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Faramarzi A, Fooladi M, Yousef Pour M, Khodamoradi E, Chehreh A, Amiri S, shavandi M, Sharini H. Clinical utility of fMRI in evaluating of LSD effect on pain-related brain networks in healthy subjects. Heliyon 2024; 10:e34401. [PMID: 39165942 PMCID: PMC11334886 DOI: 10.1016/j.heliyon.2024.e34401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/08/2024] [Accepted: 07/09/2024] [Indexed: 08/22/2024] Open
Abstract
Objective We aimed to evaluate the effect of Lysergic acid diethylamide (LSD) on the pain neural network (PNN) in healthy subjects using functional magnetic resonance imaging (fMRI). Methods Twenty healthy volunteers participated in a balanced-order crossover study, receiving intravenous administration of LSD and placebo in two fMRI scanning sessions. Brain regions associated with pain processing were analyzed by amplitude of low-frequency fluctuation (ALFF), independent component analysis (ICA), functional connectivity and dynamic casual modeling (DCM). Results ALFF analysis demonstrated that LSD effectively relieves pain due to modulation in the neural network associated with pain processing. ICA analysis showed more active voxels in anterior cingulate cortex (ACC), thalamus (THL)-left, THL-right, insula cortex (IC)-right, parietal operculum (PO)-left, PO-right and frontal pole (FP)-right in the placebo session than the LSD session. There were more active voxels in FP-left and IC-left in the LSD session compared to the placebo session. Functional brain connectivity was observed between THL-left and PO-right and between PO-left with FP-left, FP-right and IC-left in the placebo session. In the LSD session, functional connectivity of PO-left with FP-left and FP-right was observed. The effective connectivity between left anterior insula cortex (lAIC)-lAIC, lAIC-dorsolateral prefrontal cortex (dlPFC) and secondary somatosensory cortex (SII)-dlPFC were significantly different. Finally, the correlation between fMRI biomarkers and clinical pain criteria was calculated. Conclusion This study enhances our understanding of the LSD effect on the architecture and neural behavior of pain in healthy subjects and provides great promise for future research in the field of cognitive science and pharmacology.
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Affiliation(s)
- A. Faramarzi
- Department of Biomedical Engineering, Faculty of Medicine, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
| | - M. Fooladi
- Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M. Yousef Pour
- Faculty of Medicine, Aja University of Medical Science, Tehran, Iran
| | - E. Khodamoradi
- Department of Radiology and Nuclear Medicine, Faculty of Paramedical, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
| | - A. Chehreh
- Medical Physics Department, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - S. Amiri
- Department of Psychiatry, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M. shavandi
- Department of Clinical Pharmacy, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - H. Sharini
- Department of Biomedical Engineering, Faculty of Medicine, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
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2
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Zou T, Li L, Huang X, Deng C, Wang X, Gao Q, Chen H, Li R. Dynamic causal modeling analysis reveals the modulation of motor cortex and integration in superior temporal gyrus during multisensory speech perception. Cogn Neurodyn 2024; 18:931-946. [PMID: 38826672 PMCID: PMC11143173 DOI: 10.1007/s11571-023-09945-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 02/03/2023] [Accepted: 02/10/2023] [Indexed: 03/06/2023] Open
Abstract
The processing of speech information from various sensory modalities is crucial for human communication. Both left posterior superior temporal gyrus (pSTG) and motor cortex importantly involve in the multisensory speech perception. However, the dynamic integration of primary sensory regions to pSTG and the motor cortex remain unclear. Here, we implemented a behavioral experiment of classical McGurk effect paradigm and acquired the task functional magnetic resonance imaging (fMRI) data during synchronized audiovisual syllabic perception from 63 normal adults. We conducted dynamic causal modeling (DCM) analysis to explore the cross-modal interactions among the left pSTG, left precentral gyrus (PrG), left middle superior temporal gyrus (mSTG), and left fusiform gyrus (FuG). Bayesian model selection favored a winning model that included modulations of connections to PrG (mSTG → PrG, FuG → PrG), from PrG (PrG → mSTG, PrG → FuG), and to pSTG (mSTG → pSTG, FuG → pSTG). Moreover, the coupling strength of the above connections correlated with behavioral McGurk susceptibility. In addition, significant differences were found in the coupling strength of these connections between strong and weak McGurk perceivers. Strong perceivers modulated less inhibitory visual influence, allowed less excitatory auditory information flowing into PrG, but integrated more audiovisual information in pSTG. Taken together, our findings show that the PrG and pSTG interact dynamically with primary cortices during audiovisual speech, and support the motor cortex plays a specifically functional role in modulating the gain and salience between auditory and visual modalities. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09945-z.
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Affiliation(s)
- Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
| | - Liyuan Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
| | - Xinju Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
| | - Chijun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
| | - Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
| | - Qing Gao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
| | - Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 People’s Republic of China
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3
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Wang Y, Sun YX, Yang QY, Gao JH. A generalized QUCESOP method with evaluating CEST peak overlap. NMR IN BIOMEDICINE 2024; 37:e5098. [PMID: 38224670 DOI: 10.1002/nbm.5098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/26/2023] [Accepted: 12/04/2023] [Indexed: 01/17/2024]
Abstract
The overlapping peaks of the target chemical exchange saturation transfer (CEST) solutes and other unknown CEST solutes affect the quantification results and accuracy of the chemical exchange parameters-the fractional concentration, f b , exchange rate, k b , and transverse relaxation rate, R 2 b -for the target solutes. However, to date, no method has been established for assessing the overlapping peaks. This study aimed to develop a method for quantifying the f b , k b , and R 2 b values of a specific CEST solute, as well as assessing the overlap between the CEST peaks of the specific solute(s) and other unknown solutes. A simplified R 1 ρ model was proposed, assuming linear approximation of the other solutes' contributions to R 1 ρ . A CEST data acquisition scheme was applied with various saturation offsets and saturation powers. In addition to fitting the f b , k b , and R 2 b values of the specific solute, the overlapping condition was evaluated based on the root mean square error (RMSE) between the trajectories of the acquired and synthesized data. Single-solute and multi-solute phantoms with various phosphocreatine (PCr) concentrations and pH values were used to calculate the f b and k b of PCr and the corresponding RMSE. The feasibility of RMSE for evaluating the overlapping condition, and the accurate fitting of f b and k b in weak overlapping conditions, were verified. Furthermore, the method was employed to quantify the nuclear Overhauser effect signal in rat brains and the PCr signal in rat skeletal muscles, providing results that were consistent with those reported in previous studies. In summary, the proposed approach can be applied to evaluate the overlapping condition of CEST peaks and quantify the f b , k b , and R 2 b values of specific solutes, if the weak overlapping condition is satisfied.
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Affiliation(s)
- Yi Wang
- Public Health Science and Engineering College, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yi-Xuan Sun
- School of Medical Technology, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Qiu-Yu Yang
- Public Health Science and Engineering College, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
- National Biomedical Imaging Center, Peking University, Beijing, China
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4
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Kora Y, Salhi S, Davidsen J, Simon C. Global excitability and network structure in the human brain. Phys Rev E 2023; 107:054308. [PMID: 37328981 DOI: 10.1103/physreve.107.054308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 04/07/2023] [Indexed: 06/18/2023]
Abstract
We utilize a model of Wilson-Cowan oscillators to investigate structure-function relationships in the human brain by means of simulations of the spontaneous dynamics of brain networks generated through human connectome data. This allows us to establish relationships between the global excitability of such networks and global structural network quantities for connectomes of two different sizes for a number of individual subjects. We compare the qualitative behavior of such correlations between biological networks and shuffled networks, the latter generated by shuffling the pairwise connectivities of the former while preserving their distribution. Our results point towards a remarkable propensity of the brain to achieve a trade-off between low network wiring cost and strong functionality, and highlight the unique capacity of brain network topologies to exhibit a strong transition from an inactive state to a globally excited one.
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Affiliation(s)
- Youssef Kora
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
| | - Salma Salhi
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
| | - Jörn Davidsen
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
| | - Christoph Simon
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
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5
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Zhao Y, Boley M, Pelentritou A, Karoly PJ, Freestone DR, Liu Y, Muthukumaraswamy S, Woods W, Liley D, Kuhlmann L. Space-time resolved inference-based neurophysiological process imaging: application to resting-state alpha rhythm. Neuroimage 2022; 263:119592. [PMID: 36031185 DOI: 10.1016/j.neuroimage.2022.119592] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 11/28/2022] Open
Abstract
Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes in different brain states.
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Affiliation(s)
- Yun Zhao
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Mario Boley
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Andria Pelentritou
- Swinburne University of Technology, Hawthorn, Australia; Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia
| | - Dean R Freestone
- Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Seer Medical Pty Ltd, Melbourne, Australia
| | - Yueyang Liu
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - William Woods
- School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - David Liley
- Swinburne University of Technology, Hawthorn, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia.
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6
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Wei J, Wang B, Yang Y, Niu Y, Yang L, Guo Y, Xiang J. Effects of virtual lesions on temporal dynamics in cortical networks based on personalized dynamic models. Neuroimage 2022; 254:119087. [PMID: 35364277 DOI: 10.1016/j.neuroimage.2022.119087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/02/2022] [Accepted: 03/08/2022] [Indexed: 11/19/2022] Open
Abstract
The human brain dynamically shifts between a predominantly integrated state and a predominantly segregated state, each with different roles in supporting cognition and behavior. However, no studies to date have investigated lesions placed in different regions of the cerebral cortex to determine the effects on the temporal balance of integration and segregation. Here, we used the integrated state occurrence rate to measure the temporal balance of integration and segregation in the resting brain. Based on dynamic mean-field models coupled through the individual's structural white matter connections, neural activity was modeled. By lesioning different individual nodes from the model, our results implied that the impact of lesions reaches far beyond focal damage and could impair cognition by affecting system-level dynamics. Lesions in a portion of the nodes in the visual, somatomotor, limbic, and default networks significantly compromised the temporal balance of integration and segregation. Crucially, the effects of lesions in different regions could be predicted based on the hierarchical axis inferred from the T1w/T2w map and specific graph measures of structural brain networks. Taken together, our findings indicate the possibility of significant contributions of anatomical heterogeneity to the dynamics of functional network topology. Although still in its infancy, computational modeling may provide an entry point for understanding brain disorders at a causal mechanistic level, possibly leading to novel, more effective therapeutic interventions.
