1
|
Caznok Silveira AC, Antunes ASLM, Athié MCP, da Silva BF, Ribeiro dos Santos JV, Canateli C, Fontoura MA, Pinto A, Pimentel-Silva LR, Avansini SH, de Carvalho M. Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders. Front Neurosci 2024; 18:1340345. [PMID: 38445254 PMCID: PMC10912403 DOI: 10.3389/fnins.2024.1340345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.
Collapse
Affiliation(s)
- Ana Clara Caznok Silveira
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | | | - Maria Carolina Pedro Athié
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Bárbara Filomena da Silva
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Camila Canateli
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Marina Alves Fontoura
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Allan Pinto
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Simoni Helena Avansini
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Murilo de Carvalho
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| |
Collapse
|
2
|
Okabe S. Recent advances in computational methods for measurement of dendritic spines imaged by light microscopy. Microscopy (Oxf) 2021; 69:196-213. [PMID: 32244257 DOI: 10.1093/jmicro/dfaa016] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 02/04/2020] [Accepted: 03/23/2020] [Indexed: 12/13/2022] Open
Abstract
Dendritic spines are small protrusions that receive most of the excitatory inputs to the pyramidal neurons in the neocortex and the hippocampus. Excitatory neural circuits in the neocortex and hippocampus are important for experience-dependent changes in brain functions, including postnatal sensory refinement and memory formation. Several lines of evidence indicate that synaptic efficacy is correlated with spine size and structure. Hence, precise and accurate measurement of spine morphology is important for evaluation of neural circuit function and plasticity. Recent advances in light microscopy and image analysis techniques have opened the way toward a full description of spine nanostructure. In addition, large datasets of spine nanostructure can be effectively analyzed using machine learning techniques and other mathematical approaches, and recent advances in super-resolution imaging allow researchers to analyze spine structure at an unprecedented level of precision. This review summarizes computational methods that can effectively identify, segment and quantitate dendritic spines in either 2D or 3D imaging. Nanoscale analysis of spine structure and dynamics, combined with new mathematical approaches, will facilitate our understanding of spine functions in physiological and pathological conditions.
Collapse
Affiliation(s)
- Shigeo Okabe
- Department of Cellular Neurobiology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| |
Collapse
|
3
|
Chen W, Liu M, Zhan Q, Tan Y, Meijering E, Radojevic M, Wang Y. Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:527-538. [PMID: 33055023 DOI: 10.1109/tmi.2020.3031289] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article.
Collapse
|
4
|
Liu M, Chen W, Wang C, Peng H. A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1923-1934. [PMID: 30668496 DOI: 10.1109/tmi.2019.2893117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.
Collapse
|
5
|
Reberger R, Dall'Oglio A, Jung CR, Rasia-Filho AA. Structure and diversity of human dendritic spines evidenced by a new three-dimensional reconstruction procedure for Golgi staining and light microscopy. J Neurosci Methods 2017; 293:27-36. [PMID: 28887132 DOI: 10.1016/j.jneumeth.2017.09.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 08/30/2017] [Accepted: 09/03/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND Different approaches aim to unravel detailed morphological features of neural cells. Dendritic spines are multifunctional units that reflect cellular connectivity, synaptic strength and plasticity. NEW METHOD A novel three-dimensional (3D) reconstruction procedure is introduced for visualization of dendritic spines from human postmortem brain tissue using brightfield microscopy. The segmentation model was based on thresholding the intensity values of the dendritic spine image along 'z' stacks. We used median filtering and removed false positives. Fine adjustments during image processing confirmed that the reconstructed image of the spines corresponded to the actual original data. RESULTS Examples are shown for the cortical amygdaloid nucleus and the CA3 hippocampal area. Structure of spine heads and necks was evaluated at different angles. Our 3D reconstruction images display dendritic spines either isolated or in clusters, in a continuum of shapes and sizes, from simple to more elaborated forms, including the presence of spinule and complex 'thorny excrescences'. COMPARISON WITH EXISTING METHODS The procedure has the advantages already described for the adapted "single-section" Golgi method, since it provides suitable results using human brains fixed in formalin for long time, is relatively easy, requires minimal equipment, and uses an algorithm for 3D reconstruction that provides high quality images and more precise morphological data. CONCLUSION The procedure described here allows the reliable visualization and study of human dendritic spines with broad applications for normal controls and pathological studies.
