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Li D, Zhang Y, Lai L, Hao J, Wang X, Zhao Z, Cui X, Xiang J, Wang B. The impact of indirect structure on functional connectivity in schizophrenia using a multiplex brain network. J Psychiatr Res 2024; 179:257-265. [PMID: 39321524 DOI: 10.1016/j.jpsychires.2024.09.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 08/21/2024] [Accepted: 09/19/2024] [Indexed: 09/27/2024]
Abstract
It is known that abnormal functional connectivity (FC) in schizophrenia (SZ) is closely related to structural connectivity (SC). We speculate that indirect SC also have an impact on FC in SZ patients. Conventional single-layer network has limitations for studying the relationship between indirect SC and FC. Thus, this study constructed a multiplex network based on structural connectivity and functional connectivity (SC-FC). The SC-FC bandwidth and SC-FC cost are used to analyze the impact of indirect SC on FC. Moreover, this paper proposed mediation ability, mediation cost, mediated strength and mediated cost to quantify the effects of mediator nodes and mediated nodes on indirect SC. The results show that SZ patients exhibit lower SC-FC bandwidth and SC-FC cost compared to healthy controls (HC), which could be caused by the limbic and subcortical network (LSN), default mode network (DMN) and visual network (VN). The mediator and mediated nodes in indirect SC of SZ patients also showed diminished effects. These findings suggest that functional communication ability and cost in SZ patients are influenced by indirect SC. This study provides new perspectives for understanding the relationship between indirect SC and FC, and provides strong evidence for interpreting the physiological mechanisms of SZ patients.
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Affiliation(s)
- Dandan Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Yating Zhang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Luyao Lai
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jianchao Hao
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xuedong Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhenyu Zhao
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xiaohong Cui
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
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2
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Long H, Chen Z, Xu X, Zhou Q, Fang Z, Lv M, Yang XH, Xiao J, Sun H, Fan M. Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder. Neuroimage 2024; 297:120722. [PMID: 38971483 DOI: 10.1016/j.neuroimage.2024.120722] [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: 03/25/2024] [Revised: 06/24/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024] Open
Abstract
Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/β-hydrolase domain-containing 6 (ABHD6), β 1,3-N-acetylglucosaminyltransferase-9(β3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, β3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.
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Affiliation(s)
- Haixia Long
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Zihao Chen
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xinli Xu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Qianwei Zhou
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Zhaolin Fang
- Network Information Center, Zhejiang University of Technology, Hangzhou 310023, China
| | - Mingqi Lv
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xu-Hua Yang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jie Xiao
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Hui Sun
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
| | - Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.
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3
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Li WX, Lin QH, Zhang CY, Han Y, Calhoun VD. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data. Front Neurosci 2024; 18:1423014. [PMID: 39050665 PMCID: PMC11266018 DOI: 10.3389/fnins.2024.1423014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 06/21/2024] [Indexed: 07/27/2024] Open
Abstract
Background Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information. Methods We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude-magnitude and phase-phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude-phase and phase-magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals. Results Simulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2-20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features. Conclusion The proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.
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Affiliation(s)
- Wei-Xing Li
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Qiu-Hua Lin
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Chao-Ying Zhang
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Yue Han
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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4
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Falcó-Roget J, Cacciola A, Sambataro F, Crimi A. Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations. Commun Biol 2024; 7:419. [PMID: 38582867 PMCID: PMC10998892 DOI: 10.1038/s42003-024-06119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/28/2024] [Indexed: 04/08/2024] Open
Abstract
Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.
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Affiliation(s)
- Joan Falcó-Roget
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | - Fabio Sambataro
- Department of Neuroscience, University of Padova, Padua, Italy
| | - Alessandro Crimi
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
- Faculty of Computer Science, AGH University of Krakow, Kraków, Poland.
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5
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Gao Q, Luo N, Liang M, Zhou W, Li Y, Li R, Hu X, Zou T, Wang X, Yu J, Leng J, Chen H. A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:4974-4984. [PMID: 36099216 DOI: 10.1109/tnnls.2022.3202535] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. The complex brain network is also hierarchically constructed, which is particularly suited to facilitate segregated functions and the global integration of the segregated functions. Therefore, it is of great interest to explore new approach to model the hierarchical architecture of the directed network. In the present study, we proposed a new approach, namely, stepwise multivariate Granger causality (SMGC), considering both the directed and hierarchical features of brain functional network to explore the stepwise causal relationship in the network. The simulation study demonstrated that the diverse and complex hierarchical organization could be embedded in the apparently simple directed network. The proposed SMGC method could capture the multiple hierarchy of the directed network. When applying to the real functional magnetic resonance imaging (fMRI) datasets, the core triple resting-state networks in human brain showed within-network directed connections in the first-level directed network and rich and diverse between-network pathways in the second-level hierarchical network. The default mode network (DMN) had a prominent role in the resting-state acting as both the causal source and the important relay station. Further exploratory research on the adaption of directed hierarchical network in athletes suggested the enhanced bidirectional communication between the DMN and the central executive network (CEN) and the enhanced directed connections from the salience network (SN) to the CEN in the athlete group. The SMGC approach is capable of capturing the hierarchical architecture of the brain directed functional network, which refreshes the new stepwise causal relationship in the directed network. This might shed light on the potential application for exploring the altered hierarchical organization of brain directed network in neuropsychiatric disorders.
