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Lee J, Hussain S, Warnick R, Vannucci M, Menchaca I, Seitz AR, Hu X, Peters MAK, Guindani M. A predictor-informed multi-subject bayesian approach for dynamic functional connectivity. PLoS One 2024; 19:e0298651. [PMID: 38753655 PMCID: PMC11098372 DOI: 10.1371/journal.pone.0298651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/30/2024] [Indexed: 05/18/2024] Open
Abstract
Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.
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Affiliation(s)
- Jaylen Lee
- Department of Statistics, University of California, Irvine, Irvine, California, United States of America
| | - Sana Hussain
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Ryan Warnick
- Microsoft Security Research, Microsoft, Redmond, Washington, United States of America
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Isaac Menchaca
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Aaron R. Seitz
- Department of Psychology, University of California, Riverside, Riverside, California, United States of America
| | - Xiaoping Hu
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Megan A. K. Peters
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
- Department of Cognitive Sciences, University of California, Irvine, Irvine, California, United States of America
| | - Michele Guindani
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, California, United States of America
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Ahmed I, Reeves WD, Sun W, Dubrof ST, Zukaitis JG, West FD, Park HJ, Zhao Q. Nutritional supplement induced modulations in the functional connectivity of a porcine brain. Nutr Neurosci 2024; 27:147-158. [PMID: 36657164 DOI: 10.1080/1028415x.2023.2166803] [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: 01/20/2023]
Abstract
BACKGROUND Functional connectivity (FC) measures statistical dependence between cortical brain regions. Studies of FC facilitate understanding of the brain's function and architecture that underpin normal cognition, behavior, and changes associated with various factors (e.g. nutritional supplements) at a large scale. OBJECTIVE We aimed to identify modifications in FC patterns and targeted brain anatomies in piglets following perinatal intake of different nutritional diets using a graph theory based approach. METHODS Forty-four piglets from four groups of pregnant sows, who were treated with nutritional supplements, including control diet, docosahexaenoic acid (DHA), egg yolk (EGG), and DHA + EGG, went through resting-state functional magnetic resonance imaging (rs-fMRI). We introduced the use of differential degree test (DDT) to identify differentially connected edges (DCEs). Simulation studies were first conducted to compare the DDT with permutation test, using three network structures at different noise levels. DDT was then applied to rs-fMRI data acquired from piglets. RESULTS In simulations, the DDT showed a greater accuracy in detecting DCEs when compared with the permutation test. For empirical data, we found that the strength of internodal connectivity is significantly increased for more than 6% of edges in the EGG group and more than 8% of edges in the DHA and DHA + EGG groups, all compared to the control group. Moreover, differential wiring diagrams between group comparisons provided means to pinpoint brain hubs affected by nutritional supplements. CONCLUSION DDT showed a greater accuracy of detection of DCEs and demonstrated EGG, DHA, and DHA + EGG supplemented diets lead to an improved internodal connectivity in the developing piglet brain.
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Affiliation(s)
- Ishfaque Ahmed
- Department of Physics and Astronomy, University of Georgia, Athens, GA, USA
- Institute of Physics, University of Sindh, Jamshoro, Pakistan
| | - William D Reeves
- Department of Physics and Astronomy, University of Georgia, Athens, GA, USA
| | - Wenwu Sun
- Department of Physics and Astronomy, University of Georgia, Athens, GA, USA
| | - Stephanie T Dubrof
- Department of Nutritional Sciences, University of Georgia, Athens, GA, USA
| | - Jillien G Zukaitis
- Department of Nutritional Sciences, University of Georgia, Athens, GA, USA
| | - Franklin D West
- Department of Animal and Dairy Sciences, University of Georgia, Athens, GA, USA
- Regenerative Bioscience Center, Athens, GA, USA
| | - Hea Jin Park
- Department of Nutritional Sciences, University of Georgia, Athens, GA, USA
| | - Qun Zhao
- Department of Physics and Astronomy, University of Georgia, Athens, GA, USA
- Regenerative Bioscience Center, Athens, GA, USA
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Pan H, Mao Y, Liu P, Li Y, Wei G, Qiao X, Ren Y, Zhao F. Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis. Med Phys 2023; 50:6269-6282. [PMID: 36995984 DOI: 10.1002/mp.16406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/13/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases. PURPOSE To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients. METHODS We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis. RESULTS (1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum. CONCLUSIONS Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.
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Affiliation(s)
- Hongxin Pan
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yanyan Mao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Peiqiang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yuan Li
- School of Management Science and Engineering, Shandong Technology and Business University, Yantai, China
| | - Guanglan Wei
- Information Network Center, Shandong Second Provincial General Hospital, Jinan, China
| | - Xiaoyan Qiao
- School of Mathematics and Information Science, Shandong Technology and Business University, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
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Fallahi A, Hashemi-Fesharaki SS, Hoseini-Tabatabaei N, Pooyan M, Nazem-Zadeh MR. Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082832 DOI: 10.1109/embc40787.2023.10339957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.
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Ramírez-Carrillo E, G-Santoyo I, López-Corona O, Rojas-Ramos OA, Falcón LI, Gaona O, de la Fuente Rodríguez RM, Hernández Castillo A, Cerqueda-García D, Sánchez-Quinto A, Hernández-Muciño D, Nieto J. Similar connectivity of gut microbiota and brain activity networks is mediated by animal protein and lipid intake in children from a Mexican indigenous population. PLoS One 2023; 18:e0281385. [PMID: 37384745 PMCID: PMC10310019 DOI: 10.1371/journal.pone.0281385] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 01/22/2023] [Indexed: 07/01/2023] Open
Abstract
The gut microbiota-brain axis is a complex communication network essential for host health. Any long-term disruption can affect higher cognitive functions, or it may even result in several chronic neurological diseases. The type and diversity of nutrients an individual consumes are essential for developing the gut microbiota (GM) and the brain. Hence, dietary patterns might influence networks communication of this axis, especially at the age that both systems go through maturation processes. By implementing Mutual Information and Minimum Spanning Tree (MST); we proposed a novel combination of Machine Learning and Network Theory techniques to study the effect of animal protein and lipid intake on the connectivity of GM and brain cortex activity (BCA) networks in children from 5-to 10 years old from an indigenous community in the southwest of México. Socio-ecological conditions in this nonwestern lifestyle community are very homogeneous among its inhabitants but it shows high individual heterogeneity in the consumption of animal products. Results suggest that MST, the critical backbone of information flow, diminishes under low protein and lipid intake. So, under these nonwestern regimens, deficient animal protein and lipid consumption diets may significantly affect the GM-BCA connectivity in crucial development stages. Finally, MST offers us a metric that unifies biological systems of different nature to evaluate the change in their complexity in the face of environmental pressures or disturbances. Effect of Diet on gut microbiota and brain networks connectivity.
