The Data Sharing Structure of The R-fMRI Maps Project

With the R-fMRI Maps Project, we shared a list of R-fMRI indices:

1.     Amplitude of low frequency fluctuations (ALFF)

2.     Fractional ALFF (fALFF)

3.     Regional Homogeneity (ReHo)

4.     Voxel-mirrored homotopic connectivity (VMHC)

5.     Degree Centrality (DC)

6.     Functional Connectivity Matrices of

a.     Automated Anatomical Labeling (AAL) atlas

b.     Harvard-Oxford atlas

c.     Craddock’s clustering 200 ROIs

d.     Zalesky’s random parcelations

e.     Dosenbach’s 160 functional ROIs

f.     Global signal (Since DPARSF V4.2)

g.     Power's 264 functional ROIs (Since DPARSF V4.3)

h.     Schaefer's 400 Parcels (Since DPARSF V4.4)

In addition, the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) density and volume are shared as well.

 

Detailed Data Structure:

I. Results (All the R-fMRI Indices)

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1.     ALFF: Amplitude of low frequency fluctuations (Zang et al., 2007)

2.     fALFF: Fractional ALFF (Zou et al., 2008)

3.     ReHo: Regional Homogeneity (Zang et al., 2004)

4.     VMHC: Voxel-mirrored homotopic connectivity (Anderson et al., 2011; Zuo et al., 2010)

5.     DegreeCentrality: Degree Centrality (Buckner et al., 2009; Zuo et al., 2012)

6.     ROISignals: The ROI time courses of predefined ROIs.

1~116: Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002)

117~212: Harvard-Oxford atlas (Kennedy et al., 1998)– cortical areas

213~228: Harvard-Oxford atlas (Kennedy et al., 1998)– subcortical areas

229~428: Craddock’s clustering 200 ROIs (Craddock et al., 2012)

429~1408: Zalesky’s random parcelations (compact version: 980 ROIs) (Zalesky et al., 2010)

1409~1568: Dosenbach’s 160 functional ROIs (Dosenbach et al., 2010)

1569: Global signal (Since DPARSF V4.2)

1570~1833: Power’s 264 functional ROIs (Power et al., 2011) (Since DPARSF V4.3)

1834~2233: Schaefer's 400 Parcels (Schaefer et al., 2017) (Since DPARSF V4.4)

 

      The suffix of each folder means the preprocessing steps:

A - Slice timing correction

R – Realign

C - Covariates Removed (without global signal regression)

globalC - Covariates Removed (with global signal regression)

W – Spatial Normalization

F – Filter (0.01~0.1Hz)

sym - Normalized to a symmetric template

S - Smooth

 

II. S2_Results: The results for the second session (if available).

 

III. VBM: voxel-based morphometry analysis

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1.     c1: Gray matter density in native space

2.     c2: White matter density in native space

3.     c3: Cerebrospinal fluid density in native space

4.     wc1: Gray matter density in MNI space

5.     wc2: White matter density in MNI space

6.     wc3: Cerebrospinal fluid density in MNI space

7.     mwc1: Gray matter volume in MNI space

8.     mwc2: White matter volume in MNI space

9.     mwc3: Cerebrospinal fluid volume density in MNI space

10.  u_rc1: Transformation from native space to the group template

11.  Template_6.nii.gz: The group template

12.  Template_6_2mni: Transformation from group template to MNI space

 

IV. ReorientMats: The matrices for reorienting the functional and structural images. This reorienting step can help improve the accuracy of coregistration, segmentation and normalization, especially when the image’s initial orientation is largely inconsistent with the standard template.

