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Hegedűs D, Grolmusz V. Robust circuitry-based scores of structural importance of human brain areas. PLoS One 2024; 19:e0292613. [PMID: 38232101 DOI: 10.1371/journal.pone.0292613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/25/2023] [Indexed: 01/19/2024] Open
Abstract
We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others.
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Affiliation(s)
- Dániel Hegedűs
- PIT Bioinformatics Group, Eötvös University, Budapest, Hungary
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, Budapest, Hungary
- Uratim Ltd., Budapest, Hungary
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Ramsey’s Theory Meets the Human Brain Connectome. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11099-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Keresztes L, Szögi E, Varga B, Grolmusz V. Discovering sex and age implicator edges in the human connectome. Neurosci Lett 2022; 791:136913. [DOI: 10.1016/j.neulet.2022.136913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/07/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022]
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Keresztes L, Szögi E, Varga B, Grolmusz V. Introducing and applying Newtonian blurring: an augmented dataset of 126,000 human connectomes at braingraph.org. Sci Rep 2022; 12:3102. [PMID: 35197486 PMCID: PMC8866411 DOI: 10.1038/s41598-022-06697-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 02/03/2022] [Indexed: 11/09/2022] Open
Abstract
Gaussian blurring is a well-established method for image data augmentation: it may generate a large set of images from a small set of pictures for training and testing purposes for Artificial Intelligence (AI) applications. When we apply AI for non-imagelike biological data, hardly any related method exists. Here we introduce the "Newtonian blurring" in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects from the public release of the Human Connectome Project, we first repeat a probabilistic weighted braingraph construction algorithm 10 times for describing the connections of distinct cerebral areas, then for every possible set of 7 of these graphs, delete the lower and upper extremes, and average the remaining 7 - 2 = 5 edge-weights for the data of each subject. This way we augment the 1053 graph-set to 120 [Formula: see text] 1053 = 126,360 graphs. In augmentation techniques, it is an important requirement that no artificial additions should be introduced into the dataset. Gaussian blurring and also this Newtonian blurring satisfy this goal. The resulting dataset of 126,360 graphs, each in 5 resolutions (i.e., 631,800 graphs in total), is freely available at the site https://braingraph.org/cms/download-pit-group-connectomes/ . Augmenting with Newtonian blurring may also be applicable in other non-image-related fields, where probabilistic processing and data averaging are implemented.
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Affiliation(s)
- László Keresztes
- PIT Bioinformatics Group, Eötvös University, 1117, Budapest, Hungary
| | - Evelin Szögi
- PIT Bioinformatics Group, Eötvös University, 1117, Budapest, Hungary
| | - Bálint Varga
- PIT Bioinformatics Group, Eötvös University, 1117, Budapest, Hungary
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, 1117, Budapest, Hungary.
- Uratim Ltd., 1118, Budapest, Hungary.
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Keresztes L, Szögi E, Varga B, Grolmusz V. Identifying super-feminine, super-masculine and sex-defining connections in the human braingraph. Cogn Neurodyn 2021; 15:949-959. [PMID: 34786030 PMCID: PMC8572280 DOI: 10.1007/s11571-021-09687-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 04/23/2021] [Accepted: 05/29/2021] [Indexed: 11/26/2022] Open
Abstract
For more than a decade now, we can discover and study thousands of cerebral connections with the application of diffusion magnetic resonance imaging (dMRI) techniques and the accompanying algorithmic workflow. While numerous connectomical results were published enlightening the relation between the braingraph and certain biological, medical, and psychological properties, it is still a great challenge to identify a small number of brain connections closely related to those conditions. In the present contribution, by applying the 1200 Subjects Release of the Human Connectome Project (HCP) and Support Vector Machines, we identify just 102 connections out of the total number of 1950 connections in the 83-vertex graphs of 1064 subjects, which-by a simple linear test-precisely, without any error determine the sex of the subject. Next, we re-scaled the weights of the edges-corresponding to the discovered fibers-to be between 0 and 1, and, very surprisingly, we were able to identify two graph edges out of these 102, such that, if their weights are both 1, then the connectome always belongs to a female subject, independently of the other edges. Similarly, we have identified 3 edges from these 102, whose weights, if two of them are 1 and one is 0, imply that the graph belongs to a male subject-again, independently of the other edges. We call the former 2 edges superfeminine and the first two of the 3 edges supermasculine edges of the human connectome. Even more interestingly, the edge, connecting the right Pars Triangularis and the right Superior Parietal areas, is one of the 2 superfeminine edges, and it is also the third edge, accompanying the two supermasculine connections if its weight is 0; therefore, it is also a "switching" edge. Identifying such edge-sets of distinction is the unprecedented result of this work. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11571-021-09687-w.
