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opencc-by-4.0Oct 2023View details →

T1-weighted brain MRI acquired from awake and unrestrained sheep

This dataset contains T1-weighted brain MRI images acquired from 6 awake sheep, 1 anesthetized sheep and the MRI acquisition parameters. When using this data please cite: Pluchot, C., Adriaensen, H., Parias, C. et al. Sheep (Ovis aries) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. Behav Res (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a>  Note: A "Version v2" was created because the original "13332_anesthetized_T1.nii" file was corrupted.

opencc-by-4.0Oct 2019View details →

CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS

Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups.  The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI). We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.

opencc-by-4.0Jul 2023View details →

Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes

Abstract Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks. Data Description This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold.  As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those. Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets. Paper & Code The original paper for this article can be accessed here: <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> To access the codes relevant for this project, please access the project GitHub Repos: <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> If using this work, please cite it based on the above paper, or using the following BibTex: <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre>   Data Access The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi Funding ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).

opencc-by-4.0Jul 2023View details →

Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction

This dataset contains the data used in the paper <blockquote> de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32 </blockquote> A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference. This paper studied the impact of the regularization parameter \(\alpha \) on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters \(\alpha \). Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.

OpenNeuro44/100

Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 2

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openCC0Jan 2019View details →

OpenNeuro44/100

Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 3

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openCC0Jan 2019View details →

OpenNeuro44/100

7 Tesla MRI of the ex vivo human brain at 100 micron resolution

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openCC0Jan 2019View details →

zenodo44/100

Human Brain MRI Template and Myelin Atlas

The structural template, quantitative myelin water imaging atlases, tissue segmentations, and regions of interest (ROIs) generated and analyzed for An atlas for human brain myelin content throughout the adult life span <a href="https://www.nature.com/articles/s41598-020-79540-3">https://www.nature.com/articles/s41598-020-79540-3</a>

opencc-by-4.0Oct 2020View details →

zenodo44/100

Quail (Coturnix japonica) brain MRI template and whole-brain atlas

A population average MRI brain template computed from 20 male Japanese Quails and a manually segmented atlas containing 194 regions.  In this Version 2: <ul> <li>the nomenclature in the file siwiaszczyk_LUT-ITK-SNAP_v2.txt was updated</li> <li>one slice of one region was completed in the file siwiaszczyk_atlas_v2.nii.gz.</li> </ul>

opencc-by-4.0Apr 2021View details →

zenodo44/100

MRI Brain Template and Atlas of the Mouse Lemur Primate Microcebus murinus

MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus. Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; <a href="https://sammba-mri.github.io/">https://sammba-mri.github.io/</a>). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid. if used for publication please cite:  A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy Nachiket A Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq Jan 2019 NeuroImage 185, 85-95 DOI: 10.1016/J.NEUROIMAGE.2018.10.010 <a href="https://www.sciencedirect.com/science/article/pii/S1053811918319694">https://www.sciencedirect.com/science/article/pii/S1053811918319694</a>

opencc-by-4.0Oct 2023View details →

OpenNeuro40/100

CAMRI Mouse Brain MRI Data

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openCC0Jan 2021View details →

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CAMRI Rat Brain MRI Data

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openCC0Jan 2021View details →

opencc-by-4.0Nov 2023View details →

Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control

This dataset contains the data and model used in the paper <blockquote> Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." <a href="https://arxiv.org/abs/2311.04780">arXiv preprint arXiv:2311.04780</a> (2023). </blockquote> If you found this dataset useful or used it in your research, please cite this reference. This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases. These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on <a href="https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc">GitHub.</a> Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the <a href="https://arxiv.org/abs/2311.04780">paper</a>. The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the<a href="https://www.synapse.org/#!Synapse:syn25649159/wiki/610007"> Fetal Tissue Annotation Challenge</a> (FeTA). Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023. [1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nature methods 18.2 (2021): 203-211.

dryad40/100

Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.

opencc-zeroSep 2022View details →

Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

This paper mainly focuses on automated detection of White Matter Lesions of brain using fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the representation of an image into a meaningful image and makes it easier to analyze. As a first step, MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient image enhancement techniques. The pre-processed image is subjected to clustering. The clustering algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC) and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to initialization and are easily trapped in local optima. In order to obtain an optimized result, the clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. Figure 2 represents overall process of automatic detection of WMLs of brain. Since MS lesions present different characteristics from lesions in elderly individuals there are many clustering models to determine the accuracy but those methods are not directly applicable to predict the accurate lesions because of the decreased contrast between White Matter and Grey Matter in elderly people. The proposed clustering models are derived by extending the objective functions of FCM and Possibilistic clustering with a Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches.

Figure 9. Performance analysis of FCM-PSO, GPC-PSO and GFCM-PSO-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

All scans obtained from different image clustering models are manually ranked based on values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO) are applied on a dataset of 208 images and ranking is done in terms of under detected, over detected, properly detected as shown in figure 8 and figure 9. The number of images detected properly in GFCM is comparatively high than FCM and GPC. The optimized result of GFMC provides accurate detection of WMLs and it properly detects 195 images.

Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

The performance of WML quantification is evaluated using clustering algorithms. When the image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is clustered using the effective clustering algorithms. Figure 3 represents the input image for WML detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4 depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to clustering. Three clustering models are proposed to provide accurate results. All scans obtained from different image clustering models are manually ranked based on values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO) are applied on a dataset of 208 images and ranking is done in terms of under detected, over detected, properly detected as shown in figure 8 and figure 9. The number of images detected properly in GFCM is comparatively high than FCM and GPC.