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Liu Y, Ge E, Kang Z, Qiang N, Liu T, Ge B. Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI. Neuroimage 2024; 287:120519. [PMID: 38280690 DOI: 10.1016/j.neuroimage.2024.120519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/11/2024] [Accepted: 01/15/2024] [Indexed: 01/29/2024] Open
Abstract
Functional brain networks (FBNs) are spatial patterns of brain function that play a critical role in understanding human brain function. There are many proposed methods for mapping the spatial patterns of brain function, however they oversimplify the underlying assumptions of brain function and have various limitations such as linearity and independence. Additionally, current methods fail to account for the dynamic nature of FBNs, which limits their effectiveness in accurately characterizing these networks. To address these limitations, we present a novel deep learning and spatial-wise attention based model called Spatial-Temporal Convolutional Attention (STCA) to accurately model dynamic FBNs. Specifically, we train STCA in a self-supervised manner by utilizing a Convolutional Autoencoder to guide the STCA module in assigning higher attention weights to regions of functional activity. To validate the reliability of the results, we evaluate our approach on the HCP-task motor behavior dataset, the experimental results demonstrate that the STCA derived FBNs have higher spatial similarity with the templates and that the spatial similarity between the templates and the FBNs derived by STCA fluctuates with the task design over time, suggesting that STCA can reflect the dynamic changes of brain function, providing a powerful tool to better understand human brain function. Code is available at https://github.com/SNNUBIAI/STCAE.
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Affiliation(s)
- Yiheng Liu
- School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China
| | - Enjie Ge
- School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China
| | - Zili Kang
- School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China
| | - Ning Qiang
- School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China
| | - Tianming Liu
- School of Computing, University of Georgia, GA, USA
| | - Bao Ge
- School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China.
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2
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Oota SR, Yadav A, Dash A, Bapi RS, Sharma A. Attention-based fusion of multiple graphheat networks for structural to functional brain mapping. Sci Rep 2024; 14:1184. [PMID: 38216636 PMCID: PMC10786906 DOI: 10.1038/s41598-023-50408-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/19/2023] [Indexed: 01/14/2024] Open
Abstract
Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks and graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 1058 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On HCP data, we achieve a high Pearson correlation of 0.788 (Desikan-Killiany atlas with 87 regions) and 0.773 (AAL atlas with 86 regions). Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of the structure-function relation of the human brain.
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Affiliation(s)
- Subba Reddy Oota
- Computer Science, Inria Bordeaux, Bordeaux, France.
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India.
| | - Archi Yadav
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
| | - Arpita Dash
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
| | - Raju S Bapi
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
| | - Avinash Sharma
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
- CVIT Lab, International Institute of Information Technology (IIIT), Hyderabad, India
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3
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Ramezanian-Panahi M, Abrevaya G, Gagnon-Audet JC, Voleti V, Rish I, Dumas G. Generative Models of Brain Dynamics. Front Artif Intell 2022; 5:807406. [PMID: 35910192 PMCID: PMC9335006 DOI: 10.3389/frai.2022.807406] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/10/2022] [Indexed: 01/28/2023] Open
Abstract
This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
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Affiliation(s)
| | - Germán Abrevaya
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Instituto de Física de Buenos Aires (IFIBA), CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | | | - Vikram Voleti
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Irina Rish
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Guillaume Dumas
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
- Department of Psychiatry, CHU Sainte-Justine Research Center, Mila-Quebec AI Institute, Université de Montréal, Montréal, QC, Canada
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4
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Big Data in Cognitive Neuroscience: Opportunities and Challenges. BIG DATA ANALYTICS 2022. [DOI: 10.1007/978-3-031-24094-2_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
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5
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Wein S, Deco G, Tomé AM, Goldhacker M, Malloni WM, Greenlee MW, Lang EW. Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5573740. [PMID: 34135951 PMCID: PMC8177997 DOI: 10.1155/2021/5573740] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/06/2021] [Indexed: 12/12/2022]
Abstract
This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.
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Affiliation(s)
- Simon Wein
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, University Pompeu Fabra, Carrer Tanger, 122-140, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats, University Barcelona, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Ana Maria Tomé
- IEETA/DETI, University de Aveiro, Aveiro 3810-193, Portugal
| | - Markus Goldhacker
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Wilhelm M. Malloni
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Mark W. Greenlee
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Elmar W. Lang
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
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6
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Roy D, Uddin LQ. Atypical core-periphery brain dynamics in autism. Netw Neurosci 2021; 5:295-321. [PMID: 34189366 PMCID: PMC8233106 DOI: 10.1162/netn_a_00181] [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] [Accepted: 12/31/2020] [Indexed: 11/06/2022] Open
Abstract
The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.
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Affiliation(s)
- Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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Ma J, Wang Y, Liu B, Liu W. Accurately modeling the human brain functional correlations with hypergraph Laplacian. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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8
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Zhuang Y, Zhang Z, Tivarus M, Qiu X, Zhong J, Schifitto G. Whole-brain computational modeling reveals disruption of microscale brain dynamics in HIV infected individuals. Hum Brain Mapp 2020; 42:95-109. [PMID: 32941693 PMCID: PMC7721235 DOI: 10.1002/hbm.25207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/13/2020] [Accepted: 08/30/2020] [Indexed: 01/07/2023] Open
Abstract
MRI‐based neuroimaging techniques have been used to investigate brain injury associated with HIV‐infection. Whole‐brain cortical mean‐field dynamic modeling provides a way to integrate structural and functional imaging outcomes, allowing investigation of microscale brain dynamics. In this study, we adopted the relaxed mean‐field dynamic modeling to investigate structural and functional connectivity in 42 HIV‐infected subjects before and after 12‐week of combination antiretroviral therapy (cART) and compared them with 46 age‐matched healthy subjects. Microscale brain dynamics were modeled by a set of parameters including two region‐specific microscale brain properties, recurrent connection strengths, and subcortical inputs. We also analyzed the relationship between the model parameters (i.e., the recurrent connection and subcortical inputs) and functional network topological characterizations, including smallworldness, clustering coefficient, and network efficiency. The results show that untreated HIV‐infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement.
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Affiliation(s)
- Yuchuan Zhuang
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
| | - Zhengwu Zhang
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.,Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
| | - Madalina Tivarus
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA.,Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA
| | - Jianhui Zhong
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA.,Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA
| | - Giovanni Schifitto
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
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beim Graben P, Jimenez-Marin A, Diez I, Cortes JM, Desroches M, Rodrigues S. Metastable Resting State Brain Dynamics. Front Comput Neurosci 2019; 13:62. [PMID: 31551744 PMCID: PMC6743347 DOI: 10.3389/fncom.2019.00062] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/23/2019] [Indexed: 12/26/2022] Open
Abstract
Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation-BOLD-signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.
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Affiliation(s)
- Peter beim Graben
- Communication Engineering, Institute of Electrical Engineering and Information Science, Brandenburg University of Technology Cottbus – Senftenberg, Cottbus, Germany
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
- Neurology Department, Harvard Medical School, Boston, MA, United States
- Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Ikerbasque - the Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| | - Mathieu Desroches
- MathNeuro Team, Inria Sophia Antipolis Méditerranée, Valbonne, France
- Université Côte d'Azur, Nice, France
| | - Serafim Rodrigues
- Ikerbasque - the Basque Foundation for Science, Bilbao, Spain
- Mathematical, Computational and Experimental Neuroscience, Basque Center for Applied Mathematics, Bilbao, Spain
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