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Desrosiers J, Caron-Desrochers L, René A, Gaudet I, Pincivy A, Paquette N, Gallagher A. Functional connectivity development in the prenatal and neonatal stages measured by functional magnetic resonance imaging: A systematic review. Neurosci Biobehav Rev 2024; 163:105778. [PMID: 38936564 DOI: 10.1016/j.neubiorev.2024.105778] [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: 12/07/2023] [Revised: 04/28/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
The prenatal and neonatal periods are two of the most important developmental stages of the human brain. It is therefore crucial to understand normal brain development and how early connections are established during these periods, in order to advance the state of knowledge on altered brain development and eventually identify early brain markers of neurodevelopmental disorders and diseases. In this systematic review (Prospero ID: CRD42024511365), we compiled resting state functional magnetic resonance imaging (fMRI) studies in healthy fetuses and neonates, in order to outline the main characteristics of typical development of the functional brain connectivity during the prenatal and neonatal periods. A systematic search of five databases identified a total of 12 573 articles. Of those, 28 articles met pre-established selection criteria based determined by the authors after surveying and compiling the major limitations reported within the literature. Inclusion criteria were: (1) resting state studies; (2) presentation of original results; (3) use of fMRI with minimum one Tesla; (4) a population ranging from 20 weeks of GA to term birth (around 37-42 weeks of PMA); (5) singleton pregnancy with normal development (absence of any complications known to alter brain development). Exclusion criteria were: (1) preterm studies; (2) post-mortem studies; (3) clinical or pathological studies; (4) twin studies; (5) papers with a sole focus on methodology (i.e. focused on tool and analysis development); (6) volumetric studies; (7) activation map studies; (8) cortical analysis studies; (9) conference papers. A risk of bias assessment was also done to evaluate each article's methodological rigor. 1877 participants were included across all the reviewed articles. Results consistently revealed a developmental gradient of increasing functional brain connectivity from posterior to anterior regions and from proximal-to-distal regions. A decrease in local small-world organization shortly after birth was also observed; small-world characteristics were present in fetuses and newborns, but appeared weaker in the latter group. Also, the posterior-to-anterior gradient could be associated with earlier development of the sensorimotor networks in the posterior regions while more complex higher-order networks (e.g. attention-related) mature later in the anterior regions. The main limitations of this systematic review stem from the inherent limitations of functional imaging in fetuses, mainly: unevenly distributed populations and limited sample sizes; fetal movements in the womb and other imaging obstacles; and a large voxel resolution when imaging a small brain. Another limitation specific to this review is the relatively small number of included articles compared to very a large search result, which may have led to relevant articles having been overlooked.
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Affiliation(s)
- Jérémi Desrosiers
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; School of Psychoeducation, University of Montreal, QC, Canada
| | - Laura Caron-Desrochers
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Andréanne René
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Isabelle Gaudet
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Health Sciences, Université du Québec à Chicoutimi, QC, Canada
| | - Alix Pincivy
- Sainte-Justine University Health Center and Research Center Libraries, Montreal, QC, Canada
| | - Natacha Paquette
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Anne Gallagher
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada.
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Du Y, Fang S, He X, Calhoun VD. A survey of brain functional network extraction methods using fMRI data. Trends Neurosci 2024:S0166-2236(24)00091-2. [PMID: 38906797 DOI: 10.1016/j.tins.2024.05.011] [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: 02/20/2024] [Revised: 05/04/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024]
Abstract
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Songke Fang
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
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Huang ZA, Liu R, Zhu Z, Tan KC. Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:8161-8175. [PMID: 36459608 DOI: 10.1109/tnnls.2022.3225179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.
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Pinto H, Lazic I, Antonacci Y, Pernice R, Gu D, Barà C, Faes L, Rocha AP. Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1385421. [PMID: 38835949 PMCID: PMC11148466 DOI: 10.3389/fnetp.2024.1385421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/22/2024] [Indexed: 06/06/2024]
Abstract
The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics.
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Affiliation(s)
- Helder Pinto
- Departamento de Matemática, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
- Centro de Matemática da Universidade do Porto (CMUP), Porto, Portugal
| | - Ivan Lazic
- Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Danlei Gu
- Beijing Jiaotong University, Beijing, China
| | - Chiara Barà
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Ana Paula Rocha
- Departamento de Matemática, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
- Centro de Matemática da Universidade do Porto (CMUP), Porto, Portugal
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Wang Y, Yen PS, Ajilore OA, Bhaumik DK. A novel biomarker selection method using multimodal neuroimaging data. PLoS One 2024; 19:e0289401. [PMID: 38573979 PMCID: PMC10994318 DOI: 10.1371/journal.pone.0289401] [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: 12/10/2021] [Accepted: 07/18/2023] [Indexed: 04/06/2024] Open
Abstract
Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.
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Affiliation(s)
- Yue Wang
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Pei-Shan Yen
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Olusola A. Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Dulal K. Bhaumik
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States of America
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Brooks SJ, Jones VO, Wang H, Deng C, Golding SGH, Lim J, Gao J, Daoutidis P, Stamoulis C. Community detection in the human connectome: Method types, differences and their impact on inference. Hum Brain Mapp 2024; 45:e26669. [PMID: 38553865 PMCID: PMC10980844 DOI: 10.1002/hbm.26669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.
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Affiliation(s)
- Skylar J. Brooks
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- University of California BerkeleyHelen Wills Neuroscience InstituteBerkeleyCaliforniaUSA
| | - Victoria O. Jones
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Haotian Wang
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Chengyuan Deng
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | | | - Jethro Lim
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
| | - Jie Gao
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Prodromos Daoutidis
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Catherine Stamoulis
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- Harvard Medical SchoolDepartment of PediatricsBostonMassachusettsUSA
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7
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Li Q, Calhoun VD, Pham TD, Iraji A. Exploring Nonlinear Dynamics In Brain Functionality Through Phase Portraits And Fuzzy Recurrence Plots. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.06.547922. [PMID: 38405742 PMCID: PMC10888921 DOI: 10.1101/2023.07.06.547922] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Much of the complexity and diversity found in nature are driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics, and they can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.
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Affiliation(s)
- Qiang Li
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Turner Street, London E1 2AD, UK
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
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Yuan H, Li X, Wei B. Modeling default mode network patterns via a universal spatio-temporal brain attention skip network. Neuroimage 2024; 287:120522. [PMID: 38253216 DOI: 10.1016/j.neuroimage.2024.120522] [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: 09/26/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 01/24/2024] Open
Abstract
Designing a comprehensive four-dimensional resting-state functional magnetic resonance imaging (4D Rs-fMRI) based default mode network (DMN) modeling methodology to reveal the spatio-temporal patterns of individual DMN, is crucial for understanding the cognitive mechanisms of the brain and the pathogenesis of psychiatric disorders. However, there are still two limitations of existing approaches for DMN modeling. The approaches either (1) simply split the spatio-temporal components and ignore the overall character of the spatio-temporal patterns or (2) are biased in the process of feature extraction for DMN modeling, and their spatio-temporal accuracy is thus not warranted. To this end, we propose a novel Spatio-Temporal Brain Attention Skip Network (STBAS-Net) to model the personalized spatio-temporal patterns of the DMN. STBAS-Net consists of spatial and temporal components, where the multi-head attention skip connection block in the spatial component achieves detailed feature extraction and enhancement in the shallow stage. Under the guidance of spatial information, we technically fuse multiple spatio-temporal information in the temporal component, which dexterously exploits the overall spatio-temporal features and achieves mutual constraints of spatio-temporal patterns to characterize the spatio-temporal patterns of the DMN. We verify the proposed STBAS-Net on a publicly released 4D Rs-fMRI dataset and an EMCI dataset. The experimental results show that compared with existing advanced methods, the proposed network can more accurately model the personalized spatio-temporal patterns of the human brain DMN and successfully identify abnormal spatio-temporal patterns in EMCI patients. This study provides a potential tool for revealing the spatio-temporal patterns of the human brain DMN and is expected to provide an effective methodological framework for future exploration of abnormal brain spatio-temporal patterns and modeling of other functional brain networks.
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Affiliation(s)
- Hang Yuan
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China
| | - Xiang Li
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China.
