network Atlases

There are 41 network atlases included in NLA. These all follow a generic naming pattern::

<publisher>_<modifier (optional)>_<number of networks>_<ROIs/parcels>_on_<brain atlas>

Brain Atlas is either Talairach (TT) or Montreal Neurological Institute (MNI)

Provided Brain Atlases

Name	Notes
Glasser_12nets_360parcels_on_MNI
Gordon_12nets_286parcels_LR_on_MNI
Gordon_12nets_286parcels_on_MNI
Gordon_13nets_333parcels_on_MNI
GordonCort_SeitzmanSubcort_17nets_394ROI_on_MNI
Kardan_11nets_333parcels_on_MNI
Myers_24nets_283parcels_50pct_2023_on_MNI
Schaefer2018_7nets_100parcels_on_MNI
Schaefer2018_7nets_200parcels_on_MNI
Schaefer2018_7nets_300parcels_on_MNI
Schaefer2018_7nets_400parcels_on_MNI
Schaefer2018_7nets_500parcels_on_MNI
Schaefer2018_7nets_600parcels_on_MNI
Schaefer2018_7nets_700parcels_on_MNI
Schaefer2018_7nets_800parcels_on_MNI
Schaefer2018_7nets_900parcels_on_MNI
Schaefer2018_7nets_1000parcels_on_MNI
Schaefer2018_17nets_100parcels_on_MNI
Schaefer2018_17nets_200parcels_on_MNI
Schaefer2018_17nets_300parcels_on_MNI
Schaefer2018_17nets_400parcels_on_MNI
Schaefer2018_17nets_500parcels_on_MNI
Schaefer2018_17nets_600parcels_on_MNI
Schaefer2018_17nets_700parcels_on_MNI
Schaefer2018_17nets_800parcels_on_MNI
Schaefer2018_17nets_900parcels_on_MNI
Schaefer2018_17nets_1000parcels_on_MNI
Sietzman_2020_NeuroImage_17nets_300ROI_on_MNI
Sietzman_2020_NeuroImage_17nets_300ROI_on_TT
Wang_infant_group1_7nets_864parcels_on_MNI
Wang_infant_group2_9nets_864parcels_on_MNI
Wang_infant_group3_10nets_864parcels_on_MNI
Wang_infant_group4_10nets_864parcels_on_MNI
Wang_infant_group5_10nets_864parcels_on_MNI
Wang_infant_group6_10nets_864parcels_on_MNI
Wheelock_2020_CerebralCortex_15nets_288ROI_on_MNI
Wheelock_2020_CerebralCortex_15nets_288ROI_on_TT
Wheelock_2020_CerebralCortex_16nets_288ROI_on_MNI
Wheelock_2020_CerebralCortex_16nets_288ROI_on_TT
Wheelock_2020_CerebralCortex_17nets_288ROI_on_MNI
Wheelock_2020_CerebralCortex_17nets_288ROI_on_TT