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Open access 7T resting-state fMRI dataset

Open access 7T resting-state fMRI dataset

I'm looking for an open access dataset of 7-tesla resting state fMRI images of human subjects. I've been able to find one so far (http://www.nature.com/articles/sdata201454). However, I need images that were acquired using MP-RAGE (not MP2-RAGE) sequencing. I've checked the major open neuroscience repos so far with no luck. Does anyone know of an open access dataset that meets this description?


There two 7 Tesla data sets currently publicly available, bith within the OpenfMRI data collection

Here is a link to a 350 GB data set for 20 subjects watching the audio version of Forrest Gump. The authors also have all the scripts available at Github to enable reproduction. The associate publication can be found here.

The other 7T data set you allready mentioned in your question. As far as I know there are only these two as of June 2016.


The Nighres project has released some 7T MRI datasets and tools to run them. You can read the paper on the tools here, and download the datasets from NITRC here.


Abstract

Recent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting ‘Core’ cells and supragranular-projecting ‘Matrix’ cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core–Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity.


Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner

D. Rangaprakash AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA Guo-Rong Wu Department of Data Analysis, University of Ghent, Ghent, Belgium Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China Daniele Marinazzo Department of Data Analysis, University of Ghent, Ghent, Belgium Xiaoping Hu Department of Bioengineering, University of California Riverside, Riverside, CA, USA Gopikrishna Deshpande AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA Department of Psychology, Auburn University, Auburn, AL, USA Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA Corresponding author at: AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.


Open access 7T resting-state fMRI dataset - Psychology

<p>The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.</p


Abstract

Recent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting ‘Core’ cells and supragranular-projecting ‘Matrix’ cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core–Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity.


Project definition

Background

In my current PhD project one of the end results should be a open multimodal behavioural and neuroimaging dataset characterizing healthy human auditory processing. It aims to allow researchers address individual differences in auditory cognitive skills across brain functions and structures, and it will serve as a baseline for comparison with clinical populations. To achieve that, our core objectives are to create a standardized framework with which to administer a battery of curated tasks. After acquiring the data from 70 young adults and we intend to share our framework, analysis pipelines, stimuli with linked descriptors, and metadata with the community through open data repositories. The dataset contains cognitive and psychophysical tasks, as well as questionnaires designed to assess musical abilities, speech, and general auditory perception. It also includes EEG and fMRI recorded during resting state, as well as naturalistic listening to musical stimuli and speech.

During this BrainHanks School project I wanted to understand what are the needs as a researcher to easily make use of public available data and learn the basics of pre-processing raw fMRI data.

Learning Goals

I have good experience analyzing highly process data&mldr but how you get there?


AUTHOR CONTRIBUTIONS

Sanchez-Romero: Data curation Formal analysis Investigation Methodology Software Validation Visualization Writing – original draft Writing – review & editing. Joseph D. Ramsey: Conceptualization Formal analysis Investigation Methodology Software Validation Writing – review & editing. Kun Zhang: Formal analysis Investigation Methodology Software Validation Funding acquisition Writing – review & editing. Madelyn R. K. Glymour: Data curation Investigation Software Validation. Biwei Huang: Formal analysis Methodology Software Validation. Clark Glymour: Conceptualization Formal analysis Funding acquisition Investigation Methodology Project administration Supervision Writing – original draft Writing – review & editing.


Human data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators David Van Essen and Kamil Ugurbil grant 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, and by the McDonnell Center for Systems Neuroscience at Washington University. Funding for this work was provided by the Wellcome Trust (grants 203139/Z/16/Z, WT100973AIA, 103184/Z/13/Z, 202831/Z/16/Z, and 105238/Z/14/Z), the Medical Research Council (grants MR/T023007/1, MR/P024955/1, and G0902373), the Bettencourt Schueller Foundation, and Christ Church, University of Oxford. We are very grateful for the care afforded to the animals by the veterinary and technical staff at the University of Oxford.

Author contributions: A.L.-P. and J.S. designed research A.L.-P., L.R., D.F., K.M., E.F.F., N.K., M.F.S.R., and J.S. performed research A.L.-P. analyzed data and A.L.-P., M.F.S.R., and J.S. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission. P.H.R. is a guest editor invited by the Editorial Board.