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Affiliation(s)
- Elvia Ramírez-Carrillo
- NeuroEcology Lab, Department of Psychology, UNAM, CDMX, México
- Investigadoras por México, Posdoc-CONACyT, Facultad de Psicología, Universidad Nacional Autónoma de México (UNAM), CDMX, México
| | - Isaac G-Santoyo
- NeuroEcology Lab, Department of Psychology, UNAM, CDMX, México
- Unidad de Investigación en Psicobiología y Neurociencias, Department of Psychology, Universidad Nacional Autónoma de México (UNAM), CDMX, México
| | - Oliver López-Corona
- Cátedras CONACyT, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México (UNAM), CDMX, México
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, CDMX, México
| | - Olga A. Rojas-Ramos
- NeuroEcology Lab, Department of Psychology, UNAM, CDMX, México
- Coordinación de Psciobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México (UNAM), CDMX, México
| | - Luisa I. Falcón
- Laboratorio de Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México, UNAM, Parque Científico y Tecnológico de Yucatán, Mérida, México
| | - Osiris Gaona
- Laboratorio de Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México, UNAM, Parque Científico y Tecnológico de Yucatán, Mérida, México
| | | | | | - Daniel Cerqueda-García
- Consorcio de Investigación del Golfo de México (CIGoM), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida, Departamento de Recursos del Mar, Mérida, Yucatán, México
| | - Andrés Sánchez-Quinto
- Laboratorio de Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México, UNAM, Parque Científico y Tecnológico de Yucatán, Mérida, México
| | - Diego Hernández-Muciño
- Laboratorio de Agroecología Instituto de Investigaciones en Ecosistema y Sustentabilidad, UNAM, Morelia, México
| | - Javier Nieto
- Laboratorio de Aprendizaje y Adaptación, Facultad de Psicología, Universidad Nacional Autónoma de México (UNAM), CDMX, México
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Massalha Y, Maggioni E, Callari A, Brambilla P, Delvecchio G. A review of resting-state fMRI correlations with executive functions and social cognition in bipolar disorder. J Affect Disord 2023; 334:337-351. [PMID: 37003435 DOI: 10.1016/j.jad.2023.03.084] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 03/20/2023] [Accepted: 03/25/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND Deficits in executive functions (EF) and social cognition (SC) are often observed in bipolar disorder (BD), leading to a severe impairment in engaging a functional interaction with the others and the surrounding environment. Therefore, in recent years, resting-state functional magnetic resonance imaging (rs-fMRI) studies on BD tried to identify the neural underpinnings of these cognitive domains by exploring the association between the intrinsic functional connectivity (FC) and the scores in clinical scales evaluating these domains. METHODS A bibliographic search on PubMed and Scopus of studies evaluating the correlations between rs-fMRI findings and EF and/or SC in BD was conducted until March 2022. Ten studies met the inclusion criteria. RESULTS Overall, the results of the reviewed studies showed that BD patients had FC deficits compared to healthy controls (HC) in selective resting-state networks involved in EF and SC, which include the default mode network, especially the link between medial prefrontal cortex and posterior cingulate cortex, and the sensory-motor network. Finally, it also emerged the predominant role of alterations in prefrontal connections in explaining the cognitive deficits in BD patients. LIMITATIONS The heterogeneity of the reviewed studies, in terms of cognitive domains explored and neuroimaging acquisitions, limited the comparability of the findings. CONCLUSIONS rs-fMRI studies could help deepen the brain network alterations underlying EF and SC deficits in BD, pointing the attention on the neuronal underpinning of cognition, whose knowledge may lead to the development of new neurobiological-based approaches to improve the quality of life of these patients.
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Affiliation(s)
- Yara Massalha
- Department of Pathophysiology and Transplantation, University of Milan, 20122 Milan, Italy
| | - Eleonora Maggioni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20122 Milan, Italy
| | - Antonio Callari
- Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, 20122 Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, 20122 Milan, Italy; Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, 20122 Milan, Italy
| | - Giuseppe Delvecchio
- Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, 20122 Milan, Italy.
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Wei L, Zhang Y, Zhai W, Wang H, Zhang J, Jin H, Feng J, Qin Q, Xu H, Li B, Liu J. Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders. Front Neurosci 2022; 16:987248. [PMID: 36523439 PMCID: PMC9745118 DOI: 10.3389/fnins.2022.987248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2023] Open
Abstract
INTRODUCTION Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. OBJECTS The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. MATERIALS AND METHODS The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics. RESULTS For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. CONCLUSION Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.
