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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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102
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Wu H, Qi Z, Wu X, Zhang J, Wu C, Huang Z, Zang D, Fogel S, Tanabe S, Hudetz AG, Northoff G, Mao Y, Qin P. Anterior precuneus related to the recovery of consciousness. Neuroimage Clin 2022; 33:102951. [PMID: 35134706 PMCID: PMC8856921 DOI: 10.1016/j.nicl.2022.102951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
Abstract
Degree centrality of anterior precuneus correlated with Glasgow Outcome Scale scores. Anterior precuneus was shown as a hub in multiple recoverable unconscious states. Anterior precuneus had similar connectivity pattern in recoverable unconscious states.
The neural mechanism that enables the recovery of consciousness in patients with unresponsive wakefulness syndrome (UWS) remains unclear. The aim of the current study is to characterize the cortical hub regions related to the recovery of consciousness. In the current fMRI study, voxel-wise degree centrality analysis was adopted to identify the cortical hubs related to the recovery of consciousness, for which a total of 27 UWS patients were recruited, including 13 patients who emerged from UWS (UWS-E), and 14 patients who remained in UWS (UWS-R) at least three months after the experiment performance. Furthermore, other recoverable unconscious states were adopted as validation groups, including three independent N3 sleep datasets (n = 12, 9, 9 respectively) and three independent anesthesia datasets (n = 27, 14, 6 respectively). Spatial similarity of the hub characteristic with the validation groups between the UWS-E and UWS-R was compared using the dice coefficient. Finally, with the cortical regions persistently shown as hubs across UWS-E and validation states, functional connectivity analysis was further performed to explore the connectivity patterns underlying the recovery of consciousness. The results identified four cortical hubs in the UWS-E, which showed significantly higher degree centrality for UWS-E than UWS-R, including the anterior precuneus, left inferior parietal lobule, left inferior frontal gyrus, and left middle frontal gyrus, of which the degree centrality value also positively correlated with the patients’ Glasgow Outcome Scale (GOS) score that assessed global brain functioning outcome after a brain injury. Furthermore, the anterior precuneus was found with significantly higher similarity of hub characteristics as well as functional connectivity patterns between UWS-E and the validation groups. The results suggest that the recovery of consciousness may be relevant to the integrity of cortical hubs in the recoverable unconscious states, especially the anterior precuneus. The identified cortical hub regions could serve as potential treatment targets for patients with UWS.
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Affiliation(s)
- Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong 510631, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Xuehai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China; Pazhou Lab, Guangzhou 510335, China
| | - Jun Zhang
- Department of Anesthesiology, Fudan University Shanghai Cancer Center Shanghai, 200433, China
| | - Changwei Wu
- Research Center for Brain and Consciousness, Taipei Medical University, Taipei 11031, Taiwan; Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei 11031, Taiwan; Shuang-Ho Hospital, Taipei Medical University, New Taipei 23561, Taiwan
| | - Zirui Huang
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48105, USA
| | - Di Zang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Stuart Fogel
- School of Psychology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Sean Tanabe
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48105, USA
| | - Anthony G Hudetz
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48105, USA
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, ON K1Z 7K4, Canada; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China.
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong 510631, China; Pazhou Lab, Guangzhou 510335, China.
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103
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Shang Y, Yang Y, Zheng G, Zhao Z, Wang Y, Yang L, Han L, Yao Z, Hu B. Aberrant functional network topology and effective connectivity in burnout syndrome. Clin Neurophysiol 2022; 138:163-172. [DOI: 10.1016/j.clinph.2022.03.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 12/11/2022]
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104
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Networks behind the morphology and structural design of living systems. Phys Life Rev 2022; 41:1-21. [DOI: 10.1016/j.plrev.2022.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/04/2022] [Indexed: 01/06/2023]
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105
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Gu F, Gong A, Qu Y, Bao A, Wu J, Jiang C, Fu Y. From Expert to Elite? — Research on Top Archer’s EEG Network Topology. Front Hum Neurosci 2022; 16:759330. [PMID: 35280210 PMCID: PMC8916709 DOI: 10.3389/fnhum.2022.759330] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 01/14/2022] [Indexed: 12/17/2022] Open
Abstract
It is not only difficult to be a sports expert but also difficult to grow from a sports expert to a sports elite. Professional athletes are often concerned about the differences between an expert and an elite and how to eventually become an elite athlete. To explore the differences in brain neural mechanism between experts and elites in the process of motor behavior and reveal the internal connection between motor performance and brain activity, we collected and analyzed the electroencephalography (EEG) findings of 14 national archers and 14 provincial archers during aiming and resting states and constructed the EEG brain network of the two archer groups based on weighted phase lag index; the graph theory was used to analyze and compare the network characteristics via local network and global network topologies. The results showed that compared with the expert archers, the elite archers had stronger functional coupling in beta1 and beta2 bands, and the difference was evident in the frontal and central regions; in terms of global characteristics of brain network topology, the average clustering coefficient and global efficiency of elite archers were significantly higher than that of expert archers, and the eigenvector centrality of expert archers was higher; for local characteristics, elite archers had higher local efficient; and the brain network characteristics of expert archers showed a strong correlation with archery performance. This suggests that compared with expert archers, elite archers showed stronger functional coupling, higher integration efficiency of global and local information, and more independent performance in the archery process. These findings reveal the differences in brain electrical network topologies between elite and expert archers in the archery preparation stage, which is expected to provide theoretical reference for further training and promotion of professional athletes.
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Affiliation(s)
- Feng Gu
- School of Information Engineering, Engineering University of People’s Armed Police, Xi’an, China
| | - Anmin Gong
- School of Information Engineering, Engineering University of People’s Armed Police, Xi’an, China
- *Correspondence: Anmin Gong,
| | - Yi Qu
- School of Information Engineering, Engineering University of People’s Armed Police, Xi’an, China
| | - Aiyong Bao
- School of Military Basic Education, Engineering University of People’s Armed Police, Xi’an, China
| | - Jin Wu
- Department of Physical Education, Beijing City University, Beijing, China
| | - Changhao Jiang
- Key Laboratory of Sports Performance Evaluation and Technical Analysis, Capital Institute of Physical Education, Beijing, China
| | - Yunfa Fu
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, China
- Yunfa Fu,
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106
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Polver S, Quadrelli E, Turati C, Bulf H. Decoding functional brain networks through graph measures in infancy: The case of emotional faces. Biol Psychol 2022; 170:108292. [PMID: 35217132 DOI: 10.1016/j.biopsycho.2022.108292] [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: 09/30/2021] [Revised: 02/02/2022] [Accepted: 02/21/2022] [Indexed: 11/26/2022]
Abstract
Graph measures represent an optimal way to investigate neural networks' organization, yet their application is still limited in developmental samples. To uncover the organization of 7-month-old infants' functional brain networks during an emotional perception task, we combined a decoding technique (i.e., Principal Component Regression) to graph metrics computation. Nodes' Within Module Degree Z Score (WMDZ) was computed as a measure of modular organization, and we decoded networks' functional organizations across EEG alpha and theta bands in response to static and dynamic facial expressions of emotions. We found that infants' brain topological activity differentiates between static and dynamic emotional faces due to the involvement of visual streams and sensorimotor areas, as often observed in adults. Moreover, network invariances point toward an already present rudimental network structure tuned to face processing already at 7-months of age. Overall, our results affirm the fruitfulness of the application of graph measures in developmental samples, due to their flexibility and the wealth of information they provide over infants' networks functional organization.
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Affiliation(s)
- Silvia Polver
- Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano (MI), Italy.
| | - Ermanno Quadrelli
- Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano (MI), Italy; NeuroMI, Milan Center for Neuroscience, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano (MI), Italy.
| | - Chiara Turati
- Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano (MI), Italy; NeuroMI, Milan Center for Neuroscience, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano (MI), Italy.
| | - Hermann Bulf
- Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano (MI), Italy; NeuroMI, Milan Center for Neuroscience, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano (MI), Italy.
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107
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Ando M, Nobukawa S, Kikuchi M, Takahashi T. Alteration of Neural Network Activity With Aging Focusing on Temporal Complexity and Functional Connectivity Within Electroencephalography. Front Aging Neurosci 2022; 14:793298. [PMID: 35185527 PMCID: PMC8855040 DOI: 10.3389/fnagi.2022.793298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022] Open
Abstract
With the aging process, brain functions, such as attention, memory, and cognitive functions, degrade over time. In a super-aging society, the alteration of neural activity owing to aging is considered crucial for interventions for the prevention of brain dysfunction. The complexity of temporal neural fluctuations with temporal scale dependency plays an important role in optimal brain information processing, such as perception and thinking. Complexity analysis is a useful approach for detecting cortical alteration in healthy individuals, as well as in pathological conditions, such as senile psychiatric disorders, resulting in changes in neural activity interactions among a wide range of brain regions. Multi-fractal (MF) and multi-scale entropy (MSE) analyses are known methods for capturing the complexity of temporal scale dependency of neural activity in the brain. MF and MSE analyses exhibit high accuracy in detecting changes in neural activity and are superior with regard to complexity detection when compared with other methods. In addition to complex temporal fluctuations, functional connectivity reflects the integration of information of brain processes in each region, described as mutual interactions of neural activity among brain regions. Thus, we hypothesized that the complementary relationship between functional connectivity and complexity could improve the ability to detect the alteration of spatiotemporal patterns observed on electroencephalography (EEG) with respect to aging. To prove this hypothesis, this study investigated the relationship between the complexity of neural activity and functional connectivity in aging based on EEG findings. Concretely, MF and MSE analyses were performed to evaluate the temporal complexity profiles, and phase lag index analyses assessing the unique profile of functional connectivity were performed based on the EEGs conducted for young and older participants. Subsequently, these profiles were combined through machine learning. We found that the complementary relationship between complexity and functional connectivity improves the classification accuracy among aging participants. Thus, the outcome of this study could be beneficial in formulating interventions for the prevention of age-related brain dysfunction.
