151
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Thiele JA, Faskowitz J, Sporns O, Hilger K. Multitask brain network reconfiguration is inversely associated with human intelligence. Cereb Cortex 2022; 32:4172-4182. [PMID: 35136956 PMCID: PMC9528794 DOI: 10.1093/cercor/bhab473] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/18/2021] [Accepted: 11/19/2021] [Indexed: 01/08/2023] Open
Abstract
Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for the effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used functional magnetic resonance imaging data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system and replicates in two independent samples (n = 138 and n = 184). Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multitask brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold - the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multitask brain network.
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Affiliation(s)
- Jonas A Thiele
- Department of Psychology I, Würzburg University, 97070 Würzburg, Germany
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Kirsten Hilger
- Department of Psychology I, Würzburg University, 97070 Würzburg, Germany
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152
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Zhang H, Zhao R, Hu X, Guan S, Margulies DS, Meng C, Biswal BB. Cortical connectivity gradients and local timescales during cognitive states are modulated by cognitive loads. Brain Struct Funct 2022; 227:2701-2712. [PMID: 36098843 DOI: 10.1007/s00429-022-02564-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 08/29/2022] [Indexed: 11/02/2022]
Abstract
Although resting-state fMRI studies support that human brain is topographically organized regarding localized and distributed processes, it is still unclear about the task-modulated cortical hierarchy in terms of distributed functional connectivity and localized timescales. To address, current study investigated the effect of cognitive load on cortical connectivity gradients and local timescales in the healthy brain using resting state fMRI as well as 1- and 2-back working memory task fMRI. The results demonstrated that (1) increased cognitive load was associated with lower principal gradient in transmodal cortices, higher principal gradient in primary cortices, decreased decay rate and reduced timescale variability; (2) global properties including gradient variability, timescale decay rate, timescale variability and network topology were all modulated by cognitive load, with timescale variability related to behavioral performance; and (3) at 2-back state, the timescale variability was indirectly and negatively linked with global network integration, which was mediated by gradient variability. In conclusion, current study provides novel evidence for load-modulated cortical connectivity gradients and local timescales during cognitive states, which could contribute to better understanding about cognitive load theory and brain disorders with cognitive dysfunction.
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Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Rong Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Xin Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Sihai Guan
- Key Laboratory of Electronic and Information Engineering (Southwest Minzu University), State Ethnic Affairs Commission. College of Electronic and Information, Southwest Minzu University, Chengdu, 610225, China
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China.
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China. .,Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, 607 Fenster Hall, Newark, NJ, 07102, USA.
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153
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Glaubitz L, Stumme J, Lucht S, Moebus S, Schramm S, Jockwitz C, Hoffmann B, Caspers S. Association between Long-Term Air Pollution, Chronic Traffic Noise, and Resting-State Functional Connectivity in the 1000BRAINS Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:97007. [PMID: 36154234 PMCID: PMC9512146 DOI: 10.1289/ehp9737] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/04/2022] [Accepted: 07/22/2022] [Indexed: 06/02/2023]
Abstract
BACKGROUND Older adults show a high variability in cognitive performance that cannot be explained by aging alone. Although research has linked air pollution and noise to cognitive impairment and structural brain alterations, the potential impact of air pollution and noise on functional brain organization is unknown. OBJECTIVE This study examined the associations between long-term air pollution and traffic noise with measures of functional brain organization in older adults. We hypothesize that exposures to high air pollution and noise levels are associated with age-like changes in functional brain organization, shown by less segregated brain networks. METHODS Data from 574 participants (44.1% female, 56-85 years of age) in the German 1000BRAINS study (2011-2015) were analyzed. Exposure to particulate matter (PM10, PM2.5, and PM2.5 absorbance), accumulation mode particle number (PNAM), and nitrogen dioxide (NO2) was estimated applying land-use regression and chemistry transport models. Noise exposures were assessed as weighted 24-h (Lden) and nighttime (Lnight) means. Functional brain organization of seven established brain networks (visual, sensorimotor, dorsal and ventral attention, limbic, frontoparietal and default network) was assessed using resting-state functional brain imaging data. To assess functional brain organization, we determined the degree of segregation between networks by comparing the strength of functional connections within and between networks. We estimated associations between air pollution and noise exposure with network segregation, applying multiple linear regression models adjusted for age, sex, socioeconomic status, and lifestyle variables. RESULTS Overall, small associations of high exposures with lesser segregated networks were visible. For the sensorimotor networks, we observed small associations between high air pollution and noise and lower network segregation, which had a similar effect size as a 1-y increase in age [e.g., in sensorimotor network, -0.006 (95% CI: -0.021, 0.009) per 0.3 ×10-5/m increase in PM2.5 absorbance and -0.004 (95% CI: -0.006, -0.002) per 1-y age increase]. CONCLUSION High exposure to air pollution and noise was associated with less segregated functional brain networks. https://doi.org/10.1289/EHP9737.