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Affiliation(s)
- Lei Wei
- Network Center, Air Force Medical University, Xi’an, China
| | - Yao Zhang
- Military Medical Center, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Wensheng Zhai
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Junchao Zhang
- Network Center, Air Force Medical University, Xi’an, China
| | - Haojie Jin
- Network Center, Air Force Medical University, Xi’an, China
| | - Jianfei Feng
- Network Center, Air Force Medical University, Xi’an, China
| | - Qin Qin
- Network Center, Air Force Medical University, Xi’an, China
| | - Hao Xu
- Network Center, Air Force Medical University, Xi’an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Jian Liu
- Network Center, Air Force Medical University, Xi’an, China
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Zhang C, Cui X, Lian S, Xiao R, Qiao H, Li S, Lou Y, Feng Y, Zhuang L, Du J, Liu X. Intelligent algorithm for dynamic functional brain network complexity from CN to AD. INT J INTELL SYST 2022. [DOI: 10.1002/int.22737] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Chenghui Zhang
- School of Computer Science Qufu Normal University Rizhao China
- Department of Psychology and Behavioral Sciences Zhejiang University Hangzhou China
| | - Xinchun Cui
- School of Computer Science Qufu Normal University Rizhao China
- Guangxi Key Laboratory of Cryptography and Information Security Guilin China
| | - Shujun Lian
- School of Management Qufu Normal University Rizhao China
| | - Ruyi Xiao
- School of Computer Science Qufu Normal University Rizhao China
| | - Hong Qiao
- School of Business Shandong Normal University Jinan China
| | - Shancang Li
- Department of Computer Science University of the West of England Bristol UK
| | - Yue Lou
- Department of Neurology Zhejiang Hospital Hangzhou China
| | - Yue Feng
- Department of Radiology Zhejiang Hospital Hangzhou China
| | - Liying Zhuang
- Department of Neurology Zhejiang Hospital Hangzhou China
| | - Jianzong Du
- Respiratory Medicine Zhejiang Hospital Hangzhou China
| | - Xiaoli Liu
- Department of Neurology Zhejiang Hospital Hangzhou China
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Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models. J Neurosci 2021; 41:8577-8588. [PMID: 34413204 DOI: 10.1523/jneurosci.0051-21.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 07/06/2021] [Accepted: 07/11/2021] [Indexed: 01/21/2023] Open
Abstract
Neuronal ensembles are groups of neurons with coordinated activity that could represent sensory, motor, or cognitive states. The study of how neuronal ensembles are built, recalled, and involved in the guiding of complex behaviors has been limited by the lack of experimental and analytical tools to reliably identify and manipulate neurons that have the ability to activate entire ensembles. Such pattern completion neurons have also been proposed as key elements of artificial and biological neural networks. Indeed, the relevance of pattern completion neurons is highlighted by growing evidence that targeting them can activate neuronal ensembles and trigger behavior. As a method to reliably detect pattern completion neurons, we use conditional random fields (CRFs), a type of probabilistic graphical model. We apply CRFs to identify pattern completion neurons in ensembles in experiments using in vivo two-photon calcium imaging from primary visual cortex of male mice and confirm the CRFs predictions with two-photon optogenetics. To test the broader applicability of CRFs we also analyze publicly available calcium imaging data (Allen Institute Brain Observatory dataset) and demonstrate that CRFs can reliably identify neurons that predict specific features of visual stimuli. Finally, to explore the scalability of CRFs we apply them to in silico network simulations and show that CRFs-identified pattern completion neurons have increased functional connectivity. These results demonstrate the potential of CRFs to characterize and selectively manipulate neural circuits.SIGNIFICANCE STATEMENT We describe a graph theory method to identify and optically manipulate neurons with pattern completion capability in mouse cortical circuits. Using calcium imaging and two-photon optogenetics in vivo we confirm that key neurons identified by this method can recall entire neuronal ensembles. This method could be broadly applied to manipulate neuronal ensemble activity to trigger behavior or for therapeutic applications in brain prostheses.
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Tao Q, Si Y, Li F, Li P, Li Y, Zhang S, Wan F, Yao D, Xu P. Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG. Int J Neural Syst 2021; 31:2150031. [PMID: 34167448 DOI: 10.1142/s0129065721500313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.
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Affiliation(s)
- Qin Tao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yajing Si
- School of Psychology, Xinxiang Medical University, Hena, 453000, P. R. China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P. R. China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Shu Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Feng Wan
- Faculty of Science and Technology, University of Macau, 999078, Macau
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
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Ahmadi H, Fatemizadeh E, Motie-Nasrabadi A. Deep sparse graph functional connectivity analysis in AD patients using fMRI data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 201:105954. [PMID: 33567381 DOI: 10.1016/j.cmpb.2021.105954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to eliminate weak correlations, thresholding is a common method. In this routine, by adjusting a threshold the values below the threshold turn to zero and the rest remains. In this paper, in addition to thresholding, two other methods including spectral sparsification based on Effective Resistance (ER) and autoencoders are investigated for sparsing the correlation matrices. Autoencoders are based on deep learning neural networks and ER considers the network as a resistive circuit. The fMRI data of the study correspond to Alzheimer's patients and control subjects. Graph global measures are calculated and a non-parametric permutation test is reported. Results show that the autoencoder and spectral sparsification achieved more distinctive brain graphs between healthy and AD subjects. Also, more graph global features were significantly different from these two methods due to better elimination of weak correlations and preserve more informative ones. Regardless of the sparsification method features including average strength, clustering, local efficiency, modularity, and transitivity are significantly different (P-value=0.05). On the other hand, the measures radius, diameter, and eccentricity showed no significant differences in none of the methods. In addition, according to three different methods, the brain regions show fragile and solid FCs are determined.
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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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12
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Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach. Neurol Sci 2020; 42:2379-2390. [PMID: 33052576 DOI: 10.1007/s10072-020-04759-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/23/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE). METHODS Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE. RESULTS Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%. CONCLUSION Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.