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Affiliation(s)
- Momo Ando
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- *Correspondence: Sou Nobukawa
| | - Mitsuru Kikuchi
- Department of Psychiatry and Behavioral Science, Kanazawa University, Ishikawa, Japan
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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108
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Royer J, Bernhardt BC, Larivière S, Gleichgerrcht E, Vorderwülbecke BJ, Vulliémoz S, Bonilha L. Epilepsy and brain network hubs. Epilepsia 2022; 63:537-550. [DOI: 10.1111/epi.17171] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 02/06/2023]
Affiliation(s)
- Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Ezequiel Gleichgerrcht
- Department of Neurology Medical University of South Carolina Charleston South Carolina USA
| | - Bernd J. Vorderwülbecke
- EEG and Epilepsy Unit University Hospitals and Faculty of Medicine Geneva Geneva Switzerland
- Department of Neurology Epilepsy Center Berlin‐Brandenburg Charité–Universitätsmedizin Berlin Berlin Germany
| | - Serge Vulliémoz
- EEG and Epilepsy Unit University Hospitals and Faculty of Medicine Geneva Geneva Switzerland
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109
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Peng Z, Yang X, Xu C, Wu X, Yang Q, Wei Z, Zhou Z, Verguts T, Chen Q. Aberrant rich club organization in patients with obsessive-compulsive disorder and their unaffected first-degree relatives. Neuroimage Clin 2022; 32:102808. [PMID: 34500426 PMCID: PMC8430383 DOI: 10.1016/j.nicl.2021.102808] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 01/20/2023]
Abstract
Recent studies suggested that the rich club organization promoting global brain communication and integration of information, may be abnormally increased in obsessive-compulsive disorder (OCD). However, the structural and functional basis of this organization is still not very clear. Given the heritability of OCD, as suggested by previous family-based studies, we hypothesize that aberrant rich club organization may be a trait marker for OCD. In the present study, 32 patients with OCD, 30 unaffected first-degree relatives (FDR) and 32 healthy controls (HC) underwent diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). We examined the structural rich club organization and its interrelationship with functional coupling. Our results showed that rich club and peripheral connection strength in patients with OCD was lower than in HC, while it was intermediate in FDR. Finally, the coupling between structural and functional connections of the rich club, was decreased in FDR but not in OCD relative to HC, which suggests a buffering mechanism of brain functions in FDR. Overall, our findings suggest that alteration of the rich club organization may reflect a vulnerability biomarker for OCD, possibly buffered by structural and functional coupling of the rich club.
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Affiliation(s)
- Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education China, School of Psychology, Center for Studies of Psychological Application, And Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, China.
| | - Xinyi Yang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education China, School of Psychology, Center for Studies of Psychological Application, And Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, China
| | - Chuanyong Xu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education China, School of Psychology, Center for Studies of Psychological Application, And Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, China
| | - Xiangshu Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education China, School of Psychology, Center for Studies of Psychological Application, And Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, China
| | - Qiong Yang
- Southern Medical University, Guangzhou, China; Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhen Wei
- Department of Child Psychiatry and Rehabilitation, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zihan Zhou
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education China, School of Psychology, Center for Studies of Psychological Application, And Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, China
| | - Tom Verguts
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education China, School of Psychology, Center for Studies of Psychological Application, And Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, China.
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110
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Guo T, Xuan M, Zhou C, Wu J, Gao T, Bai X, Liu X, Gu L, Liu R, Song Z, Gu Q, Huang P, Pu J, Zhang B, Xu X, Guan X, Zhang M. Normalization effect of levodopa on hierarchical brain function in Parkinson’s disease. Netw Neurosci 2022; 6:552-569. [PMID: 35733432 PMCID: PMC9208001 DOI: 10.1162/netn_a_00232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 01/10/2022] [Indexed: 11/08/2022] Open
Abstract
Hierarchical brain organization, in which the rich club and diverse club situate in core position, is critical for global information integration in the human brain network. Parkinson’s disease (PD), a common movement disorder, has been conceptualized as a network disorder. Levodopa is an effective treatment for PD. Whether there is a functional divergence in the hierarchical brain system under PD pathology, and how this divergence is regulated by immediate levodopa therapy, remains unknown. We constructed a functional network in 61 PD patients and 89 normal controls and applied graph theoretical analyses to examine the neural mechanism of levodopa short response from the perspective of brain hierarchical configuration. The results revealed the following: (a) PD patients exhibited disrupted function within rich-club organization, while the diverse club preserved function, indicating a differentiated brain topological organization in PD. (b) Along the rich-club derivate hierarchical system, PD patients showed impaired network properties within rich-club and feeder subnetworks, and decreased nodal degree centrality in rich-club and feeder nodes, along with increased nodal degree in peripheral nodes, suggesting distinct functional patterns in different types of nodes. And (c) levodopa could normalize the abnormal network architecture of the rich-club system. This study provides evidence for levodopa effects on the hierarchical brain system with divergent functions. Many studies of brain networks have revealed densely connected regions forming the rich club and diverse club, which occupy the central position of the hierarchical brain system. Here, we explore the hierarchical topology in Parkinson’s disease (PD) and investigate the neural effect of levodopa on it. We show that within the core position of the hierarchical system, the function of the diverse club is preserved while the function of the rich club is impaired. Along the rich-club hierarchical system, the function of biologically costly rich-club and feeder subnetworks is disrupted, together with an increased function of peripheral nodes, which could be normalized by levodopa. Our study provides evidence of a disparity pattern between different levels of brain hierarchical systems under PD pathology.
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Affiliation(s)
- Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ruiqi Liu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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111
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Páscoa dos Santos F, Verschure PFMJ. Excitatory-Inhibitory Homeostasis and Diaschisis: Tying the Local and Global Scales in the Post-stroke Cortex. Front Syst Neurosci 2022; 15:806544. [PMID: 35082606 PMCID: PMC8785563 DOI: 10.3389/fnsys.2021.806544] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 11/29/2021] [Indexed: 12/28/2022] Open
Abstract
Maintaining a balance between excitatory and inhibitory activity is an essential feature of neural networks of the neocortex. In the face of perturbations in the levels of excitation to cortical neurons, synapses adjust to maintain excitatory-inhibitory (EI) balance. In this review, we summarize research on this EI homeostasis in the neocortex, using stroke as our case study, and in particular the loss of excitation to distant cortical regions after focal lesions. Widespread changes following a localized lesion, a phenomenon known as diaschisis, are not only related to excitability, but also observed with respect to functional connectivity. Here, we highlight the main findings regarding the evolution of excitability and functional cortical networks during the process of post-stroke recovery, and how both are related to functional recovery. We show that cortical reorganization at a global scale can be explained from the perspective of EI homeostasis. Indeed, recovery of functional networks is paralleled by increases in excitability across the cortex. These adaptive changes likely result from plasticity mechanisms such as synaptic scaling and are linked to EI homeostasis, providing a possible target for future therapeutic strategies in the process of rehabilitation. In addition, we address the difficulty of simultaneously studying these multiscale processes by presenting recent advances in large-scale modeling of the human cortex in the contexts of stroke and EI homeostasis, suggesting computational modeling as a powerful tool to tie the meso- and macro-scale processes of recovery in stroke patients.
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Affiliation(s)
- Francisco Páscoa dos Santos
- Eodyne Systems SL, Barcelona, Spain
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Department of Information and Communications Technologies (DTIC), Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Paul F. M. J. Verschure
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
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112
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Zheng W, Gu C, Yang H, Rohling JHT. Motif structure for the four subgroups within the suprachiasmatic nuclei affects its entrainment ability. Phys Rev E 2022; 105:014314. [PMID: 35193260 DOI: 10.1103/physreve.105.014314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Circadian rhythms of physiological and behavioral activities are regulated by a central clock. This clock is located in the bilaterally symmetrical suprachiasmatic nucleus (SCN) of mammals. Each nucleus contains a light-sensitive group of neurons, named the ventrolateral (VL) part, with the rest of the neurons being insensitive to light, named the dorsomedial (DM) group. While the coupling between the VL and DM subgroups have been investigated quite well, the communication among the four subgroups across the nuclei did not get a lot of attention. In this article, we theoretically analyzed seven motiflike connection patterns to investigate the network of the two nuclei of the SCN as a whole in relation to the function of the SCN. We investigated the entrainment ability of the SCN and found that the entrainment range is larger in the motifs containing a link between the two VL parts across the nuclei, but it is smaller in the motifs that contain a link between the two DM parts across the nuclei. The SCN may strengthen or weaken connections between the left and right nucleus to accomodate changes in external conditions, such as resynchronization after a jet lag, adjustment to photoperiod or for the aging SCN.