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Affiliation(s)
- Jing Wei
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China; School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China; Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Lan Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yuxiang Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
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7
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Li S, Li L, Zou L, Yan X, Zhang J, Yang M, Ding G. “Antagonistic” cooperation of control regions in bilingual language production: An effective connectivity study. Neuropsychologia 2022; 167:108165. [DOI: 10.1016/j.neuropsychologia.2022.108165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/17/2022] [Accepted: 01/23/2022] [Indexed: 11/25/2022]
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8
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Suarez A, Valdes-Hernandez PA, Moshkforoush A, Tsoukias N, Riera J. Arterial blood stealing as a mechanism of negative BOLD response: From the steady-flow with nonlinear phase separation to a windkessel-based model. J Theor Biol 2021; 529:110856. [PMID: 34363836 PMCID: PMC8507599 DOI: 10.1016/j.jtbi.2021.110856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 06/22/2021] [Accepted: 08/01/2021] [Indexed: 01/07/2023]
Abstract
Blood Oxygen Level Dependent (BOLD) signal indirectly characterizes neuronal activity by measuring hemodynamic and metabolic changes in the nearby microvasculature. A deeper understanding of how localized changes in electrical, metabolic and hemodynamic factors translate into a BOLD signal is crucial for the interpretation of functional brain imaging techniques. While positive BOLD responses (PBR) are widely considered to be linked with neuronal activation, the origins of negative BOLD responses (NBR) have remained largely unknown. As NBRs are sometimes observed in close proximity of regions with PBR, a blood "stealing" effect, i.e., redirection of blood from a passive periphery to the area with high neuronal activity, has been postulated. In this study, we used the Hagen-Poiseuille equation to model hemodynamics in an idealized microvascular network that account for the particulate nature of blood and nonlinearities arising from the red blood cell (RBC) distribution (i.e., the Fåhraeus, Fåhraeus-Lindqvist and the phase separation effects). Using this detailed model, we evaluate determinants driving this "stealing" effect in a microvascular network with geometric parameters within physiological ranges. Model simulations predict that during localized cerebral blood flow (CBF) increases due to neuronal activation-hyperemic response, blood from surrounding vessels is reallocated towards the activated region. This stealing effect depended on the resistance of the microvasculature and the uneven distribution of RBCs at vessel bifurcations. A parsimonious model consisting of two-connected windkessel regions sharing a supplying artery was proposed to simulate the stealing effect with a minimum number of parameters. Comparison with the detailed model showed that the parsimonious model can reproduce the observed response for hematocrit values within the physiological range for different species. Our novel parsimonious model promise to be of use for statistical inference (top-down analysis) from direct blood flow measurements (e.g., arterial spin labeling and laser Doppler/Speckle flowmetry), and when combined with theoretical models for oxygen extraction/diffusion will help account for some types of NBRs.
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Affiliation(s)
- Alejandro Suarez
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Pedro A Valdes-Hernandez
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States; Department of Community Dentistry and Behavioral Science, University of Florida, United States
| | - Arash Moshkforoush
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Nikolaos Tsoukias
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jorge Riera
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States.
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9
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Suarez A, Valdés-Hernández PA, Bernal B, Dunoyer C, Khoo HM, Bosch-Bayard J, Riera JJ. Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models. Front Neurol 2021; 12:659081. [PMID: 34690906 PMCID: PMC8531269 DOI: 10.3389/fneur.2021.659081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 09/03/2021] [Indexed: 11/16/2022] Open
Abstract
Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.
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Affiliation(s)
- Alejandro Suarez
- Neuronal Mass Dynamics Laboratory, Florida International University, Miami, FL, United States
| | | | - Byron Bernal
- Nicklaus Children Hospital, Miami, FL, United States
| | | | - Hui Ming Khoo
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Department of Neurosurgery, Osaka University, Suita, Japan
| | - Jorge Bosch-Bayard
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge J Riera
- Neuronal Mass Dynamics Laboratory, Florida International University, Miami, FL, United States
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10
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Cauzzo S, Callara AL, Morelli MS, Hartwig V, Esposito F, Montanaro D, Passino C, Emdin M, Giannoni A, Vanello N. Mapping dependencies of BOLD signal change to end-tidal CO 2: linear and nonlinear modeling, and effect of physiological noise correction. J Neurosci Methods 2021; 362:109317. [PMID: 34380051 DOI: 10.1016/j.jneumeth.2021.109317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 07/28/2021] [Accepted: 08/06/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Disentangling physiological noise and signal of interest is a major issue when evaluating BOLD-signal changes in response to breath holding. Currently-adopted approaches for retrospective noise correction are general-purpose, and have non-negligible effects in studies on hypercapnic challenges. NEW METHOD We provide a novel approach to the analysis of specific and non-specific BOLD-signal changes related to end-tidal CO2 (PETCO2) in breath-hold fMRI studies. Multiple-order nonlinear predictors for PETCO2 model a region-dependent nonlinear input-output relationship hypothesized in literature and possibly playing a crucial role in disentangling noise. We explore Retrospective Image-based Correction (RETROICOR) effects on the estimated BOLD response, applying our analysis both with and without RETROICOR and analyzing the linear and non-linear correlation between PETCO2 and RETROICOR regressors. RESULTS The RETROICOR model of noise related to respiratory activity correlated with PETCO2 both linearly and non-linearly. The correction affected the shape of the estimated BOLD response to hypercapnia but allowed to discard spurious activity in ventricles and white matter. Activation clusters were best detected using non-linear components in the BOLD response model. COMPARISON WITH EXISTING METHOD We evaluated the side-effects of standard physiological noise correction procedure, tailoring our analysis on challenging understudied brainstem and subcortical regions. Our novel approach allowed to characterize delays and non-linearities in BOLD response. CONCLUSIONS RETROICOR successfully avoided false positives, still broadly affecting the estimated non-linear BOLD responses. Non-linearities in the model better explained CO2-related BOLD signal fluctuations. The necessity to modify the standard procedure for physiological-noise correction in breath-hold studies was addressed, stating its crucial importance.
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Affiliation(s)
- Simone Cauzzo
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy.
| | | | - Maria Sole Morelli
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Valentina Hartwig
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | | | - Claudio Passino
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Michele Emdin
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Alberto Giannoni
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy; Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Nicola Vanello
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy
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11
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Rolls ET, Cheng W, Gilson M, Gong W, Deco G, Lo CYZ, Yang AC, Tsai SJ, Liu ME, Lin CP, Feng J. Beyond the disconnectivity hypothesis of schizophrenia. Cereb Cortex 2021; 30:1213-1233. [PMID: 31381086 DOI: 10.1093/cercor/bhz161] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 06/24/2019] [Accepted: 06/24/2019] [Indexed: 01/01/2023] Open
Abstract
To go beyond the disconnectivity hypothesis of schizophrenia, directed (effective) connectivity was measured between 94 brain regions, to provide evidence on the source of the changes in schizophrenia and a mechanistic model. Effective connectivity (EC) was measured in 180 participants with schizophrenia and 208 controls. For the significantly different effective connectivities in schizophrenia, on average the forward (stronger) effective connectivities were smaller, whereas the backward connectivities tended to be larger. Further, higher EC in schizophrenia was found from the precuneus and posterior cingulate cortex (PCC) to areas such as the parahippocampal, hippocampal, temporal, fusiform, and occipital cortices. These are backward effective connectivities and were positively correlated with the positive symptoms of schizophrenia. Lower effective connectivities were found from temporal and other regions and were negatively correlated with the symptoms, especially the negative and general symptoms. Further, a signal variance parameter was increased for areas that included the parahippocampal gyrus and hippocampus, consistent with the hypothesis that hippocampal overactivity is involved in schizophrenia. This investigation goes beyond the disconnectivity hypothesis by drawing attention to differences in schizophrenia between backprojections and forward connections, with the backward connections from the precuneus and PCC implicated in memory stronger in schizophrenia.
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Affiliation(s)
- Edmund T Rolls
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Oxford Centre for Computational Neuroscience, Oxford OX1 4BH, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain
| | - Weikang Gong
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX1 4BH, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Mu-En Liu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Institute of Neuroscience, National Yang-Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei 11221, Taiwan
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
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12
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Sumner RL, McMillan R, Spriggs MJ, Campbell D, Malpas G, Maxwell E, Deng C, Hay J, Ponton R, Sundram F, Muthukumaraswamy SD. Ketamine improves short-term plasticity in depression by enhancing sensitivity to prediction errors. Eur Neuropsychopharmacol 2020; 38:73-85. [PMID: 32763021 DOI: 10.1016/j.euroneuro.2020.07.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 06/02/2020] [Accepted: 07/16/2020] [Indexed: 12/17/2022]
Abstract
Major depressive disorder negatively impacts the sensitivity and adaptability of the brain's predictive coding framework. The current electroencephalography study into the antidepressant properties of ketamine investigated the downstream effects of ketamine on predictive coding and short-term plasticity in thirty patients with depression using the auditory roving mismatch negativity (rMMN). The rMMN paradigm was run 3-4 h after a single 0.44 mg/kg intravenous dose of ketamine or active placebo (remifentanil infused to a target plasma concentration of 1.7 ng/mL) in order to measure the neural effects of ketamine in the period when an improvement in depressive symptoms emerges. Depression symptomatology was measured using the Montgomery-Asberg Depression Rating Scale (MADRS); 70% of patients demonstrated at least a 50% reduction their MADRS global score. Ketamine significantly increased the MMN and P3a event related potentials, directly contrasting literature demonstrating ketamine's acute attenuation of the MMN. This effect was only reliable when all repetitions of the post-deviant tone were used. Dynamic causal modelling showed greater modulation of forward connectivity in response to a deviant tone between right primary auditory cortex and right inferior temporal cortex, which significantly correlated with antidepressant response to ketamine at 24 h. This is consistent with the hypothesis that ketamine increases sensitivity to unexpected sensory input and restores deficits in sensitivity to prediction error that are hypothesised to underlie depression. However, the lack of repetition suppression evident in the MMN evoked data compared to studies of healthy adults suggests that, at least within the short term, ketamine does not improve deficits in adaptive internal model calibration.