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9
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Cheng Q, Ren A, Han J, Jin X, Pylypenko D, Yu D, Wang X. Assessment of functional and structural brain abnormalities with resting-state functional MRI in patients with vestibular neuronitis. Acta Radiol 2023; 64:3024-3031. [PMID: 37807650 DOI: 10.1177/02841851231203569] [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] [Indexed: 10/10/2023]
Abstract
BACKGROUND Vestibular neuritis (VN) is a disorder manifesting as acute, isolated, spontaneous vertigo. There are few comprehensive studies on the changes in related functional and structural brain regions. PURPOSE To evaluate alterations in spontaneous neural activity, functional connectivity (FC), and gray matter volume (GMV) in patients with VN. MATERIAL AND METHODS A total of 24 patients with VN and 22 age- and sex-matched healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI) and three-dimensional T1-weighted anatomical imaging. We calculated the amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) to discern local brain abnormalities. The most abnormal brain region was selected as the region of interest (ROI) for FC analysis based on ALFF and ReHo values after Bonferroni correction. Voxel-based morphometry (VBM) was used to assess differences in GMV. RESULTS Patients with VN, compared to healthy controls, showed increased ALFF (P < 0.001), ReHo values (P = 0.002, <0.001), and DC (P = 0.013) in the left lingual gyrus and right postcentral gyrus. FC analysis demonstrated enhanced connectivity between the left lingual gyrus and the left superior frontal gyrus, and decreased connectivity with the right insula gyrus, right and left supramarginal gyrus (P = 0.012, 0.004, <0.001, 0.014). In addition, GMV was reduced in the bilateral caudate (P = 0.022, 0.014). CONCLUSIONS Patients with VN exhibit abnormal spontaneous neural activity and changes in ALFF, ReHo, DC, GMV, and FC. Understanding these functional and structural brain abnormalities may elucidate the underlying mechanisms of VN.
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Affiliation(s)
- QiChao Cheng
- Qilu Hospital of Shandong University, JiNan, Shandong Province, PR China
| | - AnLi Ren
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, JiNan, Shandong Province, PR China
| | - JingYang Han
- Affiliated Hospital of Weifang Medical University, Weifang, Shandong Province, PR China
| | - XinJuan Jin
- Qilu Hospital of Shandong University, JiNan, Shandong Province, PR China
| | | | - DeXin Yu
- Qilu Hospital of Shandong University, JiNan, Shandong Province, PR China
| | - XiZhen Wang
- Affiliated Hospital of Weifang Medical University, Weifang, Shandong Province, PR China
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Mansour L S, Di Biase MA, Smith RE, Zalesky A, Seguin C. Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource. Neuroimage 2023; 283:120407. [PMID: 37839728 DOI: 10.1016/j.neuroimage.2023.120407] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 10/17/2023] Open
Abstract
We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject's connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.
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Affiliation(s)
- Sina Mansour L
- Department of Biomedical Engineering, The University of Melbourne, VIC, Australia.
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia; Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Parkville, Victoria, Australia; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, MA, USA
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, VIC, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
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11
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Zhang W, Xia S, Tang X, Zhang X, Liang D, Wang Y. Topological analysis of functional connectivity in Parkinson's disease. Front Neurosci 2023; 17:1236128. [PMID: 37680970 PMCID: PMC10481708 DOI: 10.3389/fnins.2023.1236128] [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: 06/07/2023] [Accepted: 08/02/2023] [Indexed: 09/09/2023] Open
Abstract
Parkinson's disease (PD) is a clinically heterogeneous disorder, which mainly affects patients' motor and non-motor function. Functional connectivity was preliminary explored and studied through resting state functional magnetic resonance imaging (rsfMRI). Through the topological analysis of 54 PD scans and 31 age-matched normal controls (NC) in the Neurocon dataset, leveraging on rsfMRI data, the brain functional connection and the Vietoris-Rips (VR) complex were constructed. The barcodes of the complex were calculated to reflect the changes of functional connectivity neural circuits (FCNC) in brain network. The 0-dimensional Betti number β0 means the number of connected branches in VR complex. The average number of connected branches in PD group was greater than that in NC group when the threshold δ ≤ 0.7. Two-sample Mann-Whitney U test and false discovery rate (FDR) correction were used for statistical analysis to investigate the FCNC changes between PD and NC groups. In PD group, under threshold of 0.7, the number of FCNC involved was significantly differences and these brain regions include the Cuneus_R, Lingual_R, Fusiform_R and Heschl_R. There are also significant differences in brain regions in the Frontal_Inf_Orb_R and Pallidum_R, when the threshold increased to 0.8 and 0.9 (p < 0.05). In addition, when the length of FCNC was medium, there was a significant statistical difference between the PD group and the NC group in the Neurocon dataset and the Parkinson's Progression Markers Initiative (PPMI) dataset. Topological analysis based on rsfMRI data may provide comprehensive information about the changes of FCNC and may provide an alternative for clinical differential diagnosis.
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Affiliation(s)
- Weiwei Zhang
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Shengxiang Xia
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Xinhua Tang
- School of Cyberspace Security, Shandong University of Political Science and Law, Jinan, China
| | - Xianfu Zhang
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Di Liang
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Yinuo Wang
- School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, China
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12
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Mamah D. A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2023; 8:e230005. [PMID: 37427077 PMCID: PMC10327607 DOI: 10.20900/jpbs.20230005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The risk for developing schizophrenia is increased among first-degree relatives of those with psychotic disorders, but the risk is even higher in those meeting established criteria for clinical high risk (CHR), a clinical construct most often comprising of attenuated psychotic experiences. Conversion to psychosis among CHR youth has been reported to be about 15-35% over three years. Accurately identifying individuals whose psychotic symptoms will worsen would facilitate earlier intervention, but this has been difficult to do using behavior measures alone. Brain-based risk markers have the potential to improve the accuracy of predicting outcomes in CHR youth. This narrative review provides an overview of neuroimaging studies used to investigate psychosis risk, including studies involving structural, functional, and diffusion imaging, functional connectivity, positron emission tomography, arterial spin labeling, magnetic resonance spectroscopy, and multi-modality approaches. We present findings separately in those observed in the CHR state and those associated with psychosis progression or resilience. Finally, we discuss future research directions that could improve clinical care for those at high risk for developing psychotic disorders.
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Affiliation(s)
- Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, 63110, USA
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13
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Bedel HA, Sivgin I, Dalmaz O, Dar SUH, Çukur T. BolT: Fused window transformers for fMRI time series analysis. Med Image Anal 2023; 88:102841. [PMID: 37224718 DOI: 10.1016/j.media.2023.102841] [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: 09/19/2022] [Revised: 02/07/2023] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual representations across diverse time scales. Here, we present BolT, a blood-oxygen-level-dependent transformer model, for analyzing multi-variate fMRI time series. BolT leverages a cascade of transformer encoders equipped with a novel fused window attention mechanism. Encoding is performed on temporally-overlapped windows within the time series to capture local representations. To integrate information temporally, cross-window attention is computed between base tokens in each window and fringe tokens from neighboring windows. To gradually transition from local to global representations, the extent of window overlap and thereby number of fringe tokens are progressively increased across the cascade. Finally, a novel cross-window regularization is employed to align high-level classification features across the time series. Comprehensive experiments on large-scale public datasets demonstrate the superior performance of BolT against state-of-the-art methods. Furthermore, explanatory analyses to identify landmark time points and regions that contribute most significantly to model decisions corroborate prominent neuroscientific findings in the literature.
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Affiliation(s)
- Hasan A Bedel
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Irmak Sivgin
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Onat Dalmaz
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Salman U H Dar
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Tolga Çukur
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey; Neuroscience Program, Bilkent University, Ankara 06800, Turkey.