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Affiliation(s)
- Wenxin Zheng
- Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Changgui Gu
- Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
- Department of Molecular Cell Biology, Laboratory for Neurophysiology, Leiden University Medical Center, Leiden 2300RC, The Netherlands
| | - Huijie Yang
- Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Jos H T Rohling
- Department of Molecular Cell Biology, Laboratory for Neurophysiology, Leiden University Medical Center, Leiden 2300RC, The Netherlands
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113
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Hartig R, Karimi A, Evrard HC. Interconnected sub-networks of the macaque monkey gustatory connectome. Front Neurosci 2022; 16:818800. [PMID: 36874640 PMCID: PMC9978403 DOI: 10.3389/fnins.2022.818800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 08/24/2022] [Indexed: 02/18/2023] Open
Abstract
Macroscopic taste processing connectivity was investigated using functional magnetic resonance imaging during the presentation of sour, salty, and sweet tastants in anesthetized macaque monkeys. This examination of taste processing affords the opportunity to study the interactions between sensory regions, central integrators, and effector areas. Here, 58 brain regions associated with gustatory processing in primates were aggregated, collectively forming the gustatory connectome. Regional regression coefficients (or β-series) obtained during taste stimulation were correlated to infer functional connectivity. This connectivity was then evaluated by assessing its laterality, modularity and centrality. Our results indicate significant correlations between same region pairs across hemispheres in a bilaterally interconnected scheme for taste processing throughout the gustatory connectome. Using unbiased community detection, three bilateral sub-networks were detected within the graph of the connectome. This analysis revealed clustering of 16 medial cortical structures, 24 lateral structures, and 18 subcortical structures. Across the three sub-networks, a similar pattern was observed in the differential processing of taste qualities. In all cases, the amplitude of the response was greatest for sweet, but the network connectivity was strongest for sour and salty tastants. The importance of each region in taste processing was computed using node centrality measures within the connectome graph, showing centrality to be correlated across hemispheres and, to a smaller extent, region volume. Connectome hubs exhibited varying degrees of centrality with a prominent leftward increase in insular cortex centrality. Taken together, these criteria illustrate quantifiable characteristics of the macaque monkey gustatory connectome and its organization as a tri-modular network, which may reflect the general medial-lateral-subcortical organization of salience and interoception processing networks.
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Affiliation(s)
- Renée Hartig
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Functional and Comparative Neuroanatomy Laboratory, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karl University of Tübingen, Tübingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Ali Karimi
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Henry C Evrard
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Functional and Comparative Neuroanatomy Laboratory, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karl University of Tübingen, Tübingen, Germany.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States.,International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
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114
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Yamamoto M, Bagarinao E, Shimamoto M, Iidaka T, Ozaki N. Involvement of cerebellar and subcortical connector hubs in schizophrenia. NEUROIMAGE: CLINICAL 2022; 35:103140. [PMID: 36002971 PMCID: PMC9421528 DOI: 10.1016/j.nicl.2022.103140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/29/2022] [Accepted: 07/30/2022] [Indexed: 11/14/2022] Open
Abstract
Hubs with altered connectivity to multiple networks were identified in patients. Identified hubs were located in the cerebellum, midbrain, thalamus, and insula. In controls, these hubs were strongly connected with the basal ganglia network. Hubs’ connections to large-scale networks were associated with clinical data. Their connections were also highly predictive of patients from controls.
Background Schizophrenia is considered a brain connectivity disorder in which functional integration within the brain fails. Central to the brain’s integrative function are connector hubs, brain regions characterized by strong connections with multiple networks. Given their critical role in functional integration, we hypothesized that connector hubs, including those located in the cerebellum and subcortical regions, are severely impaired in patients with schizophrenia. Methods We identified brain voxels with significant connectivity alterations in patients with schizophrenia (n = 76; men = 43) compared to healthy controls (n = 80; men = 43) across multiple large-scale resting state networks (RSNs) using a network metric called functional connectivity overlap ratio (FCOR). From these voxels, candidate connector hubs were identified and verified using seed-based connectivity analysis. Results We found that most networks exhibited connectivity alterations in the patient group. Specifically, connectivity with the basal ganglia and high visual networks was severely affected over widespread brain areas in patients, affecting subcortical and cerebellar regions and the regions involved in visual and sensorimotor processing. Furthermore, we identified critical connector hubs in the cerebellum, midbrain, thalamus, insula, and calcarine with connectivity to multiple RSNs affected in the patients. FCOR values of these regions were also associated with clinical data and could classify patient and control groups with > 80 % accuracy. Conclusions These findings highlight the critical role of connector hubs, particularly those in the cerebellum and subcortical regions, in the pathophysiology of schizophrenia and the potential role of FCOR as a clinical biomarker for the disorder.
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Reorganization of rich clubs in functional brain networks of dementia with Lewy bodies and Alzheimer's disease. Neuroimage Clin 2021; 33:102930. [PMID: 34959050 PMCID: PMC8856913 DOI: 10.1016/j.nicl.2021.102930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/18/2021] [Accepted: 12/23/2021] [Indexed: 12/12/2022]
Abstract
DLB and AD had the different functional reorganization patterns. Rich club nodes increased in frontal-parietal network in patients with DLB. The rich club nodes in temporal lobe decreased and those in cerebellum increased for AD. Compared with HC, rich club connectivity was enhanced in the DLB and AD groups.
The purpose of this study was to reveal the patterns of reorganization of rich club organization in brain functional networks in dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD). The study found that the rich club node shifts from sensory/somatomotor network to fronto-parietal network in DLB. For AD, the rich club nodes switch between the temporal lobe with obvious structural atrophy and the frontal lobe, parietal lobe and cerebellum with relatively preserved structure and function. In addition, compared with healthy controls, rich club connectivity was enhanced in the DLB and AD groups. The connection strength of DLB patients was related to cognitive assessment. In conclusion, we revealed the different functional reorganization patterns of DLB and AD. The conversion and redistribution of rich club members may play a causal role in disease-specific outcomes. It may be used as a potential biomarker to provide more accurate prevention and treatment strategies.
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Wu Z, Cao M, Di X, Wu K, Gao Y, Li X. Regional Topological Aberrances of White Matter- and Gray Matter-Based Functional Networks for Attention Processing May Foster Traumatic Brain Injury-Related Attention Deficits in Adults. Brain Sci 2021; 12:brainsci12010016. [PMID: 35053760 PMCID: PMC8774280 DOI: 10.3390/brainsci12010016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 12/31/2022] Open
Abstract
Traumatic brain injury (TBI) is highly prevalent in adults. TBI-related functional brain alterations have been linked with common post-TBI neurobehavioral sequelae, with unknown neural substrates. This study examined the systems-level functional brain alterations in white matter (WM) and gray matter (GM) for visual sustained-attention processing, and their interactions and contributions to post-TBI attention deficits. Task-based functional MRI data were collected from 42 adults with TBI and 43 group-matched normal controls (NCs), and analyzed using the graph theoretic technique. Global and nodal topological properties were calculated and compared between the two groups. Correlation analyses were conducted between the neuroimaging measures that showed significant between-group differences and the behavioral symptom measures in attention domain in the groups of TBI and NCs, respectively. Significantly altered nodal efficiencies and/or degrees in several WM and GM nodes were reported in the TBI group, including the posterior corona radiata (PCR), posterior thalamic radiation (PTR), postcentral gyrus (PoG), and superior temporal sulcus (STS). Subjects with TBI also demonstrated abnormal systems-level functional synchronization between the PTR and STS in the right hemisphere, hypo-interaction between the PCR and PoG in the left hemisphere, as well as the involvement of systems-level functional aberrances in the PCR in TBI-related behavioral impairments in the attention domain. The findings of the current study suggest that TBI-related systems-level functional alterations associated with these two major-association WM tracts, and their anatomically connected GM regions may play critical role in TBI-related behavioral deficits in attention domains.
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Affiliation(s)
- Ziyan Wu
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
| | - Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 510630, China;
| | - Yu Gao
- Department of Psychology, Brooklyn College, The City University of New York, New York, NY 11210, USA;
- The Graduate Center, The City University of New York, New York, NY 10016, USA
| | - Xiaobo Li
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
- Correspondence: or ; Tel.: +1-973-596-5880
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Galván A. Adolescent Brain Development and Contextual Influences: A Decade in Review. JOURNAL OF RESEARCH ON ADOLESCENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR RESEARCH ON ADOLESCENCE 2021; 31:843-869. [PMID: 34820955 DOI: 10.1111/jora.12687] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Adolescence is a developmental period characterized by substantial psychological, biological, and neurobiological changes. This review discusses the past decade of research on the adolescent brain, as based on the overarching framework that development is a dynamic process both within the individual and between the individual and external inputs. As such, this review focuses on research showing that the development of the brain is influenced by multiple ongoing and dynamic elements. It highlights the implications this body of work on behavioral development and offers areas of opportunity for future research in the coming decade.
