Content

This work is a derivative from the Atlanta sample (Liu et al., 2009) found in the 1000 functional connectome project (Biswal et al., 2010), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 19 healthy subjects. Time series are packaged in a series of .mat matlab/octave (HDF5) files, one per subject. For each subject, an array featuring about 200 time points for 116 brain regions from the AAL template is available. The Atlanta AAL preprocessed time series release more specifically contains the following files: * README.md: a markdown (text) description of the release. * brain_rois.nii.gz: a 3D nifti volume of the AAL template at 3 mm isotropic resolution, in the MNI non-linear 2009a symmetric space (http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Region number I is filled with Is (background is filled with 0s). * labels_aal.mat: a .mat file with two variables: rois_aal(i) is the numrical ID of the i-th region in the AAL template (e.g. 2001, 2002, 2101, etc). labels_all{i} is a string label for the i-th region (e.g. 'Precentral_L', 'Precentral_R', etc). * tseries_rois_SUBJECT_session1_run1.mat: a matlab/octave file for each subject. Each tseries file contains the following variables: * confounds: a TxK array. Each row corresponds to a time sample, and each column to one confound that was regressed out from the time series during preprocessing. * labels_confounds: cell of strings. Each entry is the label of a confound that was regressed out from the time series. * mask_suppressed: a T2x1 vector. T2 is the number of time samples in the raw time series (before preprocessing), T2=205. Each entry corresponds to a time sample, and is 1 if the corresponding sample was removed due to excessive motion (or to wait for magnetic equilibrium at the beginning of the series). Samples that were kept are tagged with 0s. * time_frames: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume. * tseries: a TxN array, where each row is a time sample and each column a region (N=483, numbered as in brain_rois.nii.gz). Note that the number of time samples may vary, as some samples have been removed if tagged with excessive motion.

Preprocessing

The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.6.5c. The parameters of a rigid body motion were first estimated at each time frame of the fMRI dataset (no correction of inter-slice difference in acquisition time was applied). The median volume of the fMRI time series was coregistered with a T1 individual scan using Minctracc9 (Collins et al., 1994), which was itself transformed to the Montreal Neurological Institute (MNI) non-linear template (Fonov et al., 2011) using the CIVET10 pipeline (Zijdenbos et al., 2002). The rigid-body transform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of Power et al. (2012) was used to remove the volumes with excessive motion (frame displacement greater than 0.5). The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were then spatially smoothed with a 6 mm isotropic Gaussian blurring kernel. The fMRI time series were spatially averaged on each of the areas of the AAL brain template (Tzourio-Mazoyer et al., 2002). To further reduce the spatial dimension, only the 81 cortical AAL areas were included in the analysis (excluding the cerebellum, the basal ganglia and the thalamus). The clustering methods were applied to these regional time series. Note that 8 subjects were excluded because there was not enough time points left after scrubbing (a minimum number of 190 volumes was selected as acceptable), and one additional subject had to be excluded because the quality of the T1-fMRI coregistration was substandard (by visual inspection). A total of 19 subjects was thus actually released.

References

Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010.Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-roImage 51 (3), 1126–1139.URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Biswal, B. B. et al., 2010. Toward discoveryscience of human brain function. Proceedings of the National Academy ofSciences of the U.S.A. 107 (10), 4734–4739. Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlin-ear registration-based segmentation. International Journal of Pattern Recog-nition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins,D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric stud-ies. NeuroImage 54 (1), 313–327.URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009.Images-based suppression of unwanted global signals in resting-state func-tional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064.URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Liu, H., Stufflebeam, S. M., Sepulcre, J., Hedden, T., Buckner, R. L., 2009.Evidence from intrinsic activity that asymmetry of the human brain iscontrolled by multiple factors. Proceedings of the National Academy ofSciences of the U.S.A. 106 (48), 20499–20503. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E.,Feb. 2012. Spurious but systematic correlations in functional connectivityMRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154.URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018 Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard,O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomicallabeling of activations in SPM using a macroscopic anatomical parcellationof the MNI MRI single-subject brain. NeuroImage 15, 273–289. Zijdenbos, A. P., Forghani, R., Evans, A. C., 2002. Automatic ”pipeline”analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.IEEE Transactions on Medical Imaging 21 (10), 1280–1291.

Other derivatives

This dataset was used in a publication:http://arxiv.org/abs/1501.05194

Preprocessed version of voxel time-series for three rats, originally described in Becq et al., Functional connectivity is preserved but reorganized across several anesthetic regimes, NeuroImage, 2020. Used in Achard et al., Inter-regional correlation estimators for functional magnetic resonance imaging, arXiv, 2022, arXiv:2011.08269.

The files named "coord_ROI_x.txt" contain the coordinates of the voxels inside region x (each line corresponds to one voxel).

The files named "ts_ROI_x.txt" contain the BOLD signal time series of the voxels inside region x (each line corresponds to one voxel, each column to one timepoint). The voxels with time series equal to zero have been removed

The files named "weight_ROI_x.txt" contain the weights associated with the voxels inside region x (each line corresponds to one voxel). Indeed, when assigning voxels to regions, some voxels end up at the border of several regions. These weights characterize the proportion of a given voxel present inside a given region. Hence, some voxels are included in several different regions. So when we compute the voxel-to-voxel inter-correlation between two regions we sometimes end up with inter-correlations equal to 1. In the current dataset this issue has been resolved and each voxel has been assigned to a single region.

Dataset of 3D reconstructions of the foraminifer Elphidium clavatum (marine protist with a calcite shell) acquired at the Beamline BL 47XU, SPring-8 synchrotron facility (Japan). A voxel size of 0.5 µm was used. In total, 124 specimens of Elphidium clavatum were scanned. For each specimen are available: a collection of raw images ("cropped" folder), a collection of binary images ("mask" folder), a 3D reconstruction (STL file), and two snapshot images of the 3D reconstruction. Sediment cores were collected in 2013 during a cruise with R/V Skagerak at Öresund station DV, north of the Island of Ven (55°55.59′ N, 12°42.66′ E). From the sediment core, 16 sediment layers were selected, representing the last 200 years (i.e., roughly the years ~2013, ~2010, ~2005, ~2002, ~1993, ~1986, ~1978, ~1960, ~1939, ~1923, ~1906, ~1890, ~1873, ~1857, ~1840, and ~1807). Between five to ten Elphidium clavatum specimens were selected from each layer. The dataset is part of the study exploring 3D time series of microfossils recording environmental conditions in the Baltic Sea entrance from the period early industrial (the 1800s) to present-day (the 2010s). The size of the dataset is 57 GB. Please contact the main author for further details.

