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Etemadyrad N, Gao Y, Li Q, Guo X, Krueger F, Lin Q, Qiu D, Zhao L. Functional Connectivity Prediction With Deep Learning for Graph Transformation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:4862-4875. [PMID: 36037449 DOI: 10.1109/tnnls.2022.3197337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Inferring resting-state functional connectivity (FC) from anatomical brain wiring, known as structural connectivity (SC), is of enormous significance in neuroscience for understanding biological neuronal networks and treating mental diseases. Both SC and FC are networks where the nodes are brain regions, and in SC, the edges are the physical fiber nerves among the nodes, while in FC, the edges are the nodes' coactivation relations. Despite the importance of SC and FC, until very recently, the rapidly growing research body on this topic has generally focused on either linear models or computational models that rely heavily on heuristics and simple assumptions regarding the mapping between FC and SC. However, the relationship between FC and SC is actually highly nonlinear and complex and contains considerable randomness; additional factors, such as the subject's age and health, can also significantly impact the SC-FC relationship and hence cannot be ignored. To address these challenges, here, we develop a novel SC-to-FC generative adversarial network (SF-GAN) framework for mapping SC to FC, along with additional metafeatures based on a newly proposed graph neural network-based generative model that is capable of learning the stochasticity. Specifically, a new graph-based conditional generative adversarial nets model is proposed, where edge convolution layers are leveraged to encode the graph patterns in the SC in the form of a graph representation. New edge deconvolution layers are then utilized to decode the representation back to FC. Additional metafeatures of subjects' profile information are integrated into the graph representation with newly designed sparse-regularized layers that can automatically select features that impact FC. Finally, we have also proposed new post hoc explainer of our SF-GAN, which can identify which subgraphs in SC strongly influence which subgraphs in FC by a new multilevel edge-correlation-guided graph clustering problem. The results of experiments conducted to test the new model confirm that it significantly outperforms existing state-of-the-art methods, with additional interpretability for identifying important metafeatures and subgraphs.
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Nozais V, Theaud G, Descoteaux M, Thiebaut de Schotten M, Petit L. Improved Functionnectome by dissociating the contributions of white matter fiber classes to functional activation. Brain Struct Funct 2023; 228:2165-2177. [PMID: 37804431 DOI: 10.1007/s00429-023-02714-y] [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: 05/10/2023] [Accepted: 09/19/2023] [Indexed: 10/09/2023]
Abstract
Integrating the underlying brain circuit's structural and functional architecture is required to explore the functional organization of cognitive networks. In that regard, we recently introduced the Functionnectome. This structural-functional method combines an fMRI acquisition with tractography-derived white matter connectivity data to map cognitive processes onto the white matter. However, this multimodal integration faces three significant challenges: (1) the necessarily limited overlap between tractography streamlines and the grey matter, which may reduce the amount of functional signal associated with the related structural connectivity; (2) the scrambling effect of crossing fibers on functional signal, as a single voxel in such regions can be structurally connected to several cognitive networks with heterogeneous functional signals; and (3) the difficulty of interpretation of the resulting cognitive maps, as crossing and overlapping white matter tracts can obscure the organization of the studied network. In the present study, we tackled these problems by developing a streamline-extension procedure and dividing the white matter anatomical priors between association, commissural, and projection fibers. This approach significantly improved the characterization of the white matter involvement in the studied cognitive processes. The new Functionnectome priors produced are now readily available, and the analysis workflow highlighted here should also be generalizable to other structural-functional approaches. We improved the Functionnectome approach to better study the involvement of white matter in brain function by separating the analysis of the three classes of white matter fibers (association, commissural, and projection fibers). This step successfully clarified the activation maps and increased their statistical significance.
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Affiliation(s)
- Victor Nozais
- Groupe d'Imagerie Neurofonctionnelle - Institut des Maladies Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA, Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case 28, 33076, Bordeaux Cedex, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc, Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle - Institut des Maladies Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA, Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case 28, 33076, Bordeaux Cedex, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle - Institut des Maladies Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA, Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case 28, 33076, Bordeaux Cedex, France.
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3
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Yang H, Yao X, Zhang H, Meng C, Biswal B. Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification. Brain Struct Funct 2023; 228:1755-1769. [PMID: 37572108 DOI: 10.1007/s00429-023-02689-w] [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: 04/04/2023] [Accepted: 07/16/2023] [Indexed: 08/14/2023]
Abstract
As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Xing Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, 607 Fenster Hall, Newark, NJ, 07102, USA.