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14
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Barà C, Sparacino L, Pernice R, Antonacci Y, Porta A, Kugiumtzis D, Faes L. Comparison of discretization strategies for the model-free information-theoretic assessment of short-term physiological interactions. CHAOS (WOODBURY, N.Y.) 2023; 33:033127. [PMID: 37003789 DOI: 10.1063/5.0140641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/17/2023] [Indexed: 06/19/2023]
Abstract
This work presents a comparison between different approaches for the model-free estimation of information-theoretic measures of the dynamic coupling between short realizations of random processes. The measures considered are the mutual information rate (MIR) between two random processes X and Y and the terms of its decomposition evidencing either the individual entropy rates of X and Y and their joint entropy rate, or the transfer entropies from X to Y and from Y to X and the instantaneous information shared by X and Y. All measures are estimated through discretization of the random variables forming the processes, performed either via uniform quantization (binning approach) or rank ordering (permutation approach). The binning and permutation approaches are compared on simulations of two coupled non-identical Hènon systems and on three datasets, including short realizations of cardiorespiratory (CR, heart period and respiration flow), cardiovascular (CV, heart period and systolic arterial pressure), and cerebrovascular (CB, mean arterial pressure and cerebral blood flow velocity) measured in different physiological conditions, i.e., spontaneous vs paced breathing or supine vs upright positions. Our results show that, with careful selection of the estimation parameters (i.e., the embedding dimension and the number of quantization levels for the binning approach), meaningful patterns of the MIR and of its components can be achieved in the analyzed systems. On physiological time series, we found that paced breathing at slow breathing rates induces less complex and more coupled CR dynamics, while postural stress leads to unbalancing of CV interactions with prevalent baroreflex coupling and to less complex pressure dynamics with preserved CB interactions. These results are better highlighted by the permutation approach, thanks to its more parsimonious representation of the discretized dynamic patterns, which allows one to explore interactions with longer memory while limiting the curse of dimensionality.
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Affiliation(s)
- Chiara Barà
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
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15
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Jacobs NPT, Pouwels PJW, van der Krogt MM, Meyns P, Zhu K, Nelissen L, Schoonmade LJ, Buizer AI, van de Pol LA. Brain structural and functional connectivity and network organization in cerebral palsy: A scoping review. Dev Med Child Neurol 2023. [PMID: 36750309 DOI: 10.1111/dmcn.15516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 12/10/2022] [Accepted: 12/13/2022] [Indexed: 02/09/2023]
Abstract
AIM To explore altered structural and functional connectivity and network organization in cerebral palsy (CP), by clinical CP subtype (unilateral spastic, bilateral spastic, dyskinetic, and ataxic CP). METHOD PubMed and Embase databases were systematically searched. Extracted data included clinical characteristics, analyses, outcome measures, and results. RESULTS Sixty-five studies were included, of which 50 investigated structural connectivity, and 20 investigated functional connectivity using functional magnetic resonance imaging (14 studies) or electroencephalography (six studies). Five of the 50 studies of structural connectivity and one of 14 of functional connectivity investigated whole-brain network organization. Most studies included patients with unilateral spastic CP; none included ataxic CP. INTERPRETATION Differences in structural and functional connectivity were observed between investigated clinical CP subtypes and typically developing individuals on a wide variety of measures, including efferent, afferent, interhemispheric, and intrahemispheric connections. Directions for future research include extending knowledge in underrepresented CP subtypes and methodologies, evaluating the prognostic potential of specific connectivity and network measures in neonates, and understanding therapeutic effects on brain connectivity.
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Affiliation(s)
- Nina P T Jacobs
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Marjolein M van der Krogt
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
| | - Pieter Meyns
- REVAL Rehabilitation Research, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Kangdi Zhu
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Loïs Nelissen
- Department of Pediatric Neurology, Emma Children's Hospital, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands
| | - Linda J Schoonmade
- Medical Library, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Annemieke I Buizer
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands.,Emma Children's Hospital, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Laura A van de Pol
- Department of Pediatric Neurology, Emma Children's Hospital, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands
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16
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Wang C, Zhang L, Zhang J, Qiao L, Liu M. Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification. J Pers Med 2023; 13:jpm13020251. [PMID: 36836485 PMCID: PMC9958959 DOI: 10.3390/jpm13020251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/27/2022] [Accepted: 01/13/2023] [Indexed: 01/31/2023] Open
Abstract
Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the functional connections between brain regions of interest (ROIs) from a single view (e.g., by estimating FBNs through a specific strategy), failing to capture the complex interactions among ROIs in the brain. Methods: To address this problem, we propose fusion of multiview FBNs through joint embedding, which can make full use of the common information of multiview FBNs estimated by different strategies. More specifically, we first stack the adjacency matrices of FBNs estimated by different methods into a tensor and use tensor factorization to learn the joint embedding (i.e., a common factor of all FBNs) for each ROI. Then, we use Pearson's correlation to calculate the connections between each embedded ROI in order to reconstruct a new FBN. Results: Experimental results obtained on the public ABIDE dataset with rs-fMRI data reveal that our method is superior to several state-of-the-art methods in automated ASD diagnosis. Moreover, by exploring FBN "features" that contributed most to ASD identification, we discovered potential biomarkers for ASD diagnosis. The proposed framework achieves an accuracy of 74.46%, which is generally better than the compared individual FBN methods. In addition, our method achieves the best performance compared to other multinetwork methods, i.e., an accuracy improvement of at least 2.72%. Conclusions: We present a multiview FBN fusion strategy through joint embedding for fMRI-based ASD identification. The proposed fusion method has an elegant theoretical explanation from the perspective of eigenvector centrality.
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Affiliation(s)
- Chengcheng Wang
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Limei Zhang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
- Correspondence: (L.Z.); (M.L.)
| | - Jinshan Zhang
- College of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence: (L.Z.); (M.L.)
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17
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Dos Santos PMN, Mendes SL, Biazoli C, Gadelha A, Salum GA, Miguel EC, Rohde LA, Sato JR. Assessing atypical brain functional connectivity development: An approach based on generative adversarial networks. Front Neurosci 2023; 16:1025492. [PMID: 36699518 PMCID: PMC9868740 DOI: 10.3389/fnins.2022.1025492] [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: 08/23/2022] [Accepted: 11/25/2022] [Indexed: 01/11/2023] Open
Abstract
Generative Adversarial Networks (GANs) are promising analytical tools in machine learning applications. Characterizing atypical neurodevelopmental processes might be useful in establishing diagnostic and prognostic biomarkers of psychiatric disorders. In this article, we investigate the potential of GANs models combined with functional connectivity (FC) measures to build a predictive neurotypicality score 3-years after scanning. We used a ROI-to-ROI analysis of resting-state functional magnetic resonance imaging (fMRI) data from a community-based cohort of children and adolescents (377 neurotypical and 126 atypical participants). Models were trained on data from neurotypical participants, capturing their sample variability of FC. The discriminator subnetwork of each GAN model discriminated between the learned neurotypical functional connectivity pattern and atypical or unrelated patterns. Discriminator models were combined in ensembles, improving discrimination performance. Explanations for the model's predictions are provided using the LIME (Local Interpretable Model-Agnostic) algorithm and local hubs are identified in light of these explanations. Our findings suggest this approach is a promising strategy to build potential biomarkers based on functional connectivity.