124 specimens of Elphidium clavatum from 16 sediment layers were scanned. For each specimen, the following files are available: a collection of raw images in TIF format ("cropped" folder), a collection of binary images in TIF format ("mask" folder), a 3D reconstruction in STL format, and two snapshot images of the 3D reconstruction in TIF or PNG format. A voxel size of 0.5 µm was used. The data for each specimen is stored in a folder named as follows: DV(sediment depth in cm)-sp(specimen number)-(estimated year), e.g., “DV1-sp2-2013” (sediment depth: 1 cm, specimen 2, estimated year 2013). Examples of suitable software for handling the files include ImageJ and MeshLab.

Total number of files: 69,652 (plus a readme file with documentation) Total number of folders: 390 Dataset size: 57,1 GB

NOTE: This dataset is still under construction.

Participants

Participants for the English study were 51 young adults (32 females, mean age=21.3,SD=3.6) with no history of psychiatric, neurological or other medical illness that might compromise cognitive functions. They self-identified as native English speakers, and strictly qualified as right-handed on the Edinburgh handedness inventory (Oldfield 1971). All participants were paid, and gave written informed consent prior to participation, in accordance with the IRB guidelines of Cornell University.

Chinese participants were 35 healthy, right-handed young adults (15 females, meanage=19.3, SD=1.6). They self-identified as native Chinese speakers, and had no history of psychiatric, neurological, or other medical illness that could compromise cognitive functions. All participants were paid, and gave written informed consent prior to participation, in accordance with the IRB guidelines of Jiangsu Normal University.

French participants were 30 healthy, right-handed adults. They self-identifed as native French speakers and had no history of psychiatric, neurological, or other medical illness that could compromise cognitive functions. All participants gave written informed consent prior to participation, in accordance with the Regional Committee for the Protection of Persons involved in Biomedical Research.

Experiment Procedure

After giving their informed consent, participants were familiarized with the MRI facility and assumed a supine position on the scanner. Auditory stimuli were delivered through MRI-safe, high-fidelity headphones inside the head coil. The headphones were secured against the plastic frame of the coil using foam blocks. An experimenter increased the sound volume stepwise until the participants could hear clearly. The stimuli were divided into 9 sections, and each lasted for about 10 minutes. Participants listened passively to the 9 sections and completed 4 quiz questions after each section (36 questions in total). These questions were used to confirm their comprehension and were viewed by the participants via a mirror attached to the head coil and they answered through a button box. The entire session, including preparation time and practice, lasted for around 2.5 hours.

Scanner Settings

English and Chinese MRI images were acquired with a 3T MRI GE Discovery MR750 scanner with a 32-channel head coil. French MRI images were acquired with a 3T Siemens Magnetom Prisma Fit 230 scanner. Anatomical scans were acquired using a T1-weighted volumetric Magnetization Prepared RApid Gradient-Echo (MP-RAGE) pulse sequence. Facial structure was removed from anatomical scans before sharing publicly using Pydeface 2.0.0 (Gulban et al. 2019). Functional scans were acquired using a multi-echo planar imaging (ME-EPI) sequence with online reconstruction (TR=2000 ms; English and Chinese: TEs=12.8, 27.5, 43 ms; French:TEs=10, 25, 38 ms; FA=77◦; matrix size=72 x 72; FOV=240.0 mm x 240.0 mm; 2 x image acceleration; English and Chinese: 33 axial slices; French: 34 axial slices; voxel size=3.75 x 3.75 x 3.8 mm).

Data Preprocessing

All fMRI data were preprocessed using AFNI version 16 (Cox 1996). The first 4 volumes in each run were excluded from analyses to allow for T1-equilibration effects. Multi-echo independent components analysis (ME-ICA) (Kundu et al. 2012) were used to denoise data for motion, physiology and scanner artifacts. Images were then spatially normalized to the standard space of the Montreal Neurological Institute (MNI) atlas, yielding a volumetric time series resampled at 2 mm cubic voxels. These processed data files are available under the derivatives directory.

The dataset published here is based on the measurements of 5 sets of 100 sequential 3D images acquired at 12 seconds per scan at a reconstructed voxel size of 0.8µm (datasets: "10?burst_waterflood_y-9500", type: "raw") and an additional two static higher-quality scans before and after flooding (datasets: "10{0,8}_HQy-9500", type: "raw") . This repository contains these projection data in full. Furthermore, it contains 243 reconstructed tomograms with phase retrieval at different time steps that cover the main (visible) fluid displacement in the field-of-view (datasets: "10?_burst_waterflood_y-9500_phase", type: "derived") . Also the static scan reconstructions are present, both with absorption contrast and phase contrast reconstructions (datasets: "10{0,8}_HQy-9500{abs,phase}", type: "derived"). Finally, the collection also contains processed data from two sub-volumes of the full field-of-view (datasets: "BoiseWF_{tomo,seg}_sub{1,2}", type: "derived"). An additional spreadsheet provides information about the exact time points in the time series sampled by the tomographic scans (datasets: "BoiseWF_timeseries_metadata", type: "derived").

Rest1 and Rest3 are resting state Rest2 is music, three subjects had technical problems with the music and should not be used (S03, S12, S15) ratings: subjects rated the questions with a VAS after each scan. The 11D-ASC was rated at the end of the scanning day.

