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	IXI VBM dataset. 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 Jo et al. (2021)	Done	0.0.2
EdgeCentricFCSpheres	Calculate edge-centric functional connectivity over spheres placed on coordinates, as found in Jo et al. (2021)	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 Peng et al. (1995)	Done	0.0.4
MultiscaleEntropyAUC	Calculate AUC of multiscale entropy of a time series as found in Costa et al. (2002)	Done	0.0.4
PermEntropy	Calculate permutation entropy of a time series as found in Bandt at al. (2002)	Done	0.0.4
RangeEntropy	Calculate range entropy of a time series as found in Omidvarnia et al. (2018)	Done	0.0.4
RangeEntropyAUC	Calculate AUC of range entropy of a time series as found in Omidvarnia et al. (2018)	Done	0.0.4
SampleEntropy	Calculate sample entropy of a time series as found in Richman et al. (2000)	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). https://doi.org/10.1093/cercor/bhx179
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). https://doi.org/10.1016/j.neuroimage.2006.05.056
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). https://doi.org/10.1038/s41593-020-00711-6
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). https://doi.org/10.1016/j.jneumeth.2015.07.013
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). https://doi.org/10.1016/j.neuroimage.2013.05.081. 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). https://doi:10.1038/nn.4135
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). https://doi.org/10.1016/j.neuroimage.2023.120010
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). https://doi.org/10.1093/cercor/bhw157

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). http://doi.org/10.1016/j.neuroimage.2006.01.021
Glasser	Glasser, M.F., Coalson, T.S., Robinson, E.C. et al. A multi-modal parcellation of human cerebral cortex. Nature (2016). http://doi.org/10.1038/nature18933
AAL	Rolls, E.T., Huang, C.C., Lin, C.P., et al. Automated anatomical labelling atlas 3. NeuroImage, Volume 206 (2020). https://doi.org/10.1016/j.neuroimage.2019.116189
Mindboggle 101	Klein, A., & Tourville, J. 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in Neuroscience (2012). http://doi.org/10.3389/fnins.2012.00171/abstract
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). http://doi.org/10.1016/j.neuroimage.2010.06.010.
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). https://doi.org/10.1093/cercor/bhw157
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). https://doi.org/10.1152/jn.00339.2011 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). https://doi.org/10.1152/jn.00338.2011

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). https://doi.org/10.1016/j.neubiorev.2014.11.003.
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). https://doi.org/10.1016/j.neubiorev.2018.04.022.
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). https://doi.org/10.1152/jn.00783.2009 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). https://doi.org/10.1196/annals.1440.011
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). https://doi.org/10.1007/s00429-012-0380-y
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). https://doi.org/10.1007/s00429-013-0698-0
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). https://doi.org/10.1016/j.neuroimage.2017.10.020.
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). https://doi.org/10.1016/j.neuroimage.2008.04.025.
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). https://doi.org/10.1007/s00429-019-01870-4
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). https://doi.org/10.1016/j.neuroimage.2015.06.059.
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). https://doi.org/10.1016/j.neubiorev.2010.12.012.
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). https://doi.org/10.1007/s00429-012-0380-y
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). https://doi.org/10.1037/a0030694
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). https://doi.org/10.1016/j.neuroimage.2011.11.050.
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). https://doi.org/10.1016/j.neuron.2011.09.006
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). https://doi.org/10.1126/science.1194144
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). https://doi.org/10.1016/j.neuron.2013.07.035
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). https://doi.org/10.1162/jocn.2008.21029

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). https://doi.org/10.1016/j.neuroimage.2010.10.011.
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). https://doi.org/10.1162/jocn_a_01616

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. https://doi.org/10.5281/zenodo.6463123
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. https://doi.org/10.5281/zenodo.6463123
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 image histogram. See nilearn.masking.compute_epi_mask()
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. See nilearn.masking.compute_background_mask()
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. See nilearn.datasets.fetch_icbm152_brain_gm_mask()