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118
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Rezaei Z, Jafari Z, Afrashteh N, Torabi R, Singh S, Kolb BE, Davidsen J, Mohajerani MH. Prenatal stress dysregulates resting-state functional connectivity and sensory motifs. Neurobiol Stress 2021; 15:100345. [PMID: 34124321 PMCID: PMC8173309 DOI: 10.1016/j.ynstr.2021.100345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 11/24/2022] Open
Abstract
Prenatal stress (PS) can impact fetal brain structure and function and contribute to higher vulnerability to neurodevelopmental and neuropsychiatric disorders. To understand how PS alters evoked and spontaneous neocortical activity and intrinsic brain functional connectivity, mesoscale voltage imaging was performed in adult C57BL/6NJ mice that had been exposed to auditory stress on gestational days 12-16, the age at which neocortex is developing. PS mice had a four-fold higher basal corticosterone level and reduced amplitude of cortical sensory-evoked responses to visual, auditory, whisker, forelimb, and hindlimb stimuli. Relative to control animals, PS led to a general reduction of resting-state functional connectivity, as well as reduced inter-modular connectivity, enhanced intra-modular connectivity, and altered frequency of auditory and forelimb spontaneous sensory motifs. These resting-state changes resulted in a cortical connectivity pattern featuring disjoint but tight modules and a decline in network efficiency. The findings demonstrate that cortical connectivity is sensitive to PS and exposed offspring may be at risk for adult stress-related neuropsychiatric disorders.
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Affiliation(s)
- Zahra Rezaei
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4
| | - Zahra Jafari
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4
| | - Navvab Afrashteh
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4
| | - Reza Torabi
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4
| | - Surjeet Singh
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4
| | - Bryan E. Kolb
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4
| | - Jörn Davidsen
- Complexity Science Group, Department of Physics and Astronomy, Faculty of Science, University of Calgary, Calgary, AB, Canada, T2N 1N4
| | - Majid H. Mohajerani
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada, T1K 3M4
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Kesler SR, Tang T, Henneghan AM, Wright M, Gaber MW, Palesh O. Cross-Sectional Characterization of Local Brain Network Connectivity Pre and Post Breast Cancer Treatment and Distinct Association With Subjective Cognitive and Psychological Function. Front Neurol 2021; 12:746493. [PMID: 34777216 PMCID: PMC8586413 DOI: 10.3389/fneur.2021.746493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: We aimed to characterize local brain network connectivity in long-term breast cancer survivors compared to newly diagnosed patients. Methods: Functional magnetic resonance imaging (fMRI) and subjective cognitive and psychological function data were obtained from a group of 76 newly diagnosed, pre-treatment female patients with breast cancer (mean age 57 ± 7 years) and a separate group of 80, post-treatment, female breast cancer survivors (mean age 58 ± 8; mean time since treatment 44 ± 43 months). The network-based statistic (NBS) was used to compare connectivity of local brain edges between groups. Hubs were defined as nodes with connectivity indices one standard deviation or more above network mean and were further classified as provincial (higher intra-subnetwork connectivity) or connector (higher inter-subnetwork connectivity) using the participation coefficient. We determined the hub status of nodes encompassing significantly different edges and correlated the centralities of edges with behavioral measures. Results: The post-treatment group demonstrated significantly lower subjective cognitive function (W = 3,856, p = 0.004) but there were no group differences in psychological distress (W = 2,866, p = 0.627). NBS indicated significantly altered connectivity (p < 0.042, corrected) in the post-treatment group compared to the pre-treatment group largely in temporal, frontal-temporal and temporal-parietal areas. The majority of the regions projecting these connections (78%) met criteria for hub status and significantly less of these hubs were connectors in the post-treatment group (z = 1.85, p = 0.031). Subjective cognitive function and psychological distress were correlated with largely non-overlapping edges in the post-treatment group (p < 0.05). Conclusion: Widespread functional network alterations are evident in long-term survivors of breast cancer compared to newly diagnosed patients. We also demonstrated that there are both overlapping and unique brain network signatures for subjective cognitive function vs. psychological distress.
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Affiliation(s)
- Shelli R. Kesler
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Tien Tang
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | | | - Michelle Wright
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - M. Waleed Gaber
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Oxana Palesh
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
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120
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Zhang X, Shi Y, Fan T, Wang K, Zhan H, Wu W. Analysis of Correlation Between White Matter Changes and Functional Responses in Post-stroke Depression. Front Aging Neurosci 2021; 13:728622. [PMID: 34707489 PMCID: PMC8542668 DOI: 10.3389/fnagi.2021.728622] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/20/2021] [Indexed: 11/28/2022] Open
Abstract
Objective: Post-stroke depression (PSD) is one of the most common neuropsychiatric symptoms with high prevalence, however, the mechanism of the brain network in PSD and the relationship between the structural and functional network remain unclear. This research applies graph theory to structural networks and explores the relationship between structural and functional networks. Methods: Forty-five patients with acute ischemic stroke were divided into the PSD group and post-stroke without depression (non-PSD) group respectively and underwent the magnetic resonance imaging scans. Network construction and Module analysis were used to explore the structural connectivity-functional connectivity (SC-FC) coupling of multi-scale brain networks in patients with PSD. Results: Compared with non-PSD, the structural network in PSD was related to the reduction of clustering and the increase of path length, but the degree of modularity was lower. Conclusions: The SC-FC coupling may serve as a biomarker for PSD. The similarity in SC and FC is associated with cognitive dysfunction, retardation, and desperation. Our findings highlighted the distinction in brain structural-functional networks in PSD. Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT03256305, NCT03256305.
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Affiliation(s)
- Xuefei Zhang
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yu Shi
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Tao Fan
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Kangling Wang
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hongrui Zhan
- Department of Rehabilitation, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Wen Wu
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China.,Rehabilitation Medical School, Southern Medical University, Guangzhou, China
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Tarchi L, Damiani S, La Torraca Vittori P, Marini S, Nazzicari N, Castellini G, Pisano T, Politi P, Ricca V. The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO). Brain Imaging Behav 2021; 16:977-990. [PMID: 34689318 PMCID: PMC9107439 DOI: 10.1007/s11682-021-00584-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2021] [Indexed: 11/29/2022]
Abstract
Several systematic reviews have highlighted the role of multiple sources in the investigation of psychiatric illness. For what concerns fMRI, the focus of recent literature preferentially lies on three lines of research, namely: functional connectivity, network analysis and spectral analysis. Data was gathered from the UCLA Consortium for Neuropsychiatric Phenomics. The sample was composed by 130 neurotypicals, 50 participants diagnosed with Schizophrenia, 49 with Bipolar disorder and 43 with ADHD. Single fMRI scans were reduced in their dimensionality by a novel method (i-ECO) averaging results per Region of Interest and through an additive color method (RGB): local connectivity values (Regional Homogeneity), network centrality measures (Eigenvector Centrality), spectral dimensions (fractional Amplitude of Low-Frequency Fluctuations). Average images per diagnostic group were plotted and described. The discriminative power of this novel method for visualizing and analyzing fMRI results in an integrative manner was explored through the usage of convolutional neural networks. The new methodology of i-ECO showed between-groups differences that could be easily appreciated by the human eye. The precision-recall Area Under the Curve (PR-AUC) of our models was > 84.5% for each diagnostic group as evaluated on the test-set – 80/20 split. In conclusion, this study provides evidence for an integrative and easy-to-understand approach in the analysis and visualization of fMRI results. A high discriminative power for psychiatric conditions was reached. This proof-of-work study may serve to investigate further developments over more extensive datasets covering a wider range of psychiatric diagnoses.
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Affiliation(s)
- Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, viale della Maternità, Padiglione 8b, AOU Careggi, Firenze, Florence, FI, 50134, Italy.
| | - Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
| | | | - Simone Marini
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Nelson Nazzicari
- Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, Lodi, LO, Italy
| | - Giovanni Castellini
- Psychiatry Unit, Department of Health Sciences, University of Florence, viale della Maternità, Padiglione 8b, AOU Careggi, Firenze, Florence, FI, 50134, Italy
| | - Tiziana Pisano
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital, University of Florence, Florence, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, viale della Maternità, Padiglione 8b, AOU Careggi, Firenze, Florence, FI, 50134, Italy
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Zhang S, Xia T, Wang J, Zhao Y, Xie X, Wei Z, Zhang X, Song C, Song X. Role of Bacillus inoculation in rice straw composting and bacterial community stability after inoculation: Unite resistance or individual collapse. BIORESOURCE TECHNOLOGY 2021; 337:125464. [PMID: 34320744 DOI: 10.1016/j.biortech.2021.125464] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 06/13/2023]
Abstract
Bacillus is the classic inoculant in rice straw composting. However, there has been no in-depth study of the mechanism promoting the degradation of lignocellulose and the change of indigenous bacterial communities after Bacillus inoculation. Moreover, the stability of bacterial communities is a significant challenge in achieving the efficacy of inoculation. In this study, the ecological succession and yield-resource acquisition-stress tolerance (Y-A-S) framework were combined with Redundancy analysis (RDA) and changes in relative abundance, Bacillus was found to be a pioneer bacterium that adopts a resource acquisition-stress tolerance strategy. The structural equation model (SEM) revealed that in addition to exerting a degradation effect, Bacillus inoculation could also indirectly affect lignocellulose degradation by changing the bacterial community. Random forest model and network analysis indicated a change in bacterial communities after inoculation, and bacteria with more complex relationships and weaker decomposition ability were key to the stability of bacterial communities.