LSD, BOLD Pre-processing
Four different but complementary imaging software packages were used to analyse the fMRI data. Specifically, FMRIB Software Library (FSL), AFNI , Freesurfer and Advanced Normalization Tools (ANTS) were used. One subject did not complete the BOLD scans due to anxiety and an expressed desire to exit the scanner and four others were discarded from the group analyses due to excessive head movement. Principally, motion was measured using frame-wise displacement (FD) (Power et al., 2014). The criterion for exclusion was subjects with >15% scrubbed volumes when the scrubbing threshold is FD = 0.5. After discarding these subjects we reduced the threshold to FD = 0.4. The between-condition difference in mean FD for the 4 subjects that were discarded was 0.323±0.254 and for the 15 subjects that were used in the analysis the difference in mean FD was 0.046 ±0.032. The following pre-processing stages were performed: 1) removal of the first three volumes; 2) de-spiking (3dDespike, AFNI); 3) slice time correction (3dTshift, AFNI); 4) motion correction (3dvolreg, AFNI) by registering each volume to the volume most similar, in the least squares sense, to all others (in-house code); 5) brain extraction (BET, FSL); 6) rigid body registration to anatomical scans (twelve subjects with FSL’s BBR, one subject with Freesurfer’s bbregister and two subjects manually); 7) non-linear registration to 2mm MNI brain (Symmetric Normalization (SyN), ANTS); 8) scrubbing (Power et al., 2012) - using an FD threshold of 0.4 (the mean percentage of volumes scrubbed for placebo and LSD was 0.4 ±0.8% and 1.7 ±2.3%, respectively). The maximum number of scrubbed volumes per scan was 7.1%) and scrubbed volumes were replaced with the mean of the surrounding volumes. Additional pre-processing steps included: 9) spatial smoothing (FWHM) of 6mm (3dBlurInMask, AFNI); 10) band-pass filtering between 0.01 to 0.08 Hz (3dFourier, AFNI); 11) linear and quadratic de-trending (3dDetrend, AFNI); 12) regressing out 9 nuisance regressors (all nuisance regressors were bandpassed filtered with the same filter as in step 10): out of these, 6 were motion-related (3 translations, 3 rotations) and 3 were anatomically-related (not smoothed). Specifically, the anatomical nuisance regressors were: 1) ventricles (Freesurfer, eroded in 2mm space), 2) draining veins (DV) (FSL’s CSF minus Freesurfer’s Ventricles, eroded in 1mm space) and 3) local white matter (WM) (FSL’s WM minus Freesurfer’s subcortical grey matter (GM) structures, eroded in 2mm space). Regarding WM regression, AFNI’s 3dLocalstat was used to calculate the mean local WM time-series for each voxel, using a 25mm radius sphere centred on each voxel (Jo et al., 2010).

fMRI motion correction After discarding four subjects due to head motion, fifteen were left for the BOLD analysis. There was still a significant between-condition difference in motion for these subjects however (mean FD of placebo = 0.074 ±0.032, mean FD of LSD = 0.12 ±0.05, p = 0.0002). RSFC analysis is extremely sensitive to head motion (Power et al., 2012) and therefore special consideration was given to the pre-processing pipeline to account for motion. This section goes into more detail about the pre-processing steps that were performed to reduce artefacts associated with motion as well as other non-neural sources of noise. De-spiking has been shown to improve motion-correction and create more accurate FD values (Jo et al., 2013) and low-pass filtering at 0.08 Hz has been shown to perform well in removing high frequency motion (Satterthwaite et al., 2013). Six motion regressors were used as covariates in linear regression. It was decided that using more than six (e.g., “Friston 24-parameter motion regression” (Friston et al., 1996)) would be redundant and may impinge on neural signal (Bright and Murphy, 2015) (especially when other rigorous processes such as scrubbing (Power et al., 2012) and local WM were applied (Jo et al., 2010)) . Using anatomical regressors is also a common step to clean noise and ventricles, DV and local WM were used in the pipeline employed in the present analyses. local WM regression has been suggested to perform better than global WM regression (Jo et al., 2013). It has previously been shown that head motion biases functional connectivity results in a distance-dependant manner (Power et al., 2014). Therefore, as a quality control step, at the end of the pre-processing procedure, cloud plots were constructed to test for relationships between inter-node Euclidian distance and correlations between FD and RSFC across subjects. In cases in which motion is affecting the results, proximal nodes will have high FD-RSFC correlations and distal nodes will have low FD-RSFC correlations. This would result in a negative correlation between distance and FD-RSFC correlation. In the present dataset, the distance to FD-RSFC correlation was very close to zero for both the placebo and LSD conditions (Fig. S7), suggesting that the extensive pre-processing measures had successfully controlled for distance-related motion artefacts. The final quality control step was to correlate the results with mean FD across subjects (Table S6). Reassuringly, very few results correlated with mean motion (FD) and these were: vmPFC-PCC (r = -0.48, p = 0.035), V1-bilateral angular gyrus (r = 0.56, p=0.015). The significant correlation between changes in vmPFC-PCC RSFC and FD is also mentioned in (Power et al., 2012) and (Van Dijk et al., 2012); therefore, we decided not to elaborate on this result in the manuscript as it may have been an artifact of motion.

Fig. S7. Correlation between inter-node Euclidian distance (mm) and FD-RSFC correlation (r) for both LSD (a) and placebo (b). Nodes were defined using the Craddock atlas with 240 parcellations, excluding supplementary motor and motor areas. For each pair of nodes, RSFC was calculated with pearson’s r and transformed into z using fisher transformation. For each pair of nodes, a correlation across subjects was calculated between mean FD and RSFC (r) and transformed into z using fisher’s transformation. This correlation is plotted against the distance between nodes (mm). The correlations for LSD and placebo were r = -0.0009 (p = 0.089) and r = -0.025 (p < 0.001), respectively, suggesting that motion did not affect RSFC in a distant dependant manner after pre-processing.

