6. Built-in Pipeline Components#

6.1. Data Grabber#

6.1.1. Available#

Class

Description

Access

Type/Config

State

Version Added

DataladHCP1200

HCP OpenAccess dataset

Open with registration

Built-in

Done

0.0.1

JuselessDataladUKBVBM

UKB VBM dataset preprocessed with CAT.
Available for Juseless only.

Restricted

junifer.configs.juseless

Done

0.0.1

JuselessDataladCamCANVBM

CamCAN VBM dataset preprocessed with CAT.
Available for Juseless only.

Restricted

junifer.configs.juseless

Done

0.0.1

DataladAOMICID1000

AOMIC 1000 dataset

Open without registration

Built-in

Done

0.0.1

DataladAOMICPIOP1

AOMIC PIOP1 dataset

Open without registration

Built-in

Done

0.0.1

DataladAOMICPIOP2

AOMIC PIOP2 dataset

Open without registration

Built-in

Done

0.0.1

JuselessDataladAOMICID1000VBM

AOMIC ID1000 VBM dataset.
Available for Juseless only.

Restricted

junifer.configs.juseless

Done

0.0.1

JuselessDataladIXIVBM

Available for Juseless only.

Restricted

junifer.configs.juseless

Done

0.0.1

JuselessUCLA

UCLA fMRIPrep dataset.
Available for Juseless only.

Restricted

junifer.configs.juseless

Done

0.0.1

6.1.2. Planned#

Name

Description

Access

Type/Config

Reference

ENKI

ENKI dataset for Juseless

Restricted

junifer.configs.juseless

#47

6.2. Preprocessor#

6.2.1. Available#

Class

Description

State

Version Added

fMRIPrepConfoundRemover

Remove confounds from fMRIPrep-ed data

Done

0.0.1

SpaceWarper

Warp / transform data from one space to another
(subject-native or other template spaces)

Done

0.0.4

Smoothing

Apply smoothing to data, particularly useful when dealing with
fMRIPrep-ed data

In Progress

#161

6.3. Marker#

6.3.1. Available#

Class

Description

State

Version Added

ParcelAggregation

Apply parcellation and perform aggregation function

Done

0.0.1

FunctionalConnectivityParcels

Compute functional connectivity over parcellation

Done

0.0.1

CrossParcellationFC

Compute functional connectivity across two parcellations

Done

0.0.1

SphereAggregation

Spherical aggregation using mean

Done

0.0.1

FunctionalConnectivitySpheres

Compute functional connectivity over spheres placed on coordinates

Done

0.0.1

RSSETSMarker

Compute root sum of squares of edgewise timeseries

Done

0.0.1

ReHoParcels

Calculate regional homogeneity over parcellation

Done

0.0.1

ReHoSpheres

Calculate regional homogeneity over spheres placed on coordinates

Done

0.0.1

ALFFParcels

Calculate (f)ALFF and aggregate using parcellations

Done

0.0.1

ALFFSpheres

Calculate (f)ALFF and aggregate using spheres placed on coordinates

Done

0.0.1

EdgeCentricFCParcels

Calculate edge-centric functional connectivity over parcellation, as
found in

Done

0.0.2

EdgeCentricFCSpheres

Calculate edge-centric functional connectivity over spheres placed on
coordinates, as found in

Done

0.0.2

TemporalSNRParcels

Calculate temporal signal-to-noise ratio using parcellations

Done

0.0.2

TemporalSNRSpheres

Calculate temporal signal-to-noise ratio using spheres placed on
coordinates

Done

0.0.2

HurstExponent

Calculate Hurst exponent of a time series as found in

Done

0.0.4

MultiscaleEntropyAUC

Calculate AUC of multiscale entropy of a time series as found in

Done

0.0.4

PermEntropy

Calculate permutation entropy of a time series as found in

Done

0.0.4

RangeEntropy

Calculate range entropy of a time series as found in

Done

0.0.4

RangeEntropyAUC

Calculate AUC of range entropy of a time series as found in

Done

0.0.4

SampleEntropy

Calculate sample entropy of a time series as found in

Done

0.0.4

6.3.2. Planned#

Name

Description

Reference

Connectedness

Compute connectedness

#34

6.4. Parcellation#

6.4.1. Available#

Name

Options

Keys

Template Spaces

Version Added

Publication

Schaefer

n_rois, yeo_networks

Schaefer900x7, Schaefer1000x7, Schaefer100x17,
Schaefer200x17, Schaefer300x17, Schaefer400x17,
Schaefer500x17, Schaefer600x17, Schaefer700x17,
Schaefer800x17, Schaefer900x17, Schaefer1000x17

MNI152NLin6Asym

0.0.1

Schaefer, A., Kong, R., Gordon, E.M. et al.
Local-Global Parcellation of the Human Cerebral Cortex from
Intrinsic Functional Connectivity MRI
Cerebral Cortex, Volume 28(9), Pages 3095–3114 (2018).

