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Wang H, Chen J, Yuan Z, Huang Y, Lin F. NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis. Med Image Anal 2024; 97:103290. [PMID: 39094462 DOI: 10.1016/j.media.2024.103290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 02/08/2024] [Accepted: 07/25/2024] [Indexed: 08/04/2024]
Abstract
The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.
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Affiliation(s)
- Houxiang Wang
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China
| | - Jiaqing Chen
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China.
| | - Zihao Yuan
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China
| | - Yangxin Huang
- School of Public Health, University of South Florida, Tampa, FL 33612, USA
| | - Fuchun Lin
- National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei 430071, China.
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Gong X, Wang L, Guo Y, Ma Y, Li W, Zhang J, Chen M, Wang J, Meng Q, Chen K, Tian Y. Abnormal large-scale resting-state functional networks in anti-N-methyl-D-aspartate receptor encephalitis. Front Neurosci 2024; 18:1455131. [PMID: 39224578 PMCID: PMC11366611 DOI: 10.3389/fnins.2024.1455131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Background Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks. Methods Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA). Results Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN). Conclusion Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis.
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Affiliation(s)
- Xiarong Gong
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China
- Department of MR, The First People’s Hospital of Yunnan Province, Kunming, China
| | - Libo Wang
- The Second People’s Hospital of Yuxi, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Yuanyuan Guo
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yingzi Ma
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Wei Li
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Juanjuan Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Meiling Chen
- Department of Clinical Psychology, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Qiang Meng
- Department of Neurology, The First People’s Hospital of Yunnan Province, Kunming, China
| | - Kexuan Chen
- Medical School, Kunming University of Science and Technology, Kunming, China
| | - Yanghua Tian
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
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Sheng Y, Wang Y, Wang X, Zhang Z, Zhu D, Zheng W. No sex difference in maturation of brain morphology during the perinatal period. Brain Struct Funct 2024:10.1007/s00429-024-02828-x. [PMID: 39020216 DOI: 10.1007/s00429-024-02828-x] [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: 02/23/2024] [Accepted: 07/04/2024] [Indexed: 07/19/2024]
Abstract
Accumulating evidence have documented sex differences in brain anatomy from early childhood to late adulthood. However, whether sex difference of brain structure emerges in the neonatal brain and how sex modulates the development of cortical morphology during the perinatal stage remains unclear. Here, we utilized T2-weighted MRI from the Developing Human Connectome Project (dHCP) database, consisting of 41 male and 40 female neonates born between 35 and 43 postmenstrual weeks (PMW). Neonates of each sex were arranged in a continuous ascending order of age to capture the progressive changes in cortical thickness and curvature throughout the developmental continuum. The maturational covariance network (MCN) was defined as the coupled developmental fluctuations of morphology measures between cortical regions. We constructed MCNs based on the two features, respectively, to illustrate their developmental interdependencies, and then compared the network topology between sexes. Our results showed that cortical structural development exhibited a localized pattern in both males and females, with no significant sex differences in the developmental trajectory of cortical morphology, overall organization, nodal importance, and modular structure of the MCN. Furthermore, by merging male and female neonates into a unified cohort, we identified evident dependencies influences in structural development between different brain modules using the Granger causality analysis (GCA), emanating from high-order regions toward primary cortices. Our findings demonstrate that the maturational pattern of cortical morphology may not differ between sexes during the perinatal period, and provide evidence for the developmental causality among cortical structures in perinatal brains.
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Affiliation(s)
- Yucen Sheng
- School of Foreign Languages, Lanzhou Jiaotong University, Lanzhou, People's Republic of China
| | - Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, People's Republic of China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital Lanzhou, Lanzhou, People's Republic of China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
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Li WX, Lin QH, Zhang CY, Han Y, Calhoun VD. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data. Front Neurosci 2024; 18:1423014. [PMID: 39050665 PMCID: PMC11266018 DOI: 10.3389/fnins.2024.1423014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 06/21/2024] [Indexed: 07/27/2024] Open
Abstract
Background Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information. Methods We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude-magnitude and phase-phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude-phase and phase-magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals. Results Simulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2-20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features. Conclusion The proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.
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Affiliation(s)
- Wei-Xing Li
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Qiu-Hua Lin
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Chao-Ying Zhang
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Yue Han
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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Zhang J, Zhu X, Zhou H, Wang S. Behavioral and neural mechanisms of face-specific attention during goal-directed visual search. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600413. [PMID: 38979217 PMCID: PMC11230280 DOI: 10.1101/2024.06.24.600413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Goal-directed visual attention is a fundamental cognitive process that enables animals to selectively focus on specific regions of the visual field while filtering out irrelevant information. However, given the domain specificity of social behaviors, it remains unclear whether attention to faces versus non-faces recruits different neurocognitive processes. In this study, we simultaneously recorded activity from temporal and frontal nodes of the attention network while macaques performed a goal-directed visual search task. V4 and inferotemporal (IT) visual category-selective units, selected during cue presentation, discriminated fixations on targets and distractors during the search, but were differentially engaged by face and house targets. V4 and IT category-selective units also encoded fixation transitions and search dynamics. Compared to distractors, fixations on targets reduced spike-LFP coherence within the temporal cortex. Importantly, target-induced desynchronization between the temporal and prefrontal cortices was only evident for face targets, suggesting that attention to faces differentially engaged the prefrontal cortex. We further revealed bidirectional theta influence between the temporal and prefrontal cortices using Granger causality, which was again disproportionate for faces. Finally, we showed that the search became more efficient with increasing target-induced desynchronization. Together, our results suggest domain specificity for attending to faces and an intricate interplay between visual attention and social processing neural networks.
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Zhang J, Cao R, Zhu X, Zhou H, Wang S. Distinct attentional profile and functional connectivity of neurons with visual feature coding in the primate brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600401. [PMID: 38979388 PMCID: PMC11230157 DOI: 10.1101/2024.06.24.600401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Visual attention and object recognition are two critical cognitive functions that significantly influence our perception of the world. While these neural processes converge on the temporal cortex, the exact nature of their interactions remains largely unclear. Here, we systematically investigated the interplay between visual attention and object feature coding by training macaques to perform a free-gaze visual search task using natural face and object stimuli. With a large number of units recorded from multiple brain areas, we discovered that units exhibiting visual feature coding displayed a distinct attentional response profile and functional connectivity compared to units not exhibiting feature coding. Attention directed towards search targets enhanced the pattern separation of stimuli across brain areas, and this enhancement was more pronounced for units encoding visual features. Our findings suggest two stages of neural processing, with the early stage primarily focused on processing visual features and the late stage dedicated to processing attention. Importantly, feature coding in the early stage could predict the attentional effect in the late stage. Together, our results suggest an intricate interplay between visual feature and attention coding in the primate brain, which can be attributed to the differential functional connectivity and neural networks engaged in these processes.
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Thum JA, Malekmohammadi M, Toker D, Sparks H, Alijanpourotaghsara A, Choi JW, Hudson AE, Monti MM, Pouratian N. Globus pallidus externus drives increase in network-wide alpha power with propofol-induced loss-of-consciousness in humans. Cereb Cortex 2024; 34:bhae243. [PMID: 38850214 PMCID: PMC11161864 DOI: 10.1093/cercor/bhae243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 05/16/2024] [Accepted: 05/29/2024] [Indexed: 06/10/2024] Open
Abstract
States of consciousness are likely mediated by multiple parallel yet interacting cortico-subcortical recurrent networks. Although the mesocircuit model has implicated the pallidocortical circuit as one such network, this circuit has not been extensively evaluated to identify network-level electrophysiological changes related to loss of consciousness (LOC). We characterize changes in the mesocircuit in awake versus propofol-induced LOC in humans by directly simultaneously recording from sensorimotor cortices (S1/M1) and globus pallidus interna and externa (GPi/GPe) in 12 patients with Parkinson disease undergoing deep brain stimulator implantation. Propofol-induced LOC is associated with increases in local power up to 20 Hz in GPi, 35 Hz in GPe, and 100 Hz in S1/M1. LOC is likewise marked by increased pallidocortical alpha synchrony across all nodes, with increased alpha/low beta Granger causal (GC) flow from GPe to all other nodes. In contrast, LOC is associated with decreased network-wide beta coupling and beta GC from M1 to the rest of the network. Results implicate an important and possibly central role of GPe in mediating LOC-related increases in alpha power, supporting a significant role of the GPe in modulating cortico-subcortical circuits for consciousness. Simultaneous LOC-related suppression of beta synchrony highlights that distinct oscillatory frequencies act independently, conveying unique network activity.