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13
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Stability-driven non-negative matrix factorization-based approach for extracting dynamic network from resting-state EEG. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.01.071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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14
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Christiaen E, Goossens MG, Descamps B, Larsen LE, Boon P, Raedt R, Vanhove C. Dynamic functional connectivity and graph theory metrics in a rat model of temporal lobe epilepsy reveal a preference for brain states with a lower functional connectivity, segregation and integration. Neurobiol Dis 2020; 139:104808. [PMID: 32087287 DOI: 10.1016/j.nbd.2020.104808] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/21/2020] [Accepted: 02/18/2020] [Indexed: 12/14/2022] Open
Abstract
Epilepsy is a neurological disorder characterized by recurrent epileptic seizures. The involvement of abnormal functional brain networks in the development of epilepsy and its comorbidities has been demonstrated by electrophysiological and neuroimaging studies in patients with epilepsy. This longitudinal study investigated changes in dynamic functional connectivity (dFC) and network topology during the development of epilepsy using the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy (TLE). Resting state functional magnetic resonance images (rsfMRI) of 20 IPKA animals and 7 healthy control animals were acquired before and 1, 3, 6, 10 and 16 weeks after status epilepticus (SE) under medetomidine anaesthesia using a 7 T MRI system. Starting from 17 weeks post-SE, hippocampal EEG was recorded to determine the mean daily seizure frequency of each animal. Dynamic FC was assessed by calculating the correlation matrices between fMRI time series of predefined regions of interest within a sliding window of 50 s using a step length of 2 s. The matrices were classified into 6 FC states, each characterized by a correlation matrix, using k-means clustering. In addition, several time-variable graph theoretical network metrics were calculated from the time-varying correlation matrices and classified into 6 states of functional network topology, each characterized by a combination of network metrics. Our results showed that FC states with a lower mean functional connectivity, lower segregation and integration occurred more often in IPKA animals compared to control animals. Functional connectivity also became less variable during epileptogenesis. In addition, average daily seizure frequency was positively correlated with percentage dwell time (i.e. how often a state occurs) in states with high mean functional connectivity, high segregation and integration, and with the number of transitions between states, while negatively correlated with percentage dwell time in states with a low mean functional connectivity, low segregation and low integration. This indicates that animals that dwell in states of higher functional connectivity, higher segregation and higher integration, and that switch more often between states, have more seizures.
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Affiliation(s)
- Emma Christiaen
- MEDISIP, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
| | | | - Benedicte Descamps
- MEDISIP, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Lars E Larsen
- MEDISIP, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium; 4Brain Team, Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Paul Boon
- 4Brain Team, Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Robrecht Raedt
- 4Brain Team, Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Christian Vanhove
- MEDISIP, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
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15
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Disrupted salience network dynamics in Parkinson's disease patients with impulse control disorders. Parkinsonism Relat Disord 2019; 70:74-81. [PMID: 31881521 DOI: 10.1016/j.parkreldis.2019.12.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/15/2019] [Accepted: 12/15/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND Dynamic functional network analysis may add relevant information about the temporal nature of the neurocognitive alterations in PD patients with impulse control disorders (PD-ICD). Our aim was to investigate changes in dynamic functional network connectivity (dFNC) in PD-ICD patients, and topological properties of such networks. METHODS Resting state fMRI was performed on 16 PD PD-ICD patients, 20 PD patients without ICD and 17 healthy controls, whose demographic, clinical and behavioral scores were assessed. We conducted a group spatial independent component analysis, sliding window and graph-theory analyses. RESULTS PD-ICD patients, in contrast to PD-noICD and HC subjects, were engaged across time in a brain configuration pattern characterized by a lack of between-network connections at the expense of strong within-network connections (State III) in temporal, frontoinsular and cingulate cortices, all key nodes of the salience network. Moreover, this increased maintenance of State III in PD-ICD patients was positively correlated with the severity of impulsivity and novelty seeking as measured by specific scales. While in State III, these patients also exhibited increased local efficiency in all the aforementioned areas. CONCLUSIONS Our findings show for the first time that PD-ICD patients have a dynamic functional engagement of local connectivity involving the limbic circuit, leading to the inefficient modulation in emotional processing and reward-related decision-making. These results provide new insights into the pathophysiology of ICD in PD patients and indicate that the dFC study of fMRI could be a useful biomarker to identify patients at risk to develop ICD.
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16
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Spring AM, Pittman DJ, Bessemer R, Federico P. Graph index complexity as a novel surrogate marker of high frequency oscillations in delineating the seizure onset zone. Clin Neurophysiol 2019; 131:78-87. [PMID: 31756595 DOI: 10.1016/j.clinph.2019.09.019] [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: 04/29/2019] [Revised: 08/09/2019] [Accepted: 09/06/2019] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To investigate the Graph Index Complexity (uGIC) as a marker of high frequency oscillatory (HFO) activity, the seizure onset zone (SOZ), and surgical outcome. METHODS The SOZ, rates of HFOs at two thresholds (broad, strict), and uGIC were determined using EEG data from 41 patients. The correlation between HFOs and uGIC were calculated. HFOs and uGIC were compared within and outside the SOZ. Postsurgical outcome was compared to the colocalization of HFOs and resected SOZ. RESULTS There was significant correlation between uGIC and both broad (r = 0.69, p < 0.0005) and strict HFOs (r = 0.48, p < 0.0005). All were significantly greater within the SOZ overall, but only in 17/41 (strict, uGIC) or 18/41 (broad) patients. HFO markers were significantly greater within the SOZ for 8/15 patients with positive postsurgical outcomes, but not for any patients with negative outcomes (0/5). CONCLUSION The uGIC is a marker of HFO activity, while HFOs and uGIC are markers of the SOZ overall. Colocalization of HFOs and the SOZ has strong positive predictive value for postsurgical outcome, but poor negative predictive value. SIGNIFICANCE The uGIC is an objective surrogate marker of HFO activity independent of identifying discrete HFO events.
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Affiliation(s)
- Aaron M Spring
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Daniel J Pittman
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Robin Bessemer
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Paolo Federico
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada.
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17
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Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma. Front Neurosci 2019; 13:803. [PMID: 31507353 PMCID: PMC6716456 DOI: 10.3389/fnins.2019.00803] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 07/17/2019] [Indexed: 01/08/2023] Open
Abstract
Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R 2 = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.