REFERENCES Bright MG, Murphy K (2015) Is fMRI “noise” really noise? Resting state nuisance regressors remove variance with network structure. NeuroImage 114:158-169. Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R (1996) Movement‐related effects in fMRI time‐series. Magnetic resonance in medicine 35:346-355. Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW (2010) Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage 52:571-582. Jo HJ, Gotts SJ, Reynolds RC, Bandettini PA, Martin A, Cox RW, Saad ZS (2013) Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. Journal of applied mathematics 2013. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142-2154. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84:320-341. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64:240-256. Van Dijk KR, Sabuncu MR, Buckner RL (2012) The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59:431-438.

Email leor.roseman13@imperial.ac.uk for any questions

ParticipantsSixty participants (28 male, 32 female) were recruited locally from the Pittsburgh, Pennsylvania area as well as the U.S. Army Research Laboratory in Aberdeen, Maryland. Participants were neurologically healthy adults with no history of head trauma, neurological pathology, or psychological pathology. Participant ages ranged from 18 to 45 years old (mean age, 26.5 years). The study protocol for acquiring the human subjects data was reviewed and approved by the IRB at Carnegie Mellon University and written informed consent was obtained for all participants. As the present work uses de-identified human data from the original CMU study, the Penn IRB deemed this study exempt from the requirement for ethical review.MRI acquisitionAll 60 participants were scanned at the Scientific Imaging and Brain Research Center at Carnegie Mellon University on a Siemens Verio 3T magnet fitted with a 32-channel head coil. An MPRAGE sequence was used to acquire a high-resolution (1 mm3 isotropic voxels, 176 slices) T1-weighted brain image for all participants. DSI data was acquired following fMRI sequences using a 50 min, 257-direction, twice-refocused spin-echo EPI sequence with multiple q values (TR =11,400 ms, TE =128 ms, voxel size 2.4 mm3, field of view 231x 231 mm, b-max 5000 s/mm2, 51 slices). Resting state fMRI (rsfMRI) data consisting of 210 T2*-weighted volumes were collected for each participant (56 participants) with a BOLD contrast with echo planar imaging (EPI) sequence (TR 2000 ms, TE 29 ms, voxel size 3.5 mm3, field of view 224 x 224 mm, flip angle 79 degrees).Head motion is a major source of artifact in resting state fMRI data (rsfMRI). Although recently developed motion correction algorithms are far more effective than typical procedures, head motion was additionally minimized during image acquisition with a custom foam padding setup designed to minimize the variance of head motion along pitch and yaw directions. The setup also included a chin restraint that held the participant's head to the receiving coil itself. Preliminary inspection of EPI images at the imaging center showed that the setup minimized resting head motion to 1 mm maximum deviation for most subjects. Only 3 out of 56 subject were excluded from the final analysis because they moved more than 2 voxels multiple times throughout the imaging session.Diffusion MRI reconstructionDSI Studio (http://dsi-studio.labsolver.org) was used to process all DSI images using a q-space diffeomorphic reconstruction method (yeh et al 2011). A nonlinear spatial normalization approach (Ashburner et al 1999) was implemented through 16 iterations to obtain the spatial mapping function of quantitative anisotropy (QA) values from individual subject diffusion space tothe FMRIB 1 mm fractional anisotropy (FA) atlas template. QA is an orientation distribution function (ODF) based index that is scaled with spin density information that permits the removal of isotropic diffusion components from the ODF to filter false peaks, facilitating the resolution of fiber tracts using deterministic fiber tracking algorithms. For a detailed description and comparison of QA with standard FA techniques, see (Yeh et al 2013). The ODFs were reconstructed to a spatial resolution of 2 mm3 with a diffusion sampling length ratio of 1.25. Whole-brain ODF maps of all 60 subjects were averaged together to generate a template image of the average tractography space.Fiber tractography analysisFiber tractography was performed using an ODF-streamline version of the FACT algorithm (Yeh et al 2013) in DSI Studio, using the builds from September 23, 2013 and August 29, 2014. All fiber tractography was initiated from seed positions with random locations within the whole-brain seed mask with random initial fiber orientations. Using a step size of 1 mm, the directional estimates of fiber progression within each voxel were weighted by 80% of the incoming fiber direction and 20% of the previous fiber direction. A streamline was terminated when the QA index fell below 0.05 or had a turning angle greater than 75 degrees. We performed a region-based tractography to isolate streamlines between pairs of regional masks. All cortical masks were selected from an upsampled version of the original Automated Anatomical Labeling Atlas (AAL) (Tzourio et al 2002, Desikan et al 2006) containing 90 cortical and subcortical regions of interest but not containing cerebellar structures or the brainstem. This resampled version contains 600 regions and is created via a series of upsampling steps in which any given region is bisected perpendicular to its principal spatial axis in order to create 2 equally sized sub-regions (Hermundstad et al 2014). The final atlas contained regions of an average size of 268 voxels, with a standard deviation of 35 voxels. Diffusion-based tractography has been shown to exhibit a strong medial bias (Croxson et al 2005) due to partial volume effects and poor resolution of complex fiber crossings (jones et al 2010). To counter the bias away from more lateral cortical regions, tractography was generated for each cortical surface mask separately.Resting state fMRI preprocessingSPM8 (Wellcome Department of Imaging Neuroscience, London) was used to preprocess all rsfMRI collected from 53 of the 60 participants with DSI data. To estimate the normalization transformation for each EPI image, the mean EPI image was first selected as a source image and weighted by its mean across all volumes. Then, an MNI-space EPI template supplied with SPM was selected as the target image for normalization. The source image smoothing kernel was set to a FWHM of 4 mm, and all other estimation options were kept at the SPM8 defaults to generate a transformation matrix that was applied to each volume of the individual source images for further analyses. The time-series was up-sampled to a 1Hz TR using a cubic-spline interpolation. Regions from the AAL600 atlas were used as seed points for the functional connectivity analysis (Hermundstad et al 2014). A series of custom MATLAB functions were used to extract the voxel time series of activity for each region, and to remove estimated noise from the time series by selecting the first five principal components from the white matter and CSF masks.DatasetHere we provided the streamline count matrices of all 60 participants in CMU_SC.zip, which contains individual .mat files per participant. We also provided the average ROI BOLD fMRI time series of 53 participants with low head motion in CMU_BOLD.mat file.