This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).


Results

Individualized Functional Parcellation Results of the Amygdala

In the present study, bilateral amygdalae were successfully parcellated into three spatially coherent subregions for each subject, namely CM, LB, and SF, which were named after respective prior information. Parcellation results of two randomly selected subjects are shown in Figure 2.

Figure 2. 2D view of the obtained amygdala subregions in two randomly selected subjects, located at (�, 𢄧, �). The results are overlaid on the standard template in the MNI space. CM, centromedial amygdala LB, laterobasal amygdala SF, superficial amygdala.

Inter-subject Variability of the Individualized Functional Parcellation Results

Volume Measurements

Table 1 shows the mean volumes of bilateral amygdalae and their parcellated three subregions for the 20 subjects. The mean volume of the whole amygdala is 2041.71 ± 33.46 mm 3 (Mean ± SE) for left hemisphere, and is 2036.48 ± 45.24 mm 3 (Mean ± SE) for right hemisphere.

Probability Maps for the Functional Parcellation Results of the Amygdala

Probability maps for bilateral amygdala subregions, namely CM, LB, and SF, are shown in Figures 3a𠄼. Meanwhile, the obtained SSC maximum probability map (SSC-MPM) is shown in Figure 3d. For comparison, the cytoarchitectonic MPM is shown in Figure 3e as well. Besides, the relationship between the whole amygdala volume of SSC-MPM and the number of subjects who have a label (that is, share the same label) at one specific voxel is shown in Figure 4.

Figure 3. The probability map of amygdala's subregions obtained by the SSC-based brain parcellation method. (a) The probability map of the CM subregion, located at (�, 𢄧, �) in the MNI space (b) the probability map of the LB subregion, located at (�, 𢄤, �) in the MNI space (c) the probability map of the SF subregion, located at (�, 𢄥, �) in the MNI space (d) the maximum probability map of the SSC-based functional parcellation (e) the maximum probability map of the Julich cytoarchitectonic atlas obtained from the SPM Anatomy toolbox. (d,e) views are located at (�, 𢄥, �) in the MNI space. SSC, semi-supervised clustering CM, centromedial LB, laterobasal SF, superficial.

Figure 4. The volume variation of the whole amygdala and each subregion within the SSC-MPM with the change of the subjects' number that share the same voxel. The dotted and solid lines represent the left and right hemispheres, respectively. The volume of the whole amygdala and the CM, LB, as well as the SF subregion in the SSC-MPM was shown separately. SSC-MPM, semi-supervised clustering parcellation maximum probability map CM, centromedial LB, laterobasal SF, superficial.

Similarity Between the Functional and Cytoarchitectonic Parcellations

Table 2 shows the overlap degree between our functional parcellation results and the cytoarchitectonic map. The high mean Dice coefficients indicate our functionally parcellated subregions have a fine correspondence with the cytoarchitectonic subregions.

Table 2. Overlap between the SSC/NCUT parcellation and cytoarchitectonic parcellation. The overlap degree was measured using Dice Coefficients (Mean ± SE, N = 20).

Functional Homogeneity of the Parcellation Results

The mean modified SI value of SSC based brain parcellation method is 0.141 ± 0.004 (Mean ± SE) for left hemisphere, and is 0.147 ± 0.006 (Mean ± SE) for right hemisphere whereas the mean modified SI value of the cytoarchitectonic parcellation method is 0.126 ± 0.003 (Mean ± SE) for left hemisphere, and is 0.119 ± 0.005 (Mean ± SE) for right hemisphere. Results of paired t-tests have demonstrated that the modified SI values of the SSC based brain parcellation method are significantly larger than the cytoarchitectonic parcellation method with p = 0.001 and p = 6.82 × 10 𢄥 for the left and right amygdala, respectively (Figure 5).

Figure 5. Functional homogeneity comparison between the SSC-based amygdala parcellation and the Julich cytoarchitectonic atlas. The functional homogeneity is measured by the modified SI index. SSC, semi-supervised clustering SI, silhouette width.