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Affiliation(s)
- D Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, United States
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States.,U.S. Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, United States.,Department of Psychology, Auburn University, Auburn, AL, United States
| | - Jeffrey S Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Thomas S Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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18
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Xie Y, Liu T, Ai J, Chen D, Zhuo Y, Zhao G, He S, Wu J, Han Y, Yan T. Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline. Front Aging Neurosci 2019; 11:118. [PMID: 31281248 PMCID: PMC6595963 DOI: 10.3389/fnagi.2019.00118] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 05/03/2019] [Indexed: 12/31/2022] Open
Abstract
Despite subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), being widely studied in recent years, studies on centrality frequency in individuals with SCD are lacking. This study aimed to investigate the differences in centrality frequency between individuals with SCD and normal controls (NCs). Forty individuals with SCD and 53 well-matched NCs underwent a resting-state functional magnetic resonance imaging scan. We assessed individual dynamic functional connectivity using sliding window correlations. In each time window, brain regions with a high degree centrality were defined as hubs. Across the entire time window, the proportion of time that the hub appeared was characterized as centrality frequency. The centrality frequency correlated with cognitive performance differently in individuals with SCD and NCs. Our results revealed that in individuals with SCD, compared with NCs, correlations between centrality frequency of the anterior cortical regions and cognitive performance decreased (79.2% for NCs and 43.5% for individuals with SCD). In contrast, correlations between centrality frequency of the posterior cortical regions and cognitive performance increased in SCD individuals compared with NCs (20.8% for NCs and 56.5% for individuals with SCD). Moreover, the changes mainly focused on the anterior (93.3% for NCs and 45.5% for individuals with SCD) and posterior (6.7% for NCs and 54.5% for individuals with SCD) regions associated with the default mode network (DMN). In addition, we used absolute thresholds (correlation efficient r = 0.2, 0.25) and proportional thresholds (sparsity = 0.2, 0.25) to verify the results. Dynamic results are relative stable at absolute thresholds while static results are relative stable at proportional thresholds. Converging findings provide a new framework for the detection of the changes occurring in individuals with SCD via centrality frequency of the DMN.
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Affiliation(s)
- Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jing Ai
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yiran Zhuo
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Guanglei Zhao
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Shuai He
- Beijing Haidian Foreign Language Shiyan School, Beijing, China
| | - Jinglong Wu
- School of Mechatronical Engineering, Intelligent Robotics Institute, Beijing Institute of Technology, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Institute of Geriatrics, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
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19
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Saba V, Premi E, Cristillo V, Gazzina S, Palluzzi F, Zanetti O, Gasparotti R, Padovani A, Borroni B, Grassi M. Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia. Front Neurosci 2019; 13:211. [PMID: 30930736 PMCID: PMC6427927 DOI: 10.3389/fnins.2019.00211] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/25/2019] [Indexed: 12/12/2022] Open
Abstract
Brain functional disruption and cognitive shortfalls as consequences of neurodegeneration are among the most investigated aspects in current clinical research. Traditionally, specific anatomical and behavioral traits have been associated with neurodegeneration, thus directly translatable in clinical terms. However, these qualitative traits, do not account for the extensive information flow breakdown within the functional brain network that deeply affect cognitive skills. Behavioural variant Frontotemporal Dementia (bvFTD) is a neurodegenerative disorder characterized by behavioral and executive functions disturbances. Deviations from the physiological cognitive functioning can be accurately inferred and modeled from functional connectivity alterations. Although the need for unbiased metrics is still an open issue in imaging studies, the graph-theory approach applied to neuroimaging techniques is becoming popular in the study of brain dysfunction. In this work, we assessed the global connectivity and topological alterations among brain regions in bvFTD patients using a minimum spanning tree (MST) based analysis of resting state functional MRI (rs-fMRI) data. Whilst several graph theoretical methods require arbitrary criteria (including the choice of network construction thresholds and weight normalization methods), MST is an unambiguous modeling solution, ensuring accuracy, robustness, and reproducibility. MST networks of 116 regions of interest (ROIs) were built on wavelet correlation matrices, extracted from 41 bvFTD patients and 39 healthy controls (HC). We observed a global fragmentation of the functional network backbone with severe disruption of information-flow highways. Frontotemporal areas were less compact, more isolated, and concentrated in less integrated structures, respect to healthy subjects. Our results reflected such complex breakdown of the frontal and temporal areas at both intra-regional and long-range connections. Our findings highlighted that MST, in conjunction with rs-fMRI data, was an effective method for quantifying and detecting functional brain network impairments, leading to characteristic bvFTD cognitive, social, and executive functions disorders.
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Affiliation(s)
- Valentina Saba
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Enrico Premi
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Viviana Cristillo
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Stefano Gazzina
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Fernando Palluzzi
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Orazio Zanetti
- Alzheimer's Research Unit, IRCCS Fatebenefratelli, Brescia, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Mario Grassi
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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20
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Statistical model-based approaches for functional connectivity analysis of neuroimaging data. Curr Opin Neurobiol 2019; 55:48-54. [PMID: 30739880 DOI: 10.1016/j.conb.2019.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 01/06/2019] [Accepted: 01/13/2019] [Indexed: 11/21/2022]
Abstract
We present recent literature on model-based approaches to estimating functional connectivity from neuroimaging data. In contrast to the typical focus on a particular scientific question, we reframe a wider literature in terms of the underlying statistical model used. We distinguish between directed versus undirected and static versus time-varying connectivity. There are numerous advantages to a model-based approach, including easily specified inductive bias, handling limited data scenarios, and building complex models from simpler building blocks.