Dataset of 3D reconstructions of the foraminifer Elphidium clavatum (marine protist with a calcite shell) acquired at the Beamline BL 47XU, SPring-8 synchrotron facility (Japan). A voxel size of 0.5 µm was used. In total, 124 specimens of Elphidium clavatum were scanned. For each specimen are available: a collection of raw images ("cropped" folder), a collection of binary images ("mask" folder), a 3D reconstruction (STL file), and two snapshot images of the 3D reconstruction. Sediment cores were collected in 2013 during a cruise with R/V Skagerak at Öresund station DV, north of the Island of Ven (55°55.59′ N, 12°42.66′ E). From the sediment core, 16 sediment layers were selected, representing the last 200 years (i.e., roughly the years ~2013, ~2010, ~2005, ~2002, ~1993, ~1986, ~1978, ~1960, ~1939, ~1923, ~1906, ~1890, ~1873, ~1857, ~1840, and ~1807). Between five to ten Elphidium clavatum specimens were selected from each layer. The dataset is part of the study exploring 3D time series of microfossils recording environmental conditions in the Baltic Sea entrance from the period early industrial (the 1800s) to present-day (the 2010s). The size of the dataset is 57 GB. Please contact the main author for further details. 124 specimens of Elphidium clavatum from 16 sediment layers were scanned. For each specimen, the following files are available: a collection of raw images in TIF format ("cropped" folder), a collection of binary images in TIF format ("mask" folder), a 3D reconstruction in STL format, and two snapshot images of the 3D reconstruction in TIF or PNG format. A voxel size of 0.5 µm was used. The data for each specimen is stored in a folder named as follows: DV(sediment depth in cm)-sp(specimen number)-(estimated year), e.g., “DV1-sp2-2013” (sediment depth: 1 cm, specimen 2, estimated year 2013). Examples of suitable software for handling the files include ImageJ and MeshLab. Total number of files: 69,652 (plus a readme file with documentation) Total number of folders: 390 Dataset size: 57,1 GB

Dataset of 3D reconstructions of the foraminifer Elphidium clavatum (marine protist with a calcite shell) acquired at the Beamline BL 47XU, SPring-8 synchrotron facility (Japan). A voxel size of 0.5 µm was used. In total, 124 specimens of Elphidium clavatum were scanned. For each specimen are available: a collection of raw images ("cropped" folder), a collection of binary images ("mask" folder), a 3D reconstruction (STL file), and two snapshot images of the 3D reconstruction. Sediment cores were collected in 2013 during a cruise with R/V Skagerak at Öresund station DV, north of the Island of Ven (55°55.59′ N, 12°42.66′ E). From the sediment core, 16 sediment layers were selected, representing the last 200 years (i.e., roughly the years ~2013, ~2010, ~2005, ~2002, ~1993, ~1986, ~1978, ~1960, ~1939, ~1923, ~1906, ~1890, ~1873, ~1857, ~1840, and ~1807). Between five to ten Elphidium clavatum specimens were selected from each layer. The dataset is part of the study exploring 3D time series of microfossils recording environmental conditions in the Baltic Sea entrance from the period early industrial (the 1800s) to present-day (the 2010s). The size of the dataset is 57 GB. Please contact the main author for further details. 124 specimens of Elphidium clavatum from 16 sediment layers were scanned. For each specimen, the following files are available: a collection of raw images in TIF format ("cropped" folder), a collection of binary images in TIF format ("mask" folder), a 3D reconstruction in STL format, and two snapshot images of the 3D reconstruction in TIF or PNG format. A voxel size of 0.5 µm was used. The data for each specimen is stored in a folder named as follows: DV(sediment depth in cm)-sp(specimen number)-(estimated year), e.g., “DV1-sp2-2013” (sediment depth: 1 cm, specimen 2, estimated year 2013). Examples of suitable software for handling the files include ImageJ and MeshLab. Total number of files: 69,652 (plus a readme file with documentation) Total number of folders: 390 Dataset size: 57,1 GB

A network approach to brain and dynamics opens new perspectives towards understanding of its function. The functional connectivity from functional MRI recordings in humans is widely explored at large scale, and recently also at the voxel level. The networks of dynamical directed connections are far less investigated, in particular at the voxel level. To reconstruct full brain effective connectivity network and study its topological organization, we present a novel approach to multivariate Granger causality which integrates information theory and the architecture of the dynamical network to efficiently select a limited number of variables. The proposed method aggregates conditional information sets according to community organization, allowing to perform Granger causality analysis avoiding redundancy and overfitting even for high-dimensional and short datasets, such as time series from individual voxels in fMRI. We for the first time depicted the voxel-wise hubs of incoming and outgoing information, called Granger causality density (GCD), as a complement to previous repertoire of functional and anatomical connectomes. Analogies with these networks have been presented in most part of default mode network; while differences suggested differences in the specific measure of centrality. Our findings could open the way to a new description of global organization and information influence of brain function. With this approach is thus feasible to study the architecture of directed networks at the voxel level and individuating hubs by investigation of degree, betweenness and clustering coefficient.

Participants

Thirty participants (17 females, mean age=23.17±2.31 years) were recruited for the fMRI experiment at Shanghai International Studies University, Shanghai, China. An additional thirty participants (16 females, mean age=22.67±1.99 years) were recruited from the West China Hospital of Sichuan University, Chengdu, China. All participants were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorders. Before the experiment, all participants provided written informed consent and were compensated for their participation.

Experiment Procedure

The experimental procedures for both fMRI and MEG experiments were identical. Participants watched the video while inside the scanner. The video was presented via a mirror attached to the head coil in the fMRI and MEG. Audio was delivered through MRI-compatible headphones (Sinorad, Shenzhen, China) during the fMRI experiment and MEG-compatible insert earphones (ComfortBuds 24, Sinorad, Shenzhen, China) during the MEG experiment. Following the video, participants were visually presented with 5 multiple-choice questions on the screen to assess their comprehension and ensure engagement with the stimuli. Participants responded using a button press, with a maximum response time of 10 seconds per question. If no response was recorded within this time, the experiment proceeded to the next question automatically. After the quiz, participants were instructed to close their eyes for 15 minutes without an explicit task. This period allowed for the recording of neural activity, capturing spontaneous mental replay of the video stimulus. The entire experimental procedure lasted approximately 45 minutes per participant.