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Affiliation(s)
- Lina Glaubitz
- Environmental Epidemiology Group, Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Lucht
- Environmental Epidemiology Group, Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Susanne Moebus
- Institute for Urban Public Health, University of Duisburg-Essen, Essen, Germany
| | - Sara Schramm
- Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Barbara Hoffmann
- Environmental Epidemiology Group, Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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154
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Hummos A, Wang BA, Drammis S, Halassa MM, Pleger B. Thalamic regulation of frontal interactions in human cognitive flexibility. PLoS Comput Biol 2022; 18:e1010500. [PMID: 36094955 PMCID: PMC9499289 DOI: 10.1371/journal.pcbi.1010500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 09/22/2022] [Accepted: 08/19/2022] [Indexed: 11/19/2022] Open
Abstract
Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.
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Affiliation(s)
- Ali Hummos
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Bin A. Wang
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr University Bochum, Bochum, Germany
| | - Sabrina Drammis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Michael M. Halassa
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Burkhard Pleger
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr University Bochum, Bochum, Germany
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155
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Jovanova M, Falk EB, Pearl JM, Pandey P, Brook O'Donnell M, Kang Y, Bassett DS, Lydon-Staley DM. Brain system integration and message consistent health behavior change. Health Psychol 2022; 41:611-620. [PMID: 36006700 PMCID: PMC10152515 DOI: 10.1037/hea0001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Modifiable behaviors, including physical activity and sedentary behavior, are important determinants of health, and messages are important tools for influencing these behaviors. Functional neuroimaging research suggests that activity in regions of the brain's default mode and salience systems are independently associated with attending to health promoting messages. However, it remains unclear how these brain systems interact during exposure to persuasive messages and how this interaction relates to subsequent behavior change. Here, we examine how between-person differences in the relative integration between default mode and salience systems while viewing health messages relates to changes in health behavior. METHOD Using wrist-worn accelerometers, we logged physical activity in 150 participants (mean age = 33.17 years, 64% women; 43% Black, 37% white, 7% Asian, 5% Hispanic, and 8% other) continuously for an average of 10 days. Participants then viewed health messages encouraging physical activity while undergoing functional MRI (fMRI) and completed an additional month where physical activity was logged and the health messages were reinforced with daily text reminders. RESULTS Individuals with higher default mode and salience system integration during health message exposure were more likely to decrease their sedentary behavior and increase light physical activity in the month following fMRI than participants with lower brain integration. CONCLUSIONS Interactions between the salience and default mode systems are associated with message receptivity and subsequent behavior change, highlighting the value of expanding the focus from the role of single brain regions in studying health behavior change to larger-scale connectivity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Affiliation(s)
- Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania
| | - Jacob M Pearl
- Annenberg School for Communication, University of Pennsylvania
| | | | | | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania
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156
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Cheng L, Chiu Y, Lin Y, Li W, Hong T, Yang C, Shih C, Yeh T, Tseng WI, Yu H, Hsieh J, Chen L. Long-term musical training induces white matter plasticity in emotion and language networks. Hum Brain Mapp 2022; 44:5-17. [PMID: 36005832 PMCID: PMC9783470 DOI: 10.1002/hbm.26054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 07/02/2022] [Accepted: 07/15/2022] [Indexed: 02/05/2023] Open
Abstract
Numerous studies have reported that long-term musical training can affect brain functionality and induce structural alterations in the brain. Singing is a form of vocal musical expression with an unparalleled capacity for communicating emotion; however, there has been relatively little research on neuroplasticity at the network level in vocalists (i.e., noninstrumental musicians). Our objective in this study was to elucidate changes in the neural network architecture following long-term training in the musical arts. We employed a framework based on graph theory to depict the connectivity and efficiency of structural networks in the brain, based on diffusion-weighted images obtained from 35 vocalists, 27 pianists, and 33 nonmusicians. Our results revealed that musical training (both voice and piano) could enhance connectivity among emotion-related regions of the brain, such as the amygdala. We also discovered that voice training reshaped the architecture of experience-dependent networks, such as those involved in vocal motor control, sensory feedback, and language processing. It appears that vocal-related changes in areas such as the insula, paracentral lobule, supramarginal gyrus, and putamen are associated with functional segregation, multisensory integration, and enhanced network interconnectivity. These results suggest that long-term musical training can strengthen or prune white matter connectivity networks in an experience-dependent manner.