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21
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Cortical cores in network dynamics. Neuroimage 2018; 180:370-382. [DOI: 10.1016/j.neuroimage.2017.09.063] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Revised: 09/12/2017] [Accepted: 09/28/2017] [Indexed: 02/02/2023] Open
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22
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Kundu S, Ming J, Pierce J, McDowell J, Guo Y. Estimating dynamic brain functional networks using multi-subject fMRI data. Neuroimage 2018; 183:635-649. [PMID: 30048750 DOI: 10.1016/j.neuroimage.2018.07.045] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 06/08/2018] [Accepted: 07/17/2018] [Indexed: 01/13/2023] Open
Abstract
A common assumption in the study of brain functional connectivity is that the brain network is stationary. However it is increasingly recognized that the brain organization is prone to variations across the scanning session, fueling the need for dynamic connectivity approaches. One of the main challenges in developing such approaches is that the frequency and change points for the brain organization are unknown, with these changes potentially occurring frequently during the scanning session. In order to provide greater power to detect rapid connectivity changes, we propose a fully automated two-stage approach which pools information across multiple subjects to estimate change points in functional connectivity, and subsequently estimates the brain networks within each state phase lying between consecutive change points. The number and positioning of the change points are unknown and learned from the data in the first stage, by modeling a time-dependent connectivity metric under a fused lasso approach. In the second stage, the brain functional network for each state phase is inferred via sparse inverse covariance matrices. We compare the performance of the method with existing dynamic connectivity approaches via extensive simulation studies, and apply the proposed approach to a saccade block task fMRI data.
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Affiliation(s)
- Suprateek Kundu
- Department of Biostatistics, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.
| | - Jin Ming
- Department of Biostatistics, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA
| | - Jordan Pierce
- Department of Psychology, University of Georgia, 125 Baldwin Street, Athens, GA 30602, USA
| | - Jennifer McDowell
- Department of Psychology, University of Georgia, 125 Baldwin Street, Athens, GA 30602, USA
| | - Ying Guo
- Department of Biostatistics, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA
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23
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Xu H, Shen H, Wang L, Zhong Q, Lei Y, Yang L, Zeng LL, Zhou Z, Hu D, Yang Z. Impact of 36 h of total sleep deprivation on resting-state dynamic functional connectivity. Brain Res 2018; 1688:22-32. [DOI: 10.1016/j.brainres.2017.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/10/2017] [Accepted: 11/13/2017] [Indexed: 11/28/2022]
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24
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Warnick R, Guindani M, Erhardt E, Allen E, Calhoun V, Vannucci M. A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data. J Am Stat Assoc 2018; 113:134-151. [PMID: 30853734 PMCID: PMC6405235 DOI: 10.1080/01621459.2017.1379404] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 08/01/2017] [Indexed: 01/22/2023]
Abstract
Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad-hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task-based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.
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Affiliation(s)
- Ryan Warnick
- Department of Statistics, Rice University, Houston, TX
| | - Michele Guindani
- Department of Statistics, University of California at Irvine, Irvine, CA
| | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM
| | - Elena Allen
- Research Scientist, Medici Technologies, Albuquerque, NM
| | - Vince Calhoun
- Distinguished Professor, Departments of Electrical and Computer Engineering, University of New Mexico
| | - Marina Vannucci
- Noah Harding Professor and Chair, Department of Statistics, Rice University
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25
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Chiang S, Vannucci M, Goldenholz DM, Moss R, Stern JM. Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability. Epilepsia Open 2018; 3:236-246. [PMID: 29881802 PMCID: PMC5983137 DOI: 10.1002/epi4.12112] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/05/2018] [Indexed: 01/07/2023] Open
Abstract
Objective A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk. Methods Using data from SeizureTracker.com, a patient‐reported seizure diary tool containing over 1.2 million recorded seizures across 8 years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed‐effects hidden Markov model for zero‐inflated count data was developed to estimate changes in underlying seizure risk using patient‐reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (TSC) were assessed using data from SeizureTracker.com. Results EpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to TSC, four underlying seizure risk states were identified. The expected duration of each state was <12 months, providing a data‐driven estimate of the amount of time a person with TSC would be expected to remain at the same seizure risk level according to the natural course of epilepsy. Significance We propose a novel Bayesian statistical approach for evaluating seizure risk on an individual patient level using patient‐reported seizure diaries, which allows for the incorporation of external clinical variables to assess impact on seizure risk. This tool may improve the ability to distinguish true changes in seizure risk from natural variations in seizure frequency in clinical practice. Incorporation of systematic statistical approaches into antiepileptic drug (AED) management may help improve understanding of seizure unpredictability as well as timing of treatment interventions for people with epilepsy.
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Affiliation(s)
- Sharon Chiang
- School of Medicine Baylor College of Medicine Houston Texas U.S.A.,Department of Statistics Rice University Houston Texas U.S.A
| | - Marina Vannucci
- Department of Statistics Rice University Houston Texas U.S.A
| | - Daniel M Goldenholz
- Division of Epilepsy Beth Israel Deaconess Medical Center Boston Massachusetts U.S.A.,Clinical Epilepsy Section National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda Maryland U.S.A
| | - Robert Moss
- SeizureTracker.com Alexandria Virginia U.S.A
| | - John M Stern
- Department of Neurology University of California Los Angeles Los Angeles California U.S.A
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Chiang S, Vankov ER, Yeh HJ, Guindani M, Vannucci M, Haneef Z, Stern JM. Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity. PLoS One 2018; 13:e0190220. [PMID: 29320526 PMCID: PMC5761874 DOI: 10.1371/journal.pone.0190220] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 12/11/2017] [Indexed: 12/24/2022] Open
Abstract
Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas, United States of America
- Baylor College of Medicine, School of Medicine, Houston, Texas, United States of America
| | - Emilian R. Vankov
- Department of Statistics, Rice University, Houston, Texas, United States of America
- Baker Institute for Public Policy, Rice University, Houston, Texas, United States of America
| | - Hsiang J. Yeh
- Department of Neurology, University of California at Los Angeles, Los Angeles, California, United States of America
| | - Michele Guindani
- Department of Statistics, Uniersity of California at Irvine, Irvine, California, United States of America
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, Texas, United States of America
- Neurology Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, United States of America
| | - John M. Stern
- Department of Neurology, University of California at Los Angeles, Los Angeles, California, United States of America
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Bolton TAW, Tarun A, Sterpenich V, Schwartz S, Van De Ville D. Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:230-240. [PMID: 28945590 DOI: 10.1109/tmi.2017.2755369] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner. We demonstrate and validate the method on simulated data, for which we observed that the SCHMM could accurately estimate network dynamics, revealing more precise insights about direct network-to-network modulatory influences than with conventional correlational methods. On experimental resting-state fMRI data, we unraveled a set of reproducible cross-network couplings across two independent datasets. Our framework opens new perspectives for capturing complex temporal dynamics and their changes in health and disease.