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Nozais V, Forkel SJ, Petit L, Talozzi L, Corbetta M, Thiebaut de Schotten M, Joliot M. Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain. Commun Biol 2023; 6:726. [PMID: 37452124 PMCID: PMC10349117 DOI: 10.1038/s42003-023-05107-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.
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Affiliation(s)
- Victor Nozais
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Donders Institute for Brain Cognition Behaviour, Radboud University, Nijmegen, the Netherlands
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Departments of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
| | - Laurent Petit
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France
| | - Lia Talozzi
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Maurizio Corbetta
- Department of Neuroscience, Venetian Institute of Molecular Medicine and Padova Neuroscience Center, University of Padua, Padova, PD, 32122, Italy
| | - Michel Thiebaut de Schotten
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Marc Joliot
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France.
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Wu L, Calhoun V. Joint connectivity matrix independent component analysis: Auto-linking of structural and functional connectivities. Hum Brain Mapp 2023; 44:1533-1547. [PMID: 36420833 PMCID: PMC9921228 DOI: 10.1002/hbm.26155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/25/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data-driven parcellation and automated-linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion-weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data-driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity-based multimodal data fusion in brain.
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Affiliation(s)
- Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA
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Yang H, Zhang H, Meng C, Wohlschläger A, Brandl F, Di X, Wang S, Tian L, Biswal B. Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study. Hum Brain Mapp 2022; 43:3792-3808. [PMID: 35475569 PMCID: PMC9294298 DOI: 10.1002/hbm.25884] [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: 12/09/2021] [Revised: 03/05/2022] [Accepted: 04/05/2022] [Indexed: 11/09/2022] Open
Abstract
The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Afra Wohlschläger
- Department of Neuroradiology, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Felix Brandl
- Department of Psychiatry, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Xin Di
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Shuai Wang
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Lin Tian
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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7
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Pirondini E, Kinany N, Sueur CL, Griffis JC, Shulman GL, Corbetta M, Ville DVD. Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions. Neuroimage 2022; 255:119201. [PMID: 35405342 DOI: 10.1016/j.neuroimage.2022.119201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/24/2022] [Accepted: 04/07/2022] [Indexed: 02/06/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.
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Affiliation(s)
- Elvira Pirondini
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Department of Physical Medicine and Rehabilitation, University of Pittsburgh; Pittsburgh, PA, USA; Rehabilitation Neural Engineering Laboratories, University of Pittsburgh; Pittsburgh, PA, USA; Department of BioEngineering, University of Pittsburgh; Pittsburgh, PA, USA.
| | - Nawal Kinany
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineerin, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Cécile Le Sueur
- Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Neuroscience and Padua Neuroscience Center, University of Padua; Padua, Italy; Venetian Institute of Molecular Medicine (VIMM); Padua, Italy
| | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland.
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8
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Ricchi I, Tarun A, Maretic HP, Frossard P, Van De Ville D. Dynamics of Functional Network Organization Through Graph Mixture Learning. Neuroimage 2022; 252:119037. [PMID: 35219859 DOI: 10.1016/j.neuroimage.2022.119037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 12/29/2021] [Accepted: 02/23/2022] [Indexed: 12/12/2022] Open
Abstract
Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally-averaged timecourses. We use the Graph Laplacian Mixture Model (GLMM), a generative model that treats functional data as a collection of signals expressed on multiple underlying graphs. By exploiting covariance between activity of brain regions, these graphs can be learned without resorting to structural information. To validate the proposed technique, we first apply it to task fMRI with a known experimental paradigm. The probability of each graph to occur at each time-point is found to be consistent with the task timing, while the spatial patterns associated to each epoch of the task are in line with previously established activation patterns using classical regression analysis. We further on apply the technique to resting state data, which leads to extracted graphs that correspond to well-known brain functional activation patterns. The GLMM allows to learn graphs entirely from the functional activity that, in practice, turn out to reveal high degrees of similarity to the structural connectome. The Default Mode Network (DMN) is always captured by the algorithm in the different tasks and resting state data. Therefore, we compare the states corresponding to this network within themselves and with structure. Overall, this method allows us to infer relevant functional brain networks without the need of structural connectome information. Moreover, we overcome the limitations of windowing the time sequences by feeding the GLMM with the whole functional signal and neglecting the focus on sub-portions of the signals.