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Affiliation(s)
- László Keresztes
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary
| | - Evelin Szögi
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary
| | - Bálint Varga
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary
- Uratim Ltd., H-1118 Budapest, Hungary
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The braingraph.org database with more than 1000 robust human connectomes in five resolutions. Cogn Neurodyn 2021; 15:915-919. [PMID: 34603551 PMCID: PMC8448809 DOI: 10.1007/s11571-021-09670-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 02/03/2021] [Accepted: 02/13/2021] [Indexed: 01/18/2023] Open
Abstract
The human brain is the most complex object of study we encounter today. Mapping the neuronal-level connections between the more than 80 billion neurons in the brain is a hopeless task for science. By the recent advancement of magnetic resonance imaging (MRI), we are able to map the macroscopic connections between about 1000 brain areas. The MRI data acquisition and the subsequent algorithmic workflow contain several complex steps, where errors can occur. In the present contribution we describe and publish 1064 human connectomes, computed from the public release of the Human Connectome Project. Each connectome is available in 5 resolutions, with 83, 129, 234, 463 and 1015 anatomically labeled nodes. For error correction we follow an averaging and extreme value deleting strategy for each edge and for each connectome. The resulting 5320 braingraphs can be downloaded from the https://braingraph.org site. This dataset makes possible the access to this graphs for scientists unfamiliar with neuroimaging- and connectome-related tools: mathematicians, physicists and engineers can use their expertize and ideas in the analysis of the connections of the human brain. Brain scientists and computational neuroscientists also have a robust and large, multi-resolution set for connectomical studies.
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Szalkai B, Varga B, Grolmusz V. The Graph of Our Mind. Brain Sci 2021; 11:342. [PMID: 33800527 PMCID: PMC7998275 DOI: 10.3390/brainsci11030342] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/27/2021] [Accepted: 03/02/2021] [Indexed: 11/24/2022] Open
Abstract
Graph theory in the last two decades penetrated sociology, molecular biology, genetics, chemistry, computer engineering, and numerous other fields of science. One of the more recent areas of its applications is the study of the connections of the human brain. By the development of diffusion magnetic resonance imaging (diffusion MRI), it is possible today to map the connections between the 1-1.5 cm2 regions of the gray matter of the human brain. These connections can be viewed as a graph. We have computed 1015-vertex graphs with thousands of edges for hundreds of human brains from one of the highest quality data sources: the Human Connectome Project. Here we analyze the male and female braingraphs graph-theoretically and show statistically significant differences in numerous parameters between the sexes: the female braingraphs are better expanders, have more edges, larger bipartition widths, and larger vertex cover than the braingraphs of the male subjects. These parameters are closely related to the quality measures of highly parallel computer interconnection networks: the better expanding property, the large bipartition width, and the large vertex cover characterize high-quality interconnection networks. We apply the data of 426 subjects and demonstrate the statistically significant (corrected) differences in 116 graph parameters between the sexes.
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Affiliation(s)
- Balázs Szalkai
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary; (B.S.); (B.V.)
| | - Bálint Varga
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary; (B.S.); (B.V.)
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary; (B.S.); (B.V.)
- Uratim Ltd., H-1118 Budapest, Hungary
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