Comparison With the State-of-the-Art Unsupervised Parcellation Method

Parcellation results of two representative subjects using SSC and NCUT methods are shown in Figure 6. Visual inspection shows that the parcellation results generated by SSC method are more spatially continuous than the NCUT method. Besides, the average entropy of SSC parcellation results for left and right amygdala is 0.290 and 0.428, respectively. While the average entropy of NCUT parcellation results for left and right amygdala is 0.746 and 0.839, respectively, which is significantly higher than the SSC partition. The comparison results demonstrate that the SSC method has better performance than the NCUT method in terms of cross subject consistency because of adopting prior information to guide the functional parcellation. In addition, the overlap degree between the parcellation results generated by the SSC method and cytoarchitectonic map is higher than the overlap degree between the parcellation results generated by the NCUT method and cytoarchitectonic map (Table 2).

Figure 6. Comparison between the SSC partition and NCUT partition. (a) Parcellation results of two representative subjects obtained by the SSC method. (b) Parcellation results of the same two subjects obtained by the NCUT method. SSC, Semi-supervised clustering NCUT, Normalized cut CM, centromedial LB, laterobasal SF, superficial.

Functional Connectivity Patterns of the Amygdala Parcellation Results

FC Patterns of the Amygdala's Subregions

As shown in Figure 7 and Supplementary Tables 1𠄳, the group-level FC patterns are distinctive among the parcellated three amygdala subregions, which is described in detail as follows.

Figure 7. Functional Connectivity patterns of the amygdala's subregions identified using semi-supervised clustering method. The colorbar shows the t-values of one sample t-tests. Clusters were identified using one sample t-tests, significant at a threshold of p < 0.001 and an extent threshold of p < 0.05 with cluster-level family-wise error correction. CM, centromedial LB, laterobasal SF, superficial L, left hemisphere R, right hemisphere.

The brain regions, showing significantly positive FC with the left CM, are mainly located in the bilateral striatum, thalamus, insula, supramarginal gyrus, part of anterior and middle cingulate gyrus, and part of the cerebellum. The right CM has significantly positive FC with the bilateral putamen, pallidum, insula, middle cingulate gyrus, left precentral gyrus, left postcentral gyrus, right supplementary motor cortex, and part of the cerebellum. Conversely, the brain regions, showing significantly negative FC with the CM, are mainly located in the angular gyrus, precuneus, middle frontal gyrus, and part of the superior frontal gyrus.

The brain regions, showing significantly positive FC with the left LB, are primarily located in the hippocampus, parahippocampal gyrus, inferior and middle temporal gyrus, temporal pole, cingulate gyrus, left insula, precentral gyrus, and the fusiform gyrus. Besides these brain areas, the right LB also has extensive positive FC with bilateral brain regions including the precuneus, postcentral gyrus, and the precentral gyrus. On the contrary, the LB has significantly negative FC with the brain regions including the angular gyrus, precuneus, middle cingulate gyrus, medial frontal gyrus, middle and superior frontal gyrus.

The left SF has significantly positive FC with brain regions including the hippocampus, parahippocampus, pallidum, left anterior and middle cingulate gyrus, part of right precentral gyrus, and part of the cerebellum. The brain regions, showing extensive positive FC with the right SF, are the hippocampus, parahippocampus, left middle cingulate gyrus, part of right precentral gyrus, and right precuneus. Conversely, the SF has significantly negative FC with brain regions including the angular gyrus, middle frontal gyrus, inferior orbital frontal gyrus, part of the left precuneus, and part of the middle temporal gyrus.

FC Difference Between the Ipsilateral Amygdala Subregions

The differences in FC patterns between the ipsilateral amygdala subregions are described as follows, which are shown in Tables 3𠄵.

Table 3. Function connectivity difference between centromedial (CM) and laterobasal (LB) amygdala subregion.

The CM has significantly stronger positive FC with the striatum, thalamus, and part of the cerebellum anterior lobe than the ipsilateral LB or SF subregions as shown in Tables 3, 4.

Table 4. Function connectivity difference between centromedial (CM) and superficial (SF) amygdala subregion.