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Affiliation(s)
- Ilaria Ricchi
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1202, Switzerland; School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland.
| | - Anjali Tarun
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1202, Switzerland
| | - Hermina Petric Maretic
- School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland
| | - Pascal Frossard
- School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1202, Switzerland
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9
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Nozais V, Forkel SJ, Foulon C, Petit L, Thiebaut de Schotten M. Functionnectome as a framework to analyse the contribution of brain circuits to fMRI. Commun Biol 2021; 4:1035. [PMID: 34475518 PMCID: PMC8413369 DOI: 10.1038/s42003-021-02530-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/02/2021] [Indexed: 02/07/2023] Open
Abstract
In recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel our understanding of the brain's functional signatures and dysfunctions. We developed a method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionnectome combines the functional signal from fMRI with white matter circuits' anatomy to unlock and chart the first maps of functional white matter. To showcase this method's versatility, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing datasets and beyond task fMRI.
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Affiliation(s)
- Victor Nozais
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
| | - Stephanie J Forkel
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
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10
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Siffredi V, Farouj Y, Tarun A, Anderson V, Wood AG, McIlroy A, Leventer RJ, Spencer-Smith MM, Ville DVD. Large-scale functional network dynamics in human callosal agenesis: Increased subcortical involvement and preserved laterality. Neuroimage 2021; 243:118471. [PMID: 34455063 DOI: 10.1016/j.neuroimage.2021.118471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/20/2021] [Accepted: 08/11/2021] [Indexed: 10/20/2022] Open
Abstract
In the human brain, the corpus callosum is the major white-matter commissural tract enabling the transmission of sensory-motor, and higher level cognitive information between homotopic regions of the two cerebral hemispheres. Despite developmental absence (i.e., agenesis) of the corpus callosum (AgCC), functional connectivity is preserved, including interhemispheric connectivity. Subcortical structures have been hypothesised to provide alternative pathways to enable this preservation. To test this hypothesis, we used functional Magnetic Resonance Imaging (fMRI) recordings in children with AgCC and typically developing children, and a time-resolved approach to retrieve temporal characteristics of whole-brain functional networks. We observed an increased engagement of the cerebellum and amygdala/hippocampus networks in children with AgCC compared to typically developing children. There was little evidence that laterality of activation networks was affected in AgCC. Our findings support the hypothesis that subcortical structures play an essential role in the functional reconfiguration of the brain in the absence of a corpus callosum.
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Affiliation(s)
- Vanessa Siffredi
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland.
| | - Younes Farouj
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Anjali Tarun
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Vicki Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Psychological Sciences, University of Melbourne, Melbourne, Australia; Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Amanda G Wood
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham, B4 7ET UK; School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, Victoria, Australia
| | - Alissandra McIlroy
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Richard J Leventer
- Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia; Department of Neurology, Royal Children's Hospital, Melbourne, Australia
| | - Megan M Spencer-Smith
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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11
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Abramian D, Larsson M, Eklund A, Aganj I, Westin CF, Behjat H. Diffusion-informed spatial smoothing of fMRI data in white matter using spectral graph filters. Neuroimage 2021; 237:118095. [PMID: 34000402 PMCID: PMC8356807 DOI: 10.1016/j.neuroimage.2021.118095] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/07/2021] [Accepted: 04/13/2021] [Indexed: 12/15/2022] Open
Abstract
Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detectability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.
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Affiliation(s)
- David Abramian
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Martin Larsson
- Centre of Mathematical Sciences, Lund University, Lund, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden; Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Hamid Behjat
- Department of Biomedical Engineering, Lund University, Lund, Sweden; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
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12
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Behjat H, Aganj I, Abramian D, Eklund A, Westin CF. CHARACTERIZATION OF SPATIAL DYNAMICS OF FMRI DATA IN WHITE MATTER USING DIFFUSION-INFORMED WHITE MATTER HARMONICS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2021; 2021:1586-1590. [PMID: 34084267 DOI: 10.1109/isbi48211.2021.9433958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this work, we leverage the Laplacian eigenbasis of voxel-wise white matter (WM) graphs derived from diffusion-weighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.