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6
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Gherardini L, Zajdel A, Pini L, Crimi A. Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models. Cereb Cortex 2023; 33:11471-11485. [PMID: 37833822 PMCID: PMC10724880 DOI: 10.1093/cercor/bhad380] [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: 08/08/2023] [Revised: 09/23/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023] Open
Abstract
The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.
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Affiliation(s)
- Luca Gherardini
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
| | - Aleksandra Zajdel
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
| | - Lorenzo Pini
- Padua Neuroscience Center, University of Padua, Via 8 Febbraio, 2, Padua 35122, Italy
| | - Alessandro Crimi
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
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7
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Rué-Queralt J, Mancini V, Rochas V, Latrèche C, Uhlhaas PJ, Michel CM, Plomp G, Eliez S, Hagmann P. The coupling between the spatial and temporal scales of neural processes revealed by a joint time-vertex connectome spectral analysis. Neuroimage 2023; 280:120337. [PMID: 37604296 DOI: 10.1016/j.neuroimage.2023.120337] [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: 03/16/2023] [Revised: 08/10/2023] [Accepted: 08/16/2023] [Indexed: 08/23/2023] Open
Abstract
Brain oscillations are produced by the coordinated activity of large groups of neurons and different rhythms are thought to reflect different modes of information processing. These modes, in turn, are known to occur at different spatial scales. Nevertheless, how these rhythms support different spatial modes of information processing at the brain scale is not yet fully understood. Here we use "Joint Time-Vertex Spectral Analysis" to characterize the joint spectral content of brain activity both in time (temporal frequencies) and in space over the connectivity graph (spatial connectome harmonics). This method allows us to characterize the relationship between spatially localized or distributed neural processes on one side and their respective temporal frequency bands in source-reconstructed M/EEG signals. We explore this approach on two different datasets, an auditory steady-state response (ASSR) and a visual grating task. Our results suggest that different information processing mechanisms are carried out at different frequency bands: while spatially distributed activity (which may also be interpreted as integration) specifically occurs at low temporal frequencies (alpha and theta) and low graph spatial frequencies, localized electrical activity (i.e., segregation) is observed at high temporal frequencies (high and low gamma) over restricted high spatial graph frequencies. Crucially, the estimated contribution of the distributed and localized neural activity predicts performance in a behavioral task, demonstrating the neurophysiological relevance of the joint time-vertex spectral representation.
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Affiliation(s)
- Joan Rué-Queralt
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland; Perceptual Networks Lab, Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Valentina Mancini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.
| | - Vincent Rochas
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland; Human Neuroscience Platform, Fondation Campus Biotech Geneva, Switzerland
| | - Caren Latrèche
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Peter J Uhlhaas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, Scotland, United Kingdom; Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Perceptual Networks Lab, Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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8
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Bagheri A, Dehshiri M, Bagheri Y, Akhondi-Asl A, Nadjar Araabi B. Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment. PLoS One 2023; 18:e0289406. [PMID: 37594972 PMCID: PMC10437876 DOI: 10.1371/journal.pone.0289406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/18/2023] [Indexed: 08/20/2023] Open
Abstract
Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome (EC). Although current EC discovery methods have contributed to our understanding of brain organization, their performances are severely constrained by the short sample size and poor temporal resolution of fMRI data, and high dimensionality of the brain connectome. By leveraging the DTI data as prior knowledge, we introduce two Bayesian causal discovery frameworks -the Bayesian GOLEM (BGOLEM) and Bayesian FGES (BFGES) methods- that offer significantly more accurate and reliable ECs and address the shortcomings of the existing causal discovery methods in discovering ECs based on only fMRI data. Moreover, to numerically assess the improvement in the accuracy of ECs with our method on empirical data, we introduce the Pseudo False Discovery Rate (PFDR) as a new computational accuracy metric for causal discovery in the brain. Through a series of simulation studies on synthetic and hybrid data (combining DTI from the Human Connectome Project (HCP) subjects and synthetic fMRI), we demonstrate the effectiveness of our proposed methods and the reliability of the introduced metric in discovering ECs. By employing the PFDR metric, we show that our Bayesian methods lead to significantly more accurate results compared to the traditional methods when applied to the Human Connectome Project (HCP) data. Additionally, we measure the reproducibility of discovered ECs using the Rogers-Tanimoto index for test-retest data and show that our Bayesian methods provide significantly more reliable ECs than traditional methods. Overall, our study's numerical and visual results highlight the potential for these frameworks to significantly advance our understanding of brain functionality.