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Affiliation(s)
- Jasmine A Thum
- Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 540, Los Angeles, CA 90095, United States
| | - Mahsa Malekmohammadi
- Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 540, Los Angeles, CA 90095, United States
| | - Daniel Toker
- Department of Psychology, University of California, Los Angeles, 6522 Pritzker Hall, Los Angeles, CA 90095, United States
| | - Hiro Sparks
- Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 540, Los Angeles, CA 90095, United States
| | - Amirreza Alijanpourotaghsara
- Department of Neurological Surgery, UT Southwestern Medical Center, 5323 Harry Hines Blvd MC8855, Dallas, TX 75390, United States
| | - Jeong Woo Choi
- Department of Neurological Surgery, UT Southwestern Medical Center, 5323 Harry Hines Blvd MC8855, Dallas, TX 75390, United States
| | - Andrew E Hudson
- Department of Anesthesiology, University of California, Los Angeles, 747 Westwood Plaza, Los Angeles, CA 90095, United States
| | - Martin M Monti
- Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 540, Los Angeles, CA 90095, United States
- Department of Psychology, University of California, Los Angeles, 6522 Pritzker Hall, Los Angeles, CA 90095, United States
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, 5323 Harry Hines Blvd MC8855, Dallas, TX 75390, United States
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Li H, Zhang W, Song H, Zhuo L, Yao H, Sun H, Liu R, Feng R, Tang C, Lui S. Altered temporal lobe connectivity is associated with psychotic symptoms in drug-naïve adolescent patients with first-episode schizophrenia. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02485-9. [PMID: 38832962 DOI: 10.1007/s00787-024-02485-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 05/23/2024] [Indexed: 06/06/2024]
Abstract
Research on individuals with a younger onset age of schizophrenia is important for identifying neurobiological processes derived from the interaction of genes and the environment that lead to the manifestation of schizophrenia. Schizophrenia has long been recognized as a disorder of dysconnectivity, but it is largely unknown how brain connectivity changes are associated with psychotic symptoms. Twenty-one adolescent-onset schizophrenia (AOS) patients and 21 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging. Regional homogeneity (ReHo) was used to investigate local brain connectivity alterations in AOS. Regions with significant ReHo changes in patients were selected as "seeds" for further functional connectivity (FC) analysis and Granger causality analysis (GCA), and associations of the obtained functional brain measures with psychotic symptoms in patients with AOS were examined. Compared with HCs, AOS patients showed significantly increased ReHo in the right middle temporal gyrus (MTG), which was positively correlated with PANSS-positive scores, PSYRATS-delusion scores and auditory hallucination scores. With the MTG as the seed, lower connectivity with the bilateral postcentral gyrus (PCG) and higher connectivity with the right precuneus were observed in patients. The reduced FC between the right MTG and bilateral PCG was significantly and positively correlated with hallucination scores. GCA indicated decreased Granger causality from the right MTG to the left middle frontal gyrus (MFG) and from the right MFG to the right MTG in AOS patients, but such effects did not significantly associate with psychotic symptoms. Abnormalities in the connectivity within the MTG and its connectivity with other networks were identified and were significantly correlated with hallucination and delusion ratings. This region may be a key neural substrate of psychotic symptoms in AOS.
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Affiliation(s)
- Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hui Song
- Department of Psychiatry, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Lihua Zhuo
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Hongchao Yao
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ruishan Liu
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Ruohan Feng
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Chungen Tang
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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Xie P, Nie Z, Zhang T, Xu G, Sun A, Chen T, Lv Y. FNIRS based study of brain network characteristics in children with cerebral palsy during bilateral lower limb movement. Med Phys 2024; 51:4434-4446. [PMID: 38683184 DOI: 10.1002/mp.17106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/23/2024] [Accepted: 04/12/2024] [Indexed: 05/01/2024] Open
Abstract
BACKGROUND Motor dysfunctions in children with cerebral palsy (CP) are caused by nonprogressive brain damage. Understanding the functional characteristics of the brain is important for rehabilitation. PURPOSE This paper aimed to study the brain networks of children with CP during bilateral lower limb movement using functional near-infrared spectroscopy (fNIRS) and to explore effective fNIRS indices for reflecting functional brain activity. METHODS Using fNIRS, cerebral oxygenation signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) were recorded from fifteen children with spastic CP and seventeen children with typical development (CTDs) in the resting state and during bilateral lower limb movement. Functional connectivity matrices based on phase-locking values (PLVs) were calculated using Hilbert transformation, and binary networks were constructed at different sparsity levels. Network metrics such as the clustering coefficient, global efficiency, local efficiency, and transitivity were calculated. Furthermore, the time-varying curves of network metrics during movement were obtained by dividing the time window and using sparse inverse covariance matrices. Finally, conditional Granger causality (GC) was used to explore the causal relationships between different brain regions. RESULTS Compared to CTDs, the connectivity between RMC-RPFC (p = 0.017) and RMC-LMC (p = 0.002) in the brain network was decreased in children with CP, and the clustering coefficient (p = 0.003), global efficiency (p = 0.034), local efficiency (p = 0.015), and transitivity (p = 0.009) were significantly lower. The standard deviation of the changes in global efficiency of children with CP during motion was also greater than that of CTDs. Using GC, it was found that there was a significant increase in causal strength from the RMC to the RPFC (p = 0.04) and from the RMC to the LMC (p = 0.042) in children with CP during motion. Additionally, there were significant negative correlations between the PLV of LMC-RMC (p = 0.002) and the Gross Motor Function Classification System (GMFCS) and between the GMFCS and the clustering coefficient (p = 0.01). CONCLUSIONS During rehabilitation training of the lower limbs, there were significant differences in brain network indices between children with CP and CTDs. The indicators proposed in this paper are effective at evaluating motor function and the real-time impact of rehabilitation training on the brain network and have great potential for application in guiding clinical motor function assessment and planning rehabilitation strategies.
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Affiliation(s)
- Ping Xie
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China
| | - Zichao Nie
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Tengyu Zhang
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Gongcheng Xu
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Aiping Sun
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Tiandi Chen
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Nanchang City Key Laboratory of Integrated Medical and Industrial Technology, Nanchang university, Nanchang, China
| | - Yan Lv
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
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10
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Rowe EG, Garrido MI, Tsuchiya N. Feedforward connectivity patterns from visual areas to the front of the brain contain information about sensory stimuli regardless of awareness or report. Cortex 2024; 172:284-300. [PMID: 38142179 DOI: 10.1016/j.cortex.2023.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/11/2023] [Accepted: 11/21/2023] [Indexed: 12/25/2023]
Abstract
Current theories of consciousness can be categorized to some extent by their predictions about the putative role of the prefrontal cortex (PFC) in conscious perception. One family of the theories proposes that the PFC is necessary for conscious perception. The other postulates that the PFC is not necessary and that other areas (e.g., posterior cortical areas) are more important for conscious perception. No-report paradigms could potentially arbitrate the debate as they disentangle task reporting from conscious perception. While previous no-report paradigms tend to point to a reduction in PFC activity, they have not examined the critical role of the PFC in "monitoring" or "reading out" the patterns of activity in the sensory cortex to generate conscious perception. To address this, we reanalysed electroencephalography (EEG) data from a no-report inattentional blindness paradigm (Shafto & Pitts, 2015). We examined the role of feedforward input patterns to the PFC from sensory cortices. We employed nonparametric spectral Granger causality and quantified the amount of information that reflected the contents of consciousness using multivariate classifiers. Unexpectedly, regardless of whether the stimulus was consciously seen or not, we found that information relating to the current sensory stimulus was present in the pattern of inputs from visual areas to the PFC. In light of these findings, we suggest various theories of consciousness need to be revised to accommodate the fact that the contents of consciousness are decodable from the input patterns from posterior sensory regions to the PFC, regardless of awareness (or report).
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Affiliation(s)
- Elise G Rowe
- School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia; Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia.
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia; ARC Centre of Excellence for Integrative Brain Function, Victoria, Australia
| | - Naotsugu Tsuchiya
- School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia; Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia; Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan; Department of Qualia Structure, ATR Computational Neuroscience Laboratories, Seika-cho, Soraku-gun, Kyoto, Japan; ARC Centre of Excellence for Integrative Brain Function, Victoria, Australia
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11
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Gao C, Li T. Gender specificity of frontal activity based on fNIRS in distinguishing bipolar depression population from health control. JOURNAL OF BIOPHOTONICS 2024; 17:e202300346. [PMID: 37934196 DOI: 10.1002/jbio.202300346] [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: 08/27/2023] [Revised: 10/19/2023] [Accepted: 10/24/2023] [Indexed: 11/08/2023]
Abstract
Bipolar depression (BD) is a chronic psychiatric disorder characterized by recurring bouts of bipolar mania or hypomania followed by depression. In this essay, we used the functional near-infrared spectroscopy to investigate the frontal function of BD in males and females, which included a total of 43 BD patients and 28 healthy subjects. The hemodynamic response associated with the task was estimated using the generalized linear model (GLM) approach. Wavelet transforms coherence and Granger causality (GC) methods were employed to calculate brain connectivity. GLM and GC results revealed that female patients were more distinguishable from healthy controls than males. Additionally, the correlation between BD scores and GLM results showed that the brain activation of male subjects was affected by their anxiety levels. This study suggests that traditional diagnostic methods for BD may not be as sensitive in men as in women.
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Affiliation(s)
- Chenyang Gao
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
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12
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Ren Q, Xu D, Liang J, Cao Y, Zhang L, Ge S, Chen P. Poor vitamin D status was associated with increased appendicular fat deposition in US Adults: Data from 2011-2018 National Health and Nutrition Examination Survey. Nutr Res 2024; 121:108-118. [PMID: 38061321 DOI: 10.1016/j.nutres.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/04/2023] [Accepted: 11/05/2023] [Indexed: 01/07/2024]
Abstract
The aim of the study was to explore the relationship between serum 25-hydroxyvitamin D [25(OH)D] concentrations and regional body fat deposition in 2011-2018 National Health and Nutrition Examination Survey participants aged 18 to 59 years. We hypothesized that serum 25(OH)D concentrations were negatively associated with total, appendicular, and truncal fat deposition. Serum 25(OH)D concentration was categorized into sufficient (≥75.0 nM), insufficient (50.0-74.9 nM), and deficient (<50.0 nM) groups. Fat mass (FM) was measured by dual-energy X-ray absorptiometry, and FM index (FMI) was calculated by dividing FM (kg) with height2 (m2). Multivariant linear regression and Granger causal analysis were performed to assess the causal relationship between vitamin D status and regional FMIs. Overall serum 25(OH)D concentrations were negatively associated with total (β = -0.029, standard error [SE] = 0.002), trunk (β = -0.015, SE = 0.001), arms (β = -0.004, SE = 3.09 × 10-4), and legs (β = -0.010, SE = 0.001) FMIs in all participants (P < .001, respectively); however, after stratified by vitamin D status and BMI, the negative associations were only observed in individuals with vitamin D deficiency and obesity. The causal analysis indicated that serum 25(OH)D concentrations may causally reduce the arms (F = 4.917, probability [P] = 0.007), legs (F = 5.783, P = 0.003), and total (F = 3.202, P = 0.041) FMIs except for trunk FMI but not vice versa. In conclusion, poor vitamin D status was associated with increased total and appendicular body fat deposition in US adults, particularly in participants with obesity.