SUIT

space

SUITxMNI, SUITxSUIT

SUIT, MNI152Lin6Asym

0.0.1

Diedrichsen, J.
A spatially unbiased atlas template of the human cerebellum.
NeuroImage, Volume 33(1), Pages 127–138 (2006).

Tian

scale, space, magneticfield

TianxS1x3TxMNI6thgeneration, TianxS1x7TxMNI6thgeneration,
TianxS2x3TxMNI6thgeneration, TianxS2x7TxMNI6thgeneration,
TianxS3x3TxMNI6thgeneration, TianxS3x7TxMNI6thgeneration,
TianxS4x3TxMNI6thgeneration, TianxS4x7TxMNI6thgeneration,
TianxS1x3TxMNInonlinear2009cAsym,
TianxS2x3TxMNInonlinear2009cAsym,
TianxS3x3TxMNInonlinear2009cAsym,
TianxS4x3TxMNInonlinear2009cAsym

MNI152NLin6Asym, MNI152NLin2009cAsym

0.0.1

Tian, Y., Margulies, D.S., Breakspear, M. et al.
Topographic organization of the human subcortex
unveiled with functional connectivity gradients.
Nature Neuroscience, Volume 23, Pages 1421–1432 (2020).

AICHA

version

AICHA_v1, AICHA_v2

MNI152Lin6Asym

0.0.3

Joliot, M., Jobard, G., Naveau, M. et al.
AICHA: An atlas of intrinsic connectivity of homotopic areas.
Journal of Neuroscience Methods, Volume 254, Pages 46-59 (2015).

Shen

year, n_rois

Shen_2013_50, Shen_2013_100, Shen_2013_150,
Shen_2015_268, Shen_2019_368

MNI152NLin2009cAsym

0.0.3

Shen, X., Tokoglu, F., Papademetris, X., Constable, R.T.
Groupwise whole-brain parcellation from resting-state fMRI data
for network node identification.
NeuroImage, Volume 82 (2013).
Finn, E.S., Shen, X., Scheinost, D., et al.
Functional connectome fingerprinting: identifying individuals using
patterns of brain connectivity.
Nature Neuroscience, Volume 18(11), Pages 1664-1671 (2015).

Yan

n_rois, yeo_networks, kong_networks

Yan100xYeo7, Yan200xYeo7, Yan300xYeo7,
Yan400xYeo7, Yan500xYeo7, Yan600xYeo7,
Yan700xYeo7, Yan800xYeo7, Yan900xYeo7,
Yan1000xYeo7,
Yan100xYeo17, Yan200xYeo17, Yan300xYeo17,
Yan400xYeo17, Yan500xYeo17, Yan600xYeo17,
Yan700xYeo17, Yan800xYeo17, Yan900xYeo17,
Yan1000xYeo17,
Yan100xKong17, Yan200xKong17, Yan300xKong17,
Yan400xKong17, Yan500xKong17, Yan600xKong17,
Yan700xKong17, Yan800xKong17, Yan900xKong17,
Yan1000xKong17

MNI152NLin6Asym

0.0.3

Yan, X., Kong, R., Xue, A., et al.
Homotopic local-global parcellation of the human cerebral cortex from
resting-state functional connectivity.
NeuroImage, Volume 273 (2023).

Brainnetome

threshold

Brainnetome_thr0, Brainnetome_thr25, Brainnetome_thr50

MNI152NLin6Asym

0.0.4

Fan, L., Li, H., Zhuo, J., et al.
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional
Architecture
Cerebral Cortex, Volume 26(8), Pages 3508–3526 (2016).

6.4.2. Planned#

Name

Publication

Desikan-Killiany

Desikan, R.S., Ségonne, F., Fischl, B. et al.
An automated labeling system for subdividing the human cerebral cortex
on MRI scans into gyral based regions of interest.
NeuroImage, Volume 31(3), Pages 968-980 (2006).

Glasser

Glasser, M.F., Coalson, T.S., Robinson, E.C. et al.
A multi-modal parcellation of human cerebral cortex.
Nature (2016).