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Affiliation(s)
- Hamid Behjat
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.,Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.,Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - David Abramian
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.,Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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13
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Griffa A, Bommarito G, Assal F, Herrmann FR, Van De Ville D, Allali G. Dynamic functional networks in idiopathic normal pressure hydrocephalus: Alterations and reversibility by CSF tap test. Hum Brain Mapp 2020; 42:1485-1502. [PMID: 33296129 PMCID: PMC7927299 DOI: 10.1002/hbm.25308] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/02/2020] [Accepted: 11/26/2020] [Indexed: 12/19/2022] Open
Abstract
Idiopathic Normal Pressure Hydrocephalus (iNPH)—the leading cause of reversible dementia in aging—is characterized by ventriculomegaly and gait, cognitive and urinary impairments. Despite its high prevalence estimated at 6% among the elderlies, iNPH remains underdiagnosed and undertreated due to the lack of iNPH‐specific diagnostic markers and limited understanding of pathophysiological mechanisms. INPH diagnosis is also complicated by the frequent occurrence of comorbidities, the most common one being Alzheimer's disease (AD). Here we investigate the resting‐state functional magnetic resonance imaging dynamics of 26 iNPH patients before and after a CSF tap test, and of 48 normal older adults. Alzheimer's pathology was evaluated by CSF biomarkers. We show that the interactions between the default mode, and the executive‐control, salience and attention networks are impaired in iNPH, explain gait and executive disturbances in patients, and are not driven by AD‐pathology. In particular, AD molecular biomarkers are associated with functional changes distinct from iNPH functional alterations. Finally, we demonstrate a partial normalization of brain dynamics 24 hr after a CSF tap test, indicating functional plasticity mechanisms. We conclude that functional changes involving the default mode cross‐network interactions reflect iNPH pathophysiological mechanisms and track treatment response, possibly contributing to iNPH differential diagnosis and better clinical management.
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Affiliation(s)
- Alessandra Griffa
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Institute of Bioengineering, Center of Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Giulia Bommarito
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Institute of Bioengineering, Center of Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Frédéric Assal
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center of Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Gilles Allali
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York, USA
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14
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Qi X, Arfanakis K. Regionconnect: Rapidly extracting standardized brain connectivity information in voxel-wise neuroimaging studies. Neuroimage 2020; 225:117462. [PMID: 33075560 PMCID: PMC7811895 DOI: 10.1016/j.neuroimage.2020.117462] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/03/2020] [Accepted: 10/09/2020] [Indexed: 02/06/2023] Open
Abstract
Reporting white matter findings in voxel-wise neuroimaging studies typically lacks specificity in terms of brain connectivity. Therefore, the purpose of this work was to develop an approach for rapidly extracting standardized brain connectivity information for white matter regions with significant findings in voxel-wise neuroimaging studies. The new approach was named regionconnect and is based on precalculated average healthy adult brain connectivity information stored in standard space in a fashion that allows fast retrieval and integration. Towards this goal, the present work first generated and evaluated the white matter connectome of the IIT Human Brain Atlas v.5.0. It was demonstrated that the edges of the atlas connectome are representative of those of individual participants of the Human Connectome Project in terms of the spatial organization of streamlines and spatial patterns of track-density. Next, the new white matter connectome was used to develop multi-layer, connectivity-based labels for each white matter voxel of the atlas, consistent with the fact that each voxel may contain axons from multiple connections. The regionconnect algorithm was then developed to rapidly integrate information contained in the multi-layer labels across voxels of a white matter region and to generate a list of the most probable connections traversing that region. Usage of regionconnect does not require high angular resolution diffusion MRI or any MRI data. The regionconnect algorithm as well as the white matter tractogram and connectome, multi-layer, connectivity-based labels, and associated resources developed for the IIT Human Brain Atlas v.5.0 in this work are available at www.nitrc.org/projects/iit. An interactive, online version of regionconnect is also available at www.iit.edu/~mri.
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Affiliation(s)
- Xiaoxiao Qi
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, United States.
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15
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Wang T, Wilkes DM, Li M, Wu X, Gore JC, Ding Z. Hemodynamic Response Function in Brain White Matter in a Resting State. Cereb Cortex Commun 2020; 1:tgaa056. [PMID: 33073237 PMCID: PMC7552822 DOI: 10.1093/texcom/tgaa056] [Citation(s) in RCA: 8] [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: 04/30/2020] [Revised: 08/24/2020] [Accepted: 08/24/2020] [Indexed: 11/14/2022] Open
Abstract
The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.
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Affiliation(s)
- Ting Wang
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
| | - D Mitchell Wilkes
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - Muwei Li
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Zhaohua Ding
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
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