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Affiliation(s)
- Li‐Kai Cheng
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan
| | - Yu‐Hsien Chiu
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan
| | - Ying‐Chia Lin
- Center for Advanced Imaging Innovation and Research (CAIR)NYU Grossman School of MedicineNew YorkNew YorkUSA,Center for Biomedical Imaging, Department of RadiologyNYU Grossman School of MedicineNew YorkNew YorkUSA
| | - Wei‐Chi Li
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan
| | - Tzu‐Yi Hong
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan
| | - Ching‐Ju Yang
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan
| | - Chung‐Heng Shih
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan
| | - Tzu‐Chen Yeh
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Department of RadiologyTaipei Veterans General HospitalTaipeiTaiwan
| | - Wen‐Yih Isaac Tseng
- Institute of Medical Device and ImagingNational Taiwan University College of MedicineTaipeiTaiwan
| | - Hsin‐Yen Yu
- Graduate Institute of Arts and Humanities EducationTaipei National University of the ArtsTaipeiTaiwan
| | - Jen‐Chuen Hsieh
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan,Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Department of Biological Science and Technology, College of Biological Science and TechnologyNational Yang Ming Chiao Tung UniversityHsinchuTaiwan
| | - Li‐Fen Chen
- Institute of Brain ScienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan,Integrated Brain Research Unit, Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan,Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
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157
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Noble S, Mejia AF, Zalesky A, Scheinost D. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proc Natl Acad Sci U S A 2022; 119:e2203020119. [PMID: 35925887 PMCID: PMC9371642 DOI: 10.1073/pnas.2203020119] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
Abstract
Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.
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Affiliation(s)
- Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519
| | - Amanda F. Mejia
- Department of Statistics, Indiana University Bloomington, Bloomington, IN 47408
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519
- Department of Biomedical Engineering, Yale School of Medicine, New Haven, CT 06520
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520
- Department of Statistics and Data Science, Yale University, New Haven, CT 06511
- Child Study Center, Yale School of Medicine, New Haven, CT 06519
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158
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Zhao F, Pan H, Li N, Chen X, Zhang H, Mao N, Ren Y. High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder. Front Neurosci 2022; 16:976229. [PMID: 36017184 PMCID: PMC9396245 DOI: 10.3389/fnins.2022.976229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/15/2022] [Indexed: 12/04/2022] Open
Abstract
Brain functional network (BFN) based on electroencephalography (EEG) has been widely used to diagnose brain diseases, such as major depressive disorder (MDD). However, most existing BFNs only consider the correlation between two channels, ignoring the high-level interaction among multiple channels that contain more rich information for diagnosing brain diseases. In such a sense, the BFN is called low-order BFN (LO-BFN). In order to fully explore the high-level interactive information among multiple channels of the EEG signals, a scheme for constructing a high-order BFN (HO-BFN) based on the “correlation’s correlation” strategy is proposed in this paper. Specifically, the entire EEG time series is firstly divided into multiple epochs by sliding window. For each epoch, the short-term correlation between channels is calculated to construct a LO-BFN. The correlation time series of all channel pairs are formulated by these LO-BFNs obtained from all epochs to describe the dynamic change of short-term correlation along the time. To construct HO-BFN, we cluster all correlation time series to avoid the problems caused by high dimensionality, and the correlation of the average correlation time series from different clusters is calculated to reflect the high-order correlation among multiple channels. Experimental results demonstrate the efficiency of the proposed HO-BFN in MDD identification, and its integration with the LO-BFN can further improve the recognition rate.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Hongxin Pan
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Na Li
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Yande Ren,
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159
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Fanton S, Altawil R, Ellerbrock I, Lampa J, Kosek E, Fransson P, Thompson WH. Multiple spatial scale mapping of time-resolved brain network reconfiguration during evoked pain in patients with rheumatoid arthritis. Front Neurosci 2022; 16:942136. [PMID: 36017179 PMCID: PMC9397124 DOI: 10.3389/fnins.2022.942136] [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: 05/12/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Functional brain networks and the perception of pain can fluctuate over time. However, how the time-dependent reconfiguration of functional brain networks contributes to chronic pain remains largely unexplained. Here, we explored time-varying changes in brain network integration and segregation during pain over a disease-affected area (joint) compared to a neutral site (thumbnail) in 28 patients with rheumatoid arthritis (RA) in comparison with 22 healthy controls (HC). During functional magnetic resonance imaging, all subjects received individually calibrated pain pressures corresponding to visual analog scale 50 mm at joint and thumbnail. We implemented a novel approach to track changes of task-based network connectivity over time. Within this framework, we quantified measures of integration (participation coefficient, PC) and segregation (within-module degree z-score). Using these network measures at multiple spatial scales, both at the level of single nodes (brain regions) and communities (clusters of nodes), we found that PC at the community level was generally higher in RA patients compared to HC during and after painful pressure over the inflamed joint and corresponding site in HC. This shows that all brain communities integrate more in RA patients than in HC for time points following painful stimulation to a disease-relevant body site. However, the elevated community-related integration seen in patients appeared to not pertain uniquely to painful stimulation at the inflamed joint, but also at the neutral thumbnail, as integration and segregation at the community level did not differ across body sites in patients. Moreover, there was no specific nodal contribution to brain network integration or segregation. Altogether, our findings indicate widespread and persistent changes in network interaction in RA patients compared to HC in response to painful stimulation.