AAL

Rolls, E.T., Huang, C.C., Lin, C.P., et al.
Automated anatomical labelling atlas 3.
NeuroImage, Volume 206 (2020).

Mindboggle 101

Klein, A., & Tourville, J.
101 labeled brain images and a consistent human cortical labeling
protocol.
Frontiers in Neuroscience (2012).

Destrieux

Destrieux, C., Fischl, B., Dale, A., & Halgren, E.
Automatic parcellation of human cortical gyri and sulci using standard
anatomical nomenclature.
NeuroImage, Volume 53(1), Pages 1–15 (2010).

Fan

Fan, L., Li, H., Zhuo, J. et al.
The human brainnetome atlas: a new brain atlas based on connectional
architecture.
Cerebral cortex, Volume 26(8), Pages 3508-3526 (2016).

Buckner

Buckner, R.L., Krienen, F.M., Castellanos, A., Diaz, J.C., Yeo, B.T.T.
The organization of the human cerebellum estimated by intrinsic
functional connectivity.
Journal of Neurophysiology, Volume 106(5), Pages 2322–2345 (2011).
Yeo, B.T.T., Krienen, F.M., Sepulcre, J. et al.
The organization of the human cerebral cortex estimated by intrinsic
functional connectivity.
Journal of Neurophysiology, Volume 106(3), Pages 1125–1165 (2011).

6.5. Coordinates#

6.5.1. Available#

Name

Keys

Version Added

Publication

Cognitive action control

CogAC

0.0.1

Cieslik, E.C., Mueller, V.I., Eickhoff, C.R., Langner, R.,
Eickhoff, S.B.
Three key regions for supervisory attentional control: Evidence from
neuroimaging meta-analyses.
Neuroscience & Biobehavioral Reviews, Volume 48, Pages 22-34 (2015).

Cognitive action regulation

CogAR

0.0.1

Langner, R., Leiberg, S., Hoffstaedter, F., Eickhoff, S.B.
Towards a human self-regulation system: Common and distinct neural
signatures of emotional and behavioural control.
Neuroscience & Biobehavioral Reviews, Volume 90, Pages 400-410 (2018).

Default mode network

DMNBuckner

0.0.1

Van Dijk, K.R., Hedden, T., Venkataraman, A. et al.
Intrinsic functional connectivity as a tool for human connectomics:
theory, properties, and optimization.
Journal of neurophysiology, Volume 103(1), Pages 297-321 (2010).
Buckner, R.L., Andrews‐Hanna, J.R., & Schacter, D.L.
The brain’s default network: anatomy, function, and relevance to
disease.
Annals of the New York Academy of Sciences, Volume 1124(1), Pages 1-38
(2008).

Missing formal name

extDMN

0.0.1

Missing publication details

Empathic processing

Empathy

0.0.1

Bzdok, D., Schilbach, L., Vogeley, K. et al.
Parsing the neural correlates of moral cognition: ALE meta-analysis on
morality, theory of mind, and empathy.
Brain Structure and Function, Volume 217(4), Pages 783-796 (2012).

Extended social-affective default

eSAD

0.0.1

Amft, M., Bzdok, D., Laird, A.R. et al.
Definition and characterization of an extended social-affective default
network.
Brain structure & function, Volume 220, Pages 1031–1049 (2015).

Extended multiple-demand network

eMDN

0.0.1

Camilleri, J.A., Müller, V.I., Fox, P. et al.
Definition and characterization of an extended multiple-demand network.
NeuroImage, Volume 165, Pages 138-147 (2018).

Motor execution

Motor

0.0.1

Witt, S.T., Laird, A.R., Meyerand, M.E.
Functional neuroimaging correlates of finger-tapping task variations:
An ALE meta-analysis,
NeuroImage, Volume 42(1), Pages 343-356 (2008).

Multitasking

MultiTask

0.0.1

Worringer, B., Langner, R., Koch, I. et al.
Common and distinct neural correlates of dual-tasking and
task-switching: a meta-analytic review and a neuro-cognitive processing
model of human multitasking.
Brain structure & function, Volume 224(5), Pages 1845–1869 (2019).

Physiological stress

PhysioStress

0.0.1

Kogler, L., Müller, V.I., Chang, A. et al.
Psychosocial versus physiological stress — Meta-analyses on
deactivations and activations of the neural correlates of stress
reactions.
NeuroImage, Volume 119, Pages 235-251 (2015).