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Affiliation(s)
- Shubo Zhang
- College of Life Science, Northeast Agricultural University, Harbin 150030, China
| | - Tianyi Xia
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, 150081 Harbin, Heilongjiang Province, China
| | - Jialin Wang
- College of Life Science, Northeast Agricultural University, Harbin 150030, China
| | - Yue Zhao
- College of Life Science, Northeast Agricultural University, Harbin 150030, China
| | - Xinyu Xie
- College of Life Science, Northeast Agricultural University, Harbin 150030, China
| | - Zimin Wei
- College of Life Science, Northeast Agricultural University, Harbin 150030, China.
| | - Xu Zhang
- College of Life Science, Northeast Agricultural University, Harbin 150030, China
| | - Caihong Song
- Liaocheng Univ, Life Sci Coll, Liaocheng 252059, China
| | - Xinyu Song
- College of Life Science, Northeast Agricultural University, Harbin 150030, China
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Characterization of the Brain Functional Architecture of Psychostimulant Withdrawal Using Single-Cell Whole-Brain Imaging. eNeuro 2021; 8:ENEURO.0208-19.2021. [PMID: 34580158 PMCID: PMC8570684 DOI: 10.1523/eneuro.0208-19.2021] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 08/08/2021] [Accepted: 08/09/2021] [Indexed: 02/03/2023] Open
Abstract
Numerous brain regions have been identified as contributing to withdrawal behaviors, but it is unclear the way in which these brain regions as a whole lead to withdrawal. The search for a final common brain pathway that is involved in withdrawal remains elusive. To address this question, we implanted osmotic minipumps containing either saline, nicotine (24 mg/kg/d), cocaine (60 mg/kg/d), or methamphetamine (4 mg/kg/d) for one week in male C57BL/6J mice. After one week, the minipumps were removed and brains collected 8 h (saline, nicotine, and cocaine) or 12 h (methamphetamine) after removal. We then performed single-cell whole-brain imaging of neural activity during the withdrawal period when brains were collected. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical-driven to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway associated with withdrawal.
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124
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Parkes L, Moore TM, Calkins ME, Cieslak M, Roalf DR, Wolf DH, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Network Controllability in Transmodal Cortex Predicts Positive Psychosis Spectrum Symptoms. Biol Psychiatry 2021; 90:409-418. [PMID: 34099190 PMCID: PMC8842484 DOI: 10.1016/j.biopsych.2021.03.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/11/2021] [Accepted: 03/15/2021] [Indexed: 01/31/2023]
Abstract
BACKGROUND The psychosis spectrum (PS) is associated with structural dysconnectivity concentrated in transmodal cortex. However, understanding of this pathophysiology has been limited by an overreliance on examining direct interregional connectivity. Using network control theory, we measured variation in both direct and indirect connectivity to a region to gain new insights into the pathophysiology of the PS. METHODS We used psychosis symptom data and structural connectivity in 1068 individuals from the Philadelphia Neurodevelopmental Cohort. Applying a network control theory metric called average controllability, we estimated each brain region's capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Using nonlinear regression, we determined the accuracy with which average controllability could predict PS symptoms in out-of-sample testing. We also examined the predictive performance of regional strength, which indexes only direct connections to a region, as well as several graph-theoretic measures of centrality that index indirect connectivity. Finally, we assessed how the prediction performance for PS symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex. RESULTS Average controllability outperformed all other connectivity features at predicting positive PS symptoms and was the only feature to yield above-chance predictive performance. Improved prediction for average controllability was concentrated in transmodal cortex, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections through average controllability is crucial in association cortex. CONCLUSIONS Examining interindividual variation in direct and indirect structural connections to transmodal cortex is crucial for accurate prediction of positive PS symptoms.
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Monica E Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico.
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125
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Wu Z, Sabel BA. Spacetime in the brain: rapid brain network reorganization in visual processing and recovery. Sci Rep 2021; 11:17940. [PMID: 34504129 PMCID: PMC8429559 DOI: 10.1038/s41598-021-96971-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/13/2021] [Indexed: 11/14/2022] Open
Abstract
Functional connectivity networks (FCN) are the physiological basis of brain synchronization to integrating neural activity. They are not rigid but can reorganize under pathological conditions or during mental or behavioral states. However, because mental acts can be very fast, like the blink of an eye, we now used the visual system as a model to explore rapid FCN reorganization and its functional impact in normal, abnormal and post treatment vision. EEG-recordings were time-locked to visual stimulus presentation; graph analysis of neurophysiological oscillations were used to characterize millisecond FCN dynamics in healthy subjects and in patients with optic nerve damage before and after neuromodulation with alternating currents stimulation and were correlated with visual performance. We showed that rapid and transient FCN synchronization patterns in humans can evolve and dissolve in millisecond speed during visual processing. This rapid FCN reorganization is functionally relevant because disruption and recovery after treatment in optic nerve patients correlated with impaired and recovered visual performance, respectively. Because FCN hub and node interactions can evolve and dissolve in millisecond speed to manage spatial and temporal neural synchronization during visual processing and recovery, we propose “Brain Spacetime” as a fundamental principle of the human mind not only in visual cognition but also in vision restoration.
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Affiliation(s)
- Zheng Wu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.,Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
| | - Bernhard A Sabel
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.
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126
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Boschi A, Brofiga M, Massobrio P. Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks. Front Neurosci 2021; 15:705103. [PMID: 34483826 PMCID: PMC8415479 DOI: 10.3389/fnins.2021.705103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/20/2021] [Indexed: 12/24/2022] Open
Abstract
The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the “noisy” connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.
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Affiliation(s)
- Alessio Boschi
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy.,National Institute for Nuclear Physics (INFN), Genova, Italy
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127
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Shu P, Zhu H, Jin W, Zhou J, Tong S, Sun J. The Resilience and Vulnerability of Human Brain Networks Across the Lifespan. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1756-1765. [PMID: 34410925 DOI: 10.1109/tnsre.2021.3105991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Resilience, the ability for a system to maintain its basic functionality when suffering from lesions, is a critical property for human brain, especially in the brain aging process. This study adopted a novel metric of network resilience, the Resilience Index (RI), to assess human brain resilience with three different lifespan datasets. Based on the structural brain networks constructed from diffusion tensor imaging (DTI), we observed an inverted-U relationship between RI and age, that is, RI increased during development and early adulthood, reached a peak at about 35 years old, and then decreased during aging, which suggested that brain resilience could be quantified by RI. Furthermore, we studied brain network vulnerability by the decreases in RI when virtual lesions occurred to nodes (i.e., brain regions) or edges (i.e., structural brain connectivity). We found that the strong edges were markedly vulnerable, and the homotopic edges were the most prominent representatives of vulnerable edges. In other words, an arbitrary attack on homotopic edges would have a high probability to degrade brain network resilience. These findings suggest the change of human brain resilience across the lifespan and provide a new perspective for exploring human brain vulnerability.
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128
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Bazinet V, Vos de Wael R, Hagmann P, Bernhardt BC, Misic B. Multiscale communication in cortico-cortical networks. Neuroimage 2021; 243:118546. [PMID: 34478823 DOI: 10.1016/j.neuroimage.2021.118546] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/27/2021] [Accepted: 08/31/2021] [Indexed: 11/25/2022] Open
Abstract
Signaling in brain networks unfolds over multiple topological scales. Areas may exchange information over local circuits, encompassing direct neighbours and areas with similar functions, or over global circuits, encompassing distant neighbours with dissimilar functions. Here we study how the organization of cortico-cortical networks mediate localized and global communication by parametrically tuning the range at which signals are transmitted on the white matter connectome. We show that brain regions vary in their preferred communication scale. By investigating the propensity for brain areas to communicate with their neighbors across multiple scales, we naturally reveal their functional diversity: unimodal regions show preference for local communication and multimodal regions show preferences for global communication. We show that these preferences manifest as region- and scale-specific structure-function coupling. Namely, the functional connectivity of unimodal regions emerges from monosynaptic communication in small-scale circuits, while the functional connectivity of transmodal regions emerges from polysynaptic communication in large-scale circuits. Altogether, the present findings reveal that communication preferences are highly heterogeneous across the cortex, shaping regional differences in structure-function coupling.
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Affiliation(s)
- Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital (CHUV-UNIL), Lausanne, Switzerland
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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129
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Ogawa A. Time-varying measures of cerebral network centrality correlate with visual saliency during movie watching. Brain Behav 2021; 11:e2334. [PMID: 34435748 PMCID: PMC8442596 DOI: 10.1002/brb3.2334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/05/2021] [Accepted: 08/07/2021] [Indexed: 12/12/2022] Open
Abstract
The extensive development of graph-theoretic analysis for functional connectivity has revealed the multifaceted characteristics of brain networks. Network centralities identify the principal functional regions, individual differences, and hub structure in brain networks. Neuroimaging studies using movie-watching have investigated brain function under naturalistic stimuli. Visual saliency is one of the promising measures for revealing cognition and emotions driven by naturalistic stimuli. This study investigated whether the visual saliency in movies was associated with network centrality. The study examined eigenvector centrality (EC), which is a measure of a region's influence in the brain network, and the participation coefficient (PC), which reflects the hub structure in the brain, was used for comparison. Static and time-varying EC and PC were analyzed by a parcel-based technique. While EC was correlated with brain activity in parcels in the visual and auditory areas during movie-watching, it was only correlated with parcels in the visual areas in the retinotopy task. In addition, high PC was consistently observed in parcels in the putative hub both during the tasks and the resting-state condition. Time-varying EC in the parietal parcels and time-varying PC in the primary sensory parcels significantly correlated with visual saliency in the movies. These results suggest that time-varying centralities in brain networks are distinctively associated with perceptual processing and subsequent higher processing of visual saliency.