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Affiliation(s)
- Pedro Machado Nery Dos Santos
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Santo André, Brazil
| | - Sérgio Leonardo Mendes
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Santo André, Brazil
| | - Claudinei Biazoli
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Santo André, Brazil
| | - Ary Gadelha
- Laboratory of Integrative Neuroscience, Universidade Federal de São Paulo, São Paulo, Brazil,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Giovanni Abrahão Salum
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil,Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Euripedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil,Department of Psychiatry, School of Medicine, University of São Paulo, São Paulo, Brazil
| | - Luis Augusto Rohde
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil,Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil,UniEduK, Jaguariúna, Brazil
| | - João Ricardo Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Santo André, Brazil,Laboratory of Integrative Neuroscience, Universidade Federal de São Paulo, São Paulo, Brazil,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil,Big Data, Hospital Israelita Albert Einstein, São Paulo, Brazil,*Correspondence: João Ricardo Sato,
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18
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Wang M, Zhao G, Jiang Y, Lu T, Wang Y, Zhu Y, Zhang Z, Xie C, Wang Z, Ren Q. Disconnection of Network Hubs Underlying the Executive Function Deficit in Patients with Ischemic Leukoaraiosis. J Alzheimers Dis 2023; 94:1577-1586. [PMID: 37458032 DOI: 10.3233/jad-230048] [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] [Indexed: 07/18/2023]
Abstract
BACKGROUND Cognitive impairment is the most common clinical manifestation of ischemic leukoaraiosis (ILA), but the underlying neurobiological pathways have not been well elucidated. Recently, it was thought that ILA is a "disconnection syndrome". Disorganized brain connectome were considered the key neuropathology underlying cognitive deficits in ILA patients. OBJECTIVE We aimed to detect the disruption of network hubs in ILA patients using a new analytical method called voxel-based eigenvector centrality (EC) mapping. METHODS Subjects with moderate to severe white matters hyperintensities (Fazekas score ≥3) and healthy controls (HCs) (Fazekas score = 0) were included in the study. The resting-state functional magnetic resonance imaging and the EC mapping approach were performed to explore the alteration of whole-brain network connectivity in ILA patients. RESULTS Relative to the HCs, the ILA patients exhibited poorer cognitive performance in episodic memory, information processing speed, and executive function (all ps < 0.0125). Additionally, compared with HCs, the ILA patients had lower functional connectivity (i.e., EC values) in the medial parts of default-mode network (i.e., bilateral posterior cingulate gyrus and ventral medial prefrontal cortex [vMPFC]). Intriguingly, the functional connectivity strength at the right vMPFC was positively correlated with executive function deficit in the ILA patients. CONCLUSION The findings suggested disorganization of the hierarchy of the default-mode regions within the whole-brain network in patients with ILA and advanced our understanding of the neurobiological mechanism underlying executive function deficit in ILA.
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Affiliation(s)
- Mengxue Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Guofeng Zhao
- Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
- Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, China
| | - Ying Jiang
- Department of Neurology, The 962nd Hospital of the PLA Joint Logistic Support Force, Harbin, China
| | - Tong Lu
- Department of Radiology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yanjuan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yixin Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhengsheng Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qingguo Ren
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
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Lu Z, Wang J, Mao R, Lu M, Shi J. Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:476-488. [PMID: 35349448 DOI: 10.1109/tcbb.2022.3163140] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Autism spectrum disorder (ASD) is characterized by poor social communication abilities and repetitive behaviors or restrictive interests, which has brought a heavy burden to families and society. In many attempts to understand ASD neurobiology, resting-state functional magnetic resonance imaging (rs-fMRI) has been an effective tool. However, current ASD diagnosis methods based on rs-fMRI have two major defects. First, the instability of rs-fMRI leads to functional connectivity (FC) uncertainty, affecting the performance of ASD diagnosis. Second, many FCs are involved in brain activity, making it difficult to determine effective features in ASD classification. In this study, we propose an interpretable ASD classifier DeepTSK, which combines a multi-output Takagi-Sugeno-Kang (MO-TSK) fuzzy inference system (FIS) for composite feature learning and a deep belief network (DBN) for ASD classification in a unified network. To avoid the suboptimal solution of DeepTSK, a joint optimization procedure is employed to simultaneously learn the parameters of MO-TSK and DBN. The proposed DeepTSK was evaluated on datasets collected from three sites of the Autism Brain Imaging Data Exchange (ABIDE) database. The experimental results showed the effectiveness of the proposed method, and the discriminant FCs are presented by analyzing the consequent parameters of Deep MO-TSK.
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20
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Matos J, Peralta G, Heyse J, Menetre E, Seeck M, van Mierlo P. Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure. Bioengineering (Basel) 2022; 9:690. [PMID: 36421091 PMCID: PMC9687589 DOI: 10.3390/bioengineering9110690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 09/29/2023] Open
Abstract
Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient's cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models' evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.
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Affiliation(s)
- João Matos
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Guilherme Peralta
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Jolan Heyse
- Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
| | - Eric Menetre
- EEG and Epilepsy Unit, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Pieter van Mierlo
- Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
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21
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Nadvar N, Stiles N, Choupan J, Patel V, Ameri H, Shi Y, Liu Z, Jonides J, Weiland J. Sight restoration reverses blindness-induced cross-modal functional connectivity changes between the visual and somatosensory cortex at rest. Front Neurosci 2022; 16:902866. [PMID: 36213743 PMCID: PMC9539921 DOI: 10.3389/fnins.2022.902866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/29/2022] [Indexed: 11/28/2022] Open
Abstract
Resting-state functional connectivity (rsFC) has been used to assess the effect of vision loss on brain plasticity. With the emergence of vision restoration therapies, rsFC analysis provides a means to assess the functional changes following sight restoration. Our study demonstrates a partial reversal of blindness-induced rsFC changes in Argus II retinal prosthesis patients compared to those with severe retinitis pigmentosa (RP). For 10 healthy control (HC), 10 RP, and 7 Argus II subjects, four runs of resting-state functional magnetic resonance imaging (fMRI) per subject were included in our study. rsFC maps were created with the primary visual cortex (V1) as the seed. The rsFC group contrast maps for RP > HC, Argus II > RP, and Argus II > HC revealed regions in the post-central gyrus (PostCG) with significant reduction, significant enhancement, and no significant changes in rsFC to V1 for the three contrasts, respectively. These findings were also confirmed by the respective V1-PostCG ROI-ROI analyses between test groups. Finally, the extent of significant rsFC to V1 in the PostCG region was 5,961 in HC, 0 in RP, and 842 mm3 in Argus II groups. Our results showed a reduction of visual-somatosensory rsFC following blindness, consistent with previous findings. This connectivity was enhanced following sight recovery with Argus II, representing a reversal of changes in cross-modal functional plasticity as manifested during rest, despite the rudimentary vision obtained by Argus II patients. Future investigation with a larger number of test subjects into this rare condition can further unveil the profound ability of our brain to reorganize in response to vision restoration.
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Affiliation(s)
- Negin Nadvar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Noelle Stiles
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Jeiran Choupan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Vivek Patel
- Irvine School of Medicine, The University of California, Irvine, Irvine, CA, United States
| | - Hossein Ameri
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Yonggang Shi
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Zhongming Liu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States
| | - John Jonides
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - James Weiland
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, United States
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22
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Ashtiani SNM, Behnam H, Daliri MR. Diagnosis of Multiple Sclerosis Using Graph-Theoretic Measures of Cognitive-Task-Based Functional Connectivity Networks. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3081605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Seyedeh Naghmeh Miri Ashtiani
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Hamid Behnam
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Reza Daliri
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
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23
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Lv Q, Zhang J, Pan Y, Liu X, Miao L, Peng J, Song L, Zou Y, Chen X. Somatosensory Deficits After Stroke: Insights From MRI Studies. Front Neurol 2022; 13:891283. [PMID: 35911919 PMCID: PMC9328992 DOI: 10.3389/fneur.2022.891283] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/15/2022] [Indexed: 11/28/2022] Open
Abstract
Somatosensory deficits after stroke are a major health problem, which can impair patients' health status and quality of life. With the developments in human brain mapping techniques, particularly magnetic resonance imaging (MRI), many studies have applied those techniques to unravel neural substrates linked to apoplexy sequelae. Multi-parametric MRI is a vital method for the measurement of stroke and has been applied to diagnose stroke severity, predict outcome and visualize changes in activation patterns during stroke recovery. However, relatively little is known about the somatosensory deficits after stroke and their recovery. This review aims to highlight the utility and importance of MRI techniques in the field of somatosensory deficits and synthesizes corresponding articles to elucidate the mechanisms underlying the occurrence and recovery of somatosensory symptoms. Here, we start by reviewing the anatomic and functional features of the somatosensory system. And then, we provide a discussion of MRI techniques and analysis methods. Meanwhile, we present the application of those techniques and methods in clinical studies, focusing on recent research advances and the potential for clinical translation. Finally, we identify some limitations and open questions of current imaging studies that need to be addressed in future research.