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Affiliation(s)
- Qian Ren
- Department of Clinical Nutrition, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200233.
| | - Danfeng Xu
- Department of Clinical Nutrition, Huadong Hospital Affiliated to Fudan University, Shanghai, China, 200020
| | - Jinrong Liang
- Department of Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200233
| | - Yun Cao
- Department of Clinical Nutrition, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200233
| | - Lili Zhang
- Department of Cardiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200233
| | - Sheng Ge
- Department of Clinical Nutrition, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200233.
| | - Peizhan Chen
- Clinical Research Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 201821.
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13
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Zhang S, Ai H, Wang J, Liu T, Zheng X, Tian X, Bai W. Reduced Prefrontal-Thalamic Theta Flow During Working Memory Retrieval in APP/PS1 Mice. J Alzheimers Dis 2024; 97:1737-1749. [PMID: 38306044 PMCID: PMC10894573 DOI: 10.3233/jad-231078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/05/2023] [Indexed: 02/03/2024]
Abstract
Background Working memory deficits in Alzheimer's disease (AD) are linked to impairments in the retrieval of stored memory information. However, research on the mechanism of impaired working memory retrieval in Alzheimer's disease is still lacking. Objective The medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval. The purpose of this study is to investigate the functional interactions and information transmission between mPFC and MD in the AD model. Methods We recorded local field potentials from mPFC and MD while the mice (APP/PS1 transgenic model and control) performed a T-maze spatial working memory task. The temporal dynamics of oscillatory activity and bidirectional information flow between mPFC and MD were assessed during the task phases. Results We mainly found a significant decrease in theta flow from mPFC to MD in APP/PS1 mice during retrieval. Conclusions Our results indicate an important role of the mPFC-MD input for retrieval and the disrupted information transfer from mPFC to MD may be the underlying mechanism of working memory deficits in APP/PS1 mice.
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Affiliation(s)
- Shengnan Zhang
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
| | - Hongrui Ai
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
| | - Jia Wang
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
| | - Tiaotiao Liu
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
| | - Xuyuan Zheng
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
| | - Xin Tian
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
| | - Wenwen Bai
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
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14
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Gallos IK, Lehmberg D, Dietrich F, Siettos C. Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator. CHAOS (WOODBURY, N.Y.) 2024; 34:013151. [PMID: 38285718 DOI: 10.1063/5.0157881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/22/2023] [Indexed: 01/31/2024]
Abstract
We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI time series. Our approach is a three stage one. First, we exploit manifold learning and, in particular, diffusion maps (DMs) to discover a set of variables that parametrize the latent space on which the emergent high-dimensional fMRI time series evolve. Then, we construct ROMs on the embedded manifold via two techniques: Feedforward Neural Networks (FNNs) and the Koopman operator. Finally, for predicting the out-of-sample long-term dynamics of brain activity in the ambient fMRI space, we solve the pre-image problem, i.e., the construction of a map from the low-dimensional manifold to the original high-dimensional (ambient) space by coupling DMs with Geometric Harmonics (GH) when using FNNs and the Koopman modes per se. For our illustrations, we have assessed the performance of the two proposed schemes using two benchmark fMRI time series: (i) a simplistic five-dimensional model of stochastic discrete-time equations used just for a "transparent" illustration of the approach, thus knowing a priori what one expects to get, and (ii) a real fMRI dataset with recordings during a visuomotor task. We show that the proposed Koopman operator approach provides, for any practical purposes, equivalent results to the FNN-GH approach, thus bypassing the need to train a non-linear map and to use GH to extrapolate predictions in the ambient space; one can use instead the low-frequency truncation of the DMs function space of L2-integrable functions to predict the entire list of coordinate functions in the ambient space and to solve the pre-image problem.
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Affiliation(s)
- Ioannis K Gallos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece
| | - Daniel Lehmberg
- School of Computation, Information and Technology, Technical University of Munich, Munich 80333, Germany
| | - Felix Dietrich
- School of Computation, Information and Technology, Technical University of Munich, Munich 80333, Germany
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli," Universitá degli Studi di Napoli Federico II, Naples 80125, Italy
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15
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Jiang X, Wen X, Ou G, Li S, Chen Y, Zhang J, Liang Z. Propofol modulates neural dynamics of thalamo-cortical system associated with anesthetic levels in rats. Cogn Neurodyn 2023; 17:1541-1559. [PMID: 37974577 PMCID: PMC10640503 DOI: 10.1007/s11571-022-09912-0] [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: 04/20/2022] [Revised: 10/14/2022] [Accepted: 10/28/2022] [Indexed: 11/24/2022] Open
Abstract
The thalamocortical system plays an important role in consciousness. How anesthesia modulates the thalamocortical interactions is not completely known. We simultaneously recorded local field potentials(LFPs) in thalamic reticular nucleus(TRN) and ventroposteromedial thalamic nucleus(VPM), and electrocorticographic(ECoG) activities in frontal and occipital cortices in freely moving rats (n = 11). We analyzed the changes in thalamic and cortical local spectral power and connectivities, which were measured with phase-amplitude coupling (PAC), coherence and multivariate Granger causality, at the states of baseline, intravenous infusion of propofol 20, 40, 80 mg/kg/h and after recovery of righting reflex. We found that propofol-induced burst-suppression results in a synchronous decrease of spectral power in thalamus and cortex (p < 0.001 for all frequency bands). The cross-frequency PAC increased by propofol, characterized by gradually stronger 'trough-max' pattern in TRN and stronger 'peak-max' pattern in cortex. The cross-region PAC increased in the phase of TRN modulating the amplitude of cortex. The functional connectivity (FC) between TRN and cortex for α/β bands also significantly increased (p < 0.040), with increased directional connectivity from TRN to cortex under propofol anesthesia. In contrast, the corticocortical FC significantly decreased (p < 0.047), with decreased directional connectivity from frontal cortex to occipital cortex. However, the thalamothalamic functional and directional connectivities remained largely unchanged by propofol anesthesia. The spectral powers and connectivities are differentially modulated with the changes of propofol doses, suggesting the changes in neural dynamics in thalamocortical system could be used for distinguishing different vigilance levels caused by propofol. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09912-0.
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Affiliation(s)
- Xuliang Jiang
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 People’s Republic of China
| | - Xin Wen
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004 People’s Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, 066004 People’s Republic of China
| | - Guoyao Ou
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040 People’s Republic of China
| | - Shitong Li
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040 People’s Republic of China
| | - Yali Chen
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 People’s Republic of China
| | - Jun Zhang
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 People’s Republic of China
| | - Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004 People’s Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, 066004 People’s Republic of China
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16
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Chao-Écija A, López-González MV, Dawid-Milner MS. CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions. BIOLOGY 2023; 12:1438. [PMID: 37998037 PMCID: PMC10669071 DOI: 10.3390/biology12111438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/25/2023]
Abstract
CardioRVAR is a new R package designed for the complete evaluation of closed-loop cardiovascular interactions and baroreflex sensitivity estimated from continuous non-invasive heart rate and blood pressure recordings. In this work, we highlight the importance of this software tool in the context of human cardiovascular and autonomic neurophysiology. A summary of the main algorithms that CardioRVAR uses are reviewed, and the workflow of this package is also discussed. We present the results obtained from this tool after its application in three clinical settings. These results support the potential clinical and scientific applications of this tool. The open-source tool can be downloaded from a public GitHub repository, as well as its specific Shiny application, CardioRVARapp. The open-source nature of the tool may benefit the future continuation of this work.
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Affiliation(s)
- Alvaro Chao-Écija
- Autonomic Nervous System Unit, CIMES, School of Medicine, University of Málaga, 29071 Malaga, Spain; (A.C.-É.); (M.V.L.-G.)
| | - Manuel Víctor López-González
- Autonomic Nervous System Unit, CIMES, School of Medicine, University of Málaga, 29071 Malaga, Spain; (A.C.-É.); (M.V.L.-G.)
- Biomedical Research Institute of Málaga (IBIMA), 29590 Malaga, Spain
| | - Marc Stefan Dawid-Milner
- Autonomic Nervous System Unit, CIMES, School of Medicine, University of Málaga, 29071 Malaga, Spain; (A.C.-É.); (M.V.L.-G.)
- Biomedical Research Institute of Málaga (IBIMA), 29590 Malaga, Spain
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17
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Das P, Babadi B. Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO. IEEE TRANSACTIONS ON INFORMATION THEORY 2023; 69:7439-7460. [PMID: 38646067 PMCID: PMC11025718 DOI: 10.1109/tit.2023.3296336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Granger causality is among the widely used data-driven approaches for causal analysis of time series data with applications in various areas including economics, molecular biology, and neuroscience. Two of the main challenges of this methodology are: 1) over-fitting as a result of limited data duration, and 2) correlated process noise as a confounding factor, both leading to errors in identifying the causal influences. Sparse estimation via the LASSO has successfully addressed these challenges for parameter estimation. However, the classical statistical tests for Granger causality resort to asymptotic analysis of ordinary least squares, which require long data duration to be useful and are not immune to confounding effects. In this work, we address this disconnect by introducing a LASSO-based statistic and studying its non-asymptotic properties under the assumption that the true models admit sparse autoregressive representations. We establish fundamental limits for reliable identification of Granger causal influences using the proposed LASSO-based statistic. We further characterize the false positive error probability and test power of a simple thresholding rule for identifying Granger causal effects and provide two methods to set the threshold in a data-driven fashion. We present simulation studies and application to real data to compare the performance of our proposed method to ordinary least squares and existing LASSO-based methods in detecting Granger causal influences, which corroborate our theoretical results.
