This is the repository for the JuSpace toolbox allowing for cross-modal correlations between imaging data and positron emission tomography derived information
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| WELCOME to JuSpace v 1.5 | | | | | | Tool for spatial correlation analyses of magnetic resonance | | imaging data with positron emission | | tomography derived receptor maps | | |
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Created by Juergen Dukart, at INM7, Research Center Jülich, Jülich, Germany
Please contact me at juergen.dukart@gmail.com for reports on bugs or other questions with respect to the toolbox
Please cite the following publication when using the toolbox:
Dukart J, Holiga S, Rullmann M, Lanzenberger R, Hawkins PCT, Mehta MA, Hesse S, Barthel H, Sabri O, Jech R, Eickhoff SB. JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum Brain Mapp. 2021 Feb 15;42(3):555-566. doi: 10.1002/hbm.25244. Epub 2020 Oct 20.
PET and SPECT data sources are listed in "Sources_templates_release.txt". Please cite the sources corresponding to respective PET or SPECT maps. Please note that some of the PET maps are released under a different license as indicated in the .txt file.
INTRODUCTION: JuSpace is a software package for the integration of different imaging modalities with positron emission tomography derived neurophysiological measures. The toolbox is written for Matlab 2017b and following (The Mathworks Insc., MA, USA). It further requires SPM12 to be installed (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) Although it has been designed on a Windows platform, it can be used on any other OS like Unix or Linux where the Matlab package and the Stats toolbox are installed.
GETTING STARTED: Once the program is unpackaged in its directory, start Matlab. Use the ‘current directory’ tab to go to the JuSpace directory and type
JuSpace
The user interface should appear.
Alternative the computing function "compute_DomainGauges" can be called directly, see help for this function on its usage To compute exact permutation based p-values for within and between-subject designs the function "compute_exact_pvalue" needs to be called. The function requires inputs provided by the "compute_DomainGauges" function
An example call looks as following: [res,p_all,stats,data,D1,D2, data_PET,Resh,T1] = compute_DomainGauges(list1,list2,filesPET,atlas, options,image_save); [p_exact,dist_r] = compute_exact_pvalue(D1,D2,data_PET,res,N_permutations,options,T1);
JuSpace saves a file with the following outputs:
Outputs: res --> Fisher's z transformed correlation / multiple linear regression coefficient matrix, rows correspond to input files, columns to PET maps p_all --> p-values from one-sample t-test testing if the Fisher's z transformed correlations /regression coefficients are different from zero stats --> Summary statistics stats.CorrOrig --> original (not Fisher's z transformed) individual correlation coefficients stats.p_ind --> p-values for correlations (CorrOrig) of individual files with PET maps stats.res_ind --> Individual Fisher's z-transformed correlation coefficients / regression coefficients stats.ci95 --> 95% confidence interval of the one sample t-test for the group test if the correlation coefficient distribution is different from 0 (see also the p_all output) D1 --> data matrix from list 1 D2 --> data matrix for list 2 (if not empty) data_PET --> PET data matrix Resh --> Reshaped results matrix [file_index PET_map Correlation_result p-value file_path]
If relevant, atlas labels per region ID are provided as a separate excel table.
More information about generation of the neuromorphometrics atlas included in the toolbox can be found on the following pages:
General Segmentation: http://neuromorphometrics.com/Seg/
BrainCOLOR: http://neuromorphometrics.com/ParcellationProtocol_2010-04-05.PDF
A list of anatomical labels for the for the included neuromorphometrics (asymetric - 119 regions version) atlas is available using the following link:
https://github.com/neurodebian/spm12/blob/master/tpm/labels_Neuromorphometrics.xml
Version history:
25.05.2020
- fixed some visualization color mismatch for linux,
- fixed a setting for computational option 4 (list 1 each image) and option 8 (allowing to test correlation distribution for all files from list 1 against null)
25.02.2021
- fixed a bug in computation of an exact p-value (leaded to error message under certain conditions)
30.07.2021 - Version 1.2 release
- A major update adding exact spatial permutations statistics for options 3,4 and 8 (highly recommended to use instead of the parametric p-value). Random permutations are performed by creating randomly permuted PET maps and reintroducing spatial auto-correlation (if present in the original). Auto-correlation is reintroduced by smoothing the permuted data (similar to the concept introduced by Burt et al. 2018, Nature Neuroscience, 21(9):1251–1259).
01.12.2021 - Version 1.3 release
- Fixed a bug in visualization of bar plots (shifted x labels when too many PET maps in one plot)
- Added many new PET maps
03.02.2022
- Fixed a bug in loading the maps when files with different dimensions were loaded into the same list.
18.02.2022
- Fixed a bug in generate_spatial_nullMaps (while loop did not converge for some of the newly added PET maps)
28.02.2022
- A major bug fixed for computing exact spatial correlation option (was computing Pearson spatial null for Spearman and vice versa)
29.05.2022 - Version 1.4 release
- Major updates to visualization functionalities
05.06.2023 - Version 1.5 release
- New visualization functionalities (including rank plots)
- NMDA map added
- Parametric p-values replaced with permutation based for all options