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Quesada J, Sathidevi L, Liu R, Ahad N, Jackson JM, Azabou M, Xiao J, Liding C, Jin M, Urzay C, Gray-Roncal W, Johnson EC, Dyer EL. MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2022; 35:5299-5314. [PMID: 38414814 PMCID: PMC10898440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.
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Affiliation(s)
| | | | - Ran Liu
- Georgia Institute of Technology
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Yelmen B, Decelle A, Ongaro L, Marnetto D, Tallec C, Montinaro F, Furtlehner C, Pagani L, Jay F. Creating artificial human genomes using generative neural networks. PLoS Genet 2021; 17:e1009303. [PMID: 33539374 PMCID: PMC7861435 DOI: 10.1371/journal.pgen.1009303] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/08/2020] [Indexed: 12/13/2022] Open
Abstract
Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.
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Affiliation(s)
- Burak Yelmen
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Paris, France
| | - Aurélien Decelle
- Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Paris, France
- Departamento de Física Téorica I, Universidad Complutense, Madrid, Spain
| | - Linda Ongaro
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | | | - Corentin Tallec
- Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Paris, France
| | - Francesco Montinaro
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Biology-Genetics, University of Bari, Bari, Italy
| | - Cyril Furtlehner
- Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Paris, France
| | - Luca Pagani
- Institute of Genomics, University of Tartu, Tartu, Estonia
- APE Lab, Department of Biology, University of Padova, Padova, Italy
| | - Flora Jay
- Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Paris, France
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Timonidis N, Bakker R, Tiesinga P. Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data. Neuroinformatics 2020; 18:611-626. [PMID: 32448958 PMCID: PMC7498447 DOI: 10.1007/s12021-020-09471-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.
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Affiliation(s)
- Nestor Timonidis
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands.
| | - Rembrandt Bakker
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands.,Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Wilhelm-Johnen-Strasse, 52425, Jülich, Germany
| | - Paul Tiesinga
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands
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Prasad JA, Balwani AH, Johnson EC, Miano JD, Sampathkumar V, De Andrade V, Fezzaa K, Du M, Vescovi R, Jacobsen C, Kording KP, Gürsoy D, Gray Roncal W, Kasthuri N, Dyer EL. A three-dimensional thalamocortical dataset for characterizing brain heterogeneity. Sci Data 2020; 7:358. [PMID: 33082340 PMCID: PMC7576781 DOI: 10.1038/s41597-020-00692-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 09/17/2020] [Indexed: 02/07/2023] Open
Abstract
Neural microarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures from the mouse brain . In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.
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Affiliation(s)
- Judy A Prasad
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
| | - Aishwarya H Balwani
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Erik C Johnson
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Joseph D Miano
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | - Kamel Fezzaa
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | - Ming Du
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | - Rafael Vescovi
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
- Department of Physics & Astronomy, Northwestern University, Evanston, IL, USA
| | - Konrad P Kording
- Department of Biomedical Engineering, University of Pennsylvania, Philadelpha, PA, USA
| | - Doga Gürsoy
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL, USA
| | | | | | - Eva L Dyer
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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Specht K. Current Challenges in Translational and Clinical fMRI and Future Directions. Front Psychiatry 2020; 10:924. [PMID: 31969840 PMCID: PMC6960120 DOI: 10.3389/fpsyt.2019.00924] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 11/20/2019] [Indexed: 12/15/2022] Open
Abstract
Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as resting-state fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within- and between-subject variability, which affects the reliability of both task-related, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.
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Affiliation(s)
- Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway
- Department of Education, UiT/The Arctic University of Norway, Tromsø, Norway
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