 

V. RealignParameter: The head motion parameters

1.     HeadMotion.csv: head motion statistics for all the subjects

2.     HeadMotion.mat: .mat form of the head motion statistics for all the subjects

3.     [SubID]->

a.     rp_arest.txt: the realign parameters

b.     meanarest.nii.gz: the mean functional image after realignment

c.     Bet_meanarest.nii.gz: the betted (skullstripped) version of mean functional image

d.     wmeanarest.nii.gz: the mean functional image in MNI space

e.     FD_Jenkinson_***.txt: framewise displacement defined by (Jenkinson et al., 2002)

f.      FD_Power_***.txt: framewise displacement defined by (Power et al., 2012)

g.     FD_VanDijk_***.txt: framewise displacement defined by (Van Dijk et al., 2012)

 

VI. Masks: The masks

1.     AutoMasks: the automasks generated based on functional images (similar to AFNI’s 3dAutoMask). Files initialed with “w” means normalized to MNI space

2.     SegmentationMasks: the white matter and cerebrospinal fluid masks generated based on structural image segmentation results

3.     WarpedMasks: the white matter and cerebrospinal fluid masks in native space, by warping the standard masks back to the native space.

 

VII. PicturesForChkNormalization: The pictures for checking spatial normalization. The normalized functional image was overlaid on a high-resolution 3D anatomical image (the opaque one with skull) in the MNI space.

 

If you have further questions, please refer to http://rfmri.org/RfMRIMapsDiscussion to discuss.

 

References

Anderson, J.S., Druzgal, T.J., Froehlich, A., DuBray, M.B., Lange, N., Alexander, A.L., Abildskov, T., Nielsen, J.A., Cariello, A.N., Cooperrider, J.R., Bigler, E.D., Lainhart, J.E., 2011. Decreased interhemispheric functional connectivity in autism. Cerebral cortex 21, 1134-1146.

Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., Andrews-Hanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci 29, 1860-1873.

Craddock, R.C., James, G.A., Holtzheimer, P.E., 3rd, Hu, X.P., Mayberg, H.S., 2012. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33, 1914-1928.

Dosenbach, N.U., Nardos, B., Cohen, A.L., Fair, D.A., Power, J.D., Church, J.A., Nelson, S.M., Wig, G.S., Vogel, A.C., Lessov-Schlaggar, C.N., Barnes, K.A., Dubis, J.W., Feczko, E., Coalson, R.S., Pruett, J.R., Jr., Barch, D.M., Petersen, S.E., Schlaggar, B.L., 2010. Prediction of individual brain maturity using fMRI. Science 329, 1358-1361.

Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825-841.

Kennedy, D.N., Lange, N., Makris, N., Bates, J., Meyer, J., Caviness, V.S., Jr., 1998. Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cereb Cortex 8, 372-384.

Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C., Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E., 2011. Functional network organization of the human brain. Neuron 72, 665-678.

Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142-2154.

Schaefer, A., Kong, R., Gordon, E.M., Laumann, T.O., Zuo, X.N., Holmes, A.J., Eickhoff, S.B., Yeo, B.T.T., 2017. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex, 1-20.

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273-289.

Van Dijk, K.R., Sabuncu, M.R., Buckner, R.L., 2012. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431-438.

Zalesky, A., Fornito, A., Harding, I.H., Cocchi, L., Yucel, M., Pantelis, C., Bullmore, E.T., 2010. Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50, 970-983.

Zang, Y.F., He, Y., Zhu, C.Z., Cao, Q.J., Sui, M.Q., Liang, M., Tian, L.X., Jiang, T.Z., Wang, Y.F., 2007. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 29, 83-91.

Zang, Y.F., Jiang, T.Z., Lu, Y.L., He, Y., Tian, L.X., 2004. Regional homogeneity approach to fMRI data analysis. Neuroimage 22, 394-400.

Zou, Q.H., Zhu, C.Z., Yang, Y., Zuo, X.N., Long, X.Y., Cao, Q.J., Wang, Y.F., Zang, Y.F., 2008. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172, 137-141.

Zuo, X.N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F.X., Sporns, O., Milham, M.P., 2012. Network Centrality in the Human Functional Connectome. Cereb Cortex 22, 1862-1875.

Zuo, X.N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D.S., Bangaru, S., Grzadzinski, R., Evans, A.C., Zang, Y.F., Castellanos, F.X., Milham, M.P., 2010. Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J Neurosci 30, 15034-15043.

 

Hi, I did my preprocessing and have obtained a output size of 61, 73, 61. I would like to know which brain mask (binary) I can use to vectorize my data (obtain only voxels that fall within the brain). Thank you.