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Affiliation(s)
| | | | - Meg J Spriggs
- Centre for Psychedelic Research, Department of Medicine, Imperial College London, UK; Brain Research New Zealand; School of Psychology, University of Auckland, New Zealand
| | - Doug Campbell
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Gemma Malpas
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Elizabeth Maxwell
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Carolyn Deng
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - John Hay
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Rhys Ponton
- School of Pharmacy, University of Auckland, New Zealand
| | - Frederick Sundram
- Department of Psychological Medicine, University of Auckland, New Zealand
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13
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Tyrer A, Gilbert JR, Adams S, Stiles AB, Bankole AO, Gilchrist ID, Moran RJ. Lateralized memory circuit dropout in Alzheimer’s disease patients. Brain Commun 2020; 2:fcaa212. [PMID: 33409493 PMCID: PMC7772115 DOI: 10.1093/braincomms/fcaa212] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 11/05/2020] [Accepted: 11/06/2020] [Indexed: 11/25/2022] Open
Abstract
Altered connectivity within neuronal networks is often observed in Alzheimer’s disease. However, delineating pro-cognitive compensatory changes from pathological network decline relies on characterizing network and task effects together. In this study, we interrogated the dynamics of occipito-temporo-frontal brain networks responsible for implicit and explicit memory processes using high-density EEG and dynamic causal modelling. We examined source-localized network activity from patients with Alzheimer’s disease (n = 21) and healthy controls (n = 21), while they performed both visual recognition (explicit memory) and implicit priming tasks. Parametric empirical Bayes analyses identified significant reductions in temporo-frontal connectivity and in subcortical visual input in patients, specifically in the left hemisphere during the recognition task. There was also slowing in frontal left hemisphere signal transmission during the implicit priming task, with significantly more distinct dropout in connectivity during the recognition task, suggesting that these network drop-out effects are affected by task difficulty. Furthermore, during the implicit memory task, increased right frontal activity was correlated with improved task performance in patients only, suggesting that right-hemisphere compensatory mechanisms may be employed to mitigate left-lateralized network dropout in Alzheimer’s disease. Taken together, these findings suggest that Alzheimer’s disease is associated with lateralized memory circuit dropout and potential compensation from the right hemisphere, at least for simpler memory tasks.
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Affiliation(s)
- Ashley Tyrer
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK
| | | | - Sarah Adams
- School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | | | - Azziza O Bankole
- Department of Psychiatry and Behavioural Medicine, Virginia Tech Carilion School of Medicine, Roanoke, VA 24016, USA
| | - Iain D Gilchrist
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - Rosalyn J Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK
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14
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Koike T, Tanabe HC, Adachi-Abe S, Okazaki S, Nakagawa E, Sasaki AT, Shimada K, Sugawara SK, Takahashi HK, Yoshihara K, Sadato N. Role of the right anterior insular cortex in joint attention-related identification with a partner. Soc Cogn Affect Neurosci 2020; 14:1131-1145. [PMID: 31919530 DOI: 10.1093/scan/nsz087] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 09/04/2019] [Accepted: 09/30/2019] [Indexed: 12/30/2022] Open
Abstract
Understanding others as intentional agents is critical in social interactions. We perceive others' intentions through identification, a categorical judgment that others should work like oneself. The most primitive form of understanding others' intentions is joint attention (JA). During JA, an initiator selects a shared object through gaze (initiative joint attention, IJA), and the responder follows the direction of the initiator's gaze (reactive joint attention, RJA). Therefore, both participants share the intention of object selection. However, the neural underpinning of shared intention through JA remains unknown. In this study, we hypothesized that JA is represented by inter-individual neural synchronization of the intention-related activity. Additionally, JA requires eye contact that activates the limbic mirror system; therefore, we hypothesized that this system is involved in shared attention through JA. To test these hypotheses, participants underwent hyperscanning fMRI while performing JA tasks. We found that IJA-related activation of the right anterior insular cortex of participants was positively correlated with RJA-related activation of homologous regions in their partners. This area was activated by volitional selection of the target during IJA. Therefore, identification with others by JA is likely accomplished by the shared intentionality of target selection represented by inter-individual synchronization of the right anterior insular cortex.
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Affiliation(s)
- Takahiko Koike
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan
| | - Hiroki C Tanabe
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Division of Psychology, Department of Cognitive and Psychological Sciences, Graduate School of Informatics, Nagoya University, 464-8601, Nagoya, Japan
| | - Saori Adachi-Abe
- Department of Medicine, Tokyo Medical and Dental University, 113-8510, Tokyo, Japan
| | - Shuntaro Okazaki
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Research Center for Child Mental Development, University of Fukui, 910-1193, Fukui, Japan
| | - Eri Nakagawa
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Graduate School of Intercultural Studies School of Languages and Communication, Kobe University, 657-8501, Kobe, Japan
| | - Akihiro T Sasaki
- Pathophysiological and Health Science Team, RIKEN Center for Life Science Technologies, 650-0047, Kobe, Japan.,Department of Physiology, Osaka City University Graduate School of Medicine, 558-8585, Osaka, Japan
| | - Koji Shimada
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Graduate School of Intercultural Studies School of Languages and Communication, Kobe University, 657-8501, Kobe, Japan
| | - Sho K Sugawara
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Japan Society for the Promotion of Science
| | - Haruka K Takahashi
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Department of Physiological Sciences, School of Life Sciences, The Graduate University for Advanced Studies, 240-0193, Kanagawa, Japan
| | - Kazufumi Yoshihara
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Department of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University, 819-0395, Fukuoka, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences (NIPS), 444-8585, Okazaki, Japan.,Biomedical Imaging Research Center (BIRC), University of Fukui, 910-1193, Fukui, Japan.,Department of Physiological Sciences, School of Life Sciences, The Graduate University for Advanced Studies, 240-0193, Kanagawa, Japan
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15
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Arora A, Pletzer B, Aichhorn M, Perner J. What's in a Hub?-Representing Identity in Language and Mathematics. Neuroscience 2020; 432:104-114. [PMID: 32112913 PMCID: PMC7100012 DOI: 10.1016/j.neuroscience.2020.02.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 11/17/2022]
Abstract
Hubs emerge in structural and resting state network analysis as areas highly connected to other parts of the brain and have been shown to respond to several task domains in functional imaging studies. A cognitive explanation for this multi-functionality is still wanting. We propose, that hubs subserve domain-general meta-cognitive functions, relevant to a variety of domain-specific networks and test this hypothesis for the example of processing explicit identity information. To isolate this meta-cognitive function from the processing of domain-specific context, we investigate the overlapping activations to linguistic identity processes (e.g. Mr. Dietrich is the dentist) on the one hand and numerical identity processes (e.g. do "3 × 8" and "36-12" give the same number) on the other hand. The main question was, whether these overlapping activations would fall within areas, consistently identified as hubs by network-based analyses. Indeed, the two contrasts showed significant conjunctions in the left inferior parietal lobe (IPL), precuneus (PC), and posterior cingulate. Accordingly, identity processing may well be one domain-general meta-cognitive function that hub-areas provide to domain-specific networks. For the parietal lobe we back up our hypothesis further with existing reports of activation peaks for other tasks that depend on identity processing, e.g., episodic recollection, theory of mind, and visual perspective taking.
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Affiliation(s)
- Aditi Arora
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Belinda Pletzer
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Markus Aichhorn
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Josef Perner
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria.
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16
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Portet S. A primer on model selection using the Akaike Information Criterion. Infect Dis Model 2020; 5:111-128. [PMID: 31956740 PMCID: PMC6962709 DOI: 10.1016/j.idm.2019.12.010] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 12/27/2019] [Indexed: 11/24/2022] Open
Abstract
A powerful investigative tool in biology is to consider not a single mathematical model but a collection of models designed to explore different working hypotheses and select the best model in that collection. In these lecture notes, the usual workflow of the use of mathematical models to investigate a biological problem is described and the use of a collection of model is motivated. Models depend on parameters that must be estimated using observations; and when a collection of models is considered, the best model has then to be identified based on available observations. Hence, model calibration and selection, which are intrinsically linked, are essential steps of the workflow. Here, some procedures for model calibration and a criterion, the Akaike Information Criterion, of model selection based on experimental data are described. Rough derivation, practical technique of computation and use of this criterion are detailed.