The fMRI experiment was approved by the Ethics Committee of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior (No. 2024BC028), and the MEG experiment was approved by the West China Hospital of Sichuan University Biomedical Research Ethics Committee (No. 2024[657]).

Stimuli

The video stimulus was extracted from the first episode of the Chinese reality TV show “Where Are We Going, Dad? (Season 1)”, which originally aired in 2013. The show features unscripted interactions between fathers and their child as they travel to a rural village and engage in daily activities. The selected excerpt has a total duration of 25 minutes and 19 seconds. The original video had a resolution of 640×368 pixels with a frame rate of 15 frames per second. It was presented in full-color (RGB) format, without embedded subtitles or captions.

Acquisition

The fMRI data was collected in a 3.0 T Siemens Prisma MRI scanner at Shanghai International Studies University, Shanghai. Anatomical scans were obtained using a Magnetization Prepared RApid Gradient-Echo (MP-RAGE) ANDI iPAT2 pulse sequence with T1-weighted contrast (192 single-shot interleaved sagittal slices with A/P phase encoding direction; voxel size=1×1×1 mm; FOV=256 mm; TR=2300 ms; TE=2.98 ms; TI=900 ms; flip angle=9°; acquisition time=6 min; GRAPPA in-plane acceleration factor=2). Functional scans were acquired using T2-weighted echo planar imaging (63 interleaved axial slices with A/P phase encoding direction, voxel size=2.5×2.5×2.5 mm; FOV=220 mm; TR=2000ms; TE=30 ms; acceleration factor=3; flip angle=60°).

MEG data were recorded at West China Hospital of Sichuan University using a 64-channel optically pumped magnetometer (OPM) MEG system (Quanmag, Beijing, China). OPM-MEG is a new type of MEG instrumentation that offers several advantages over conventional MEG systems. These include higher signal sensitivity, improved spatial resolution, and more uniform scalp coverage. Additionally, OPM-MEG allows for greater participant comfort and compliance, supports free movement during scanning, and features lower system complexity, making it a promising tool for more flexible and accessible neuroimaging. The MEG Data were sampled at 1,000 Hz and bandpass-filtered online between 0 and 500 Hz. To facilitate source localization, T1-weighted MRI scans were acquired from the participants using a 3.0 T Siemens TrioTim MRI scanner at West China Hospital of Sichuan University (176 single-shot interleaved sagittal slices with A/P phase encoding direction; voxel size = 1×1×1 mm; FOV = 256 mm; TR = 1900 ms; TE = 2.3 ms; TI = 900 ms; flip angle = 9°; acquisition time = 7 min).

Preprocessing

All Digital Imaging and Communications in Medicine (DICOM) files of the raw fMRI data were first converted into the Brain Imaging Data Structure (BIDS) format using dcm2bids (v3.1.1) and subsequently transformed into Neuroimaging Informatics Technology Initiative (NIfTI) format via dcm2niix (v1.0.20220505). Facial features were removed from anatomical images using PyDeface (v2.0.2). Preprocessing was carried out with fMRIPrep (v20.2.0), following standard neuroimaging pipelines. For anatomical images, T1-weighted scans underwent bias field correction, skull stripping, and tissue segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). These images were then spatially normalized to the Montreal Neurological Institute (MNI) space using the MNI152NLin2009cAsym:res-2 template, ensuring consistent alignment across participants. Functional MRI preprocessing included skull stripping, motion correction, slice-timing correction, and co-registration to the T1-weighted anatomical reference. The blood-oxygen-level-dependent (BOLD) time series were resampled in both native and MNI space, and various confound regressors were computed to improve signal quality. Noise correction was applied to enhance the signal-to-noise ratio, and motion outliers were identified to mitigate potential artifacts in further analyses.

MEG data preprocessing was conducted using MNE-Python (v1.8.0). We first applied a bandpass filter (1–38 Hz) to remove low-frequency drifts and high-frequency noise. We then identified bad channels through visual inspection and cross-validated using PyPREP (v0.4.3), these bad channels were interpolated to maintain data integrity. To mitigate physiological artifacts, we performed independent component analysis (ICA) and removed components corresponding to heartbeat and eye movements. The data were then segmented into three task-related epochs, corresponding to the video watching, question answering, and post-task replay conditions, with each epoch defined strictly based on event markers without additional pre- or post-stimulus time windows. T1-weighted MRI data were converted to NIfTI format and processed with FreeSurfer (v7.3.2) to reconstruct cortical surfaces and generate boundary element model (BEM) surfaces using a single-layer conductivity of 0.3 S/m. MEG-MRI coregistration was performed with fiducial points and refined via MNE-Python’s graphical interface. A source space (resolution=5mm) was generated using a fourth-order icosahedral mesh, and a BEM solution was computed to model head conductivity. A forward model was then created based on anatomical MRI and digitized head shape. Noise covariance matrices were estimated from raw MEG recordings, and inverse operators were constructed using minimum norm estimation. Source reconstruction employed dynamic statistical parametric mapping (dSPM) for noise-normalized estimates. Task-related epochs (video watching, question answering, post-task replay) were used to compute source estimates, which were morphed onto the FreeSurfer average brain template for group-level comparisons.

The poster for our ICIP paper "A GPU Accelerated Interactive Interface for Exploratory Functional Connectivity Analysis of fMRI Data"

Abstract Functional connectivity analysis is a way to investigate how different parts of the brain are connected and interact. A common measure of connectivity is the temporal correlation between a reference voxel time series and all the other time series in a functional MRI data set. An fMRI data set generally contains more than 20,000 within-brain voxels, making a complete correlation analysis between all possible combinations of voxels heavy to compute, store, visualize and explore. In this paper, a GPU-accelerated interactive tool for investigating functional connectivity in fMRI data is presented. A reference voxel can be moved by the user and the correlations to all other voxels are calculated in real-time using the graphics processing unit (GPU). The resulting correlation map is updated in real-time and visualized as a 3D volume rendering together with a high resolution anatomical volume. This tool greatly facilitates the search for interesting connectivity patterns in the brain.