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Affiliation(s)
- Akitoshi Ogawa
- Faculty of Medicine, Juntendo University, Bunkyo-ku, Tokyo, Japan.,Brain Science Institute, Tamagawa University, Machida, Tokyo, Japan
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130
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You J, Zhang J, Shang S, Gu W, Hu L, Zhang Y, Xiong Z, Chen YC, Yin X. Altered Brain Functional Network Topology in Lung Cancer Patients After Chemotherapy. Front Neurol 2021; 12:710078. [PMID: 34408724 PMCID: PMC8367296 DOI: 10.3389/fneur.2021.710078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/05/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose: This study aimed to explore the topological features of brain functional network in lung cancer patients before and after chemotherapy using graph theory. Methods: Resting-state functional magnetic resonance imaging scans were obtained from 44 post-chemotherapy and 46 non-chemotherapy patients as well as 49 healthy controls (HCs). All groups were age- and gender-matched. Then, the topological features of brain functional network were assessed using graph theory analysis. Results: At the global level, compared with the HCs, both the non-chemotherapy group and the post-chemotherapy group showed significantly increased values in sigma (p < 0.05), gamma (p < 0.05), and local efficiency, Eloc (p < 0.05). The post-chemotherapy group and the non-chemotherapy group did not differ significantly in the above-mentioned parameters. At the nodal level, when non-chemotherapy or post-chemotherapy patients were compared with the HCs, abnormal nodal centralities were mainly observed in widespread brain regions. However, when the post-chemotherapy group was compared with the non-chemotherapy group, significantly decreased nodal centralities were observed primarily in the prefrontal–subcortical regions. Conclusions: These results indicate that lung cancer and chemotherapy can disrupt the topological features of functional networks, and chemotherapy may cause a pattern of prefrontal–subcortical brain network abnormality. As far as we know, this is the first study to report that altered functional brain networks are related to lung cancer and chemotherapy.
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Affiliation(s)
- Jia You
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Juan Zhang
- Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing, China
| | - Song'an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wei Gu
- Department of Respiratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Lanyue Hu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yujie Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhenyu Xiong
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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131
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Wang P. Statistical Identification of Important Nodes in Biological Systems. JOURNAL OF SYSTEMS SCIENCE AND COMPLEXITY 2021; 34:1454-1470. [PMID: 34393461 PMCID: PMC8353063 DOI: 10.1007/s11424-020-0013-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/23/2020] [Indexed: 06/13/2023]
Abstract
Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.
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Affiliation(s)
- Pei Wang
- School of Mathematics and Statistics, Institute of Applied Mathematics, Laboratory of Data Analysis Technology, Henan University, Kaifeng, 475004 China
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132
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Suárez LE, Richards BA, Lajoie G, Misic B. Learning function from structure in neuromorphic networks. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00376-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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133
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Li J, Hong L, Bi HY, Yang Y. Functional brain networks underlying automatic and controlled handwriting in Chinese. BRAIN AND LANGUAGE 2021; 219:104962. [PMID: 33984629 DOI: 10.1016/j.bandl.2021.104962] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
This study aimed to identify the functional brain networks underlying the distinctions between automatic and controlled handwriting in Chinese. Network-based analysis was applied to functional magnetic resonance imaging data collected while adult participants performed a copying task under automatic and speed-controlled conditions. We found significant differences between automatic and speed-controlled handwriting in functional connectivity within and between the frontoparietal network, default mode network, dorsal attention network, somatomotor network and visual network; these differences reflect the variations in general attentional control and task-relevant visuomotor operations. However, no differences in brain activation were detected between the two handwriting conditions, suggesting that the reorganization of functional networks, rather than the modulation of local brain activation, underlies the dissociations between automatic and controlled handwriting in Chinese. Our findings illustrate the brain basis of handwriting automaticity, shedding new light on how handwriting automaticity may be disrupted in individuals with neurological disorders.
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Affiliation(s)
- Junjun Li
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Hong
- Department of Foreign Languages, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hong-Yan Bi
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Yang
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
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134
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Wang X, Hu T, Yang Q, Jiao D, Yan Y, Liu L. Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy. J Clin Neurosci 2021; 91:276-282. [PMID: 34373040 DOI: 10.1016/j.jocn.2021.07.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 06/16/2021] [Accepted: 07/15/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND AND PURPOSE The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI). METHODS RS-fMRI data from 35 CAE patients were collected and compared with findings from 35 age and gender matched healthy controls (HCs). The patients were treated with AEDs for 46.03 weeks before undergoing a second RS-fMRI scan. RESULTS CAE children at baseline showed increased DC in thalamus, postcentral and precentral and reduced DC in medial frontal cortex, superior frontal cortex, middle temporal cortex, angular and precuneus. However, those abnormalities showed a clear renormalization after AEDs treatments. We then explored the viability of graph-theory-based degree centrality to accurately classify effectiveness to AEDs. Support Vector Machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.22% [sensitivity 78.76%, specificity 89.65%, and area under the receiver operating characteristic curve (AUC) 0.96] for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the right thalamus, bilateral middle temporal gyrus, right medial frontal gyrus, right inferior frontal gyrus, left precuneus, bilateral angular right precentral and left postcentral. Furthermore, the DC change within the bilateral angular are positively correlated with the symptom improvements after AEDs treatment. CONCLUSION These findings suggest that graph-theory-based measures, such as DC, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of AEDs.
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Affiliation(s)
- Xueyu Wang
- Department of Pediatrics, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Department of Pediatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China.
| | - Tian Hu
- Department of Radiology, Yanan University Affiliated Hospital, China
| | - Qi Yang
- Department of Radiology, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, China
| | - Dongmei Jiao
- Department of Internal Medicine, The Second Affiliated Hospital of Shandong Traditional Chinese Medicine University, Jinan, China
| | - Yibing Yan
- Department of Pediatrics, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Libo Liu
- Department of Cardiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China.
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135
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Savi MK, Callo-Concha D, Tonnang HEZ, Borgemeister C. Emerging properties of malaria transmission and persistence in urban Accra, Ghana: evidence from a participatory system approach. Malar J 2021; 20:321. [PMID: 34281554 PMCID: PMC8287558 DOI: 10.1186/s12936-021-03851-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/12/2021] [Indexed: 11/10/2022] Open
Abstract
Background Several studies that aim to enhance the understanding of malaria transmission and persistence in urban settings failed to address its underlining complexity. This study aims at doing that by applying qualitative and participatory-based system analysis and mapping to elicit the system’s emergent properties. Methods In two experts’ workshops, the system was sketched and refined. This system was represented through a causal loop diagram, where the identification of leverage points was done using network analysis. Results 45 determinants interplaying through 56 linkages, and three subsystems: urbanization-related transmission, infection-prone behaviour and healthcare efficiency, and Plasmodium resistance were identified. Apart from the number of breeding sites and malaria-positive cases, other determinants such as drug prescription and the awareness of householders were identified by the network analysis as leverage points and emergent properties of the system of transmission and persistence of malaria. Conclusion Based on the findings, the ongoing efforts to control malaria, such as the use of insecticide-treated bed nets and larvicide applications should continue, and new ones focusing on the public awareness and malaria literacy of city dwellers should be included. The participatory approach strengthened the legitimacy of the recommendations and the co-learning of participants. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-021-03851-7.
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Affiliation(s)
| | - Daniel Callo-Concha
- Center for Development Research (ZEF), University of Bonn, 53113, Bonn, Germany.,Institute for Environmental Sciences (iES), University of Koblenz-Landau, 76829, Landau, Germany
| | - Henri E Z Tonnang
- International Centre for Insect Physiology and Ecology (Icipe), Nairobi, Kenya
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136
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Yang D, Zhu X, Yan C, Peng Z, Bagonis M, Laurienti PJ, Styner M, Wu G. Joint hub identification for brain networks by multivariate graph inference. Med Image Anal 2021; 73:102162. [PMID: 34274691 DOI: 10.1016/j.media.2021.102162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 11/19/2022]
Abstract
Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder.
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Affiliation(s)
- Defu Yang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China; Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - Xiaofeng Zhu
- School of Natural and Computational Science, Massey University, Auckland, New Zealand
| | - Chenggang Yan
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Ziwen Peng
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | | | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA.
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137
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Hartwigsen G, Bengio Y, Bzdok D. How does hemispheric specialization contribute to human-defining cognition? Neuron 2021; 109:2075-2090. [PMID: 34004139 PMCID: PMC8273110 DOI: 10.1016/j.neuron.2021.04.024] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/22/2021] [Accepted: 04/26/2021] [Indexed: 12/30/2022]
Abstract
Uniquely human cognitive faculties arise from flexible interplay between specific local neural modules, with hemispheric asymmetries in functional specialization. Here, we discuss how these computational design principles provide a scaffold that enables some of the most advanced cognitive operations, such as semantic understanding of world structure, logical reasoning, and communication via language. We draw parallels to dual-processing theories of cognition by placing a focus on Kahneman's System 1 and System 2. We propose integration of these ideas with the global workspace theory to explain dynamic relay of information products between both systems. Deepening the current understanding of how neurocognitive asymmetry makes humans special can ignite the next wave of neuroscience-inspired artificial intelligence.