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Affiliation(s)
- Qiuyi Lv
- Department of Neurology and Stroke Center, Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China
| | - Junning Zhang
- Department of Integrative Oncology, China-Japan Friendship Hospital, Beijing, China
| | - Yuxing Pan
- Institute of Neuroscience, Chinese Academy of Science, Shanghai, China
| | - Xiaodong Liu
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | | | - Jing Peng
- Department of Neurology and Stroke Center, Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China
| | - Lei Song
- Department of Neurology and Stroke Center, Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China
| | - Yihuai Zou
- Department of Neurology and Stroke Center, Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China
| | - Xing Chen
- Department of Neurology and Stroke Center, Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China
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Weiler M, Casseb RF, de Campos BM, Crone JS, Lutkenhoff ES, Vespa PM, Monti MM. Evaluating denoising strategies in resting-state functional magnetic resonance in traumatic brain injury (EpiBioS4Rx). Hum Brain Mapp 2022; 43:4640-4649. [PMID: 35723510 PMCID: PMC9491287 DOI: 10.1002/hbm.25979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/17/2022] [Accepted: 05/29/2022] [Indexed: 11/11/2022] Open
Abstract
Resting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity (FC) of traumatic brain injury (TBI) patients. We applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 TBI patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial. Pipelines were evaluated by three quality control (QC) metrics across three exclusion regimes based on the participant's head movement profile. While no pipeline eliminated noise effects on FC, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data-driven methods performed comparatively worse. In this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related FC in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as TBI datasets.
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Affiliation(s)
- Marina Weiler
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Raphael F Casseb
- Neuroimaging Laboratory, University of Campinas, Campinas, São Paulo, Brazil
| | - Brunno M de Campos
- Neuroimaging Laboratory, University of Campinas, Campinas, São Paulo, Brazil
| | - Julia S Crone
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Paul M Vespa
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.,Department of Neurosurgery, Brain Injury Research Center, University of California Los Angeles, Los Angeles, California, USA
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25
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Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage 2022; 246:118774. [PMID: 34861391 PMCID: PMC10569447 DOI: 10.1016/j.neuroimage.2021.118774] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/26/2021] [Accepted: 11/29/2021] [Indexed: 12/23/2022] Open
Abstract
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Boris Duka
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
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26
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Kulkarni PH, Merchant S, Awate SP. Mixed-Dictionary Models and Variational Inference in Task fMRI for Shorter Scans and Better Image Quality. Med Image Anal 2022; 78:102392. [DOI: 10.1016/j.media.2022.102392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 10/31/2021] [Accepted: 02/10/2022] [Indexed: 11/28/2022]
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27
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Hu X, Zhao M, Ma Y, Ge Y, He H, Wang S, Qian Y. Alteration of segregation of brain systems in the severe depressive disorder after electroconvulsive therapy. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2022. [DOI: 10.1016/j.jadr.2021.100299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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28
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Li JM, Acland BT, Brenner AS, Bentley WJ, Snyder LH. Relationships between correlated spikes, oxygen and LFP in the resting-state primate. Neuroimage 2021; 247:118728. [PMID: 34923136 DOI: 10.1016/j.neuroimage.2021.118728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 11/09/2021] [Accepted: 11/12/2021] [Indexed: 11/28/2022] Open
Abstract
Resting-state functional MRI (rsfMRI) provides a view of human brain organization based on correlation patterns of blood oxygen level dependent (BOLD) signals recorded across the whole brain. The neural basis of resting-state BOLD fluctuations and their correlation remains poorly understood. We simultaneously recorded oxygen level, spikes, and local field potential (LFP) at multiple sites in awake, resting monkeys. Following a spike, the average local oxygen and LFP voltage responses each resemble a task-driven BOLD response, with LFP preceding oxygen by 0.5 s. Between sites, features of the long-range correlation patterns of oxygen, LFP, and spikes are similar to features seen in rsfMRI. Most of the variance shared between sites lies in the infraslow frequency band (0.01-0.1 Hz) and in the infraslow envelope of higher-frequency bands (e.g. gamma LFP). While gamma LFP and infraslow LFP are both strong correlates of local oxygen, infraslow LFP explains significantly more of the variance shared between correlated oxygen signals than any other electrophysiological signal. Together these findings are consistent with a causal relationship between infraslow LFP and long-range oxygen correlations in the resting state.
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Affiliation(s)
- Jingfeng M Li
- Department of Neuroscience, Washington University School of Medicine, 660 S Euclid Ave, Box 8108, St Louis, MO 63110, USA
| | - Benjamin T Acland
- Department of Neuroscience, Washington University School of Medicine, 660 S Euclid Ave, Box 8108, St Louis, MO 63110, USA
| | - Alexander S Brenner
- Department of Biomedical Engineering, Washington University, St Louis, MO 63130, USA
| | - William J Bentley
- Department of Neuroscience, Washington University School of Medicine, 660 S Euclid Ave, Box 8108, St Louis, MO 63110, USA
| | - Lawrence H Snyder
- Department of Neuroscience, Washington University School of Medicine, 660 S Euclid Ave, Box 8108, St Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University, St Louis, MO 63130, USA.
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29
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Resting-State Functional Magnetic Resonance Imaging for Surgical Neuro-Oncology Planning: Towards a Standardization in Clinical Settings. Brain Sci 2021; 11:brainsci11121613. [PMID: 34942915 PMCID: PMC8699779 DOI: 10.3390/brainsci11121613] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 02/03/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rest-f-MRI) is a neuroimaging technique that has demonstrated its potential in providing new insights into brain physiology. rest-f-MRI can provide useful information in pre-surgical mapping aimed to balancing long-term survival by maximizing the extent of resection of brain neoplasms, while preserving the patient’s functional connectivity. Rest-fMRI may replace or can be complementary to task-driven fMRI (t-fMRI), particularly in patients unable to cooperate with the task paradigm, such as children or sedated, paretic, aphasic patients. Although rest-fMRI is still under standardization, this technique has been demonstrated to be feasible and valuable in the routine clinical setting for neurosurgical planning, along with intraoperative electrocortical mapping. In the literature, there is growing evidence that rest-fMRI can provide valuable information for the depiction of glioma-related functional brain network impairment. Accordingly, rest-fMRI could allow a tailored glioma surgery improving the surgeon’s ability to increase the extent of resection (EOR), and simultaneously minimize the risk of damage of eloquent brain structures and neuronal networks responsible for the integrity of executive functions. In this article, we present a review of the literature and illustrate the feasibility of rest-fMRI in the clinical setting for presurgical mapping of eloquent networks in patients affected by brain tumors, before and after tumor resection.
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30
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Improving the accuracy of brain activation maps in the group-level analysis of fMRI data utilizing spatiotemporal Gaussian process model. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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31
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Li G, Liu Y, Zheng Y, Wu Y, Li D, Liang X, Chen Y, Cui Y, Yap PT, Qiu S, Zhang H, Shen D. Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder. Neuroimage Clin 2021; 31:102758. [PMID: 34284335 PMCID: PMC8313604 DOI: 10.1016/j.nicl.2021.102758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 11/15/2022]
Abstract
Major depressive disorder (MDD) represents a grand challenge to human health and society, but the underlying pathophysiological mechanisms remain elusive. Previous neuroimaging studies have suggested that MDD is associated with abnormal interactions and dynamics in two major neural systems including the default mode - salience (DMN-SAL) network and the executive - limbic (EXE-LIM) network, but it is not clear which network plays a central role and which network plays a subordinate role in MDD pathophysiology. To address this question, we refined a newly developed Multiscale Neural Model Inversion (MNMI) framework and applied it to test whether MDD is more affected by impaired circuit interactions in the DMN-SAL network or the EXE-LIM network. The model estimates the directed connection strengths between different neural populations both within and between brain regions based on resting-state fMRI data collected from normal healthy subjects and patients with MDD. Results show that MDD is primarily characterized by abnormal circuit interactions in the EXE-LIM network rather than the DMN-SAL network. Specifically, we observe reduced frontoparietal effective connectivity that potentially contributes to hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined with increased excitation from the superior parietal cortex (SPC) that potentially lead to amygdala hyperactivity, together resulting in activation imbalance in the PFC-amygdala circuit that pervades in MDD. Moreover, the model reveals reduced PFC-to-hippocampus excitation but decreased SPC-to-thalamus inhibition in MDD population that potentially lead to hypoactivity in the hippocampus and hyperactivity in the thalamus, consistent with previous experimental data. Overall, our findings provide strong support for the long-standing limbic-cortical dysregulation model in major depression but also offer novel insights into the multiscale pathophysiology of this debilitating disease.