Data Acquisition

The cohort consists of a total of 27 healthy participants (age 35 ± 6.8 years) and 27 schizophrenic patients (age 41 ± 9.6), scanned in a 3-Tesla MRI scanner (Trio, Siemens Medical, Germany) using a 32-channel head-coil. The schizophrenic patients are from the Service of General Psychiatry at the Lausanne University Hospital (CHUV). All of them were diagnosed with schizophrenic and schizoaffective disorders after meeting the DSM-IV criteria (American Psychiatric Association (2000): Diagnostic and Statistical Manual of Mental Disorders, 4th ed. DSM-IV-TR. American Psychiatric Pub, Arlington, VA22209, USA). The Diagnostic Interview for Genetic Studies assessment was used to recruits the healthy controls (Preisig et al. 1999). 24 out of the 27 schizophrenics were under medication with mean chlorpromazine equivalent dose (CPZ) of 431 ± 288 mg. The written consent was obtained for all subjects - in accordance with institutional guidelines of the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland, #82/14, #382/11, #26.4.2005). All subjects were fully anonymised.

The session protocol consisted of (1) a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence sensitive to white/gray matter contrast (1-mm in-plane resolution, 1.2-mm slice thickness), (2) a Diffusion Spectrum Imaging (DSI) sequence (128 diffusion-weighted volumes and a single b0 volume, maximum b-value 8,000 s/mm², 2.2x2.2x3.0 mm voxel size), and (3) a gradient echo EPI sequence sensitive to BOLD contrast (3.3-mm in-plane resolution and slice thickness with a 0.3-mm gap, TE 30 ms, TR 1,920 ms, resulting in 280 images per participant). During the fMRI scan, participants were not engaged in any overt task, and the scan was treated as eyes-open resting-state fMRI (rs-fMRI).

Data Pre-processing

Initial signal processing of all MPRAGE, DSI, and rs-fMRI data was performed using the Connectome Mapper pipeline (Daducci et al. 2012). Grey and white matter were segmented from the MPRAGE volume using freesurfer (Desikan et al. 2006) and parcellated into 83 cortical and subcortical areas. The parcels were then further subdivided into 129, 234, 463 and 1015 approximately equally sized parcels according to the Lausanne anatomical atlas following the method proposed by (Cammoun et al. 2012). DSI data were reconstructed following the protocol described by (Wedeen et al. 2005), allowing us to estimate multiple diffusion directions per voxel. The diffusion probability density function was reconstructed as the discrete 3D Fourier transform of the signal modulus. The orientation distribution function (ODF) was calculated as the radial summation of the normalized 3D probability distribution function. Thus, the ODF is defined on a discrete sphere and captures the diffusion intensity in every direction.

Structural Connectivity

Structural connectivity matrices were estimated for individual participants using deterministic streamline tractography on reconstructed DSI data, initiating 32 streamline propagations per diffusion direction, per white matter voxel (Wedeen et al. 2008). Structural connectivity between pairs of regions was measured in terms of fiber density, defined as the number of streamlines between the two regions, normalized by the average length of the streamlines and average surface area of the two regions (Hagmann et al. 2008). The goal of this normalization was to compensate for the bias toward longer fibers inherent in the tractography procedure, as well as differences in region size. The number of fibers and fiber length were also included in the dataset. For the quantitative measure of structural connectivity, the generalised fractional anisotropy (gFA, Tuch et al. 2004) and average apparent diffusion coefficient (ADC, Sener et al. 2001) were also computed for each tract.

Functional Connectivity

Functional data were pre-processed using routines designed to facilitate subsequent network exploration (Murphy et al. 2009, Power et al. 2012). The first four time points were excluded from subsequent analysis to allow the time series to stabilize. The signal was linearly detrended and further physiological (white-matter and cerebrospinal fluid regressors) and motion artefacts (three translational and three rotational regressors) confounds were regressed. Then, the signal was spatially smoothed and bandpass-filtered between 0.01-0.1 Hz with Hamming windowed sinc FIR filter. To obtain the brain regions for different atlas scales the signal was linearly registered to the MPRAGE image and averaged within a given region (Jenkinson et al. 2012). Functional matrices were obtained by computing Pearson’s correlation between the individual pairs of regions. All of the above was carried out in subject’s native space (Daducci et al. 2012, Griffa et al. 2017).

Brain cortical bert freesurfer rendering for the 5 scales of the Lausanne2008 atlas is available on https://github.com/jvohryzek/bert4lausanne2008.

Data set of raw anatomical and functional MR data from 72 patients with Schizophrenia and 75 healthy controls (ages ranging from 18 to 65 in each group). All subjects were screened and excluded if they had: history of neurological disorder, history of mental retardation, history of severe head trauma with more than 5 minutes loss of consciousness, history of substance abuse or dependence within the last 12 months. Diagnostic information was collected using the Structured Clinical Interview used for DSM Disorders (SCID). A multi-echo MPRAGE (MEMPR) sequence was used with the following parameters: TR/TE/TI = 2530/(1.64, 3.5, 5.36, 7.22, 9.08)/900 ms, flip angle = 7��, FOV = 256x256 mm, Slab thickness = 176 mm, Matrix = 256x256x176, Voxel size =1x1x1 mm, Number of echos = 5, Pixel bandwidth =650 Hz, Total scan time = 6 min. With 5 echoes, the TR, TI and time to encode partitions for the MEMPR are similar to that of a conventional MPRAGE, resulting in similar GM/WM/CSF contrast. Rest data was collected with single-shot full k-space echo-planar imaging (EPI) with ramp sampling correction using the intercomissural line (AC-PC) as a reference (TR: 2 s, TE: 29 ms, matrix size: 64x64, 32 slices, voxel size: 3x3x4 mm3). Slice Acquisition Order: Rest scan - collected in the Axial plane - series ascending - multi slice mode - interleaved MPRAGE - collected in the Sag plane - series interleaved - multi slice mode - single shot The following data are released for every participant: * Resting fMRI * Anatomical MRI * Phenotypic data for every participant including: gender, age, handedness and diagnostic information.