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Affiliation(s)
- Gesa Hartwigsen
- Max Planck Institute for Human Cognitive and Brain Sciences, Lise Meitner Research Group Cognition and Plasticity, Leipzig, Germany.
| | - Yoshua Bengio
- Mila, Montreal, QC, Canada; University of Montreal, Montreal, QC, Canada
| | - Danilo Bzdok
- Mila, Montreal, QC, Canada; Montreal Neurological Institute, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, Faculty of Medicine, and School of Computer Science, McGill University, Montreal, QC, Canada.
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138
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Miller DR, Guenther DT, Maurer AP, Hansen CA, Zalesky A, Khoshbouei H. Dopamine Transporter Is a Master Regulator of Dopaminergic Neural Network Connectivity. J Neurosci 2021; 41:5453-5470. [PMID: 33980544 PMCID: PMC8221606 DOI: 10.1523/jneurosci.0223-21.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/19/2021] [Accepted: 05/01/2021] [Indexed: 12/13/2022] Open
Abstract
Dopaminergic neurons of the substantia nigra pars compacta (SNC) and ventral tegmental area (VTA) exhibit spontaneous firing activity. The dopaminergic neurons in these regions have been shown to exhibit differential sensitivity to neuronal loss and psychostimulants targeting dopamine transporter. However, it remains unclear whether these regional differences scale beyond individual neuronal activity to regional neuronal networks. Here, we used live-cell calcium imaging to show that network connectivity greatly differs between SNC and VTA regions with higher incidence of hub-like neurons in the VTA. Specifically, the frequency of hub-like neurons was significantly lower in SNC than in the adjacent VTA, consistent with the interpretation of a lower network resilience to SNC neuronal loss. We tested this hypothesis, in DAT-cre/loxP-GCaMP6f mice of either sex, when activity of an individual dopaminergic neuron is suppressed, through whole-cell patch clamp electrophysiology, in either SNC or VTA networks. Neuronal loss in the SNC increased network clustering, whereas the larger number of hub-neurons in the VTA overcompensated by decreasing network clustering in the VTA. We further show that network properties are regulatable via a dopamine transporter but not a D2 receptor dependent mechanism. Our results demonstrate novel regulatory mechanisms of functional network topology in dopaminergic brain regions.SIGNIFICANCE STATEMENT In this work, we begin to untangle the differences in complex network properties between the substantia nigra pars compacta (SNC) and VTA, that may underlie differential sensitivity between regions. The methods and analysis employed provide a springboard for investigations of network topology in multiple deep brain structures and disorders.
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Affiliation(s)
- Douglas R Miller
- Department of Neuroscience, University of Florida, Gainesville, Florida
| | - Dylan T Guenther
- Department of Neuroscience, University of Florida, Gainesville, Florida
| | - Andrew P Maurer
- Department of Neuroscience, University of Florida, Gainesville, Florida
| | - Carissa A Hansen
- Department of Neuroscience, University of Florida, Gainesville, Florida
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Victoria 3010, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, Victoria 3010, Australia
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139
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Dynamic reorganization of the frontal parietal network during cognitive control and episodic memory. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 20:76-90. [PMID: 31811557 DOI: 10.3758/s13415-019-00753-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Higher cognitive functioning is supported by adaptive reconfiguration of large-scale functional brain networks. Cognitive control (CC), which plays a vital role in flexibly guiding cognition and behavior in accordance with our goals, supports a range of executive functions via distributed brain networks. These networks process information dynamically and can be represented as functional connectivity changes between network elements. Using graph theory, we explored context-dependent network reorganization in 56 healthy adults performing fMRI tasks from two cognitive domains that varied in CC and episodic-memory demands. We examined whole-brain modular structure during the DPX task, which engages proactive CC in the frontal-parietal cognitive-control network (FPN), and the RiSE task, which manipulates CC demands at encoding and retrieval during episodic-memory processing, and engages FPN, the medial-temporal lobe and other memory-related networks in a context dependent manner. Analyses revealed different levels of network integration and segregation. Modularity analyses revealed greater brain-wide integration across tasks in high CC conditions compared to low CC conditions. Greater network reorganization occurred in the RiSE memory task, which is thought to require coordination across multiple brain networks, than in the DPX cognitive-control task. Finally, FPN, ventral attention, and visual systems showed within network connectivity effects of cognitive control; however, these cognitive systems displayed varying levels of network reorganization. These findings provide insight into how brain networks reorganize to support differing task contexts, suggesting that the FPN flexibly segregates during focused proactive control and integrates to support control in other domains such as episodic memory.
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140
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Simpson-Kent IL, Fried EI, Akarca D, Mareva S, Bullmore ET, Kievit RA. Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners. J Intell 2021; 9:32. [PMID: 34204009 PMCID: PMC8293355 DOI: 10.3390/jintelligence9020032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022] Open
Abstract
Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies.
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Affiliation(s)
- Ivan L. Simpson-Kent
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Eiko I. Fried
- Department of Clinical Psychology, Leiden University, 2300 RA Leiden, The Netherlands;
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Silvana Mareva
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, Cambridgeshire CB2 0SP, UK;
| | - the CALM Team
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Rogier A. Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
- Cognitive Neuroscience Department, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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141
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Yin Z, Wang Y, Dong M, Ren S, Hu H, Yin K, Liang J. Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study. Front Neurosci 2021; 15:652920. [PMID: 34177446 PMCID: PMC8221185 DOI: 10.3389/fnins.2021.652920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/17/2021] [Indexed: 12/03/2022] Open
Abstract
Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0-100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100-200 and 200-300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.
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Affiliation(s)
- Zhongliang Yin
- School of Electronic Engineering, Xidian University, Xi'an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Yue Wang
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Shenghan Ren
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Haihong Hu
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Kuiying Yin
- Nanjing Research Institute of Electronics Technology, Nanjing, China
| | - Jimin Liang
- School of Electronic Engineering, Xidian University, Xi'an, China
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142
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Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison. Brain Sci 2021; 11:brainsci11060735. [PMID: 34073098 PMCID: PMC8227272 DOI: 10.3390/brainsci11060735] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 11/28/2022] Open
Abstract
Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.
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143
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Phase fMRI defines brain resting-state functional hubs within central and posterior regions. Brain Struct Funct 2021; 226:1925-1941. [PMID: 34050790 DOI: 10.1007/s00429-021-02301-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 05/12/2021] [Indexed: 10/21/2022]
Abstract
From a brain functional connectivity (FC) matrix, we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for one node's centrality but also all other nodes' centrality values through correlation connections in an eigenvector of the FC matrix. For the resting-state functional MRI (fMRI) data (complex-valued EPI images in nature), both magnitude and phase images are useful for brain FC analysis. We herein report on brain functional hubness analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping. In our study, we collected a cohort of 160 complex-valued fMRI dataset (consisting of magnitude and phase in pairs), and performed independent component analysis (ICA), FC matrix calculation (in size of 50 × 50) and FC matrix eigen decomposition; thereby obtained the 50-node eigencentrality values in the eigenvector associated with the largest eigenvalue. We also compared the hub structures inferred from FC matrices under different thresholding. Alternatively, we obtained the geometric hubs among p value the 50 nodes involved in the FC matrix through the use of harmonic centrality metric. Our results showed that phase fMRI data analysis defines the resting-state brain functional hubs primarily in the central region (subcortex) and the posterior region (parieto-occipital lobes and cerebella). The brain central hubness was supported by the geometric central hubness, which, however, is distinct from the magnitude-inferred hubness in brain superior region (frontal and parietal lobes). Our findings pose a new understanding of (or a debate over) brain functional connectivity architecture.
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144
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Fouladivanda M, Kazemi K, Makki M, Khalilian M, Danyali H, Gervain J, Aarabi A. Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study. J Neural Eng 2021; 18. [PMID: 33930878 DOI: 10.1088/1741-2552/abfd46] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/30/2021] [Indexed: 12/11/2022]
Abstract
Objective.Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-state fMRI data.Approach.A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100-900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales.Main results.Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus.Significance.Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.
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Affiliation(s)
- Mahshid Fouladivanda
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Malek Makki
- Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, Amiens, France
| | - Maedeh Khalilian
- Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, Amiens, France
| | - Habibollah Danyali
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Judit Gervain
- Integrative Neuroscience and Cognition Center, CNRS & Université de Paris, Paris, France.,Department of Developmental Psychology and Socialization, University of Padua, Padua, Italy
| | - Ardalan Aarabi
- Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, Amiens, France.,Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
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145
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Sheng J, Zhang L, Feng J, Liu J, Li A, Chen W, Shen Y, Wang J, He Y, Xue G. The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases. Neuroimage 2021; 237:118187. [PMID: 34020011 DOI: 10.1016/j.neuroimage.2021.118187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SDBOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSDBOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSDBOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSDBOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSDBOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSDBOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.
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Affiliation(s)
- Jintao Sheng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Liang Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Junjiao Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Jing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Anqi Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang 310000, PR China
| | - Yuedi Shen
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang 310000, PR China
| | - Jinhui Wang
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou 510631, PR China; Key Laboratory of Brain, Ministry of Education, Cognition and Education Sciences (South China Normal University), PR China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China.