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Affiliation(s)
- Stéphanie Portet
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
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17
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Wang Y, Wang Y, Lui YW. Biophysically interpretable recurrent neural network for functional magnetic resonance imaging analysis and sparsity based causal architecture discovery. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:275-278. [PMID: 30440391 DOI: 10.1109/embc.2018.8512200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Recent efforts use state-of-the-art Recurrent Neural Networks (RNN) to gain insight into neuroscience. A limitation of these works is that the used generic RNNs lack biophysical meaning, making the interpretation of the results in a neuroscience context difficult. In this paper, we propose a biophysically interpretable RNN built on the Dynamic Causal Modelling (DCM). DCM is an advanced nonlinear generative model typically used to test hypotheses of brain causal architectures and associated effective connectivities. We show that DCM can be cast faithfully as a special form of a new generalized RNN. In the resulting DCM-RNN, the hidden states are neural activity, blood flow, blood volume, and deoxyhemoglobin content and the parameters are biological quantities such as effective connectivity, oxygen extraction fraction and vessel wall elasticity. DCM-RNN is a versatile tool for neuroscience with great potential especially when combined with deep learning networks. In this study, we explore sparsity- based causal architecture discovery with DCM-RNN. In the experiments, we demonstrate that DCM-RNN equipped with $l_{1}$ connectivity regulation is more robust to noise and more powerful at discovering sparse architectures than classic DCM with $l_{2}$ connectivity regulation.
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18
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Turner BM, Palestro JJ, Miletić S, Forstmann BU. Advances in techniques for imposing reciprocity in brain-behavior relations. Neurosci Biobehav Rev 2019; 102:327-336. [PMID: 31128445 DOI: 10.1016/j.neubiorev.2019.04.018] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 03/18/2019] [Accepted: 04/25/2019] [Indexed: 01/01/2023]
Abstract
To better understand human behavior, the emerging field of model-based cognitive neuroscience seeks to anchor psychological theory to the biological substrate from which behavior originates: the brain. Despite complex dynamics, many researchers in this field have demonstrated that fluctuations in brain activity can be related to fluctuations in components of cognitive models, which instantiate psychological theories. In this review, we discuss a number of approaches for relating brain activity to cognitive models, and expand on a framework for imposing reciprocity in the inference of mental operations from the combination of brain and behavioral data.
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Affiliation(s)
- Brandon M Turner
- Department of Psychology, The Ohio State University, Columbus, OH, USA.
| | - James J Palestro
- Department of Psychology, The Ohio State University, Columbus, OH, USA.
| | - Steven Miletić
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
| | - Birte U Forstmann
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
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19
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Wu H, Lu M, Zeng Y. State Estimation of Hemodynamic Model for fMRI Under Confounds: SSM Method. IEEE J Biomed Health Inform 2019; 24:804-814. [PMID: 31095502 DOI: 10.1109/jbhi.2019.2917093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Through hemodynamic models, the change of neuronal state can be estimated from functional magnetic resonance imaging (fMRI) signals. Usually, there are confounds in the fMRI signal, which will degrade the performance of the estimation for the neuronal state change. For the reason, this paper introduces a state-space model with confounds, from a conventional hemodynamic model. In this model, a successive state estimation method requires a state value vector, an error covariance, an innovation covariance, and a cross covariance to be re-derived. Thus, a confounds square-root cubature Kalman smoothing (CSCKS) algorithm is proposed in this paper. We use a Balloon-Windkessel model to generate simulation data and add confounds signals to evaluate the performance of the proposed algorithm. The experiment results show that when the signal-to-interference ratio is less than 21 dB, the CSCKS proposed in this paper reduced estimation error to 16%, whereas the traditional algorithm reduced it to only 73%.
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20
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Hosseini GS, Nasrabadi AM. Effective connectivity of mental fatigue: Dynamic causal modeling of EEG data. Technol Health Care 2019; 27:343-352. [PMID: 30932904 DOI: 10.3233/thc-181480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recognition of sources in the brain and their interaction with mental fatigue states are interesting subjects for researchers. OBJECTIVE The aim of this study was to investigate the mental fatigue effects on brain areas by dynamic casual modeling (DCM) parameters that are extracted from event-related potential (ERP) signals which were then estimated based on mental fatigue data with visual stimulation. METHODS ERP were recorded based on a Continuous Performance Task in four consecutive trials. Active regions and brain sources were extracted by a Multiple Sparse Priors algorithm. RESULTS Four models are proposed for DCM. The parameters and the structure of the best model were obtained by SPM software for ERP in each of the four trials. CONCLUSION The results illustrate that an increase of mental fatigue through trials leads to increased likelihood of choosing forward models.
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Affiliation(s)
- Ghazaleh Sadat Hosseini
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
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21
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Yeldesbay A, Fink GR, Daun S. Reconstruction of effective connectivity in the case of asymmetric phase distributions. J Neurosci Methods 2019; 317:94-107. [PMID: 30786248 DOI: 10.1016/j.jneumeth.2019.02.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 02/13/2019] [Accepted: 02/15/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND The interaction of different brain regions is supported by transient synchronization between neural oscillations at different frequencies. Different measures based on synchronization theory are used to assess the strength of the interactions from experimental data. One method of estimating the effective connectivity between brain regions, within the framework of the theory of weakly coupled phase oscillators, was implemented in Dynamic Causal Modelling (DCM) for phase coupling (Penny et al., 2009). However, the results of such an approach strongly depend on the observables used to reconstruct the equations (Kralemann et al., 2008). In particular, an asymmetric distribution of the observables could result in a false estimation of the effective connectivity between the network nodes. NEW METHOD In this work we built a new modelling part into DCM for phase coupling, and extended it with a distortion function that accommodates departures from purely sinusoidal oscillations. RESULTS By analysing numerically generated data sets with an asymmetric phase distribution, we demonstrated that the extended DCM for phase coupling with the additional modelling component, correctly estimates the coupling functions. COMPARISON WITH EXISTING METHODS The new method allows for different intrinsic frequencies among coupled neuronal populations and provides results that do not depend on the distribution of the observables. CONCLUSIONS The proposed method can be used to analyse effective connectivity between brain regions within and between different frequency bands, to characterize m:n phase coupling, and to unravel underlying mechanisms of the transient synchronization.
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Affiliation(s)
- Azamat Yeldesbay
- University of Cologne, Institute of Zoology, Heisenberg Research Group of Computational Neuroscience - Modeling Neural Network Function, Zülpicher Str. 47b, 50674 Cologne, Germany; Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Cognitive Neuroscience, 52425 Jülich, Germany.
| | - Gereon R Fink
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Cognitive Neuroscience, 52425 Jülich, Germany; University of Cologne, Department of Neurology, Medical Faculty and University Hospital Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Silvia Daun
- University of Cologne, Institute of Zoology, Heisenberg Research Group of Computational Neuroscience - Modeling Neural Network Function, Zülpicher Str. 47b, 50674 Cologne, Germany; Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Cognitive Neuroscience, 52425 Jülich, Germany.
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22
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McDowell AR, Carmichael DW. Optimal repetition time reduction for single subject event-related functional magnetic resonance imaging. Magn Reson Med 2018; 81:1890-1897. [PMID: 30230635 PMCID: PMC6519282 DOI: 10.1002/mrm.27498] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Revised: 07/23/2018] [Accepted: 07/28/2018] [Indexed: 11/08/2022]
Abstract
PURPOSE Short TRs are increasingly used for fMRI as fast sequences such as simultaneous multislice excitation become available. These have been associated with apparent sensitivity improvements, although greater temporal autocorrelation at shorter TRs can inflate sensitivity measurements leading to uncertainty regarding the optimal approach. METHODS In volunteers (n = 10), the optimal TR was assessed at the single subject level for event-related designs (visual stimulation) with 4 frequencies of presentation at 4 TR values (412-2550 ms). T-values in the visual cortex localized in each individual were obtained and receiver operating characteristics (ROC) analysis was performed by counting voxels within and outside expected task active regions at different thresholds. This analysis was repeated using 4 different autoregressive (AR) models; SPM AR(1) and SPM AR(fast) which globally estimate autocorrelation, and fMRIstat AR(1) and AR(5) that use a local estimate. RESULTS The use of modest multiband factors of 2 or 3 with a reduction in TR to 1000 ± 200 ms had greater sensitivity and specificity as shown by higher T-values in visual cortex and ROC analysis. At these TRs, the ROC analysis demonstrated that a local AR model fit improved performance while high order AR models were unnecessary. CONCLUSIONS Modest TR reductions (to 1000 ± 200 ms) optimally improved event-related fMRI performance independent of design frequency. Autoregressive models with a local as opposed to global fit performed better, while low order autoregressive models were sufficient at the optimal TR.
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Affiliation(s)
| | - David W Carmichael
- UCL GOS Institute of Child Health, London, UK.,EPSCRC / Wellcome Centre for Medical Engineering, Kings College London, UK
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23
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Chu R, Meltzer JA, Bitan T. Interhemispheric interactions during sentence comprehension in patients with aphasia. Cortex 2018; 109:74-91. [PMID: 30312780 DOI: 10.1016/j.cortex.2018.08.022] [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: 10/05/2017] [Revised: 05/03/2018] [Accepted: 08/28/2018] [Indexed: 02/06/2023]
Abstract
Right-hemisphere involvement in language processing following left-hemisphere damage may reflect either compensatory processes, or a release from homotopic transcallosal inhibition, resulting in excessive right-to-left suppression that is maladaptive for language performance. Using fMRI, we assessed inter-hemispheric effective connectivity in fifteen patients with post-stroke aphasia, along with age-matched and younger controls during a sentence comprehension task. Dynamic Causal Modeling was used with four bilateral regions including inferior frontal gyri (IFG) and primary auditory cortices (A1). Despite the presence of lesions, satisfactory model fit was obtained in 9/15 patients. In young controls, the only significant homotopic connection (RA1-LA1), was excitatory, while inhibitory connections emanated from LIFG to both left and right A1's. Interestingly, these connections were also correlated with language comprehension scores in patients. The results for homotopic connections show that excitatory connectivity from RA1-to-LA1 and inhibitory connectivity from LA1-to-RA1 are associated with general auditory verbal comprehension. Moreover, negative correlations were found between sentence comprehension and top-down coupling for both heterotopic (LIFG-to-RA1) and intra-hemispheric (LIFG-to-LA1) connections. These results do not show an emergence of a new compensatory right to left excitation in patients nor do they support the existence of left to right transcallosal suppression in controls. Nevertheless, the correlations with performance in patients are consistent with some aspects of both the compensation model, and the transcallosal suppression account for the role of the RH. Altogether our results suggest that changes to both excitatory and inhibitory homotopic and heterotopic connections due to LH damage may be maladaptive, as they disrupt the normal inter-hemispheric coordination and communication.