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Affiliation(s)
- Proloy Das
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114 USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering and the Institute for Systems Research, University of Maryland, College Park, MD, 20742 USA
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18
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Agrimi J, Menicucci D, Qu JH, Laurino M, Mackey CD, Hasnain L, Tarasova YS, Tarasov KV, McDevitt RA, Hoover DB, Gemignani A, Paolocci N, Lakatta EG. Enhanced Myocardial Adenylyl Cyclase Activity Alters Heart-Brain Communication. JACC Clin Electrophysiol 2023; 9:2219-2235. [PMID: 37737772 DOI: 10.1016/j.jacep.2023.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/14/2023] [Accepted: 07/17/2023] [Indexed: 09/23/2023]
Abstract
BACKGROUND The central nervous system's influence on cardiac function is well described; however, direct evidence for signaling from heart to brain remains sparse. Mice with cardiac-selective overexpression of adenylyl cyclase type 8 (TGAC8) display elevated heart rate/contractility and altered neuroautonomic surveillance. OBJECTIVES In this study the authors tested whether elevated adenylyl cyclase type 8-dependent signaling at the cardiac cell level affects brain activity and behavior. METHODS A telemetry system was used to record electrocardiogram (ECG) and electroencephalogram (EEG) in TGAC8 and wild-type mice simultaneously. The Granger causality statistical approach evaluated variations in the ECG/EEG relationship. Mouse behavior was assessed via elevated plus maze, open field, light-dark box, and fear conditioning tests. Transcriptomic and proteomic analyses were performed on brain tissue lysates. RESULTS Behavioral testing revealed increased locomotor activity in TGAC8 that included a greater total distance traveled (+43%; P < 0.01), a higher average speed (+38%; P < 0.01), and a reduced freezing time (-45%; P < 0.01). Dual-lead telemetry recording confirmed a persistent heart rate elevation with a corresponding reduction in ECG-R-waves interval variability and revealed increased EEG-gamma activity in TGAC8 vs wild-type. Bioinformatic assessment of hippocampal tissue indicated upregulation of dopamine 5, gamma-aminobutyric acid A, and metabotropic glutamate 1/5 receptors, major players in gamma activity generation. Granger causality analyses of ECG and EEG recordings showed a marked increase in informational flow between the TGAC8 heart and brain. CONCLUSIONS Perturbed signals arising from the heart cause changes in brain activity, altering mouse behavior. More specifically, the brain interprets augmented myocardial humoral/functional output as a "sustained exercise-like" situation and responds by activating central nervous system output controlling locomotion.
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Affiliation(s)
- Jacopo Agrimi
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institutes of Health Biomedical Research Center (BRC), Baltimore, Maryland, USA; Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Danilo Menicucci
- Department of Surgical, Medical, Molecular Pathology, and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Jia-Hua Qu
- Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institutes of Health Biomedical Research Center (BRC), Baltimore, Maryland, USA
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Chelsea D Mackey
- Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institutes of Health Biomedical Research Center (BRC), Baltimore, Maryland, USA
| | - Laila Hasnain
- Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institutes of Health Biomedical Research Center (BRC), Baltimore, Maryland, USA
| | - Yelena S Tarasova
- Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institutes of Health Biomedical Research Center (BRC), Baltimore, Maryland, USA
| | - Kirill V Tarasov
- Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institutes of Health Biomedical Research Center (BRC), Baltimore, Maryland, USA
| | - Ross A McDevitt
- Center of Excellence in Inflammation, Infectious Disease, and Immunity, East Tennessee State University, Johnson City, Tennessee, USA
| | - Donald B Hoover
- The Comparative Medicine Section, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA; Department of Biomedical Sciences, James H. Quillen College of Medicine, East Tennessee State University, Johnson City, Tennessee, USA; Center of Excellence in Inflammation, Infectious Disease, and Immunity, East Tennessee State University, Johnson City, Tennessee, USA
| | - Angelo Gemignani
- Department of Surgical, Medical, Molecular Pathology, and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Nazareno Paolocci
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Biomedical Sciences, University of Padova, Padova, Italy.
| | - Edward G Lakatta
- Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institutes of Health Biomedical Research Center (BRC), Baltimore, Maryland, USA.
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19
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Wang HL, Kuo YT, Lo YC, Kuo CH, Chen BW, Wang CF, Wu ZY, Lee CE, Yang SH, Lin SH, Chen PC, Chen YY. Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task. Int J Neural Syst 2023; 33:2350051. [PMID: 37632142 DOI: 10.1142/s012906572350051x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.
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Affiliation(s)
- Han-Lin Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Yun-Ting Kuo
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Yu-Chun Lo
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 235235, Taiwan
| | - Chao-Hung Kuo
- Department of Neurosurgery, Neurological Institute Taipei Veterans General Hospital, No. 201, Sec. 2 Shipai Rd., Taipei 11217, Taiwan
| | - Bo-Wei Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
- Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Zu-Yu Wu
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Chi-En Lee
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Shih-Hung Yang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1, University Rd., Tainan 70101, Taiwan
| | - Sheng-Huang Lin
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3 Zhongyang Rd., Hualien 97002, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien 97004, Taiwan
| | - Po-Chuan Chen
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 235235, Taiwan
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20
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Shi X, Sau A, Li X, Patel K, Bajaj N, Varela M, Wu H, Handa B, Arnold A, Shun-Shin M, Keene D, Howard J, Whinnett Z, Peters N, Christensen K, Jensen HJ, Ng FS. Information theory-based direct causality measure to assess cardiac fibrillation dynamics. J R Soc Interface 2023; 20:20230443. [PMID: 37817583 PMCID: PMC10565370 DOI: 10.1098/rsif.2023.0443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.
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Affiliation(s)
- Xili Shi
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Xinyang Li
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Kiran Patel
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Marta Varela
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Balvinder Handa
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Ahran Arnold
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Matthew Shun-Shin
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Daniel Keene
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - James Howard
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Zachary Whinnett
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Nicholas Peters
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Kim Christensen
- Department of Physics, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
| | - Henrik Jeldtoft Jensen
- Department of Mathematics, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
- Department of Cardiology, Chelsea and Westminster NHS Foundation Trust, London, UK
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21
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Kodama T, Kojima T, Honda Y, Hosokawa T, Karashima A, Watanabe M. Contribution of default mode network to game and delayed-response task performance: Power and connectivity analyses of theta oscillation in the monkey. Neurosci Lett 2023; 814:137465. [PMID: 37659700 DOI: 10.1016/j.neulet.2023.137465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023]
Abstract
Neuroimaging studies have demonstrated the presence of a default mode network (DMN) which shows greater activity during rest, and an executive network (EN) which is activated during cognitive tasks. DMN and EN are thought to have competing functions. However, recent studies reported that the two networks show coactivation during some cognitive tasks. To clarify how DMN works and how DMN interacts with EN for cognitive control, we recorded EEG activities in the medial prefrontal (anterior DMN: aDMN), posterior cingulate/precuneus (posterior DMN: pDMN), and lateral prefrontal (EN) areas in the monkey. As cognitive tasks, we employed a monkey-monkey competitive video game (GAME) and a delayed-response (DR) task. We focused on theta oscillation because of its importance in cognitive control. We also examined theta band connectivity among the three network areas using the Granger causality analysis. DMN and EN were found to work cooperatively in both tasks. In all the three network areas, we found GAME-task-related, but no DR-task-related, increase in theta power from the resting level, maybe because of the higher cognitive demand associated with the GAME task performance. The information flow conveyed by the theta oscillation was directed more to aDMN than from aDMN for both tasks. The GAME-task-related increase in theta power in aDMN is supposed to be supported by more information flow conveyed by the theta oscillation from EN and pDMN.
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Affiliation(s)
- Tohru Kodama
- Department of Physiological Psychology, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan
| | - Takashi Kojima
- Department of Physiological Psychology, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan
| | - Yoshiko Honda
- Department of Physiological Psychology, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan
| | - Takayuki Hosokawa
- Department of Orthoptics, Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Okayama 701-0193, Japan
| | - Akihiro Karashima
- Department of Electrical and Electronic Engineering, Tohoku Institute of Technology, Sendai, Miyagi 982-8577, Japan
| | - Masataka Watanabe
- Department of Physiological Psychology, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan.
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22
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Wu X, Guo Y, Xue J, Dong Y, Sun Y, Wang B, Xiang J, Liu Y. Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs. Brain Sci 2023; 13:1331. [PMID: 37759932 PMCID: PMC10526475 DOI: 10.3390/brainsci13091331] [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: 07/31/2023] [Revised: 08/27/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.
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Affiliation(s)
- Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yanqing Dong
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital, Taiyuan 030013, China
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23
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Chen Z, Ji X, Li T, Gao C, Li G, Liu S, Zhang Y. Lateralization difference in functional activity during Stroop tasks: a functional near-infrared spectroscopy and EEG simultaneous study. Front Psychiatry 2023; 14:1221381. [PMID: 37680451 PMCID: PMC10481867 DOI: 10.3389/fpsyt.2023.1221381] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/08/2023] [Indexed: 09/09/2023] Open
Abstract
Introduction Conflict monitoring and processing is an important part of the human cognitive system, it plays a key role in many studies of cognitive disorders. Methods Based on a Chinese word-color match Stroop task, which included incongruent and neutral stimuli, the Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) signals were recorded simultaneously. The Pearson correlation coefficient matrix was calculated to analyze brain connectivity based on EEG signals. Granger Causality (GC) method was employed to analyze the effective connectivity of bilateral frontal lobes. Wavelet Transform Coherence (WTC) was used to analyze the functional connectivity of the bilateral hemisphere and ipsilateral hemisphere. Results Results indicated that brain connectivity analysis on EEG signals did not show any significant lateralization, while fNIRS analysis results showed the frontal lobes especially the left frontal lobe play the leading role in dealing with conflict tasks. The human brain shows leftward lateralization while processing the more complicated incongruent stimuli. This is demonstrated by the higher functional connectivity in the left frontal lobe and the information flow from the left frontal lobe to the right frontal lobe. Discussion Our findings in brain connectivity during cognitive conflict processing demonstrated that the dual modality method combining EEG and fNIRS is a valuable tool to excavate more information through cognitive and physiological studies.