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146
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Distinctive Alterations of Functional Connectivity Strength between Vascular and Amnestic Mild Cognitive Impairment. Neural Plast 2021; 2021:8812490. [PMID: 34104193 PMCID: PMC8159649 DOI: 10.1155/2021/8812490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 11/02/2020] [Accepted: 04/30/2021] [Indexed: 11/18/2022] Open
Abstract
Widespread structural and functional alterations have been reported in the two highly prevalent mild cognitive impairment (MCI) subtypes, amnestic MCI (aMCI) and vascular MCI (VaMCI). However, the changing pattern in functional connectivity strength (FCS) remains largely unclear. The aim of the present study is to detect the differences of FCS and to further explore the detailed resting-state functional connectivity (FC) alterations among VaMCI subjects, aMCI subjects, and healthy controls (HC). Twenty-six aMCI subjects, 31 VaMCI participants, and 36 HC participants underwent cognitive assessments and resting-state functional MRI scans. At first, one-way ANCOVA and post hoc analysis indicated significant decreased FCS in the left middle temporal gyrus (MTG) in aMCI and VaMCI groups compared to HC, especially in the VaMCI group. Then, we selected the left MTG as a seed to further explore the detailed resting-state FC alterations among the three groups, and the results indicated that FC between the left MTG and some frontal brain regions were significantly decreased mainly in VaMCI. Finally, partial correlation analysis revealed that the FC values between the left MTG and left inferior frontal gyrus were positively correlated with the cognitive performance episodic memory and negatively related to the living status. The present study demonstrated that different FCS alterations existed in aMCI and VaMCI. These findings may provide a novel insight into the understanding of pathophysiological mechanisms underlying different MCI subtypes.
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147
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Zorzi G, Cecchin D, Bussè C, Perini G, Corbetta M, Cagnin A. Changes of Metabolic Connectivity in Dementia with Lewy Bodies with Visual Hallucinations: A 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Study. Brain Connect 2021; 11:518-528. [PMID: 33757301 DOI: 10.1089/brain.2020.0988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: Recurrent complex visual hallucinations (VHs) are common in dementia with Lewy bodies (DLB). Previous investigations suggest that VHs are associated with connectivity changes within and between large scale networks involved in visual processing and attention. Aim: To examine more directly whether VH in DLB reflects direct changes in neuronal activity between cortical regions assessing metabolic connectivity with 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/magnetic resonance and graph theory. Methods: Twenty-six patients with probable DLB (13 VHs and 13 no-VHs; mean age: 72.9 ± 6.87 years vs. 70.2 ± 7.96 years) were enrolled. T1-weighted 3T-MR images and FDG-PET data were coacquired using an integrated PET/MR scanner. MR images defined cortical parcels of the Shaefer-Yeo atlas for multiple functional networks. We computed in each parcel the regional standardized-uptake-values (SUV) corrected for partial volume and normalized to the cerebellar cortex. Strength degree, clustering coefficient, characteristic path length, and hubs were analyzed with graph analysis. Results: The mean 18F-FDG-PET SUVr of parcels belonging to the visual and dorsal attention networks (DANs) were significantly lower in the VH group (p = 0.01). Metabolism in the right temporoparietal cortex correlated with VH severity (R = -0.58; p < 0.01). VH patients showed weaker metabolic connectivity in the parietal, temporal, and occipital cortex of the default mode network, DAN, and visual networks, but more robust connectivity in the right insula and orbitofrontal cortex. A lower global efficiency characterized the VH group, except for ventral attention network and limbic network. Conclusions: VHs in DLB correlate with lower glucose metabolism and weaker metabolic connectivity in the parietal-occipital cortex, but stronger connectivity in the limbic system. Impact statement This study shows that application of the graph theory to 18F-fluorodeoxyglucose-positron emission tomography data, commonly acquired during the diagnostic workflow in neurodegenerative diseases, could be used to obtain information of functional connectivity at a group level, with results that are consistent with other data commonly used in brain functional investigation (e.g., electroencephalography or functional magnetic resonance). New network-based methods of metabolic image analyses, such as graph analysis, are a recent area of research with a potential capacity to extract information on alterations of metabolic connectivity that may become pharmacological and neuromodulation targets of the physiopathology of recurrent complex visual hallucinations.
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Affiliation(s)
- Giovanni Zorzi
- Department of Neuroscience, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Diego Cecchin
- Padova Neuroscience Center, University of Padova, Padova, Italy.,Nuclear Medicine Unit, Department of Medicine, University of Padova, Padova, Italy
| | - Cinzia Bussè
- Department of Neuroscience, University of Padova, Padova, Italy
| | | | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Annachiara Cagnin
- Department of Neuroscience, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
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148
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Garcia-Ramos C, Struck AF, Cook C, Prabhakaran V, Nair V, Maganti R, Binder JR, Meyerand M, Conant LL, Hermann B. Network topology of the cognitive phenotypes of temporal lobe epilepsy. Cortex 2021; 141:55-65. [PMID: 34029858 DOI: 10.1016/j.cortex.2021.03.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/04/2021] [Accepted: 03/28/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE The neuropsychological complications of temporal lobe epilepsy are characterized by a spectrum of reproducible cognitive phenotypes that vary in the presence, type and degree of impairment. The nature of the disruptions to the neuropsychological networks that underlie these phenotypes remain to be characterized and represent the subject of this investigation. METHODS Participants included 30 healthy controls and 104 patients with temporal lobe epilepsy who fell into three cognitive phenotypes (intact, focal impairment, generalized impairment). Eighteen neuropsychological measures representing multiple cognitive domains (language, memory, executive function, visuoperception, motor speed) were examined by graph theory techniques within the control and each epilepsy cognitive phenotype group to characterize their global and local network properties. RESULTS Across the control and epilepsy cognitive phenotype groups (intact to focal to generalized impairment), there was: 1) an orderly breakdown in the positive manifold reflected by a stepwise reduction of positive associations among the neuropsychological tests, 2) stepwise abnormal increases in global measures including the normalized clustering coefficient and modularity index, 3) stepwise abnormal decreases in normalized global efficiency, 4) a community structure demonstrating well organized modules within the control group while each epilepsy group showed deviations from controls, and 5) lower strength, compared to controls, across 8 nodes in the focal and generalized impairment groups compared to only 3 nodes in the no-impairment epilepsy group, pointing to the superior integration of local connections in controls. DISCUSSION The cognitive phenotypes of temporal lobe epilepsy are characterized by orderly abnormalities in their underlying neuropsychological networks. These findings inform the network perturbations that underlie the taxonomy of cognitive abnormality in temporal lobe epilepsy and provide a model for examination of similar issues in other focal and generalized epilepsies.
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Affiliation(s)
- Camille Garcia-Ramos
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Middleton Veterans Administration Hospital, Madison, WI, USA
| | - Cole Cook
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Veena Nair
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rama Maganti
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Marybeth Meyerand
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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149
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Simpson S, Chen Y, Wellmeyer E, Smith LC, Aragon Montes B, George O, Kimbrough A. The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Front Syst Neurosci 2021; 15:595507. [PMID: 33967705 PMCID: PMC8097000 DOI: 10.3389/fnsys.2021.595507] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 03/26/2021] [Indexed: 12/18/2022] Open
Abstract
A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.
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Affiliation(s)
- Sierra Simpson
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Yueyi Chen
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States.,Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Emma Wellmeyer
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Lauren C Smith
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Brianna Aragon Montes
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Olivier George
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Adam Kimbrough
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.,Purdue Institute for Inflammation, Immunology, and Infectious Disease, West Lafayette, IN, United States
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150
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Lou C, Cross AM, Peters L, Ansari D, Joanisse MF. Rich-club structure contributes to individual variance of reading skills via feeder connections in children with reading disabilities. Dev Cogn Neurosci 2021; 49:100957. [PMID: 33894677 PMCID: PMC8093404 DOI: 10.1016/j.dcn.2021.100957] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/29/2021] [Accepted: 04/15/2021] [Indexed: 01/18/2023] Open
Abstract
The present work considers how connectome-wide differences in brain organization might distinguish good and poor readers. The connectome comprises a ‘rich-club’ organization in which a small number of hub regions play a focal role in assisting global communication across the whole brain. Prior work indicates that this rich-club structure is associated with typical and impaired cognitive function although no work so far has examined how this relates to skilled reading or its disorders. Here we investigated the rich-club structure of brain’s white matter connectome and its relationship to reading subskills in 64 children with and without reading disabilities. Among three types of white matter connections, the strength of feeder connections that connect hub and non-hub nodes was significantly correlated with word reading efficiency and phonemic decoding. Phonemic decoding was also positively correlated with connectivity between connectome-wide hubs and nodes within the left-hemisphere reading network, as well as the local efficiency of the reading network. Exploratory analyses also identified sex differences indicating these effects were stronger in girls. This work highlights the independent roles of connectome-wide structure and the more narrowly-defined reading network in understanding the neural bases of skilled and impaired reading in children.
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Affiliation(s)
- Chenglin Lou
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada.
| | - Alexandra M Cross
- Brain and Mind Institute, The University of Western Ontario, London, Canada; Health and Rehabilitation Sciences, The University of Western Ontario, London, Canada
| | - Lien Peters
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada
| | - Daniel Ansari
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada; Faculty of Education, The University of Western Ontario, London, Canada
| | - Marc F Joanisse
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada; Haskins Laboratories, New Haven, CT, USA
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