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Affiliation(s)
- Ronald Chu
- Baycrest Health Sciences, Rotman Research Institute, Toronto, ON, Canada; University of Toronto, Department of Psychology, Toronto, ON, Canada.
| | - Jed A Meltzer
- Baycrest Health Sciences, Rotman Research Institute, Toronto, ON, Canada; University of Toronto, Department of Psychology, Toronto, ON, Canada; University of Toronto, Department of Speech-Language Pathology, Toronto, ON, Canada; Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada
| | - Tali Bitan
- University of Toronto, Department of Speech-Language Pathology, Toronto, ON, Canada; University of Haifa, Department of Psychology and IIPDM, Haifa, Israel
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24
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Sumner RL, Spriggs MJ, McMillan RL, Sundram F, Kirk IJ, Muthukumaraswamy SD. Neural plasticity is modified over the human menstrual cycle: Combined insight from sensory evoked potential LTP and repetition suppression. Neurobiol Learn Mem 2018; 155:422-434. [PMID: 30172951 DOI: 10.1016/j.nlm.2018.08.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 08/18/2018] [Accepted: 08/29/2018] [Indexed: 01/18/2023]
Abstract
In healthy women, fluctuations in hormones including progesterone and oestradiol lead to functional changes in the brain over the course of each menstrual cycle. Though considerable attention has been directed towards understanding changes in human cognition over the menstrual cycle, changes in underlying processes such as neural plasticity have largely only been studied in animals. In this study we explored predictive coding and repetition suppression via the roving mismatch negativity paradigm as a model of short-term plasticity (Garrido, Kilner, Kiebel, et al., 2009), and Hebbian learning via visual sensory long-term potentiation (LTP) as a model of long-term plasticity (Teyler et al., 2005). Electroencephalography (EEG) was recorded in 20 females during their early follicular and mid-luteal phases. Event-related potential (ERP) analyses were complemented with dynamic causal modelling (DCM) to characterise changes in the underlying neural architecture. More sustained variability in the ERP response to a change in tone during the luteal phase are interpreted as a delayed habituation of the P3a component in the luteal relative to the follicular phase. The additional increased forward connection strength over tone repetitions compared to the follicular phase suggests that, in this phase, females may be less efficient when processing deviations from predicted sensory input (error). In contrast, there appears to be no reliable change in sensory LTP. This suggests that predictive coding, but not Hebbian plasticity is modified in the mid-luteal compared to the follicular phase, at least at the days of the menstrual cycle tested. This finding implicates the human menstrual cycle in complex changes in neural plasticity and provides further evidence for the importance of considering the menstrual cycle when including females in electrophysiological research.
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Affiliation(s)
- R L Sumner
- School of Pharmacy, The University of Auckland, Auckland, New Zealand.
| | - M J Spriggs
- School of Psychology, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand, New Zealand
| | - R L McMillan
- School of Pharmacy, The University of Auckland, Auckland, New Zealand
| | - F Sundram
- Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand
| | - I J Kirk
- School of Psychology, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand, New Zealand
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25
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Straube B, Wroblewski A, Jansen A, He Y. The connectivity signature of co-speech gesture integration: The superior temporal sulcus modulates connectivity between areas related to visual gesture and auditory speech processing. Neuroimage 2018; 181:539-549. [PMID: 30025854 DOI: 10.1016/j.neuroimage.2018.07.037] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 07/04/2018] [Accepted: 07/15/2018] [Indexed: 10/28/2022] Open
Abstract
Humans integrate information communicated by speech and gestures. Functional magnetic resonance imaging (fMRI) studies suggest that the posterior superior temporal sulcus (STS) and adjacent gyri are relevant for multisensory integration. However, a connectivity model representing this essential combinatory process is still missing. Here, we used dynamic causal modeling for fMRI to analyze the effective connectivity pattern between middle temporal gyrus (MTG), occipital cortex (OC) and STS associated with auditory verbal, visual gesture-related, and integrative processing, respectively, to unveil the neural mechanisms underlying integration of intrinsically meaningful gestures (e.g., "Thumbs-up gesture") and corresponding speech. 20 participants were presented videos of an actor either performing intrinsic meaningful gestures in the context of German or Russian sentences, or speaking a German sentence without gesture, while performing a content judgment task. The connectivity analyses resulted in a winning model that included bidirectional intrinsic connectivity between all areas. Furthermore, the model included modulations of both connections to the STS (OC→STS; MTG→STS), and non-linear modulatory effects of the STS on bidirectional connections between MTG and OC. Coupling strength in the occipital pathway (OC→STS) correlated with gesture related advantages in task performance, whereas the temporal pathway (MTG→STS) correlated with performance in the speech only condition. Coupling between MTG and OC correlated negatively with subsequent memory performance for sentences of the Gesture-German condition. Our model provides a first step towards a better understanding of speech-gesture integration on network level. It corroborates the importance of the STS during audio-visual integration by showing that this region inhibits direct auditory-visual coupling.
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Affiliation(s)
- Benjamin Straube
- Translational Neuroimaging Marburg (TNM), Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior - CMBB, Philipps-University Marburg, Germany.
| | - Adrian Wroblewski
- Translational Neuroimaging Marburg (TNM), Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior - CMBB, Philipps-University Marburg, Germany
| | - Andreas Jansen
- Laboratory for Multimodal Neuroimaging (LMN), Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior - CMBB, Philipps-University Marburg, Germany; Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Germany
| | - Yifei He
- Translational Neuroimaging Marburg (TNM), Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior - CMBB, Philipps-University Marburg, Germany
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26
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Ma C, Chen HS, Lai YC, Zhang HF. Statistical inference approach to structural reconstruction of complex networks from binary time series. Phys Rev E 2018; 97:022301. [PMID: 29548109 DOI: 10.1103/physreve.97.022301] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Indexed: 12/20/2022]
Abstract
Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.
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Affiliation(s)
- Chuang Ma
- School of Mathematical Science, Anhui University, Hefei 230601, China
| | - Han-Shuang Chen
- School of Physics and Material Science, Anhui University, Hefei 230601, China
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA
| | - Hai-Feng Zhang
- School of Mathematical Science, Anhui University, Hefei 230601, China.,Center of Information Support and Assurance Technology, Anhui University, Hefei 230601, China.,Department of Communication Engineering, North University of China, Taiyuan, Shan'xi 030051, China
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27
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Wang Y, Wang Y, Lui YW. Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis. Neuroimage 2018; 178:385-402. [PMID: 29782993 DOI: 10.1016/j.neuroimage.2018.05.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 04/23/2018] [Accepted: 05/17/2018] [Indexed: 11/16/2022] Open
Abstract
Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks.
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Affiliation(s)
- Yuan Wang
- NYU WIRELESS, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA.
| | - Yao Wang
- NYU WIRELESS, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA
| | - Yvonne W Lui
- Center for Advanced Imaging Innovation and Research (CAI2R), School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA
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28
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Spriggs MJ, Sumner RL, McMillan RL, Moran RJ, Kirk IJ, Muthukumaraswamy SD. Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian learning mechanisms in the cerebral cortex. Neuroimage 2018; 176:290-300. [PMID: 29715566 DOI: 10.1016/j.neuroimage.2018.04.060] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 03/13/2018] [Accepted: 04/25/2018] [Indexed: 11/17/2022] Open
Abstract
The Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aim of the current study was to compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain. Forty participants were presented with both paradigms during EEG recording. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. This therefore indicates that both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations.
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Affiliation(s)
- M J Spriggs
- School of Psychology, The University of Auckland, New Zealand; Brain Research New Zealand, New Zealand.