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28
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Chiang S, Guindani M, Yeh HJ, Dewar S, Haneef Z, Stern JM, Vannucci M. A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection. Front Neurosci 2017; 11:669. [PMID: 29259537 PMCID: PMC5723403 DOI: 10.3389/fnins.2017.00669] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 11/17/2017] [Indexed: 01/19/2023] Open
Abstract
We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, TX, United States.,School of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, Irvine, CA, United States
| | - Hsiang J Yeh
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sandra Dewar
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - John M Stern
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, TX, United States
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29
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Chen JE, Rubinov M, Chang C. Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data. Neuroimaging Clin N Am 2017; 27:547-560. [PMID: 28985928 PMCID: PMC5679015 DOI: 10.1016/j.nic.2017.06.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Functional MR imaging (fMR imaging) studies have recently begun to examine spontaneous changes in interregional interactions (functional connectivity) over seconds to minutes, and their relation to natural shifts in cognitive and physiologic states. This practice opens the potential for uncovering structured, transient configurations of coordinated brain activity whose features may provide novel cognitive and clinical biomarkers. However, analysis of these time-varying phenomena requires careful differentiation between neural and nonneural contributions to the fMR imaging signal and thorough validation and statistical testing. In this article, the authors present an overview of methodological and interpretational considerations in this emerging field.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Mikail Rubinov
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Catie Chang
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
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30
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Dynamics of large-scale fMRI networks: Deconstruct brain activity to build better models of brain function. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2017. [DOI: 10.1016/j.cobme.2017.09.008] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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31
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Monge ZA, Geib BR, Siciliano RE, Packard LE, Tallman CW, Madden DJ. Functional modular architecture underlying attentional control in aging. Neuroimage 2017; 155:257-270. [PMID: 28476664 PMCID: PMC5512538 DOI: 10.1016/j.neuroimage.2017.05.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 04/11/2017] [Accepted: 05/01/2017] [Indexed: 12/11/2022] Open
Abstract
Previous research suggests that age-related differences in attention reflect the interaction of top-down and bottom-up processes, but the cognitive and neural mechanisms underlying this interaction remain an active area of research. Here, within a sample of community-dwelling adults 19-78 years of age, we used diffusion reaction time (RT) modeling and multivariate functional connectivity to investigate the behavioral components and whole-brain functional networks, respectively, underlying bottom-up and top-down attentional processes during conjunction visual search. During functional MRI scanning, participants completed a conjunction visual search task in which each display contained one item that was larger than the other items (i.e., a size singleton) but was not informative regarding target identity. This design allowed us to examine in the RT components and functional network measures the influence of (a) additional bottom-up guidance when the target served as the size singleton, relative to when the distractor served as the size singleton (i.e., size singleton effect) and (b) top-down processes during target detection (i.e., target detection effect; target present vs. absent trials). We found that the size singleton effect (i.e., increased bottom-up guidance) was associated with RT components related to decision and nondecision processes, but these effects did not vary with age. Also, a modularity analysis revealed that frontoparietal module connectivity was important for both the size singleton and target detection effects, but this module became central to the networks through different mechanisms for each effect. Lastly, participants 42 years of age and older, in service of the target detection effect, relied more on between-frontoparietal module connections. Our results further elucidate mechanisms through which frontoparietal regions support attentional control and how these mechanisms vary in relation to adult age.
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Affiliation(s)
- Zachary A Monge
- Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA.
| | - Benjamin R Geib
- Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA
| | - Rachel E Siciliano
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - Lauren E Packard
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - Catherine W Tallman
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA
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32
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Thompson WH, Brantefors P, Fransson P. From static to temporal network theory: Applications to functional brain connectivity. Netw Neurosci 2017; 1:69-99. [PMID: 29911669 PMCID: PMC5988396 DOI: 10.1162/netn_a_00011] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 03/29/2017] [Indexed: 11/25/2022] Open
Abstract
Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain's network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.
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Affiliation(s)
| | - Per Brantefors
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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33
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Geier C, Lehnertz K. Long-term variability of importance of brain regions in evolving epileptic brain networks. CHAOS (WOODBURY, N.Y.) 2017; 27:043112. [PMID: 28456162 DOI: 10.1063/1.4979796] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We investigate the temporal and spatial variability of the importance of brain regions in evolving epileptic brain networks. We construct these networks from multiday, multichannel electroencephalographic data recorded from 17 epilepsy patients and use centrality indices to assess the importance of brain regions. Time-resolved indications of highest importance fluctuate over time to a greater or lesser extent, however, with some periodic temporal structure that can mostly be attributed to phenomena unrelated to the disease. In contrast, relevant aspects of the epileptic process contribute only marginally. Indications of highest importance also exhibit pronounced alternations between various brain regions that are of relevance for studies aiming at an improved understanding of the epileptic process with graph-theoretical approaches. Nonetheless, these findings may guide new developments for individualized diagnosis, treatment, and control.
