cubnm.datasets

Example datasets

_get_lut_full(parc)

Load the full (cortical+subcortical) lookup table for the given parcellation

_clean_micamics_sc(micamics_dir, measure, parc, sub[, ...])

Clean SC matrix of the given subject and parcellation

_clean_micamics_bold(micamics_dir, parc, sub[, ...])

Clean BOLD time series of the given subject and parcellation

load_sc(measure, parc[, sub, exc_subcortex, norm, ...])

Load example structural connectivity matrix from MICA-MICs dataset

load_bold(parc[, sub, exc_subcortex, micamics_dir])

Load example BOLD data from the MICA-MICs dataset

load_maps(names, parc[, norm])

Loads example heterogeneity maps
cubnm.datasets._get_lut_full(parc)

Load the full (cortical+subcortical) lookup table for the given parcellation

Parameters

parc: {‘schaefer-[100, 200, … 1000]’, ‘aparc’, ‘glasser-360’}

Parcellation

Returns

pandas.DataFrame

Full lookup table

cubnm.datasets._clean_micamics_sc(micamics_dir, measure, parc, sub, exc_subcortex=True, norm='mean001', out_dir=None)

Clean SC matrix of the given subject and parcellation

Parameters

micamics_dir: str

Path to the directory containing the micapipe outputs of micamics (https://osf.io/x7qr2 unzipped)

measure: {‘strength’, ‘length’}

  • ‘strength’: SC strength (normalized tract counts)

  • ‘length’: SC tracts length

parc: {‘schaefer-[100, 200, … 1000]’, ‘aparc’, ‘glasser-360’}

Parcellation

sub: str

Subject ID, e.g. “sub-HC001”

exc_subcortex: bool, optional

Whether to exclude subcortical regions. Default: True

norm: {‘mean001’, ‘none’}

SC strength normalization method - ‘mean001’: normalize to mean 0.01. Default - ‘none’: no normalization

out_dir: str, optional

Path to save the cleaned SC matrix

cubnm.datasets._clean_micamics_bold(micamics_dir, parc, sub, exc_subcortex=True, out_dir=None)

Clean BOLD time series of the given subject and parcellation

Parameters

micamics_dir: str

Path to the directory containing the micapipe outputs of micamics (https://osf.io/x7qr2 unzipped)

parc: {‘schaefer-[100, 200, … 1000]’, ‘aparc’, ‘glasser-360’}

Parcellation

sub: str

Subject ID, e.g. “sub-HC001”

exc_subcortex: bool, optional

Whether to exclude subcortical regions. Default: True

out_dir: str, optional

Path to save the cleaned BOLD time series

cubnm.datasets.load_sc(measure, parc, sub='sub-HC001', exc_subcortex=True, norm='mean001', micamics_dir=None)

Load example structural connectivity matrix from MICA-MICs dataset (https://www.nature.com/articles/s41597-022-01682-y)

Parameters

measure: {‘strength’, ‘length’}

  • ‘strength’: SC strength (normalized tract counts)

  • ‘length’: SC tracts length

parc: {‘schaefer-[100, 200, … 1000]’, ‘aparc’, ‘glasser-360’}

Parcellation

sub: str

Subject ID, e.g. “sub-HC001”

exc_subcortex: bool, optional

Whether to exclude subcortical regions. Default: True

norm: {‘mean001’, ‘none’}

SC strength normalization method - ‘mean001’: normalize to mean 0.01. Default - ‘none’: no normalization

micamics_dir: str, optional

Path to the directory containing the micapipe outputs of micamics (https://osf.io/x7qr2 unzipped). Required if subjects other than ‘sub-HC001’ are requested and/or exc_subcortex is False and/or norm is ‘none’.

Returns

np.ndarray or str

Structural connectivity matrix or path to its text file. Shape: (nodes, nodes)

cubnm.datasets.load_bold(parc, sub='sub-HC001', exc_subcortex=True, micamics_dir=None)

Load example BOLD data from the MICA-MICs dataset (https://www.nature.com/articles/s41597-022-01682-y)

Parameters

parc: ‘schaefer-100’

parcellation

sub: str

Subject ID, e.g. “sub-HC001”

exc_subcortex: bool, optional

Whether to exclude subcortical regions. Default: True

micamics_dir: str, optional

Path to the directory containing the micapipe outputs of micamics (https://osf.io/x7qr2 unzipped). Required if subjects other than ‘sub-HC001’ are requested and/or exc_subcortex is False.

Returns

np.ndarray or str

Path to a text file or numpy array including the BOLD signal. Shape: (nodes, volumes)

cubnm.datasets.load_maps(names, parc, norm='minmax')

Loads example heterogeneity maps

Parameters

names: str or list

One or more maps selected from this list: - ‘myelinmap’ - ‘thickness’ - ‘fcgradient01’ - ‘genepc1’ - ‘nmda’ - ‘gabaa’ - ‘yeo7’

parc: {‘schaefer-100’}

parcellation

norm: {‘zscore’, ‘minmax’, None}

  • ‘zscore’: maps are z-score normalized

  • ‘minmax’: maps are min-max normalized to [0, 1]

Returns

np.ndarray or str

Maps arrays or path to their text file. Shape: (maps, nodes)

Notes

For more information and code on how these maps were obtained and parcellated see utils.datasets.load_maps in https://github.com/amnsbr/eidev. The set of maps included here are limited and provided just as examples. We recommend users to use neuromaps and similar tools to obtain and parcellate further maps.