| | - R L Sumner
- School of Psychology, The University of Auckland, New Zealand
| | - R L McMillan
- School of Pharmacy, The University of Auckland, New Zealand
| | - R J Moran
- Department Engineering Mathematics, University of Bristol, BS8 1TH, UK; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - I J Kirk
- School of Psychology, The University of Auckland, New Zealand; Brain Research New Zealand, New Zealand
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29
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Marchina S, Norton A, Kumar S, Schlaug G. The Effect of Speech Repetition Rate on Neural Activation in Healthy Adults: Implications for Treatment of Aphasia and Other Fluency Disorders. Front Hum Neurosci 2018; 12:69. [PMID: 29535619 PMCID: PMC5835070 DOI: 10.3389/fnhum.2018.00069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 02/07/2018] [Indexed: 11/13/2022] Open
Abstract
Functional imaging studies have provided insight into the effect of rate on production of syllables, pseudowords, and naturalistic speech, but the influence of rate on repetition of commonly-used words/phrases suitable for therapeutic use merits closer examination. Aim: To identify speech-motor regions responsive to rate and test the hypothesis that those regions would provide greater support as rates increase, we used an overt speech repetition task and functional magnetic resonance imaging (fMRI) to capture rate-modulated activation within speech-motor regions and determine whether modulations occur linearly and/or show hemispheric preference. Methods: Twelve healthy, right-handed adults participated in an fMRI task requiring overt repetition of commonly-used words/phrases at rates of 1, 2, and 3 syllables/second (syll./sec.). Results: Across all rates, bilateral activation was found both in ventral portions of primary sensorimotor cortex and middle and superior temporal regions. A repeated measures analysis of variance with pairwise comparisons revealed an overall difference between rates in temporal lobe regions of interest (ROIs) bilaterally (p < 0.001); all six comparisons reached significance (p < 0.05). Five of the six were highly significant (p < 0.008), while the left-hemisphere 2- vs. 3-syll./sec. comparison, though still significant, was less robust (p = 0.037). Temporal ROI mean beta-values increased linearly across the three rates bilaterally. Significant rate effects observed in the temporal lobes were slightly more pronounced in the right-hemisphere. No significant overall rate differences were seen in sensorimotor ROIs, nor was there a clear hemispheric effect. Conclusion: Linear effects in superior temporal ROIs suggest that sensory feedback corresponds directly to task demands. The lesser degree of significance in left-hemisphere activation at the faster, closer-to-normal rate may represent an increase in neural efficiency (and therefore, decreased demand) when the task so closely approximates a highly-practiced function. The presence of significant bilateral activation during overt repetition of words/phrases at all three rates suggests that repetition-based speech production may draw support from either or both hemispheres. This bihemispheric redundancy in regions associated with speech-motor control and their sensitivity to changes in rate may play an important role in interventions for nonfluent aphasia and other fluency disorders, particularly when right-hemisphere structures are the sole remaining pathway for production of meaningful speech.
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Affiliation(s)
- Sarah Marchina
- Music, Stroke Recovery, and Neuroimaging Laboratories, Department of Neurology, Harvard Medical School, Harvard University, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Andrea Norton
- Music, Stroke Recovery, and Neuroimaging Laboratories, Department of Neurology, Harvard Medical School, Harvard University, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Sandeep Kumar
- Music, Stroke Recovery, and Neuroimaging Laboratories, Department of Neurology, Harvard Medical School, Harvard University, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Gottfried Schlaug
- Music, Stroke Recovery, and Neuroimaging Laboratories, Department of Neurology, Harvard Medical School, Harvard University, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Boston, MA, United States
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30
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Timmermann C, Spriggs MJ, Kaelen M, Leech R, Nutt DJ, Moran RJ, Carhart-Harris RL, Muthukumaraswamy SD. LSD modulates effective connectivity and neural adaptation mechanisms in an auditory oddball paradigm. Neuropharmacology 2017; 142:251-262. [PMID: 29101022 DOI: 10.1016/j.neuropharm.2017.10.039] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 10/18/2017] [Accepted: 10/30/2017] [Indexed: 12/20/2022]
Abstract
Under the predictive coding framework, perceptual learning and inference are dependent on the interaction between top-down predictions and bottom-up sensory signals both between and within regions in a network. However, how such feedback and feedforward connections are modulated in the state induced by lysergic acid diethylamide (LSD) is poorly understood. In this study, an auditory oddball paradigm was presented to healthy participants (16 males, 4 female) under LSD and placebo, and brain activity was recorded using magnetoencephalography (MEG). Scalp level Event Related Fields (ERF) revealed reduced neural adaptation to familiar stimuli, and a blunted neural 'surprise' response to novel stimuli in the LSD condition. Dynamic causal modelling revealed that both the presentation of novel stimuli and LSD modulate backward extrinsic connectivity within a task-activated fronto-temporal network, as well as intrinsic connectivity in the primary auditory cortex. These findings show consistencies with those of previous studies of schizophrenia and ketamine but also studies of reduced consciousness - suggesting that rather than being a marker of conscious level per se, backward connectivity may index modulations of perceptual learning common to a variety of altered states of consciousness, perhaps united by a shared altered sensitivity to environmental stimuli. Since recent evidence suggests that the psychedelic state may correspond to a heightened 'level' of consciousness with respect to the normal waking state, our data warrant a re-examination of the top-down hypotheses of conscious level and suggest that several altered states may feature this specific biophysical effector. This article is part of the Special Issue entitled 'Psychedelics: New Doors, Altered Perceptions'.
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Affiliation(s)
- Christopher Timmermann
- Psychedelic Research Group, Centre for Psychiatry, Division of Brain Sciences, Imperial College London, W12 0NN, UK; Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Medicine, Imperial College London, W12 0NN, UK.
| | - Meg J Spriggs
- Cognitive Neuroscience Research Group, School of Psychology, University of Auckland, New Zealand; Centre for Brain Research, University of Auckland, New Zealand; Brain Research, New Zealand
| | - Mendel Kaelen
- Psychedelic Research Group, Centre for Psychiatry, Division of Brain Sciences, Imperial College London, W12 0NN, UK
| | - Robert Leech
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Medicine, Imperial College London, W12 0NN, UK
| | - David J Nutt
- Psychedelic Research Group, Centre for Psychiatry, Division of Brain Sciences, Imperial College London, W12 0NN, UK
| | - Rosalyn J Moran
- Department of Engineering Mathematics, University of Bristol, BS8 1TH, UK
| | - Robin L Carhart-Harris
- Psychedelic Research Group, Centre for Psychiatry, Division of Brain Sciences, Imperial College London, W12 0NN, UK
| | - Suresh D Muthukumaraswamy
- Cognitive Neuroscience Research Group, School of Psychology, University of Auckland, New Zealand; Centre for Brain Research, University of Auckland, New Zealand; School of Pharmacy, University of Auckland, New Zealand; CUBRIC, School of Psychology, Cardiff University, Cardiff CF103AT, UK
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31
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Zambri B, Djellouli R, Laleg-Kirati TM. An efficient multistage algorithm for full calibration of the hemodynamic model from BOLD signal responses. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2875. [PMID: 28226417 DOI: 10.1002/cnm.2875] [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: 07/15/2016] [Revised: 11/10/2016] [Accepted: 02/19/2017] [Indexed: 06/06/2023]
Abstract
We propose a computational strategy that falls into the category of prediction/correction iterative-type approaches, for calibrating the hemodynamic model. The proposed method is used to estimate consecutively the values of the two sets of model parameters. Numerical results corresponding to both synthetic and real functional magnetic resonance imaging measurements for a single stimulus as well as for multiple stimuli are reported to highlight the capability of this computational methodology to fully calibrate the considered hemodynamic model.
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Affiliation(s)
- Brian Zambri
- Department of Mathematics & Interdisciplinary Research Institute for the Sciences, California State University, Northridge, CA 91330, USA
| | - Rabia Djellouli
- Department of Mathematics & Interdisciplinary Research Institute for the Sciences, California State University, Northridge, CA 91330, USA
| | - Taous-Meriem Laleg-Kirati
- Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, KSA
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32
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Yang Y, Guliyev B, Schouten AC. Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations. Front Neurosci 2017; 11:518. [PMID: 28955197 PMCID: PMC5601387 DOI: 10.3389/fnins.2017.00518] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 09/01/2017] [Indexed: 11/13/2022] Open
Abstract
Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms) are thought be involved in the "rapid" transcortical reaction to the perturbation while the late cortical responses (>100 ms) are related to the "slow" transcortical reaction. Although previous studies indicated that both responses involve the primary motor cortex, it remains unclear if both responses are engaged by the same effective connectivity in the cortical network. To answer this question, we investigated the effective connectivity cortical network after a "ramp-and-hold" mechanical perturbation, in both the early (<100 ms) and late (>100 ms) periods, using dynamic causal modeling. Ramp-and-hold perturbations were applied to the wrist joint while the subject maintained an isometric wrist flexion. Cortical activity was recorded using a 128-channel electroencephalogram (EEG). We investigated how the perturbation modulated the effective connectivity for the early and late periods. Bayesian model comparisons suggested that different effective connectivity networks are engaged in these two periods. For the early period, we found that only a few cortico-cortical connections were modulated, while more complicated connectivity was identified in the cortical network during the late period with multiple modulated cortico-cortical connections. The limited early cortical network likely allows for a rapid muscle response without involving high-level cognitive processes, while the complexity of the late network may facilitate coordinated responses.
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Affiliation(s)
- Yuan Yang
- Neuromuscular Control Laboratory, Department of Biomechanical Engineering, Delft University of TechnologyDelft, Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, United States
| | - Bekir Guliyev
- Neuromuscular Control Laboratory, Department of Biomechanical Engineering, Delft University of TechnologyDelft, Netherlands
| | - Alfred C Schouten
- Neuromuscular Control Laboratory, Department of Biomechanical Engineering, Delft University of TechnologyDelft, Netherlands.,Department of Biomechanical Engineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of TwenteEnschede, Netherlands
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Ziegler G, Ridgway GR, Blakemore SJ, Ashburner J, Penny W. Multivariate dynamical modelling of structural change during development. Neuroimage 2017; 147:746-762. [PMID: 27979788 PMCID: PMC5315058 DOI: 10.1016/j.neuroimage.2016.12.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 10/28/2016] [Accepted: 12/08/2016] [Indexed: 01/07/2023] Open
Abstract
Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e.g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter differences between boys and girls. There is evidence that the regional pattern of sensitivity to dynamic hidden growth factors in late childhood is similar across genders and shows a consistent anterior-posterior gradient with strongest impact to prefrontal cortex (PFC) brain changes. Finally, we demonstrate the potential of the framework to explore the coupling of structural changes across a priori defined subnetworks using an example of previously established resting state functional connectivity.