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Affiliation(s)
- Zemeng Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Xiang Ji
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Chenyang Gao
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Guorui Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Shuyu Liu
- Applied Physiology and Kinesiology, College of Health and Human Performance, University of Florida, Gainesville, FL, United States
| | - Yingyuan Zhang
- Academy of Opto-Electronics, China Electronics Technology Group Corporation, Tianjin, China
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24
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Hadjiabadi D, Soltesz I. From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks. J Physiol 2023; 601:3011-3024. [PMID: 35815823 PMCID: PMC10655857 DOI: 10.1113/jp282749] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/27/2022] [Indexed: 11/08/2022] Open
Abstract
The convergence of advanced single-cell in vivo functional imaging techniques, computational modelling tools and graph-based network analytics has heralded new opportunities to study single-cell dynamics across large-scale networks, providing novel insights into principles of brain communication and pointing towards potential new strategies for treating neurological disorders. A major recent finding has been the identification of unusually richly connected hub cells that have capacity to synchronize networks and may also be critical in network dysfunction. While hub neurons are traditionally defined by measures that consider solely the number and strength of connections, novel higher-order graph analytics now enables the mining of massive networks for repeating subgraph patterns called motifs. As an illustration of the power offered by higher-order analysis of neuronal networks, we highlight how recent methodological advances uncovered a new functional cell type, the superhub, that is predicted to play a major role in regulating network dynamics. Finally, we discuss open questions that will be critical for assessing the importance of higher-order cellular-scale network analytics in understanding brain function in health and disease.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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25
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Platiša MM, Radovanović NN, Pernice R, Barà C, Pavlović SU, Faes L. Information-Theoretic Analysis of Cardio-Respiratory Interactions in Heart Failure Patients: Effects of Arrhythmias and Cardiac Resynchronization Therapy. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1072. [PMID: 37510019 PMCID: PMC10378632 DOI: 10.3390/e25071072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023]
Abstract
The properties of cardio-respiratory coupling (CRC) are affected by various pathological conditions related to the cardiovascular and/or respiratory systems. In heart failure, one of the most common cardiac pathological conditions, the degree of CRC changes primarily depend on the type of heart-rhythm alterations. In this work, we investigated CRC in heart-failure patients, applying measures from information theory, i.e., Granger Causality (GC), Transfer Entropy (TE) and Cross Entropy (CE), to quantify the directed coupling and causality between cardiac (RR interval) and respiratory (Resp) time series. Patients were divided into three groups depending on their heart rhythm (sinus rhythm and presence of low/high number of ventricular extrasystoles) and were studied also after cardiac resynchronization therapy (CRT), distinguishing responders and non-responders to the therapy. The information-theoretic analysis of bidirectional cardio-respiratory interactions in HF patients revealed the strong effect of nonlinear components in the RR (high number of ventricular extrasystoles) and in the Resp time series (respiratory sinus arrhythmia) as well as in their causal interactions. We showed that GC as a linear model measure is not sensitive to both nonlinear components and only model free measures as TE and CE may quantify them. CRT responders mainly exhibit unchanged asymmetry in the TE values, with statistically significant dominance of the information flow from Resp to RR over the opposite flow from RR to Resp, before and after CRT. In non-responders this asymmetry was statistically significant only after CRT. Our results indicate that the success of CRT is related to corresponding information transfer between the cardiac and respiratory signal quantified at baseline measurements, which could contribute to a better selection of patients for this type of therapy.
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Affiliation(s)
- Mirjana M Platiša
- Laboratory for Biosignals, Institute of Biophysics, Faculty of Medicine, University of Belgrade, Višegradska 26-2, 11000 Belgrade, Serbia
| | - Nikola N Radovanović
- Pacemaker Center, University Clinical Center of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Chiara Barà
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Siniša U Pavlović
- Pacemaker Center, University Clinical Center of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
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26
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Nemirovsky IE, Popiel NJM, Rudas J, Caius M, Naci L, Schiff ND, Owen AM, Soddu A. An implementation of integrated information theory in resting-state fMRI. Commun Biol 2023; 6:692. [PMID: 37407655 DOI: 10.1038/s42003-023-05063-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/22/2023] [Indexed: 07/07/2023] Open
Abstract
Integrated Information Theory was developed to explain and quantify consciousness, arguing that conscious systems consist of elements that are integrated through their causal properties. This study presents an implementation of Integrated Information Theory 3.0, the latest version of this framework, to functional MRI data. Data were acquired from 17 healthy subjects who underwent sedation with propofol, a short-acting anaesthetic. Using the PyPhi software package, we systematically analyze how Φmax, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φmax to other proposed measures of conscious level, including the previous version of integrated information, Granger causality, and correlation-based functional connectivity. Our results indicate that Φmax presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φmax closely reflect changes to subjects' conscious level in the frontoparietal and dorsal attention networks, which are responsible for higher-order cognitive functions. In conclusion, our findings present important insight into different measures of conscious level that will be useful in future implementations to functional MRI and other forms of neuroimaging.
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Affiliation(s)
- Idan E Nemirovsky
- Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada.
| | - Nicholas J M Popiel
- Cavendish Laboratory, University of Cambridge, Cambridge, CB3 0HE, United Kingdom
| | - Jorge Rudas
- Institute of Biotechnology, Universidad Nacional de Colombia, Cra 45, Bogotá, Colombia
| | - Matthew Caius
- Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
- Department of Medical Biophysics, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Nicholas D Schiff
- Feil Family Brain Mind Research Institute, Weill Cornell Medical College, New York, NY, 10065, USA
| | - Adrian M Owen
- Department of Physiology and Pharmacology and Department of Psychology, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Andrea Soddu
- Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
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27
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Zhang F, Yang Y, Xin Y, Sun Y, Wang C, Zhu J, Tang T, Zhang J, Xu K. Efficacy of different strategies of responsive neurostimulation on seizure control and their association with acute neurophysiological effects in rats. Epilepsy Behav 2023; 143:109212. [PMID: 37172446 DOI: 10.1016/j.yebeh.2023.109212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/01/2023] [Indexed: 05/15/2023]
Abstract
Responsive neurostimulation (RNS) has shown promising but limited efficacy in the treatment of drug-resistant epilepsy. The clinical utility of RNS is hindered by the incomplete understanding of the mechanism behind its therapeutic effects. Thus, assessing the acute effects of responsive stimulation (AERS) based on intracranial EEG recordings in the temporal lobe epilepsy rat model may provide a better understanding of the potential therapeutic mechanisms underlying the antiepileptic effect of RNS. Furthermore, clarifying the correlation between AERS and seizure severity may help guide the optimization of RNS parameter settings. In this study, RNS with high (130 Hz) and low frequencies (5 Hz) was applied to the subiculum (SUB) and CA1. To quantify the changes induced by RNS, we calculated the AERS during synchronization by Granger causality and analyzed the band power ratio in the classic power band after different stimulations were delivered in the interictal and seizure onset periods, respectively. This demonstrates that only targets combined with an appropriate stimulation frequency could be efficient for seizure control. High-frequency stimulation of CA1 significantly shortened the ongoing seizure duration, which may be causally related to increased synchronization after stimulation. Both high-frequency stimulation of the CA1 and low-frequency stimulation delivered to the SUB reduced seizure frequency, and the reduced seizure risk may correlate with the change in power ratio near the theta band. It indicated that different stimulations may control seizures in diverse manners, perhaps with disparate mechanisms. More focus should be placed on understanding the correlation between seizure severity and synchronization and rhythm around theta bands to simplify the process of parameter optimization.
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Affiliation(s)
- Fang Zhang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Yufang Yang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Yanjie Xin
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Yuting Sun
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Chang Wang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Junming Zhu
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; The MOE Frontier Science Center for Brain Science and Brain-machine Integration, China; Department of Neurosurgery, Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang University, Zhejiang, China
| | - Tao Tang
- Zhejiang Lab, Hangzhou 311100, China
| | - Jianmin Zhang
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; The MOE Frontier Science Center for Brain Science and Brain-machine Integration, China; Department of Neurosurgery, Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang University, Zhejiang, China
| | - Kedi Xu
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China; The MOE Frontier Science Center for Brain Science and Brain-machine Integration, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
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28
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Liang Z, Wang X, Yu Z, Tong Y, Li X, Ma Y, Guo H. Age-dependent neurovascular coupling characteristics in children and adults during general anesthesia. BIOMEDICAL OPTICS EXPRESS 2023; 14:2240-2259. [PMID: 37206124 PMCID: PMC10191645 DOI: 10.1364/boe.482127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/21/2023]
Abstract
General anesthesia is an indispensable procedure in clinical practice. Anesthetic drugs induce dramatic changes in neuronal activity and cerebral metabolism. However, the age-related changes in neurophysiology and hemodynamics during general anesthesia remain unclear. Therefore, the objective of this study was to explore the neurovascular coupling between neurophysiology and hemodynamics in children and adults during general anesthesia. We analyzed frontal electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals recorded from children (6-12 years old, n = 17) and adults (18-60 years old, n = 25) during propofol-induced and sevoflurane-maintained general anesthesia. The neurovascular coupling was evaluated in wakefulness, maintenance of a surgical state of anesthesia (MOSSA), and recovery by using correlation, coherence and Granger-causality (GC) between the EEG indices [EEG power in different bands and permutation entropy (PE)], and hemodynamic responses the oxyhemoglobin (Δ[HbO]) and deoxy-hemoglobin (Δ[Hb]) from fNIRS in the frequency band in 0.01-0.1 Hz. The PE and Δ[Hb] performed well in distinguishing the anesthesia state (p > 0.001). The correlation between PE and Δ[Hb] was higher than those of other indices in the two age groups. The coherence significantly increased during MOSSA (p < 0.05) compared with wakefulness, and the coherences between theta, alpha and gamma, and hemodynamic activities of children are significantly stronger than that of adults' bands. The GC from neuronal activities to hemodynamic responses decreased during MOSSA, and can better distinguish anesthesia state in adults. Propofol-induced and sevoflurane-maintained combination exhibited age-dependent neuronal activities, hemodynamics, and neurovascular coupling, which suggests the need for separate rules for children's and adults' brain states monitoring during general anesthesia.