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Affiliation(s)
- Guoshi Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yujie Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Yanting Zheng
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Ye Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Danian Li
- Cerebropathy Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xinyu Liang
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yaoping Chen
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ying Cui
- Cerebropathy Center, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
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32
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Wang Z, Xin J, Wang Z, Yao Y, Zhao Y, Qian W. Brain functional network modeling and analysis based on fMRI: a systematic review. Cogn Neurodyn 2021; 15:389-403. [PMID: 34040667 PMCID: PMC8131458 DOI: 10.1007/s11571-020-09630-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 08/05/2020] [Accepted: 08/20/2020] [Indexed: 12/12/2022] Open
Abstract
In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.
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Affiliation(s)
- Zhongyang Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Junchang Xin
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, Shenyang, China
| | - Zhiqiong Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ USA
| | - Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Wei Qian
- College of Engineering, The University of Texas at El Paso, El Paso, TX USA
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Philiastides MG, Tu T, Sajda P. Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI. Annu Rev Neurosci 2021; 44:315-334. [PMID: 33761268 DOI: 10.1146/annurev-neuro-100220-093239] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.
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Affiliation(s)
- Marios G Philiastides
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8AD, Scotland;
| | - Tao Tu
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Paul Sajda
- Departments of Biomedical Engineering, Electrical Engineering, and Radiology and the Data Science Institute, Columbia University, New York, NY 10027, USA;
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Yang T, Kim JH, Kim J, Kim SP. Involvement of bilateral insula in brand extension evaluation: an fMRI study. Sci Rep 2021; 11:3387. [PMID: 33564059 PMCID: PMC7873197 DOI: 10.1038/s41598-021-83057-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 01/21/2021] [Indexed: 11/09/2022] Open
Abstract
The present study aims to investigate functional involvement of brain areas in consumers' evaluation of brand extension that refers to the use of well-established brand for launching new offerings. During functional magnetic resonance imaging (fMRI) scanning, participants viewed a beverage brand name followed by an extension goods name selected from the beverage or household appliance categories. They responded acceptability to given brand extension. Both acceptability responses and reaction time revealed a noticeable pattern that participants responded to acceptable stimuli more carefully. General linear model (GLM) analyses revealed the involvement of insular activity in brand extension evaluation. Especially, insular activity was lateralized according to valence. Furthermore, its activity could explain behavioral response in parametric modulation model. According to these results, we speculate that insula activity is relevant to emotional processing. Finally, we divided neural activities during brand extension into separated clusters using a hierarchical clustering-based connectivity analysis. Excluding two of them related to sensorimotor functions for behavioral responses, the remaining cluster, including bilateral insula, was likely to reflect brand extension assessment. Hence, we speculate that consumers' brand extension evaluation may involve emotional processes, shown as insular activity.
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Affiliation(s)
- Taeyang Yang
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, UNIST-Gil 50, Ulsan, 44919, Republic of Korea
| | - Ji-Hyun Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, UNIST-Gil 50, Ulsan, 44919, Republic of Korea
| | - Junsuk Kim
- Department of Industrial ICT Engineering, Dong-Eui University, Eomgwangno 176, Busan, 47340, Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, UNIST-Gil 50, Ulsan, 44919, Republic of Korea.
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Pernice R, Antonacci Y, Zanetti M, Busacca A, Marinazzo D, Faes L, Nollo G. Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress. Front Neurosci 2021; 14:602584. [PMID: 33613173 PMCID: PMC7890264 DOI: 10.3389/fnins.2020.602584] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain-body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η-ρ and δ-θ, θ-α, α-β), but also statistically significant interactions between the two subnetworks (mainly η-β and η-δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain-heart interactions and of brain-brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Yuri Antonacci
- Department of Physics and Chemistry “Emilio Segrè,” University of Palermo, Palermo, Italy
| | - Matteo Zanetti
- Department of Industrial Engineering, University of Trento, Trento, Italy
| | | | | | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, Trento, Italy
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36
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Januszko P, Gmaj B, Piotrowski T, Kopera M, Klimkiewicz A, Wnorowska A, Wołyńczyk-Gmaj D, Brower KJ, Wojnar M, Jakubczyk A. Delta resting-state functional connectivity in the cognitive control network as a prognostic factor for maintaining abstinence: An eLORETA preliminary study. Drug Alcohol Depend 2021; 218:108393. [PMID: 33158664 DOI: 10.1016/j.drugalcdep.2020.108393] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 10/11/2020] [Accepted: 10/26/2020] [Indexed: 12/28/2022]
Abstract
BACKGROUND Cortical regions that support cognitive control are increasingly well recognized, but the functional mechanisms that promote such control over emotional and behavioral hyperreactivity to alcohol in recently abstinent alcohol-dependent patients are still insufficiently understood. This study aimed to identify neurophysiological biomarkers of maintaining abstinence in alcohol-dependent individuals after alcohol treatment by investigating the resting-state EEG-based functional connectivity in the cognitive control network (CCN). METHODS Lagged phase synchronization between CCN areas by means of eLORETA as well as the Barratt Impulsiveness Scale (BIS-11) and Beck Depression Inventory (BDI) were assessed in abstinent alcohol-dependent patients recruited from treatment centers. A preliminary prospective study design was used to classify participants into those who did and did not maintain abstinence during a follow-up period (median 12 months) after discharge from residential treatment. RESULTS Alcohol-dependent individuals, who maintained abstinence (N = 18), showed significantly increased lagged phase synchronization between the left dorsolateral prefrontal cortex (DLPFC) and the left posterior parietal cortex (IPL) as well as between the right anterior insula cortex/frontal operculum (IA/FO) and the right inferior frontal junction (IFJ) in the delta band compared to those who later relapsed (N = 16). Regression analysis showed that the increased left frontoparietal delta connectivity in the early period of abstinence significantly predicted maintaining abstinence over the ensuing 12 months. Furthermore, right frontoinsular delta connectivity correlated negatively with impulsivity and depression measures. CONCLUSIONS These results suggest that the increased delta resting-state functional connectivity in the CCN may be a promising neurophysiological predictor of maintaining abstinence in individuals with alcohol dependence.
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Affiliation(s)
- Piotr Januszko
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland
| | - Bartłomiej Gmaj
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland.
| | - Tadeusz Piotrowski
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland
| | - Maciej Kopera
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland
| | - Anna Klimkiewicz
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland
| | - Anna Wnorowska
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland
| | - Dorota Wołyńczyk-Gmaj
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland
| | - Kirk J Brower
- Department of Psychiatry, Addiction Center, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Marcin Wojnar
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland; Department of Psychiatry, Addiction Center, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Andrzej Jakubczyk
- Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland
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37
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Long Q, Bhinge S, Calhoun VD, Adali T. Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia. J Neurosci Methods 2020; 350:109039. [PMID: 33370561 DOI: 10.1016/j.jneumeth.2020.109039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/08/2020] [Accepted: 12/09/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND Dynamic functional network connectivity (dFNC) summarizes associations among time-varying brain networks and is widely used for studying dynamics. However, most previous studies compute dFNC using temporal variability while spatial variability started receiving increasing attention. It is hence desirable to investigate spatial variability and the interaction between temporal and spatial variability. NEW METHOD We propose to use an adaptive variant of constrained independent vector analysis to simultaneously capture temporal and spatial variability, and introduce a goal-driven scheme for addressing a key challenge in dFNC analysis---determining the number of transient states. We apply our methods to resting-state functional magnetic resonance imaging data of schizophrenia patients (SZs) and healthy controls (HCs). RESULTS The results show spatial variability provides more features discriminative between groups than temporal variability. A comprehensive study of graph-theoretical (GT) metrics determines the optimal number of spatial states and suggests centrality as a key metric. Four networks yield significantly different levels of involvement in SZs and HCs. The high involvement of a component that relates to multiple distributed brain regions highlights dysconnectivity in SZ. One frontoparietal component and one frontal component demonstrate higher involvement in HCs, suggesting a more efficient cognitive control system relative to SZs. COMPARISON WITH EXISTING METHODS Spatial variability is more informative than temporal variability. The proposed goal-driven scheme determines the optimal number of states in a more interpretable way by making use of discriminative features. CONCLUSION GT analysis is promising in dFNC analysis as it identifies distinctive transient spatial states of dFNC and reveals unique biomedical patterns in SZs.