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Affiliation(s)
- Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany.
| | - Gerard R Ridgway
- FMRIB Centre, University of Oxford, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK; Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
| | | | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
| | - Will Penny
- Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
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34
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Pletzer B. Sex differences in number processing: Differential systems for subtraction and multiplication were confirmed in men, but not in women. Sci Rep 2016; 6:39064. [PMID: 27966612 PMCID: PMC5155285 DOI: 10.1038/srep39064] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 11/17/2016] [Indexed: 11/12/2022] Open
Abstract
Neuroimaging studies suggest segregated neuronal systems underlying number magnitude processing (e.g. subtraction) and arithmetic fact retrieval (e.g. multiplication). While number magnitude processing is thought to rely on the intraparietal sulcus (IPS) bilaterally, arithmetic fact retrieval is thought to rely on the left angular gyrus (AG). However, evidence from brain damaged patients and brain stimulation challenges this view and suggests considerable overlap between the systems underlying number magnitude processing and arithmetic fact retrieval. This study investigates, whether sex differences in number processing can account for these conflicting findings. A subtraction and a multiplication task were administered to 40 men and 34 women in their luteal phase during functional MRI. Replicating previous studies in men, we found the IPS to be more strongly activated during subtraction than multiplication, and the AG to be more strongly activated during multiplication than subtraction. However, no differences between the two tasks were observed in women.
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Affiliation(s)
- Belinda Pletzer
- Department of Psychology &Centre for Cognitive Neuroscience, University of Salzburg, Salzburg Austria
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35
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Croce P, Basti A, Marzetti L, Zappasodi F, Gratta CD. EEG-fMRI Bayesian framework for neural activity estimation: a simulation study. J Neural Eng 2016; 13:066017. [PMID: 27788127 DOI: 10.1088/1741-2560/13/6/066017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. APPROACH We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). MAIN RESULTS First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. SIGNIFICANCE The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.
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Affiliation(s)
- Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, "G.d'Annunzio" University, Chieti, Italy. Institute of Advanced Biomedical Technologies, "G.d'Annunzio" University, Chieti, Italy
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Raman S, Deserno L, Schlagenhauf F, Stephan KE. A hierarchical model for integrating unsupervised generative embedding and empirical Bayes. J Neurosci Methods 2016; 269:6-20. [DOI: 10.1016/j.jneumeth.2016.04.022] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 04/23/2016] [Accepted: 04/24/2016] [Indexed: 11/25/2022]
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Gibson WS, Jo HJ, Testini P, Cho S, Felmlee JP, Welker KM, Klassen BT, Min HK, Lee KH. Functional correlates of the therapeutic and adverse effects evoked by thalamic stimulation for essential tremor. Brain 2016; 139:2198-210. [PMID: 27329768 PMCID: PMC4958905 DOI: 10.1093/brain/aww145] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/03/2016] [Indexed: 01/05/2023] Open
Abstract
Thalamic deep brain stimulation (DBS) is an effective therapy for essential tremor. Gibson et al. use functional MRI to reveal patterns of activation that correlate with stimulation-induced therapeutic and adverse effects. Their results suggest that thalamic DBS controls tremor, and induces paraesthesias, through distal modulation of tremor-related network nodes. Deep brain stimulation is an established neurosurgical therapy for movement disorders including essential tremor and Parkinson’s disease. While typically highly effective, deep brain stimulation can sometimes yield suboptimal therapeutic benefit and can cause adverse effects. In this study, we tested the hypothesis that intraoperative functional magnetic resonance imaging could be used to detect deep brain stimulation-evoked changes in functional and effective connectivity that would correlate with the therapeutic and adverse effects of stimulation. Ten patients receiving deep brain stimulation of the ventralis intermedius thalamic nucleus for essential tremor underwent functional magnetic resonance imaging during stimulation applied at a series of stimulation localizations, followed by evaluation of deep brain stimulation-evoked therapeutic and adverse effects. Correlations between the therapeutic effectiveness of deep brain stimulation (3 months postoperatively) and deep brain stimulation-evoked changes in functional and effective connectivity were assessed using region of interest-based correlation analysis and dynamic causal modelling, respectively. Further, we investigated whether brain regions might exist in which activation resulting from deep brain stimulation might correlate with the presence of paraesthesias, the most common deep brain stimulation-evoked adverse effect. Thalamic deep brain stimulation resulted in activation within established nodes of the tremor circuit: sensorimotor cortex, thalamus, contralateral cerebellar cortex and deep cerebellar nuclei (FDR q < 0.05). Stimulation-evoked activation in all these regions of interest, as well as activation within the supplementary motor area, brainstem, and inferior frontal gyrus, exhibited significant correlations with the long-term therapeutic effectiveness of deep brain stimulation (P < 0.05), with the strongest correlation (P < 0.001) observed within the contralateral cerebellum. Dynamic causal modelling revealed a correlation between therapeutic effectiveness and attenuated within-region inhibitory connectivity in cerebellum. Finally, specific subregions of sensorimotor cortex were identified in which deep brain stimulation-evoked activation correlated with the presence of unwanted paraesthesias. These results suggest that thalamic deep brain stimulation in tremor likely exerts its effects through modulation of both olivocerebellar and thalamocortical circuits. In addition, our findings indicate that deep brain stimulation-evoked functional activation maps obtained intraoperatively may contain predictive information pertaining to the therapeutic and adverse effects induced by deep brain stimulation.
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Affiliation(s)
- William S Gibson
- 1 Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA 55905, USA
| | - Hang Joon Jo
- 1 Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA 55905, USA
| | - Paola Testini
- 1 Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA 55905, USA
| | - Shinho Cho
- 1 Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA 55905, USA
| | - Joel P Felmlee
- 2 Department of Radiology, Mayo Clinic, Rochester, MN, USA 55905, USA
| | - Kirk M Welker
- 2 Department of Radiology, Mayo Clinic, Rochester, MN, USA 55905, USA
| | - Bryan T Klassen
- 3 Department of Neurology, Mayo Clinic, Rochester, MN, USA 55905, USA
| | - Hoon-Ki Min
- 1 Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA 55905, USA 2 Department of Radiology, Mayo Clinic, Rochester, MN, USA 55905, USA 4 Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kendall H Lee
- 1 Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA 55905, USA 4 Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
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Aslan S, Cemgil AT, Akın A. Joint state and parameter estimation of the hemodynamic model by particle smoother expectation maximization method. J Neural Eng 2016; 13:046010. [PMID: 27265063 DOI: 10.1088/1741-2560/13/4/046010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In this paper, we aimed for the robust estimation of the parameters and states of the hemodynamic model by using blood oxygen level dependent signal. APPROACH In the fMRI literature, there are only a few successful methods that are able to make a joint estimation of the states and parameters of the hemodynamic model. In this paper, we implemented a maximum likelihood based method called the particle smoother expectation maximization (PSEM) algorithm for the joint state and parameter estimation. MAIN RESULTS Former sequential Monte Carlo methods were only reliable in the hemodynamic state estimates. They were claimed to outperform the local linearization (LL) filter and the extended Kalman filter (EKF). The PSEM algorithm is compared with the most successful method called square-root cubature Kalman smoother (SCKS) for both state and parameter estimation. SCKS was found to be better than the dynamic expectation maximization (DEM) algorithm, which was shown to be a better estimator than EKF, LL and particle filters. SIGNIFICANCE PSEM was more accurate than SCKS for both the state and the parameter estimation. Hence, PSEM seems to be the most accurate method for the system identification and state estimation for the hemodynamic model inversion literature. This paper do not compare its results with Tikhonov-regularized Newton-CKF (TNF-CKF), a recent robust method which works in filtering sense.
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Khoram N, Zayane C, Djellouli R, Laleg-Kirati TM. A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements. J Neurosci Methods 2016; 262:93-109. [PMID: 26802187 DOI: 10.1016/j.jneumeth.2016.01.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Revised: 01/11/2016] [Accepted: 01/12/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND The calibration of the hemodynamic model that describes changes in blood flow and blood oxygenation during brain activation is a crucial step for successfully monitoring and possibly predicting brain activity. This in turn has the potential to provide diagnosis and treatment of brain diseases in early stages. NEW METHOD We propose an efficient numerical procedure for calibrating the hemodynamic model using some fMRI measurements. The proposed solution methodology is a regularized iterative method equipped with a Kalman filtering-type procedure. The Newton component of the proposed method addresses the nonlinear aspect of the problem. The regularization feature is used to ensure the stability of the algorithm. The Kalman filter procedure is incorporated here to address the noise in the data. RESULTS Numerical results obtained with synthetic data as well as with real fMRI measurements are presented to illustrate the accuracy, robustness to the noise, and the cost-effectiveness of the proposed method. COMPARISON WITH EXISTING METHOD(S) We present numerical results that clearly demonstrate that the proposed method outperforms the Cubature Kalman Filter (CKF), one of the most prominent existing numerical methods. CONCLUSION We have designed an iterative numerical technique, called the TNM-CKF algorithm, for calibrating the mathematical model that describes the single-event related brain response when fMRI measurements are given. The method appears to be highly accurate and effective in reconstructing the BOLD signal even when the measurements are tainted with high noise level (as high as 30%).