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Affiliation(s)
- Abdolmahdi Bagheri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mahdi Dehshiri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yamin Bagheri
- Department of Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran
| | - Alireza Akhondi-Asl
- Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Babak Nadjar Araabi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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9
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Nagle A, Gerrelts JP, Krause BM, Boes AD, Bruss JE, Nourski KV, Banks MI, Van Veen B. High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors. Neuroimage 2023; 277:120211. [PMID: 37385393 PMCID: PMC10528866 DOI: 10.1016/j.neuroimage.2023.120211] [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: 12/07/2022] [Revised: 04/20/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023] Open
Abstract
Multivariate autoregressive (MVAR) model estimation enables assessment of causal interactions in brain networks. However, accurately estimating MVAR models for high-dimensional electrophysiological recordings is challenging due to the extensive data requirements. Hence, the applicability of MVAR models for study of brain behavior over hundreds of recording sites has been very limited. Prior work has focused on different strategies for selecting a subset of important MVAR coefficients in the model to reduce the data requirements of conventional least-squares estimation algorithms. Here we propose incorporating prior information, such as resting state functional connectivity derived from functional magnetic resonance imaging, into MVAR model estimation using a weighted group least absolute shrinkage and selection operator (LASSO) regularization strategy. The proposed approach is shown to reduce data requirements by a factor of two relative to the recently proposed group LASSO method of Endemann et al (Neuroimage 254:119057, 2022) while resulting in models that are both more parsimonious and more accurate. The effectiveness of the method is demonstrated using simulation studies of physiologically realistic MVAR models derived from intracranial electroencephalography (iEEG) data. The robustness of the approach to deviations between the conditions under which the prior information and iEEG data is obtained is illustrated using models from data collected in different sleep stages. This approach allows accurate effective connectivity analyses over short time scales, facilitating investigations of causal interactions in the brain underlying perception and cognition during rapid transitions in behavioral state.
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Affiliation(s)
- Alliot Nagle
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, 53706, WI, USA; Department of Anesthesiology, University of Wisconsin, Madison, 53706, WI, USA
| | - Josh P Gerrelts
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, 53706, WI, USA
| | - Bryan M Krause
- Department of Anesthesiology, University of Wisconsin, Madison, 53706, WI, USA
| | - Aaron D Boes
- Department of Neurology, The University of Iowa, Iowa City, 52242, IA, USA
| | - Joel E Bruss
- Department of Neurology, The University of Iowa, Iowa City, 52242, IA, USA
| | - Kirill V Nourski
- Department of Neurosurgery, The University of Iowa, Iowa City, 52242, IA, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, 52242, IA, USA
| | - Matthew I Banks
- Department of Anesthesiology, University of Wisconsin, Madison, 53706, WI, USA.
| | - Barry Van Veen
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, 53706, WI, USA
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10
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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] [MESH Headings] [Grants] [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) CenterGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
- Department of Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) CenterGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
- Department of Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueNew MexicoUSA
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11
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Wierzbiński M, Falcó-Roget J, Crimi A. Community detection in brain connectomes with hybrid quantum computing. Sci Rep 2023; 13:3446. [PMID: 36859591 PMCID: PMC9977923 DOI: 10.1038/s41598-023-30579-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/27/2023] [Indexed: 03/03/2023] Open
Abstract
Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with an efficient integration-segregation balance that facilitates different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explored community detection within brain connectomes using the power of quantum annealers, and in particular the Leap's Hybrid Solver in D-Wave. By reframing the modularity optimization problem into a Discrete Quadratic Model, we show that quantum annealers achieved higher modularity indices compared to the Louvain Community Detection Algorithm without the need to overcomplicate the mathematical formulation. We also found that the number of communities detected in brain connectomes slightly differed while still being biologically interpretable. These promising preliminary results, together with recent findings, strengthen the claim that quantum optimization methods might be a suitable alternative against classical approaches when dealing with community assignment in networks.