As shown in Table 3, the left LB has significantly stronger FC with the bilateral fusiform gyrus, and part of the left middle occipital lobe than the left CM, while the right LB has higher FC with the bilateral fusiform gyrus, part of the bilateral middle occipital lobe, left inferior occipital lobe, left superior parietal lobe, and part of the right postcentral gyrus than the right CM. Compared with the SF, the LB has higher FC with the hippocampus, parahippocampus, middle temporal lobe, and part of the precentral gyrus (Table 5).

Table 5. Function connectivity difference between laterobasal (LB) and superficial (SF) amygdala subregion.

The SF has significantly higher FC with part of the occipital gyrus and the lingual gyrus than the CM (Table 4). Compared with the LB, the SF has higher FC with the striatum, the middle cingulate gyrus, and part of the precuneus (Table 5).

Asymmetry in FC of the Bilateral Amygdala Subregions

The asymmetry in FC of the bilateral amygdala's subregions was found in this study. Particularly, the left and right CM amygdala have significant difference in FC patterns (Figure 8), while no significant difference was found in bilateral LB amygdala and bilateral SF amygdala (P < 0.05, cluster level FWE correction). The left CM has higher FC with the left pallidum than the right CM, while the right CM has higher FC with the right hippocampus and the posterior cingulate cortex than the left CM.

Figure 8. Asymmetry in FC patterns of the left CM amygdala and right CM amygdala. (a) Brain regions that shows higher FC with the left CM than the right CM. (b) Brain regions that shows higher FC with the right CM than the left CM. The colorbar shows the t-values of paired t-tests. The clusters are identified using the paired t-test, significant at a threshold of p < 0.001 and an extent threshold of p < 0.05 with cluster-level family-wise error correction. FC, Functional Connectivity CM, centromedial.


3. Results

3.1 System stability

In any fMRI experiment it is important to establish that the system hardware is not introducing systematic noise into the analysis and that any system fluctuations are much less than the variations in signal to be measured. The Biomedical Informatics Research Network website offers publically available software for system stability measures (http://www.birncommunity.org/resources/tools/). This analysis results in a detailed output of the system stability.

Three major metrics can be used as a reliable measure of system stability. They are percentage signal fluctuation, signal drift and Radius of Decorrelation (RDC). A gel phantom was used for these measurements. The exact formula for the phantom is provided on the BIRN website. The percentage fluctuation is calculated as the standard deviations of the mean signal intensity of a time series divided by the mean signal intensity. The signal drift is the percentage signal variation of the time series (the percentage fluctuation is corrected for signal drift and is therefore a measure of shot to shot stability). The radius of decorrelation is a metric resulting from the Weisskoff stability test (Weisskoff, 1996). As a region of interest (ROI) gets larger, the standard deviation gets smaller through averaging of an increasing number of voxels. If the neighboring voxels are truly independent, then the standard deviation of a time series divided by the mean of the time series should be inversely proportional to the square root of the number of voxels in the ROI. The RDC is the size at which the statistical independence of the voxels is lost (Weisskoff, 1996). A 15 ml tube filled with gel as specified by the BIRN website was used in these experiments with the same acquisition parameters as the in vivo experiments. The percent fluctuation was 0.09% with a 1% signal drift and a RDC of 18 using a 20휠 pixel ROI. The BOLD activation is typically 1𠄲 % so the hardware fluctuations are significantly less than the neuronal blood flow modulations of the MR signal.

Figure 1 shows the results of the Independent Component Analysis (ICA) using the FSL’s MELODIC software (www.fmrib.ox.ac.uk/fsl/) from the gel phantom with the in vivo acquisition parameters. Only one component was detected which explained 0.64 % of the total variance. Also seen from Figure 1 , the temporal and spatial distributions were fairly uniform and did not indicate any clear region or frequency range as representative of the phantom scan.

3.2 In Vivo Results

We found evidence of a DMN for lateral cortical and medial cortical seeds (see Figure 2 and Table 1 ). Functional connectivity for these seeds included cortical regions, and, in the case of the right lateral and medial seed, subcortical regions such as the hippocampus, that are often included in the DMN. Evidence was less clear-cut for insular and prelimbic seeds ( Figure 3 ), which showed more circumscribed regional FC.


Abstract

The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.


Watch the video: Lesson9-session1Resting-state fMRI Analysis (January 2022).