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Affiliation(s)
- Christian Geier
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany
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34
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O'Donoghue S, Holleran L, Cannon DM, McDonald C. Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI. J Affect Disord 2017; 209:217-228. [PMID: 27930915 DOI: 10.1016/j.jad.2016.11.015] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 08/16/2016] [Accepted: 11/14/2016] [Indexed: 11/15/2022]
Abstract
BACKGROUND The dysconnectivity hypothesis suggests that psychotic illnesses arise not from regionally specific focal pathophysiology, but rather from impaired neuroanatomical integration across networks of brain regions. Decreased white matter organization has been hypothesized to be a feature of psychotic illnesses in general, which is supported by meta-analyses of DTI studies in bipolar disorder and schizophrenia. Although many diffusion MRI studies investigate bipolar disorder and schizophrenia alone, relatively few studies directly compare structural features in these psychotic illnesses. Recently, the application of graph theory analyses to DTI data has supported the dysconnectivity hypothesis in bipolar disorder and schizophrenia, employing topological properties to assess neuroanatomical dysconnectivity. METHODS This selective review evaluates white matter alterations using Diffusion Tensor Imaging (DTI) in bipolar disorder and schizophrenia, with a focus upon direct comparison DTI studies in both psychotic illnesses. We then expand in more detail on the development of network analyses and the application of these techniques in bipolar disorder and schizophrenia. RESULTS Converging evidence indicates that frontal connectivity alterations are common to both disorders, with prominent fronto-temporal deficits identified in schizophrenia and inter-hemispheric and limbic alterations reported in bipolar disorder. LIMITATIONS In bipolar disorder, most connectome reports use cortical maps alone, which given the importance of the limbic system in emotional regulation may limit the scope of network approaches in mood disorders. CONCLUSIONS Further direct connectivity comparisons between these psychotic illnesses may assist in unravelling the neuroanatomical deviations underpinning the overlapping features of psychosis and cognitive impairment, and the more diagnostically distinctive features of affective disturbance in bipolar disorder and deficit syndrome in schizophrenia.
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Affiliation(s)
- Stefani O'Donoghue
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland.
| | - Laurena Holleran
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Dara M Cannon
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Colm McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
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35
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Mahyari AG, Zoltowski DM, Bernat EM, Aviyente S. A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG. IEEE Trans Biomed Eng 2017; 64:225-237. [DOI: 10.1109/tbme.2016.2553960] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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36
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Preti MG, Bolton TA, Van De Ville D. The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage 2016; 160:41-54. [PMID: 28034766 DOI: 10.1016/j.neuroimage.2016.12.061] [Citation(s) in RCA: 737] [Impact Index Per Article: 92.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 11/09/2016] [Accepted: 12/20/2016] [Indexed: 12/22/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.
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Affiliation(s)
- Maria Giulia Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
| | - Thomas Aw Bolton
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
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Chiang S, Guindani M, Yeh HJ, Haneef Z, Stern JM, Vannucci M. Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data. Hum Brain Mapp 2016; 38:1311-1332. [PMID: 27862625 DOI: 10.1002/hbm.23456] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 10/13/2016] [Accepted: 10/25/2016] [Indexed: 11/05/2022] Open
Abstract
In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hsiang J Yeh
- Department of Neurology, University of California Los Angeles, Los Angeles, California
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, Texas
| | - John M Stern
- Department of Neurology, University of California Los Angeles, Los Angeles, California
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38
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Alexander A, Maroso M, Soltesz I. Organization and control of epileptic circuits in temporal lobe epilepsy. PROGRESS IN BRAIN RESEARCH 2016; 226:127-54. [PMID: 27323941 PMCID: PMC5140277 DOI: 10.1016/bs.pbr.2016.04.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
When studying the pathological mechanisms of epilepsy, there are a seemingly endless number of approaches from the ultrastructural level-receptor expression by EM-to the behavioral level-comorbid depression in behaving animals. Epilepsy is characterized as a disorder of recurrent seizures, which are defined as "a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain" (Fisher et al., 2005). Such abnormal activity typically does not occur in a single isolated neuron; rather, it results from pathological activity in large groups-or circuits-of neurons. Here we choose to focus on two aspects of aberrant circuits in temporal lobe epilepsy: their organization and potential mechanisms to control these pathological circuits. We also look at two scales: microcircuits, ie, the relationship between individual neurons or small groups of similar neurons, and macrocircuits, ie, the organization of large-scale brain regions. We begin by summarizing the large body of literature that describes the stereotypical anatomical changes in the temporal lobe-ie, the anatomical basis of alterations in microcircuitry. We then offer a brief introduction to graph theory and describe how this type of mathematical analysis, in combination with computational neuroscience techniques and using parameters obtained from experimental data, can be used to postulate how microcircuit alterations may lead to seizures. We then zoom out and look at the changes which are seen over large whole-brain networks in patients and animal models, and finally we look to the future.
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Affiliation(s)
- A Alexander
- Stanford University, Stanford, CA, United States
| | - M Maroso
- Stanford University, Stanford, CA, United States
| | - I Soltesz
- Stanford University, Stanford, CA, United States.
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39
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Wang Y, Msghina M, Li TQ. Studying Sub-Dendrograms of Resting-State Functional Networks with Voxel-Wise Hierarchical Clustering. Front Hum Neurosci 2016; 10:75. [PMID: 27014015 PMCID: PMC4781834 DOI: 10.3389/fnhum.2016.00075] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 02/15/2016] [Indexed: 11/24/2022] Open
Abstract
Hierarchical clustering is a useful data-driven approach to classify complex data and has been used to analyze resting-state functional magnetic resonance imaging (fMRI) data and derive functional networks of the human brain at very large scale, such as the entire visual or sensory-motor cortex. In this study, we developed a voxel-wise, whole-brain hierarchical clustering framework to perform multi-stage analysis of group-averaged resting-state fMRI data in different levels of detail. With the framework we analyzed particularly the somatosensory motor and visual systems in fine details and constructed the corresponding sub-dendrograms, which corroborate consistently with the known modular organizations from previous clinical and experimental studies. The framework provides a useful tool for data-driven analysis of resting-state fMRI data to gain insight into the hierarchical organization and degree of functional modulation among the sub-units.
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Affiliation(s)
- Yanlu Wang
- Department of Clinical Science, Intervention, and Technology, Karolinska Institute Stockholm, Sweden
| | - Mussie Msghina
- Department of Clinical Neuroscience, Karolinska University Hospital Huddinge, Sweden
| | - Tie-Qiang Li
- Department of Clinical Science, Intervention, and Technology, Karolinska InstituteStockholm, Sweden; Department of Medical Physics, Karolinska University HospitalHuddinge, Sweden
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