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Affiliation(s)
- Zhenhu Liang
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Xin Wang
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Zhenyang Yu
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Xiaoli Li
- Center for Cognition and Neuroergonomics, Beijing Normal University (Zhuhai), Zhuhai, Guangdong, 519087, China
| | - Yaqun Ma
- Department of Anesthesiology, the Seventh Medical Center to Chinese PLA General Hospital, Beijing, 100700, China
| | - Hang Guo
- Department of Anesthesiology, the Seventh Medical Center to Chinese PLA General Hospital, Beijing, 100700, China
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29
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Cong J, Zhuang W, Liu Y, Yin S, Jia H, Yi C, Chen K, Xue K, Li F, Yao D, Xu P, Zhang T. Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis. Hum Brain Mapp 2023; 44:2279-2293. [PMID: 36661190 PMCID: PMC10028659 DOI: 10.1002/hbm.26209] [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: 10/17/2022] [Revised: 12/26/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023] Open
Abstract
Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo-parietal junction showed more inter-regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In-Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter-regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD.
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Affiliation(s)
- Jing Cong
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Yunhong Liu
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Shunjie Yin
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Hai Jia
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Kai Chen
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
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Pan L, Liu J, Zhan C, Zhang X, Cui M, Su X, Wang Z, Zhao L, Liu J, Song Y. Effects of indoor exposure to low level toluene on neural network alterations during working memory encoding. CHEMOSPHERE 2023; 321:138153. [PMID: 36804498 DOI: 10.1016/j.chemosphere.2023.138153] [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: 10/20/2022] [Revised: 02/08/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVE While high concentrations of toluene are known to affect multiple human organ systems, research concerning the influence of immediate, short-term exposure to toluene indoors and at low concentrations is scarce. Here, we studied effects of indoor toluene exposure on neural network alterations during working memory (WM) encoding. METHODS A total of 23 healthy college students were recruited. All participants were situated in a closed environmental chamber with a full fresh air system. Each participant was subjected to four exposure experiments with different toluene concentrations (0, 17.5, 35, and 70 ppb, named Group A, B, C and D, respectively), with at least one week between each experiment. WM Behavioral and 19-channel electroencephalogram (EEG) recordings in a pre-set environmental chamber were conducted simultaneously during each toluene exposure experiment. Neural networks relevant to WM encoding were visualized analyzing the obtained data. RESULTS 1. No significant difference in WM behavioral performance among the four groups was found. However, a significant increase in whole brain neural network functional connectivity was noted, especially in the frontal region. 2. An outflow directional transfer function (DTFoutflow) revealed higher frontal region values among Group D (the 70 ppb group) as compared to Group A, B and C (the0, 17.5 ppb and 35 ppb groups, respectively), although no differences in frontal region DTFinflow values among the four groups were noted. 3. The DTFFZ-F7, DTFFZ-T5, DTFFZ-P4, DTFFZ-P3, DTFFP2-O2, DTFP3-T4, DTFP3-F4, DTFP4-CZ and DTFP4-T4 values of Group D were found to be higher as compared to those of Group A and B. Furthermore, DTFFZ-F7 and DTFP4-T4 values of Group C were higher as compared to those of Group A. The DTFFZ-F7 values of Group D were higher as compared to those of the Group C. CONCLUSION Short-term toluene exposure significantly influences neural networks during cognitive processes such as WM encoding, even at low concentration.
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Affiliation(s)
- Liping Pan
- General Medicine Department, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jie Liu
- General Medicine Department, Tianjin Medical University General Hospital, Tianjin, 300052, China; Tianjin Medical University, Tianjin, 300070, China
| | - Changqing Zhan
- Department of Neurology, Wuhu No.2 People's Hospital, Wuhu, Anhui, 241000, China
| | - Xin Zhang
- General Medicine Department, Tianjin Medical University General Hospital, Tianjin, 300052, China; Tianjin Medical University, Tianjin, 300070, China
| | - Mingrui Cui
- General Medicine Department, Tianjin Medical University General Hospital, Tianjin, 300052, China; Tianjin Medical University, Tianjin, 300070, China
| | - Xiao Su
- General Medicine Department, Tianjin Medical University General Hospital, Tianjin, 300052, China; Tianjin Medical University, Tianjin, 300070, China
| | - Zukun Wang
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300000, China
| | - Lei Zhao
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300000, China
| | - Junjie Liu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300000, China.
| | - Yijun Song
- General Practice Center & Emergency Department, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300000, China; General Medicine Department, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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Chiarion G, Sparacino L, Antonacci Y, Faes L, Mesin L. Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends. Bioengineering (Basel) 2023; 10:bioengineering10030372. [PMID: 36978763 PMCID: PMC10044923 DOI: 10.3390/bioengineering10030372] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.
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Affiliation(s)
- Giovanni Chiarion
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
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Esmailpour H, Raman R, Vogels R. Inferior temporal cortex leads prefrontal cortex in response to a violation of a learned sequence. Cereb Cortex 2023; 33:3124-3141. [PMID: 35780398 DOI: 10.1093/cercor/bhac265] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/09/2022] [Accepted: 06/09/2022] [Indexed: 11/13/2022] Open
Abstract
Primates learn statistical regularities that are embedded in visual sequences, a form of statistical learning. Single-unit recordings in macaques showed that inferior temporal (IT) neurons are sensitive to statistical regularities in visual sequences. Here, we asked whether ventrolateral prefrontal cortex (VLPFC), which is connected to IT, is also sensitive to the transition probabilities in visual sequences and whether the statistical learning signal in IT originates in VLPFC. We recorded simultaneously multiunit activity (MUA) and local field potentials (LFPs) in IT and VLPFC after monkeys were exposed to triplets of images with a fixed presentation order. In both areas, the MUA was stronger to images that violated the learned sequence (deviants) compared to the same images presented in the learned triplets. The high-gamma and beta LFP power showed an enhanced and suppressed response, respectively, to the deviants in both areas. The enhanced response was present also for the image following the deviant, suggesting a sensitivity for temporal adjacent dependencies in IT and VLPFC. The increased response to the deviant occurred later in VLPFC than in IT, suggesting that the deviant response in IT was not inherited from VLPFC. These data support predictive coding theories that propose a feedforward flow of prediction errors.
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Affiliation(s)
- Hamideh Esmailpour
- Laboratorium voor Neuro-en Psychofysiologie, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Leuven Brain Institute, KU Leuven, ON V Herestraat 49, 3000 Leuven, Belgium
| | - Rajani Raman
- Laboratorium voor Neuro-en Psychofysiologie, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Leuven Brain Institute, KU Leuven, ON V Herestraat 49, 3000 Leuven, Belgium
| | - Rufin Vogels
- Laboratorium voor Neuro-en Psychofysiologie, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Leuven Brain Institute, KU Leuven, ON V Herestraat 49, 3000 Leuven, Belgium
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Fairclough SH, Stamp K, Dobbins C. Functional connectivity across dorsal and ventral attention networks in response to task difficulty and experimental pain. Neurosci Lett 2023; 793:136967. [PMID: 36379390 DOI: 10.1016/j.neulet.2022.136967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/15/2022]
Abstract
The dorsal and ventral attention networks (DAN & VAN) provide a framework for studying attentional modulation of pain. It has been argued that cognitive demand distracts attention from painful stimuli via top-down reinforcement of task goals (DAN), whereas pain exerts an interruptive effect on cognitive performance via bottom-up pathways (VAN). The current study explores this explanatory framework by manipulating pain and task demand in combination with functional near-infrared spectroscopy (fNIRS) and Granger Causal Connectivity Analyses (GCCA). Twenty-one participants played a racing game at low and high difficulty levels with or without experimental pain (administered via a cold pressor test). Six channels of fNIRS were collected from bilateral frontal eye fields and intraparietal sulci (DAN), with right-lateralised channels at the inferior frontal gyrus and temporoparietal junction (VAN). Our first analysis revealed increased G-causality from bottom-up pathways (VAN) during the cold pressor test. However, an equivalent experience of experimental pain during gameplay increased G-causality in top-down (DAN) pathways, with the left intraparietal sulcus serving a hub of connectivity. High game difficulty increased G-causality via top-down pathways and implicated the right inferior frontal gyrus as an interhemispheric hub. Our results are discussed with reference to existing models of both networks and attentional modulation of pain.
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Affiliation(s)
| | - Kellyann Stamp
- School of Computer Science and Mathematics, Liverpool John Moores University, UK
| | - Chelsea Dobbins
- School of Information Technology and Electrical Engineering, The University of Queensland, Australia
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Pidnebesna A, Sanda P, Kalina A, Hammer J, Marusic P, Vlcek K, Hlinka J. Tackling the challenges of group network inference from intracranial EEG data. Front Neurosci 2022; 16:1061867. [PMID: 36532288 PMCID: PMC9752888 DOI: 10.3389/fnins.2022.1061867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/15/2022] [Indexed: 09/11/2023] Open
Abstract
INTRODUCTION Intracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients. METHODS We review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function-asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p < 0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance. RESULTS The analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area. DISCUSSION To summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition.