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Affiliation(s)
- Qunfang Long
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA.
| | - Suchita Bhinge
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, 87131, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA
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38
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Ahmadi H, Fatemizadeh E, Motie-Nasrabadi A. Identifying brain functional connectivity alterations during different stages of Alzheimer's disease. Int J Neurosci 2020; 132:1005-1013. [PMID: 33297814 DOI: 10.1080/00207454.2020.1860037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed.Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated individually in the early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), AD, and control subjects. Results: In the whole-brain analysis, during one year of disease progression, no significant changes were observed in none of the study groups. However, the intergroup comparison showed that in different stages (from healthy to AD) the efficiencies, clustering coefficient, transitivity, and modularity of the brain network have significantly changed. In the DMN network analysis, the EMCI subjects demonstrated significant alterations but no significant changes were observed in other study groups. In the nodal analysis of the DMN, the participation, clustering, and degree were among the measures significantly changed with the AD progression. Conclusions: Functional connectivity alterations are more in the first stage of AD. Since AD progresses slowly whole brain alterations are not significant in one year but DMN exhibits significant changes. Cingulum anterior and posterior areas were the first affected regions of interest (ROI) in the DMN network afterwards, the frontal superior medial ROI was declined in the functional connectivity.
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Affiliation(s)
- Hessam Ahmadi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Emad Fatemizadeh
- School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
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39
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Cao X, Lee K, Huang Q. Bayesian variable selection in logistic regression with application to whole-brain functional connectivity analysis for Parkinson's disease. Stat Methods Med Res 2020; 30:826-842. [PMID: 33308007 DOI: 10.1177/0962280220978990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Parkinson's disease is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this paper, we propose a Bayesian model to predict Parkinson's disease employing a functional MRI (fMRI) based radiomics approach. We consider a spike and slab prior for variable selection in high-dimensional logistic regression models, and present an approximate Gibbs sampler by replacing a logistic distribution with a t-distribution. Under mild conditions, we establish model selection consistency of the induced posterior and illustrate the performance of the proposed method outperforms existing state-of-the-art methods through simulation studies. In fMRI analysis, 6216 whole-brain functional connectivity features are extracted for 50 healthy controls along with 70 Parkinson's disease patients. We apply our method to the resulting dataset and further show its benefits with a higher average prediction accuracy of 0.83 compared to other contenders based on 10 random splits. The model fitting procedure also reveals the most discriminative brain regions for Parkinson's disease. These findings demonstrate that the proposed Bayesian variable selection method has the potential to support radiological diagnosis for patients with Parkinson's disease.
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Affiliation(s)
- Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati
| | - Kyoungjae Lee
- Department of Statistics, Inha University, Incheon, Korea
| | - Qingling Huang
- Department of Radiology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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40
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Zanin M, Belkoura S, Gomez J, Alfaro C, Cano J. Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients. Brain Topogr 2020; 34:6-18. [PMID: 33044705 DOI: 10.1007/s10548-020-00799-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 10/04/2020] [Indexed: 11/30/2022]
Abstract
In spite of the large attention received by brain activity analyses through functional networks, the effects of uncertainty on such representations have mostly been neglected. We here elaborate the hypothesis that such uncertainty is not just a nuisance, but that on the contrary is condition-dependent. We test this hypothesis by analysing a large set of EEG brain recordings corresponding to control subjects and patients suffering from alcoholism, through the reconstruction of the corresponding Maximum Spanning Trees (MSTs), the assessment of their topological differences, and the comparison of two frequentist and Bayesian reconstruction approaches. A machine learning model demonstrates that the Bayesian reconstruction encodes more information than the frequentist one, and that such additional information is related to the uncertainty of the topological structures. We finally show how the Bayesian approach is more effective in the validation of generative models, over and above the frequentist one, by proposing and disproving two models based on additive noise.
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Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
| | - Seddik Belkoura
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier Gomez
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain
| | - César Alfaro
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain
| | - Javier Cano
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain.,Department of Statistics, University of Auckland, Auckland, New Zealand
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41
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Kim M, Bao J, Liu K, Park BY, Park H, Shen L. Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2020; 12436:292-302. [PMID: 34766171 PMCID: PMC8580103 DOI: 10.1007/978-3-030-59861-7_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The connectivity analysis is a powerful technique for investigating a hard-wired brain architecture as well as flexible, functional dynamics tied to human cognition. Recent multi-modal connectivity studies had the challenge of combining functional and structural connectivity information into one integrated network. In this paper, we proposed a simplex regression model with graph-constrained Elastic Net (GraphNet) to estimate functional networks enriched by structural connectivity in a biologically meaningful way with a low model complexity. Our model constructed the functional networks using sparse simplex regression framework and enriched structural connectivity information based on GraphNet constraint. We applied our model on the real neuroimaging datasets to show its ability for predicting a clinical score. Our results demonstrated that integrating multi-modal features could detect more sensitive and subtle brain biomarkers than using a single modality.
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Affiliation(s)
- Mansu Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, USA
| | - Jingxaun Bao
- School of Arts and Sciences, University of Pennsylvania, USA
| | - Kefei Liu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, USA
| | - Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Canada
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Korea
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, USA
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42
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Xiao L, Zhang A, Cai B, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data. IEEE Trans Biomed Eng 2020; 68:1154-1165. [PMID: 32894705 DOI: 10.1109/tbme.2020.3022335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Recently, functional magnetic resonance imaging (fMRI)-derived brain functional connectivity (FC) patterns have been used as fingerprints to predict individual differences in phenotypic measures, and cognitive dysfunction associated with brain diseases. In these applications, how to accurately estimate FC patterns is crucial yet technically challenging. METHODS In this article, we propose a correlation guided graph learning (CGGL) method to estimate FC patterns for establishing brain-behavior relationships. Different from the existing graph learning methods which only consider the graph structure across brain regions-of-interest (ROIs), our proposed CGGL takes into account both the temporal correlation of ROIs across time points, and the graph structure across ROIs. The resulting FC patterns reflect substantial inter-individual variations related to the behavioral measure of interest. RESULTS We validate the effectiveness of our proposed CGGL on the Philadelphia Neurodevelopmental Cohort data for separately predicting three behavioral measures based on resting-state fMRI. Experimental results demonstrate that the proposed CGGL outperforms other competing FC pattern estimation methods. CONCLUSION Our method increases the predictive power of the constructed FC patterns when establishing brain-behavior relationships, and gains meaningful insights into relevant biological mechanisms. SIGNIFICANCE The proposed CGGL offers a more powerful, and reliable method to estimate FC patterns, which can be used as fingerprints in many brain network studies.
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43
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Racz FS, Stylianou O, Mukli P, Eke A. Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia. Front Syst Neurosci 2020; 14:49. [PMID: 32792917 PMCID: PMC7394222 DOI: 10.3389/fnsys.2020.00049] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022] Open
Abstract
Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.
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Affiliation(s)
| | | | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
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44
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Chen K, Azeez A, Chen DY, Biswal BB. Resting-State Functional Connectivity: Signal Origins and Analytic Methods. Neuroimaging Clin N Am 2020; 30:15-23. [PMID: 31759568 DOI: 10.1016/j.nic.2019.09.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Resting state functional connectivity (RSFC) has been widely studied in functional magnetic resonance imaging (fMRI) and is observed by a significant temporal correlation of spontaneous low-frequency signal fluctuations (SLFs) both within and across hemispheres during rest. Different hypotheses of RSFC include the biophysical origin hypothesis and cognitive origin hypothesis, which show that the role of SLFs and RSFC is still not completely understood. Furthermore, RSFC and age studies have shown an "age-related compensation" phenomenon. RSFC data analysis methods include time domain analysis, seed-based correlation, regional homogeneity, and principal and independent component analyses. Despite advances in RSFC, the authors also discuss challenges and limitations, ranging from head motion to methodological limitations.
