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Ling Q, Liu A, Li Y, McKeown MJ, Chen X. fMRI-based spatio-temporal parcellations of the human brain. Curr Opin Neurol 2024; 37:369-380. [PMID: 38804205 DOI: 10.1097/wco.0000000000001280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
PURPOSE OF REVIEW Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research. RECENT FINDINGS Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth". SUMMARY While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.
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Affiliation(s)
- Qinrui Ling
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Aiping Liu
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Yu Li
- Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Martin J McKeown
- Department of Medicine, University of British Columbia, Vancouver, Vancouver V6T2B5, Canada
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
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Zhao F, Wu Z, Wang L, Lin W, Li G. Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning. Med Image Anal 2024; 96:103193. [PMID: 38823362 PMCID: PMC11292586 DOI: 10.1016/j.media.2024.103193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/31/2024] [Accepted: 05/02/2024] [Indexed: 06/03/2024]
Abstract
Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions. Essentially, longitudinal cortical surface registration and parcellation are highly correlated tasks with inherently shared constraints on both spatial and temporal feature representations, which are unfortunately ignored in existing methods. To this end, we unprecedentedly propose a novel semi-supervised learning framework to exploit these inherent relationships from limited labeled data and extensive unlabeled data for more robust and consistent registration and parcellation of longitudinal cortical surfaces. Our method utilizes the spherical topology characteristic of cortical surfaces. It employs a spherical network to function as an encoder, which extracts high-level cortical features. Subsequently, we build two specialized decoders dedicated to the tasks of registration and parcellation, respectively. To extract more meaningful spatial features, we design a novel parcellation map similarity loss to utilize the relationship between registration and parcellation tasks, i.e., the parcellation map warped by the deformation field in registration should match the atlas parcellation map, thereby providing extra supervision for the registration task and augmented data for parcellation task by warping the atlas parcellation map to unlabeled surfaces. To enable temporally more consistent feature representation, we additionally enforce longitudinal consistency among longitudinal surfaces after registering them together using their concatenated features. Experiments on two longitudinal datasets of infants and adults have shown that our method achieves significant improvements on both registration/parcellation accuracy and longitudinal consistency compared to existing methods, especially in small and challenging cortical regions.
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Affiliation(s)
- Fenqiang Zhao
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA.
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Irastorza-Valera L, Soria-Gómez E, Benitez JM, Montáns FJ, Saucedo-Mora L. Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM). Biomimetics (Basel) 2024; 9:362. [PMID: 38921242 PMCID: PMC11202129 DOI: 10.3390/biomimetics9060362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- PIMM Laboratory, ENSAM–Arts et Métiers ParisTech, 151 Bd de l’Hôpital, 75013 Paris, France
| | - Edgar Soria-Gómez
- Achúcarro Basque Center for Neuroscience, Barrio Sarriena, s/n, 48940 Leioa, Spain;
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain
- Department of Neurosciences, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
| | - José María Benitez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
| | - Francisco J. Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave, Cambridge, MA 02139, USA
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Irastorza-Valera L, Benítez JM, Montáns FJ, Saucedo-Mora L. An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates. Biomimetics (Basel) 2024; 9:101. [PMID: 38392147 PMCID: PMC10886514 DOI: 10.3390/biomimetics9020101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/16/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024] Open
Abstract
The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- PIMM Laboratory, Arts et Métiers Institute of Technology, 151 Bd de l'Hôpital, 75013 Paris, France
| | - José María Benítez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
| | - Francisco J Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Liu S, Ye H, Yang B, Li M, Cao F. A joint parcellation and boundary network with multi-rate-shared dilated graph attention for cortical surface parcellation. Med Biol Eng Comput 2024; 62:537-549. [PMID: 37945794 DOI: 10.1007/s11517-023-02942-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Abstract
Cortical surface parcellation aims to segment the surface into anatomically and functionally significant regions, which are crucial for diagnosing and treating numerous neurological diseases. However, existing methods generally ignore the difficulty in learning labeling patterns of boundaries, hindering the performance of parcellation. To this end, this paper proposes a joint parcellation and boundary network (JPBNet) to promote the effectiveness of cortical surface parcellation. Its core is developing a multi-rate-shared dilated graph attention (MDGA) module and incorporating boundary learning into the parcellation process. The former, in particular, constructs a dilated graph attention strategy, extending the dilated convolution from regular data to irregular graph data. We fuse it with different dilated rates to extract context information in various scales by devising a shared graph attention layer. After that, a boundary enhancement module and a parcellation enhancement module based on graph attention mechanisms are built in each layer, forcing MDGA to capture informative and valuable features for boundary detection and parcellation tasks. Integrating MDGA, the boundary enhancement module, and the parcellation enhancement module at each layer to supervise boundary and parcellation information, an effective JPBNet is formed by stacking multiple layers. Experiments on the public dataset reveal that the proposed method outperforms comparison methods and performs well on boundaries for cortical surface parcellation.
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Affiliation(s)
- Siqi Liu
- College of Sciences, China Jiliang University, Hangzhou, 310018, China
| | - Hailiang Ye
- College of Sciences, China Jiliang University, Hangzhou, 310018, China.
| | - Bing Yang
- College of Sciences, China Jiliang University, Hangzhou, 310018, China
| | - Ming Li
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, 321004, China
| | - Feilong Cao
- College of Sciences, China Jiliang University, Hangzhou, 310018, China.
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Kim T, Chen D, Hornauer P, Emmenegger V, Bartram J, Ronchi S, Hierlemann A, Schröter M, Roqueiro D. Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks. Front Neuroinform 2023; 16:1032538. [PMID: 36713289 PMCID: PMC9874697 DOI: 10.3389/fninf.2022.1032538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/13/2022] [Indexed: 01/12/2023] Open
Abstract
Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.
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Affiliation(s)
- Taehoon Kim
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Dexiong Chen
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Philipp Hornauer
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Vishalini Emmenegger
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Julian Bartram
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Silvia Ronchi
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Andreas Hierlemann
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Manuel Schröter
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Damian Roqueiro
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
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