Reward-related decision making

Rew

0.0.1

Liu, X., Hairston, J., Schrier, M., Fan, J.
Common and distinct networks underlying reward valence and processing
stages: A meta-analysis of functional neuroimaging studies.
Neuroscience & Biobehavioral Reviews, Volume 35(5), Pages 1219-1236
(2011).

Missing formal name

Somatosensory

0.0.1

Missing publication details

Theory-of-mind cognition

ToM

0.0.1

Bzdok, D., Schilbach, L., Vogeley, K. et al.
Parsing the neural correlates of moral cognition: ALE meta-analysis on
morality, theory of mind, and empathy.
Brain Structure and Function, Volume 217(4), Pages 783-796 (2012).

Vigilant attention

VigAtt

0.0.1

Langner, R., & Eickhoff, S.B.
Sustaining attention to simple tasks: a meta-analytic review of the
neural mechanisms of vigilant attention.
Psychological bulletin, Volume 139 4, Pages 870-900 (2013).

Working memory

WM

0.0.1

Rottschy, C., Langner, R., Dogan, I. et al.
Modelling neural correlates of working memory: A coordinate-based
meta-analysis.
NeuroImage, Volume 60, Pages 830-846 (2012).

Areal functional network from Power et al. (2011)

Power2011

0.0.2

Power, J. D., Cohen, A. L., Nelson, S. M. et al.
Functional network organization of the human brain.
Neuron, Volume 72(4), Pages 665–678 (2011).

Brain maturity functional connections from Dosenbach et al. (2010)

Dosenbach

0.0.2

Dosenbach, N.U.F., Nardos, B., Cohen, A.L. et al.
Prediction of Individual Brain Maturity Using fMRI
Science, Volume 329(5997), Pages 1358-1361 (2010).

Areal functional network from Power et al. (2013)

Power2013

0.0.4

Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., &
Petersen, S. E.
Evidence for hubs in human functional brain networks.
Neuron, Volume 79(4), Pages 798–813 (2013).

Autobiographical Memory from Spreng et al. (2009)

AutobiographicalMemory

0.0.4

Spreng, R. N., Mar, R. A., Kim, A. S. N.
The Common Neural Basis of Autobiographical Memory, Prospection,
Navigation, Theory of Mind, and the Default Mode: A Quantitative
Meta-analysis.
Journal of Cognitive Neuroscience, Volume 21(3), Pages 489–510 (2009).

6.5.2. Planned#

Name

Publication

Emotional scene and face processing (EmoSF)

Sabatinelli, D., Fortune, E.E., Li, Q. et al.
Emotional perception: Meta-analyses of face and natural scene
processing.
NeuroImage, Volume 54(3), Pages 2524-2533 (2011).

Perceptuo-motor network

Heckner, M.K., Cieslik, E.C., Eickhoff, S.B. et al.
The Aging Brain and Executive Functions Revisited: Implications from
Meta-analytic and Functional-Connectivity Evidence.
Journal of Cognitive Neuroscience, Volume 33(9), Pages 1716–1752 (2021).

6.6. Mask#

6.6.1. Available#

Name

Keys

Template Space

Version Added

Description - Publication

Vickery-Patil (Gray Matter)

GM_prob0.2

MNI152Lin6Asym

0.0.1

Vickery, Sam, & Patil, Kaustubh. (2022).
Chimpanzee and Human Gray Matter Masks [Data set]. Zenodo.

Vickery-Patil (Cortex + Basal Ganglia)

GM_prob0.2_cortex

MNI152Lin6Asym

0.0.1

Vickery, Sam, & Patil, Kaustubh. (2022).
Chimpanzee and Human Gray Matter Masks [Data set]. Zenodo.

junifer’s custom brain mask

compute_brain_mask

Adapts to the target data

0.0.2

Compute the whole-brain, gray-matter or white-matter mask using
the template and the resolution from the target image. The
templates are obtained via templateflow.

nilearn’s mask computed from fMRI data

compute_epi_mask

Adapts to the target data

0.0.2

Compute a brain mask from fMRI data. This is based on an heuristic
proposed by T.Nichols: find the least dense point of the histogram,
between fractions lower_cutoff and upper_cutoff of the total

nilearn’s background mask

compute_background_mask

Adapts to the target data

0.0.2

Compute a brain mask for the images by guessing the value of the
background from the border of the image.

nilearn’s ICBM152 template gray-matter mask

fetch_icbm152_brain_gm_mask

MNI152NLin2009aAsym

0.0.2

Compute a gray-matter mask from the asymmetrical ICBM152 2009 template,
release a.