Content

This work is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (INDI), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ). The COBRE preprocessed fMRI release more specifically contains the following files:* README.md: a markdown (text) description of the release. * cobre_model_group.csv A comma-separated value file, with the sz (1: patient with schizophrenia, 0: control), age, sex, and FD (frame displacement, as defined by Power et al. 2012) variables. Each column codes for one variable, starting with the label, and each line has the label of the corresponding subject.* fmri_szxxxSUBJECT_session1_run1.nii.gz: a 3D+t nifti volume at 3 mm isotropic resolution, in the MNI non-linear 2009a symmetric space(http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Note that the number of time samples may vary, as some samples have beenremoved if tagged with excessive motion. See the _extra.mat file below for more info.* fmri_szxxxSUBJECT_session1_run1_extra.mat: a matlab/octave file for each subject. Each .mat file contains the following variables:* confounds: a TxK array. Each row corresponds to a time sample, and each column to one confound that was regressed out from the time series during preprocessing.* labels_confounds: cell of strings. Each entry is the label of a confound that was regressed out from the time series.* mask_suppressed: a T2x1 vector. T2 is the number of time samples in the raw time series (before preprocessing), T2=119. Each entry corresponds to a time sample, and is 1 if the corresponding sample was removed due to excessive motion (or to wait for magnetic equilibrium at the beginning of the series). Samples that were kept are tagged with 0s.* time_frames: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume.

Preprocessing

The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.14, under CentOS version 6.3 with Octave(http://gnu.octave.org) version 3.8.1 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18.Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-bodytransform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of (Power et al., 2012), was used to remove the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~180 s of acquisition, was then required for further analysis. For this reason, 16 controls and 29 schizophrenia patients were rejected from the subsequent analyses. The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were finally spatially smoothed with a 6 mm isotropic Gaussian blurring kernel.

References

Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-roImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327.URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018

Other derivatives

This dataset was used in a publication, see the link below.https://github.com/SIMEXP/glm_connectome

COBRE dataset, preprocessed and functional connectivity features extracted at 7 resolutions (7,12,20,36,64,122,197,325,444). Pearson correlation was used to compute functional connectivity between time series. The resolution are based on a partition using Cambridge dataset availlable at http://dx.doi.org/10.6084/m9.figshare.1285615

Content

This work is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (INDI), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ).

• cobre_model_group.csv A comma-separated value file, with the sz (1: patient with schizophrenia, 0: control), age, sex, and FD (frame displacement, as defined by Power et al. 2012) variables. Each column codes for one variable, starting with the label, and each line has the label of the corresponding subject.
• cobre_resolution_xx.mat: a .mat (octave/matlab) structure with two variables: data a NxF (N subjects x F features) and a subj_idx the subject id of each row. The features are a vetororized for of the connectome. ### Preprocessing The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.14, under CentOS version 6.3 with Octave(http://gnu.octave.org) version 3.8.1 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18.Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-bodytransform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of (Power et al., 2012), was used to remove the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~180 s of acquisition, was then required for further analysis. For this reason, 16 controls and 29 schizophrenia patients were rejected from the subsequent analyses. The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were finally spatially smoothed with a 6 mm isotropic Gaussian blurring kernel. ### References Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-roImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011.Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327.URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018

Accurate diagnosis of the initial phase of Alzheimer’s disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer’s and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01–.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson’s correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study’s primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.

Responses time series of voxels in each visual field map region of interest.

This study is driven by the complex and specialized nature of magnetic resonance spectroscopy imaging (MRSI) data processing, particularly within the scope of brain tumor assessments. Traditional methods often involve intricate manual procedures that demand considerable expertise. In response, we investigate the application of deep neural networks directly to raw MRSI data in the time domain. Given the significant health risks associated with brain tumors, the necessity for early and accurate detection is crucial for effective treatment. While conventional MRI techniques encounter limitations in the rapid and precise spatial evaluation of diffuse gliomas, both accuracy and efficiency are often compromised. MRSI presents a promising alternative by providing detailed insights into tissue chemical composition and metabolic changes. Our proposed model, which utilizes deep neural networks, is specifically designed for the analysis and classification of spectral time series data. Trained on a dataset that includes both synthetic and real MRSI data from brain tumor patients, the model aims to distinguish MRSI voxels that indicate pathological conditions from healthy ones. Our findings demonstrate the model’s robustness in classifying glioma-related MRSI voxels from those of healthy tissue, achieving an area under the receiver operating characteristic curve of 0.95. Overall, these results highlight the potential of deep learning approaches to harness raw MR data for clinical applications, signaling a transformative impact on diagnostic and prognostic assessments in brain tumor examinations. Ongoing research is focused on validating these approaches across larger datasets, to establish standardized guidelines and enhance their clinical utility.

The dataset contains 336 participants from East Asia, each with self-reported resilience measures (i.e., CD-RISC, BRS, and RSA) and the PCA result for these three measurements, represented by PC1 and PC2. Column G to O contain fMRI brain imaging derivatives.Here's a breakdown of the abbreviations: CD-RISC: Connor-Davidson Resilience Scale; BRS: Brief Resilience Scale; RSA: Resilience Scale for Adults; PCA: Principal Component Analysis; DMNsd: DMN time series standard deviation; DMNrMSSD: Root mean square of successive differences in default-mode network; DMNmV: multi-voxel DMN mean state transition velocity; SensorymV: multi-voxel Sensory Network mean state transition velocity; ActionmV: multi-voxel Action Network mean state transition velocity; FDmean: mean framewise displacement; DMNsize = number of ROI voxels in DMN; CSFmV: multi-voxel mean state transition velocity in selected CSF regions; WMmV: multi-voxel mean state transition velocity in selected White Matter regions