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Affiliation(s)
- Kai Chen
- 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, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China
| | - Azeezat Azeez
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ 07102, USA
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ 07102, USA
| | - Bharat B 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, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ 07102, USA.
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45
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Del Campo-Vera RM, Gogia AS, Chen KH, Sebastian R, Kramer DR, Lee MB, Peng T, Tafreshi A, Barbaro MF, Liu CY, Kellis S, Lee B. Beta-band power modulation in the human hippocampus during a reaching task. J Neural Eng 2020; 17:036022. [PMID: 32413878 PMCID: PMC8544757 DOI: 10.1088/1741-2552/ab937f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Characterize the role of the beta-band (13-30 Hz) in the human hippocampus during the execution of voluntary movement. APPROACH We recorded electrophysiological activity in human hippocampus during a reach task using stereotactic electroencephalography (SEEG). SEEG has previously been utilized to study the theta band (3-8 Hz) in conflict processing and spatial navigation, but most studies of hippocampal activity during movement have used noninvasive measures such as fMRI. We analyzed modulation in the beta band (13-30 Hz), which is known to play a prominent role throughout the motor system including the cerebral cortex and basal ganglia. We conducted the classic 'center-out' direct-reach experiment with nine patients undergoing surgical treatment for medically refractory epilepsy. MAIN RESULTS In seven of the nine patients, power spectral analysis showed a statistically significant decrease in power within the beta band (13-30 Hz) during the response phase, compared to the fixation phase, of the center-out direct-reach task using the Wilcoxon signed-rank hypothesis test (p < 0.05). SIGNIFICANCE This finding is consistent with previous literature suggesting that the hippocampus may be involved in the execution of movement, and it is the first time that changes in beta-band power have been demonstrated in the hippocampus using human electrophysiology. Our findings suggest that beta-band modulation in the human hippocampus may play a role in the execution of voluntary movement.
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46
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The Effects of Cognitive Training on Brain Network Activity and Connectivity in Aging and Neurodegenerative Diseases: a Systematic Review. Neuropsychol Rev 2020; 30:267-286. [PMID: 32529356 PMCID: PMC7305076 DOI: 10.1007/s11065-020-09440-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 05/03/2020] [Indexed: 12/12/2022]
Abstract
Cognitive training (CT) is an increasingly popular, non-pharmacological intervention for improving cognitive functioning in neurodegenerative diseases and healthy aging. Although meta-analyses support the efficacy of CT in improving cognitive functioning, the neural mechanisms underlying the effects of CT are still unclear. We performed a systematic review of literature in the PubMed, Embase and PsycINFO databases on controlled CT trials (N > 20) in aging and neurodegenerative diseases with pre- and post-training functional MRI outcomes up to November 23rd 2018 (PROSPERO registration number CRD42019103662). Twenty articles were eligible for our systematic review. We distinguished between multi-domain and single-domain CT. CT induced both increases and decreases in task-related functional activation, possibly indicative of an inverted U-shaped curve association between regional brain activity and task performance. Functional connectivity within ‘cognitive’ brain networks was consistently reported to increase after CT while a minority of studies additionally reported increased segregation of frontoparietal and default mode brain networks. Although we acknowledge the large heterogeneity in type of CT, imaging methodology, in-scanner task paradigm and analysis methods between studies, we propose a working model of the effects of CT on brain activity and connectivity in the context of current knowledge on compensatory mechanisms that are associated with aging and neurodegenerative diseases.
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47
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Mohammadzadeh L, Latifi H, Khaksar S, Feiz MS, Motamedi F, Asadollahi A, Ezzatpour M. Measuring the Frequency-Specific Functional Connectivity Using Wavelet Coherence Analysis in Stroke Rats Based on Intrinsic Signals. Sci Rep 2020; 10:9429. [PMID: 32523058 PMCID: PMC7286921 DOI: 10.1038/s41598-020-66246-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/17/2020] [Indexed: 12/28/2022] Open
Abstract
Optical intrinsic signal imaging (OISi) method is an optical technique to evaluate the functional connectivity (FC) of the cortex in animals. Already, using OISi, the FC of the cortex has been measured in time or frequency domain separately, and at frequencies below 0.08 Hz, which is not in the frequency range of hemodynamic oscillations which are able to track fast cortical events, including neurogenic, myogenic, cardiac and respiratory activities. In the current work, we calculated the wavelet coherence (WC) transform of the OISi time series to evaluate the cerebral response changes in the stroke rats. Utilizing WC, we measured FC at frequencies up to 4.5 Hz, and could monitor the time and frequency dependency of the FC simultaneously. The results showed that the WC of the brain diminished significantly in ischemic motor and somatosensory cortices. According to the statistical results, the signal amplitude, responsive area size, correlation, and wavelet coherence of the motor and the somatosensory cortices for stroke hemisphere were found to be significantly lower compared to the healthy hemisphere. The obtained results confirm that the OISi-based WC analysis is an efficient method to diagnose the relative severity of infarction and the size of the infarcted region after ischemic stroke.
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Affiliation(s)
- Leila Mohammadzadeh
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Hamid Latifi
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran. .,Department of Physics, Shahid Beheshti University, Tehran, 1983963113, Iran.
| | - Sepideh Khaksar
- Department of Plant Sciences, Faculty of Biological Sciences, Alzahra University, Tehran, 1993893973, Iran
| | - Mohammad-Sadegh Feiz
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Fereshteh Motamedi
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran
| | - Amir Asadollahi
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Marzieh Ezzatpour
- Department of Physics, Shahid Beheshti University, Tehran, 1983963113, Iran
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48
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Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia. Neuroimage 2020; 216:116872. [PMID: 32353485 DOI: 10.1016/j.neuroimage.2020.116872] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 04/13/2020] [Accepted: 04/21/2020] [Indexed: 11/22/2022] Open
Abstract
The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study of the group-specific as well as distinct information of different populations. A number of subspace analysis algorithms have been developed and successfully applied to data fusion, however they are limited to joint analysis of only a couple of datasets. Since subspace analysis is very promising for analysis of multi-subject medical imaging data as well, we focus on this problem and propose a new method based on independent vector analysis (IVA) for common subspace extraction (IVA-CS) for multi-subject data analysis. IVA-CS leverages the strength of IVA in identification of a complete subspace structure across multiple datasets along with an efficient solution that uses only second-order statistics. We propose a subset analysis approach within IVA-CS to mitigate issues in estimation in IVA due to high dimensionality, both in terms of components estimated and the number of datasets. We introduce a scheme to determine a desirable size for the subset that is high enough to exploit the dependence across datasets and is not affected by the high dimensionality issue. We demonstrate the success of IVA-CS in extracting complex subset structures and apply the method to analysis of functional magnetic resonance imaging data from 179 subjects and show that it successfully identifies shared and complementary brain patterns from patients with schizophrenia (SZ) and healthy controls group. Two components with linked resting-state networks are identified to be unique to the SZ group providing evidence of functional dysconnectivity. IVA-CS also identifies subgroups of SZs that show significant differences in terms of their brain networks and clinical symptoms.
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49
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DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI. J Neurosci Methods 2020; 335:108506. [DOI: 10.1016/j.jneumeth.2019.108506] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 11/05/2019] [Accepted: 11/07/2019] [Indexed: 11/18/2022]
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50
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Xia CH, Ma Z, Cui Z, Bzdok D, Thirion B, Bassett DS, Satterthwaite TD, Shinohara RT, Witten DM. Multi-scale network regression for brain-phenotype associations. Hum Brain Mapp 2020; 41:2553-2566. [PMID: 32216125 PMCID: PMC7383128 DOI: 10.1002/hbm.24982] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/31/2020] [Accepted: 02/26/2020] [Indexed: 02/03/2023] Open
Abstract
Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.
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Affiliation(s)
- Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Zongming Ma
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Danilo Bzdok
- Department of Psychiatry, Psychopathology and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,Université Paris-Saclay, CEA, Inria, Gif-sur-Yvette, France.,Department of Bioengineering, McGill University, Montreal, Canada
| | | | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Physics and Astronomy, School of Arts and Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Russell T Shinohara
- Penn Statistics and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Imaging Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniela M Witten
- Department of Statistics, College of Arts and Science, University of Washington, Seattle, Washington, USA.,Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, USA
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