Collapse
Affiliation(s)
- Roman Reberger
- Friedrich Alexander Universität Erlangen-Nürnberg, Medical Engineering Program, Erlangen, Germany; Federal University of Rio Grande do Sul, Institute of Informatics, Porto Alegre, Brazil
| | - Aline Dall'Oglio
- Federal University of Health Sciences, Department of Basic Sciences/Physiology, Porto Alegre, Brazil
| | - Claudio R Jung
- Federal University of Rio Grande do Sul, Institute of Informatics, Porto Alegre, Brazil
| | - Alberto A Rasia-Filho
- Federal University of Health Sciences, Department of Basic Sciences/Physiology, Porto Alegre, Brazil; Federal University of Rio Grande do Sul, Neuroscience Program, Porto Alegre, Brazil.
| |
Collapse
|
6
|
Singh PK, Hernandez-Herrera P, Labate D, Papadakis M. Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks. Neuroinformatics 2017; 15:303-319. [DOI: 10.1007/s12021-017-9332-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
7
|
Bokota G, Magnowska M, Kuśmierczyk T, Łukasik M, Roszkowska M, Plewczynski D. Computational Approach to Dendritic Spine Taxonomy and Shape Transition Analysis. Front Comput Neurosci 2016; 10:140. [PMID: 28066226 PMCID: PMC5180374 DOI: 10.3389/fncom.2016.00140] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 12/05/2016] [Indexed: 12/16/2022] Open
Abstract
The common approach in morphological analysis of dendritic spines of mammalian neuronal cells is to categorize spines into subpopulations based on whether they are stubby, mushroom, thin, or filopodia shaped. The corresponding cellular models of synaptic plasticity, long-term potentiation, and long-term depression associate the synaptic strength with either spine enlargement or spine shrinkage. Although a variety of automatic spine segmentation and feature extraction methods were developed recently, no approaches allowing for an automatic and unbiased distinction between dendritic spine subpopulations and detailed computational models of spine behavior exist. We propose an automatic and statistically based method for the unsupervised construction of spine shape taxonomy based on arbitrary features. The taxonomy is then utilized in the newly introduced computational model of behavior, which relies on transitions between shapes. Models of different populations are compared using supplied bootstrap-based statistical tests. We compared two populations of spines at two time points. The first population was stimulated with long-term potentiation, and the other in the resting state was used as a control. The comparison of shape transition characteristics allowed us to identify the differences between population behaviors. Although some extreme changes were observed in the stimulated population, statistically significant differences were found only when whole models were compared. The source code of our software is freely available for non-commercial use. CONTACT d.plewczynski@cent.uw.edu.pl.
Collapse
Affiliation(s)
- Grzegorz Bokota
- Centre of New Technologies, University of WarsawWarsaw, Poland
| | - Marta Magnowska
- Nencki Institute of Experimental Biology, Polish Academy of SciencesWarsaw, Poland
| | - Tomasz Kuśmierczyk
- Department of Computer and Information Science, Norwegian University of Science and TechnologyTrondheim, Norway
| | - Michał Łukasik
- Department of Computer Science, University of SheffieldSheffield, UK
| | - Matylda Roszkowska
- Nencki Institute of Experimental Biology, Polish Academy of SciencesWarsaw, Poland
| | - Dariusz Plewczynski
- Centre of New Technologies, University of WarsawWarsaw, Poland
- Faculty of Pharmacy, Medical University of WarsawWarsaw, Poland
| |
Collapse
|
8
|
Detrez JR, Verstraelen P, Gebuis T, Verschuuren M, Kuijlaars J, Langlois X, Nuydens R, Timmermans JP, De Vos WH. Image Informatics Strategies for Deciphering Neuronal Network Connectivity. ADVANCES IN ANATOMY, EMBRYOLOGY, AND CELL BIOLOGY 2016; 219:123-48. [PMID: 27207365 DOI: 10.1007/978-3-319-28549-8_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Amongst the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardization of both image acquisition and image analysis, it has become possible to extract statistically relevant readouts from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.
Collapse
Affiliation(s)
- Jan R Detrez
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Peter Verstraelen
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Titia Gebuis
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - Marlies Verschuuren
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Jacobine Kuijlaars
- Neuroscience Department, Janssen Research and Development, Turnhoutseweg 30, 2340, Beerse, Belgium
- Laboratory for Cell Physiology, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan, 3590, Diepenbeek, Belgium
| | - Xavier Langlois
- Neuroscience Department, Janssen Research and Development, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Rony Nuydens
- Neuroscience Department, Janssen Research and Development, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Jean-Pierre Timmermans
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Winnok H De Vos
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium.
- Cell Systems and Cellular Imaging, Department Molecular Biotechnology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.
| |
Collapse
|
9
|
Quan T, Li J, Zhou H, Li S, Zheng T, Yang Z, Luo Q, Gong H, Zeng S. Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model. Sci Rep 2014; 4:4970. [PMID: 24829141 PMCID: PMC4021323 DOI: 10.1038/srep04970] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Accepted: 04/09/2014] [Indexed: 02/03/2023] Open
Abstract
Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology, and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution, and diversity in cell size, and morphology. We believe this method would speed drawing the neural circuits and boost brain studies.
Collapse
Affiliation(s)
- Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Mathematics and Statistics, Hubei University of Education, Wuhan 430205, China
- These authors contributed equally to this work
| | - Jing Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shiwei Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ting Zheng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhongqing Yang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| |
Collapse
|