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Affiliation(s)
- Anna Pidnebesna
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
- National Institute of Mental Health, Prague, Czechia
| | - Pavel Sanda
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Adam Kalina
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czechia
| | - Jiri Hammer
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czechia
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czechia
| | - Kamil Vlcek
- Department of Neurophysiology of Memory, Institute of Physiology of the Czech Academy of Sciences, Prague, Czechia
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
- National Institute of Mental Health, Prague, Czechia
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35
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Wang J, Zhang S, Liu T, Zheng X, Tian X, Bai W. Directional prefrontal-thalamic information flow is selectively required during spatial working memory retrieval. Front Neurosci 2022; 16:1055986. [PMID: 36507330 PMCID: PMC9726760 DOI: 10.3389/fnins.2022.1055986] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Spatial working memory is a kind of short-term memory that allows temporarily storing and manipulating spatial information. Evidence suggests that spatial working memory is processed through three distinctive phases: Encoding, maintenance, and retrieval. Though the medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval, how the functional interactions and information transfer between mPFC and MD remains largely unclear. Methods We recorded local field potentials (LFPs) from mPFC and MD while mice performed a spatial working memory task in T-maze. The temporal dynamics of functional interactions and bidirectional information flow between mPFC and MD was quantitatively assessed by using directed transfer function. Results Our results showed a significantly elevated information flow from mPFC to MD, varied in time and frequency (theta in particular), accompanying successful memory retrieval. Discussion Elevated theta information flow, a feature that was absent on error trials, indicates an important role of the directional information transfer from mPFC to MD for memory retrieval.
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36
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Li F, Liu Y, Lu L, Li H, Xing C, Chen H, Yuan F, Yin X, Chen YC. Causal interactions with an insular-cortical network in mild traumatic brain injury. Eur J Radiol 2022; 157:110594. [DOI: 10.1016/j.ejrad.2022.110594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/28/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022]
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Aversive memory formation in humans involves an amygdala-hippocampus phase code. Nat Commun 2022; 13:6403. [PMID: 36302909 PMCID: PMC9613775 DOI: 10.1038/s41467-022-33828-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 10/05/2022] [Indexed: 12/25/2022] Open
Abstract
Memory for aversive events is central to survival but can become maladaptive in psychiatric disorders. Memory enhancement for emotional events is thought to depend on amygdala modulation of hippocampal activity. However, the neural dynamics of amygdala-hippocampal communication during emotional memory encoding remain unknown. Using simultaneous intracranial recordings from both structures in human patients, here we show that successful emotional memory encoding depends on the amygdala theta phase to which hippocampal gamma activity and neuronal firing couple. The phase difference between subsequently remembered vs. not-remembered emotional stimuli translates to a time period that enables lagged coherence between amygdala and downstream hippocampal gamma. These results reveal a mechanism whereby amygdala theta phase coordinates transient amygdala -hippocampal gamma coherence to facilitate aversive memory encoding. Pacing of lagged gamma coherence via amygdala theta phase may represent a general mechanism through which the amygdala relays emotional content to distant brain regions to modulate other aspects of cognition, such as attention and decision-making.
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38
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Chapeton JI, Wittig JH, Inati SK, Zaghloul KA. Micro-scale functional modules in the human temporal lobe. Nat Commun 2022; 13:6263. [PMID: 36271010 PMCID: PMC9587217 DOI: 10.1038/s41467-022-34018-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 10/11/2022] [Indexed: 12/25/2022] Open
Abstract
The sensory cortices of many mammals are often organized into modules in the form of cortical columns, yet whether modular organization at this spatial scale is a general property of the human neocortex is unknown. The strongest evidence for modularity arises when measures of connectivity, structure, and function converge. Here we use microelectrode recordings in humans to examine functional connectivity and neuronal spiking responses in order to assess modularity in submillimeter scale networks. We find that the human temporal lobe consists of temporally persistent spatially compact modules approximately 1.3mm in diameter. Functionally, the information coded by single neurons during an image categorization task is more similar for neurons belonging to the same module than for neurons from different modules. The geometry, connectivity, and spiking responses of these local cortical networks provide converging evidence that the human temporal lobe is organized into functional modules at the micro scale.
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Affiliation(s)
- Julio I. Chapeton
- grid.416870.c0000 0001 2177 357XSurgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892 USA
| | - John H. Wittig
- grid.416870.c0000 0001 2177 357XSurgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892 USA
| | - Sara K. Inati
- grid.416870.c0000 0001 2177 357XSurgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892 USA
| | - Kareem A. Zaghloul
- grid.416870.c0000 0001 2177 357XSurgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892 USA
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 PMCID: PMC10190110 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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Affiliation(s)
- Manuel R Mercier
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
| | | | - François Tadel
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA United States of America
| | - Pietro Avanzini
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Outer St, Beijing 100875, China
| | - Dillan Cellier
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America
| | - Maria Del Vecchio
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Liberty S Hamilton
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States of America; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, United States of America; Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States of America
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Robert T Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States of America
| | - Anais Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
| | - Pierre Megevand
- Department of Clinical neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lucia Melloni
- Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, Frankfurt am Main 60322, Germany; Department of Neurology, NYU Grossman School of Medicine, 145 East 32nd Street, Room 828, New York, NY 10016, United States of America
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Vitória Piai
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Medical Psychology, Radboudumc, Donders Centre for Medical Neuroscience, Nijmegen, the Netherlands
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN, United States of America
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Göttingen, Germany; Perception and Plasticity Group, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Sydney E Smith
- Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America
| | - Arjen Stolk
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
| | - Nicole C Swann
- University of Oregon in the Department of Human Physiology, United States of America
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Bradley Voytek
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America; Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America; Halıcıoğlu Data Science Institute, University of California, La Jolla, San Diego, United States of America; Kavli Institute for Brain and Mind, University of California, La Jolla, San Diego, United States of America
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jean-Philippe Lachaux
- Lyon Neuroscience Research Center, EDUWELL Team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
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Stone SSD, Park EH, Bolton J, Harini C, Libenson MH, Rotenberg A, Takeoka M, Tsuboyama M, Pearl PL, Madsen JR. Interictal Connectivity Revealed by Granger Analysis of Stereoelectroencephalography: Association With Ictal Onset Zone, Resection, and Outcome. Neurosurgery 2022; 91:583-589. [PMID: 36084171 PMCID: PMC10553068 DOI: 10.1227/neu.0000000000002079] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 05/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Stereoelectroencephalography (sEEG) facilitates electrical sampling and evaluation of complex deep-seated, dispersed, and multifocal locations. Granger causality (GC), previously used to study seizure networks using interictal data from subdural grids, may help identify the seizure-onset zone from interictal sEEG recordings. OBJECTIVE To examine whether statistical analysis of interictal sEEG helps identify surgical target sites and whether surgical resection of highly ranked nodes correspond to favorable outcomes. METHODS Ten minutes of extraoperative recordings from sequential patients who underwent sEEG evaluation were analyzed (n = 20). GC maps were compared with clinically defined surgical targets using rank order statistics. Outcomes of patients with focal resection/ablation with median follow-up of 3.6 years were classified as favorable (Engel 1, 2) or poor (Engel 3, 4) to assess their relationship with the removal of highly ranked nodes using the Wilcoxon rank-sum test. RESULTS In 12 of 20 cases, the rankings of contacts (based on the sum of outward connection weights) mapped to the seizure-onset zone showed higher causal node connectivity than predicted by chance ( P ≤ .02). A very low aggregate probability ( P < 10 -18 , n = 20) suggests that causal node connectivity predicts seizure networks. In 8 of 16 with outcome data, causal connectivity in the resection was significantly greater than in the remaining contacts ( P ≤ .05). We found a significant association between favorable outcome and the presence of highly ranked nodes in the resection ( P < .05). CONCLUSION Granger analysis can identify seizure foci from interictal sEEG and correlates highly ranked nodes with favorable outcome, potentially informing surgical decision-making without reliance on ictal recordings.
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Affiliation(s)
- Scellig S. D. Stone
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Eun-Hyoung Park
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey Bolton
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Chellamani Harini
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark H. Libenson
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Rotenberg
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Masanori Takeoka
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Melissa Tsuboyama
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Phillip L. Pearl
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joseph R. Madsen
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Liu J, Ji J, Xun G, Zhang A. Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5993-6006. [PMID: 33886478 DOI: 10.1109/tnnls.2021.3072149] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an emerging method for inferring effective connectivity. However, the previous score functions ignore the temporal information from functional magnetic resonance imaging (fMRI) series data and may not be able to determine all orientations in some cases. In this article, we propose a novel score function for inferring effective connectivity from fMRI data based on the conditional entropy and transfer entropy (TE) between brain regions. The new score employs the TE to capture the temporal information and can effectively infer connection directions between brain regions. Experimental results on both simulated and real-world data demonstrate the efficacy of our proposed score function.
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Xue J, Yao R, Cui X, Wang B, Wei J, Wu X, Sun J, Yang Y, Xiang J, Liu Y. Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer’s disease. Cereb Cortex 2022; 33:4230-4247. [PMID: 36104855 DOI: 10.1093/cercor/bhac339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/14/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.
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Affiliation(s)
- Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Rong Yao
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jing Wei
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xubin Wu
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Sun
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital , No. 3, Workers New Street, Taiyuan, Shanxi, 030013 , China
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Funane T, Jun H, Sutoko S, Saido TC, Kandori A, Igarashi KM. Impaired sharp-wave ripple coordination between the medial entorhinal cortex and hippocampal CA1 of knock-in model of Alzheimer’s disease. Front Syst Neurosci 2022; 16:955178. [PMID: 36090186 PMCID: PMC9452631 DOI: 10.3389/fnsys.2022.955178] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022] Open
Abstract
Clinical evidence suggests that the entorhinal cortex is a primary brain area triggering memory impairments in Alzheimer’s disease (AD), but the underlying brain circuit mechanisms remain largely unclear. In healthy brains, sharp-wave ripples (SWRs) in the hippocampus and entorhinal cortex play a critical role in memory consolidation. We tested SWRs in the MEC layers 2/3 of awake amyloid precursor protein knock-in (APP-KI) mice, recorded simultaneously with SWRs in the hippocampal CA1. We found that MEC→CA1 coordination of SWRs, found previously in healthy brains, was disrupted in APP-KI mice even at a young age before the emergence of spatial memory impairments. Intriguingly, long-duration SWRs critical for memory consolidation were mildly diminished in CA1, although SWR density and amplitude remained intact. Our results point to SWR incoordination in the entorhinal-hippocampal circuit as an early network symptom that precedes memory impairment in AD.