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Affiliation(s)
- Nafiseh Khoram
- Department of Mathematics & Interdisciplinary Research Institute for the Sciences, IRIS, California State University Northridge (CSUN), Northridge, USA..
| | - Chadia Zayane
- Department of Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
| | - Rabia Djellouli
- Department of Mathematics & Interdisciplinary Research Institute for the Sciences, IRIS, California State University Northridge (CSUN), Northridge, USA..
| | - Taous-Meriem Laleg-Kirati
- Department of Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
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Comparison of the hemodynamic filtering methods and particle filter with extended Kalman filter approximated proposal function as an efficient hemodynamic state estimation method. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2015.10.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Aslan S, Cemgil AT, Aslan MŞ, Töreyin BU, Akın A. Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2015.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Aponte EA, Raman S, Sengupta B, Penny WD, Stephan KE, Heinzle J. mpdcm: A toolbox for massively parallel dynamic causal modeling. J Neurosci Methods 2016; 257:7-16. [DOI: 10.1016/j.jneumeth.2015.09.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Revised: 08/15/2015] [Accepted: 09/08/2015] [Indexed: 11/26/2022]
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Koike T, Tanabe HC, Okazaki S, Nakagawa E, Sasaki AT, Shimada K, Sugawara SK, Takahashi HK, Yoshihara K, Bosch-Bayard J, Sadato N. Neural substrates of shared attention as social memory: A hyperscanning functional magnetic resonance imaging study. Neuroimage 2016; 125:401-412. [DOI: 10.1016/j.neuroimage.2015.09.076] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 08/31/2015] [Accepted: 09/26/2015] [Indexed: 10/22/2022] Open
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Heinzle J, Koopmans PJ, den Ouden HE, Raman S, Stephan KE. A hemodynamic model for layered BOLD signals. Neuroimage 2016; 125:556-570. [DOI: 10.1016/j.neuroimage.2015.10.025] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Revised: 10/09/2015] [Accepted: 10/10/2015] [Indexed: 01/16/2023] Open
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Huneau C, Benali H, Chabriat H. Investigating Human Neurovascular Coupling Using Functional Neuroimaging: A Critical Review of Dynamic Models. Front Neurosci 2015; 9:467. [PMID: 26733782 PMCID: PMC4683196 DOI: 10.3389/fnins.2015.00467] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 11/23/2015] [Indexed: 01/26/2023] Open
Abstract
The mechanisms that link a transient neural activity to the corresponding increase of cerebral blood flow (CBF) are termed neurovascular coupling (NVC). They are possibly impaired at early stages of small vessel or neurodegenerative diseases. Investigation of NVC in humans has been made possible with the development of various neuroimaging techniques based on variations of local hemodynamics during neural activity. Specific dynamic models are currently used for interpreting these data that can include biophysical parameters related to NVC. After a brief review of the current knowledge about possible mechanisms acting in NVC we selected seven models with explicit integration of NVC found in the literature. All these models were described using the same procedure. We compared their physiological assumptions, mathematical formalism, and validation. In particular, we pointed out their strong differences in terms of complexity. Finally, we discussed their validity and their potential applications. These models may provide key information to investigate various aspects of NVC in human pathology.
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Affiliation(s)
- Clément Huneau
- Laboratoire d'Imagerie Biomédicale, UPMC Paris 06, Centre National de la Recherche Scientifique U7371, Institut National de la Santé et de la Recherche Médicale U1146, Sorbonne UniversitésParis, France; Institut National de la Santé et de la Recherche Médicale U1161, Université Paris Diderot, Sorbonne Paris CitéParis, France
| | - Habib Benali
- Laboratoire d'Imagerie Biomédicale, UPMC Paris 06, Centre National de la Recherche Scientifique U7371, Institut National de la Santé et de la Recherche Médicale U1146, Sorbonne Universités Paris, France
| | - Hugues Chabriat
- Institut National de la Santé et de la Recherche Médicale U1161, Université Paris Diderot, Sorbonne Paris CitéParis, France; AP-HP, Hôpital Lariboisière, Service de Neurologie and DHU NeuroVascParis, France
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Schiavio A, Altenmüller E. Exploring Music-Based Rehabilitation for Parkinsonism through Embodied Cognitive Science. Front Neurol 2015; 6:217. [PMID: 26539155 PMCID: PMC4609849 DOI: 10.3389/fneur.2015.00217] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 09/26/2015] [Indexed: 11/25/2022] Open
Abstract
Recent embodied approaches in cognitive sciences emphasize the constitutive roles of bodies and environment in driving cognitive processes. Cognition is thus seen as a distributed system based on the continuous interaction of bodies, brains, and environment. These categories, moreover, do not relate only causally, through a sequential input-output network of computations; rather, they are dynamically enfolded in each other, being mutually implemented by the concrete patterns of actions adopted by the cognitive system. However, while this claim has been widely discussed across various disciplines, its relevance and potential beneficial applications for music therapy remain largely unexplored. With this in mind, we provide here an overview of the embodied approaches to cognition, discussing their main tenets through the lenses of music therapy. In doing so, we question established methodological and theoretical paradigms and identify possible novel strategies for intervention. In particular, we refer to the music-based rehabilitative protocols adopted for Parkinson's disease patients. Indeed, in this context, it has recently been observed that music therapy not only affects movement-related skills but that it also contributes to stabilizing physiological functions and improving socio-affective behaviors. We argue that these phenomena involve previously unconsidered aspects of cognition and (motor) behavior, which are rooted in the action-perception cycle characterizing the whole living system.
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Affiliation(s)
- Andrea Schiavio
- School of Music, The Ohio State University, Columbus, OH, USA
- Department of Music, The University of Sheffield, Sheffield, UK
| | - Eckart Altenmüller
- Institute of Music Physiology and Musician’s Medicine, University of Music, Drama and Media Hannover, Hannover, Germany
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Rosa PN, Figueiredo P, Silvestre CJ. On the distinguishability of HRF models in fMRI. Front Comput Neurosci 2015; 9:54. [PMID: 26106322 PMCID: PMC4460732 DOI: 10.3389/fncom.2015.00054] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Accepted: 04/24/2015] [Indexed: 11/13/2022] Open
Abstract
Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.
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Affiliation(s)
- Paulo N Rosa
- Flight Systems Business Unit, Aerospace, Defense & Systems Department, Deimos Engenharia, Lda. Lisboa, Portugal
| | - Patricia Figueiredo
- Institute for Systems and Robotics and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa Portugal
| | - Carlos J Silvestre
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau Taipa, Macau, China
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Kozunov VV, Ossadtchi A. GALA: group analysis leads to accuracy, a novel approach for solving the inverse problem in exploratory analysis of group MEG recordings. Front Neurosci 2015; 9:107. [PMID: 25954141 PMCID: PMC4404950 DOI: 10.3389/fnins.2015.00107] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 03/12/2015] [Indexed: 11/13/2022] Open
Abstract
Although MEG/EEG signals are highly variable between subjects, they allow characterizing systematic changes of cortical activity in both space and time. Traditionally a two-step procedure is used. The first step is a transition from sensor to source space by the means of solving an ill-posed inverse problem for each subject individually. The second is mapping of cortical regions consistently active across subjects. In practice the first step often leads to a set of active cortical regions whose location and timecourses display a great amount of interindividual variability hindering the subsequent group analysis. We propose Group Analysis Leads to Accuracy (GALA)-a solution that combines the two steps into one. GALA takes advantage of individual variations of cortical geometry and sensor locations. It exploits the ensuing variability in electromagnetic forward model as a source of additional information. We assume that for different subjects functionally identical cortical regions are located in close proximity and partially overlap and their timecourses are correlated. This relaxed similarity constraint on the inverse solution can be expressed within a probabilistic framework, allowing for an iterative algorithm solving the inverse problem jointly for all subjects. A systematic simulation study showed that GALA, as compared with the standard min-norm approach, improves accuracy of true activity recovery, when accuracy is assessed both in terms of spatial proximity of the estimated and true activations and correct specification of spatial extent of the activated regions. This improvement obtained without using any noise normalization techniques for both solutions, preserved for a wide range of between-subject variations in both spatial and temporal features of regional activation. The corresponding activation timecourses exhibit significantly higher similarity across subjects. Similar results were obtained for a real MEG dataset of face-specific evoked responses.
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Affiliation(s)
- Vladimir V Kozunov
- MEG Centre, Moscow State University of Psychology and Education Moscow, Russia
| | - Alexei Ossadtchi
- Centre for Cognition and Decision Making, National Research University Higher School of Economics Moscow, Russia ; Laboratory of Control of Complex Systems, Institute of Problems of Mechanical Engineering Russian Academy of Sciences St. Petersburg, Russia
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Sreenivasan KR, Havlicek M, Deshpande G. Nonparametric hemodynamic deconvolution of FMRI using homomorphic filtering. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1155-1163. [PMID: 25531878 DOI: 10.1109/tmi.2014.2379914] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity which is modeled as a convolution of the latent neuronal response and the hemodynamic response function (HRF). Since the sources of HRF variability can be nonneural in nature, the measured fMRI signal does not faithfully represent underlying neural activity. Therefore, it is advantageous to deconvolve the HRF from the fMRI signal. However, since both latent neural activity and the voxel-specific HRF is unknown, the deconvolution must be blind. Existing blind deconvolution approaches employ highly parameterized models, and it is unclear whether these models have an over fitting problem. In order to address these issues, we 1) present a nonparametric deconvolution method based on homomorphic filtering to obtain the latent neuronal response from the fMRI signal and, 2) compare our approach to the best performing existing parametric model based on the estimation of the biophysical hemodynamic model using the Cubature Kalman Filter/Smoother. We hypothesized that if the results from nonparametric deconvolution closely resembled that obtained from parametric deconvolution, then the problem of over fitting during estimation in highly parameterized deconvolution models of fMRI could possibly be over stated. Both simulations and experimental results demonstrate support for our hypothesis since the estimated latent neural response from both parametric and nonparametric methods were highly correlated in the visual cortex. Further, simulations showed that both methods were effective in recovering the simulated ground truth of the latent neural response.
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50
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Nonlinear Bayesian estimation of BOLD signal under non-Gaussian noise. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:389875. [PMID: 25691911 PMCID: PMC4321086 DOI: 10.1155/2015/389875] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2014] [Revised: 10/13/2014] [Accepted: 10/20/2014] [Indexed: 11/18/2022]
Abstract
Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the e-mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF.
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