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Affiliation(s)
- Marcin Wierzbiński
- grid.425010.20000 0001 2286 5863University of Warsaw, Institute of Mathematics, Warsaw, 02-097 Poland ,Sano Center for Compuational Personalised Medicine, Computer Vision Group, Krakow, 30-054 Poland
| | - Joan Falcó-Roget
- Sano Center for Compuational Personalised Medicine, Computer Vision Group, Krakow, 30-054 Poland
| | - Alessandro Crimi
- Sano Center for Compuational Personalised Medicine, Computer Vision Group, Krakow, 30-054, Poland.
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12
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Benozzo D, Baron G, Coletta L, Chiuso A, Gozzi A, Bertoldo A. Macroscale coupling between structural and effective connectivity in the mouse brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529400. [PMID: 36865122 PMCID: PMC9980133 DOI: 10.1101/2023.02.22.529400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.
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Affiliation(s)
- Danilo Benozzo
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giorgia Baron
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Chiuso
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padova, Italy
- Padova Neuroscience Center, Padova, Italy
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Previously Marzena Szkodo MOR, Micai M, Caruso A, Fulceri F, Fazio M, Scattoni ML. Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review. Neurosci Biobehav Rev 2023; 145:105021. [PMID: 36581169 DOI: 10.1016/j.neubiorev.2022.105021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022]
Abstract
In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.
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Affiliation(s)
| | - Martina Micai
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Angela Caruso
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Francesca Fulceri
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Maria Fazio
- Department of Mathematics, Computer Science, Physics and Earth Sciences (MIFT), University of Messina, Viale F. Stagno d'Alcontres, 31, 98166 Messina, Italy.
| | - Maria Luisa Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
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Wang B, Guo M, Pan T, Li Z, Li Y, Xiang J, Cui X, Niu Y, Yang J, Wu J, Liu M, Li D. Altered higher-order coupling between brain structure and function with embedded vector representations of connectomes in schizophrenia. Cereb Cortex 2022; 33:5447-5456. [PMID: 36482789 DOI: 10.1093/cercor/bhac432] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 12/13/2022] Open
Abstract
Abstract
It has been shown that the functional dependency of the brain exists in both direct and indirect regional relationships. Therefore, it is necessary to map higher-order coupling in brain structure and function to understand brain dynamic. However, how to quantify connections between not directly regions remains unknown to schizophrenia. The word2vec is a common algorithm through create embeddings of words to solve these problems. We apply the node2vec embedding representation to characterize features on each node, their pairwise relationship can give rise to correspondence relationships between brain regions. Then we adopt pearson correlation to quantify the higher-order coupling between structure and function in normal controls and schizophrenia. In addition, we construct direct and indirect connections to quantify the coupling between their respective functional connections. The results showed that higher-order coupling is significantly higher in schizophrenia. Importantly, the anomalous cause of coupling mainly focus on indirect structural connections. The indirect structural connections play an essential role in functional connectivity–structural connectivity (SC–FC) coupling. The similarity between embedded representations capture more subtle network underlying information, our research provides new perspectives for understanding SC–FC coupling. A strong indication that the structural backbone of the brain has an intimate influence on the resting-state functional.
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Affiliation(s)
- Bin Wang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Min Guo
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Tingting Pan
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Zhifeng Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Ying Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jiajia Yang
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, 3-1-1 Tsushimanaka, kita-ku, Okayama-shi, Okayama, 700-8530, Japan
| | - Jinglong Wu
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, 3-1-1 Tsushimanaka, kita-ku, Okayama-shi, Okayama, 700-8530, Japan
| | - Miaomiao Liu
- School of Psychology, Shenzhen University, No. 3688, Nanhai Avenue, Nanshan District, Shenzhen, 518061, China
| | - Dandan Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
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Pascucci D, Rubega M, Rué-Queralt J, Tourbier S, Hagmann P, Plomp G. Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors. Netw Neurosci 2022; 6:401-419. [PMID: 35733424 PMCID: PMC9205420 DOI: 10.1162/netn_a_00218] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/23/2021] [Indexed: 12/03/2022] Open
Abstract
The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural connectivity (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis.
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Affiliation(s)
- David Pascucci
- Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland
| | - Maria Rubega
- Department of Neurosciences, University of Padova, Padova, Italy
| | - Joan Rué-Queralt
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne (CHUV-SUNIL), Lausanne, Switzerland
| | - Sebastien Tourbier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne (CHUV-SUNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne (CHUV-SUNIL), Lausanne, Switzerland
| | - Gijs Plomp
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland
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