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Affiliation(s)
- Tsukasa Funane
- Center for Exploratory Research, Hitachi, Ltd., Kokubunji, Japan
- *Correspondence: Tsukasa Funane,
| | - Heechul Jun
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, CA, United States
| | - Stephanie Sutoko
- Center for Exploratory Research, Hitachi, Ltd., Kokubunji, Japan
| | - Takaomi C. Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Japan
| | - Akihiko Kandori
- Center for Exploratory Research, Hitachi, Ltd., Kokubunji, Japan
| | - Kei M. Igarashi
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, CA, United States
- Kei M. Igarashi,
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Nuernberger M, Schaller D, Klingner C, Witte O, Brodoehl S. Acoustic Stimuli Can Improve and Impair Somatosensory Perception. Front Neurosci 2022; 16:930932. [PMID: 35812213 PMCID: PMC9259856 DOI: 10.3389/fnins.2022.930932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/30/2022] [Indexed: 11/28/2022] Open
Abstract
The integration of stimuli from different sensory modalities forms the basis for human perception. While the relevant impact of visual stimuli on the perception of other sensory modalities is recognized, much less is known about the impact of auditory stimuli on general sensory processing. This study aims to investigate the effect of acoustic stimuli on the processing of somatosensory stimuli using real noise (i.e., unpleasant everyday noise, RN) and neutral white noise (WN). To this purpose, we studied 20 healthy human subjects between 20 and 29 years of age (mean: 24, SD: ±1.9 years sex ratio 1:1). Somatosensory perception was evaluated using mechanical detection threshold (MDT) of the skin on the back of the dominant hand. To investigate the underlying mechanisms in the brain, fMRI was performed while applying acoustic stimulation (RN and WN) and tactile stimulation of the dominant hand. Here we show that acoustic stimulation with noise alters the perception of touch on the skin. We found that the effect of RN and WN differed. RN leads to an improved tactile perception, whereas WN impaired tactile perception. These changes go along with significant differences in brain activity and connectivity. WN is associated with a significant increase in brain activity in multiple brain areas such as the auditory and somatosensory cortex, parietal association cortex, and the thalamus compared to RN. With tactile stimulation of the skin, the flow of information in these brain areas is altered. While with RN the information flow from the thalamus to the somatosensory cortex is prominent, the network activity pattern changes under WN revealing an increase in interaction between multiple networks. Unpleasant noise inhibits the multisensory integration and enables a more efficient unimodal perception in the somatosensory system, improving perception. Whether this is to be interpreted as a temporary increase in phasic alertness or by a stronger filter function of the thalamus with a preference for unimodal stimuli is still open for debate.
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Affiliation(s)
- Matthias Nuernberger
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
- *Correspondence: Matthias Nuernberger,
| | - Denise Schaller
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Carsten Klingner
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Otto Witte
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Stefan Brodoehl
- Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
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45
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Causal Inference in Time Series in Terms of Rényi Transfer Entropy. ENTROPY 2022; 24:e24070855. [PMID: 35885081 PMCID: PMC9321760 DOI: 10.3390/e24070855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/10/2022] [Accepted: 06/11/2022] [Indexed: 12/10/2022]
Abstract
Uncovering causal interdependencies from observational data is one of the great challenges of a nonlinear time series analysis. In this paper, we discuss this topic with the help of an information-theoretic concept known as Rényi’s information measure. In particular, we tackle the directional information flow between bivariate time series in terms of Rényi’s transfer entropy. We show that by choosing Rényi’s parameter α, we can appropriately control information that is transferred only between selected parts of the underlying distributions. This, in turn, is a particularly potent tool for quantifying causal interdependencies in time series, where the knowledge of “black swan” events, such as spikes or sudden jumps, are of key importance. In this connection, we first prove that for Gaussian variables, Granger causality and Rényi transfer entropy are entirely equivalent. Moreover, we also partially extend these results to heavy-tailed α-Gaussian variables. These results allow establishing a connection between autoregressive and Rényi entropy-based information-theoretic approaches to data-driven causal inference. To aid our intuition, we employed the Leonenko et al. entropy estimator and analyzed Rényi’s information flow between bivariate time series generated from two unidirectionally coupled Rössler systems. Notably, we find that Rényi’s transfer entropy not only allows us to detect a threshold of synchronization but it also provides non-trivial insight into the structure of a transient regime that exists between the region of chaotic correlations and synchronization threshold. In addition, from Rényi’s transfer entropy, we could reliably infer the direction of coupling and, hence, causality, only for coupling strengths smaller than the onset value of the transient regime, i.e., when two Rössler systems are coupled but have not yet entered synchronization.
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46
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Analysis of time-varying cause-effect relations based on qualitative trends and change amplitudes. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Xiang W, Karfoul A, Yang C, Shu H, Le Bouquin Jeannès R. Investigation of two neural mass models for DCM-based effective connectivity inference in temporal epilepsy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106840. [PMID: 35550455 DOI: 10.1016/j.cmpb.2022.106840] [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: 12/17/2021] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around β and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. METHODS To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. RESULTS The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. CONCLUSIONS Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.
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Affiliation(s)
- Wentao Xiang
- Key Laboratory of Clinical and Medical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China; Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Ahmad Karfoul
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Chunfeng Yang
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Huazhong Shu
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Régine Le Bouquin Jeannès
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France.
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Forstenpointner J, Maallo AMS, Elman I, Holmes S, Freeman R, Baron R, Borsook D. The Solitary Nucleus Connectivity to Key Autonomic Regions in Humans MRI and Literature based Considerations. Eur J Neurosci 2022; 56:3938-3966. [PMID: 35545280 DOI: 10.1111/ejn.15691] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 11/03/2022]
Abstract
The nucleus tractus solitarius (NTS), is a key brainstem structure relaying interoceptive peripheral information to the interrelated brain centers for eliciting rapid autonomic responses and for shaping longer-term neuroendocrine and motor patterns. Structural and functional NTS' connectivity has been extensively investigated in laboratory animals. But there is limited information about NTS' connectome in humans. Using MRI, we examined diffusion and resting state data from 20 healthy participants in the Human Connectome Project. The regions within the brainstem (n=8), subcortical (n=6), cerebellar (n=2) and cortical (n=5) parts of the brain were selected via a systematic review of the literature and their white matter NTS connections were evaluated via probabilistic tractography along with functional and directional (i.e., Granger-causality) analyses. The underlying study confirms previous results from animal models and provides novel aspects on NTS integration in humans. Two key findings can be summarized: (i) the NTS predominantly processes afferent input and (ii) a lateralization towards a predominantly left-sided NTS processing. Our results lay the foundations for future investigations into the NTS' tripartite role comprised of interoreceptors' input integration, the resultant neurochemical outflow and cognitive/affective processing. The implications of these data add to the understanding of NTS' role in specific aspects of autonomic functions.
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Affiliation(s)
- Julia Forstenpointner
- Center for Pain and the Brain, Boston Children's Hospital, Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.,Division of Neurological Pain Research and Therapy, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Anne Margarette S Maallo
- Center for Pain and the Brain, Boston Children's Hospital, Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA
| | - Igor Elman
- Center for Pain and the Brain, Boston Children's Hospital, Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.,Cambridge Health Alliance, Harvard Medical School, Cambridge, MA, USA
| | - Scott Holmes
- Center for Pain and the Brain, Boston Children's Hospital, Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA
| | - Roy Freeman
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ralf Baron
- Division of Neurological Pain Research and Therapy, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - David Borsook
- Center for Pain and the Brain, Boston Children's Hospital, Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.,Department of Radiology and Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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49
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Zhang L, Huang G, Liang Z, Li L, Zhang Z. Estimating scale-free dynamic effective connectivity networks from fMRI using group-wise spatial–temporal regularizations. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.02.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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50
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Teng C, Liu T, Zhang N, Zhong Y, Wang C. Cognitive behavioral therapy may rehabilitate abnormally functional communication pattern among the triple-network in major depressive disorder: A follow-up study. J Affect Disord 2022; 304:28-39. [PMID: 35192866 DOI: 10.1016/j.jad.2022.02.050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/12/2022] [Accepted: 02/15/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Cognitive behavioral therapy (CBT) is an established treatment for Major Depressive Disorder (MDD). MDD is characterized by imbalanced communication patterns among three networks: the central executive network (CEN), the default mode network (DMN) and the salience network (SN). The effect of CBT in restoring communications among these networks in MDD is unknown. METHODS Thirty-three patients with MDD and 27 healthy controls (HC) participated in the study. Patients were treated with CBT. Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained in patients at three stages (T0: before treatment; T1: after 6 weeks CBT; T2: after 28 weeks CBT) and in HC (only T0). Both independent component analysis (ICA) and granger causality analysis (GCA) were used to explore dynamic causal communication patterns among the three networks (CEN, DMN, SN) over a course of CBT treatment. RESULTS In the HC group, the SN had an inhibitory causal effect on CEN; the CEN and DMN had an excitatory causal effect on the SN. The SN had an inhibitory causal effect on the CEN and the DMN; only the DMN had an excitatory causal effect on the SN in the MDD patients at the T0 stage. As the CBT treatment went on for MDD patients, the CEN restored excitatory causal effect on the SN, and the SN lost inhibitory effect on the DMN. This result mimicked the one found in the HC group. Four regions, left ventromedial prefrontal cortex (lvmPFC), posterior cingulate gyrus (PCC), right inferior parietal lobule (rIPL) and right insula, were implicated in mediating network communications. LIMITATIONS The findings should be considered preliminary given the small sample sizes, and assessed only one stage in HC subjects. CONCLUSION CBT may enhance the regulatory function of the SN, and rehabilitate the imbalanced brain network communication mode in the MDD. PCC, lvmPFC and rIPL may all be potential targets of CBT.
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Affiliation(s)
- Changjun Teng
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Tianchen Liu
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Ning Zhang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
| | - Chun Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
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