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Affiliation(s)
- Silvia Fanton
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Reem Altawil
- Rheumatology Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Isabel Ellerbrock
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jon Lampa
- Rheumatology Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Eva Kosek
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - William H. Thompson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Division of Cognition and Communication, Department of Applied IT, University of Gothenburg, Gothenburg, Sweden
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160
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Rao B, Wang S, Yu M, Chen L, Miao G, Zhou X, Zhou H, Liao W, Xu H. Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment. Front Aging Neurosci 2022; 14:893297. [PMID: 36003999 PMCID: PMC9393744 DOI: 10.3389/fnagi.2022.893297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundNeural reorganization occurs after a stroke, and dynamic functional network connectivity (dFNC) pattern is associated with cognition. We hypothesized that dFNC alterations resulted from neural reorganization in post-stroke cognitive impairment (PSCI) patients, and specific dFNC patterns characterized different pathological types of PSCI.MethodsResting-state fMRI data were collected from 16 PSCI patients with hemorrhagic stroke (hPSCI group), 21 PSCI patients with ischemic stroke (iPSCI group), and 21 healthy controls (HC). We performed the dFNC analysis for the dynamic connectivity states, together with their topological and temporal features.ResultsWe identified 10 resting-state networks (RSNs), and the dFNCs could be clustered into four reoccurring states (modular, regional, sparse, and strong). Compared with HC, the hPSCI and iPSCI patients showed lower standard deviation (SD) and coefficient of variation (CV) in the regional and modular states, respectively (p < 0.05). Reduced connectivities within the primary network (visual, auditory, and sensorimotor networks) and between the primary and high-order cognitive control domains were observed (p < 0.01).ConclusionThe transition trend to suboptimal states may play a compensatory role in patients with PSCI through redundancy networks. The reduced exploratory capacity (SD and CV) in different suboptimal states characterized cognitive impairment and pathological types of PSCI. The functional disconnection between the primary and high-order cognitive control network and the frontoparietal network centered (FPN-centered) incomplete compensation may be the pathological mechanism of PSCI. These results emphasize the flexibility of neural reorganization during self-repair.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Sirui Wang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Minhua Yu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Linglong Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Guofu Miao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoli Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hong Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Weijing Liao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Weijing Liao,
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Haibo Xu,
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161
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Alvand A, Kuruvilla-Mathew A, Kirk IJ, Roberts RP, Pedersen M, Purdy SC. Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study. Neuroimage Clin 2022; 35:103139. [PMID: 36002970 PMCID: PMC9421544 DOI: 10.1016/j.nicl.2022.103139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/19/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022]
Abstract
Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties -i.e., an average of all brain regions- and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level -i.e., single-brain regions-, we observed decreased participation coefficient (PC - a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.
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Affiliation(s)
- Ashkan Alvand
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand.
| | - Abin Kuruvilla-Mathew
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand.
| | - Ian J Kirk
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
| | - Reece P Roberts
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
| | - Mangor Pedersen
- School of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.
| | - Suzanne C Purdy
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
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162
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Li Z, Yi C, Chen C, Liu C, Zhang S, Li S, Gao D, Cheng L, Zhang X, Sun J, He Y, Xu P. Predicting individual muscle fatigue tolerance by resting-state EEG brain network. J Neural Eng 2022; 19. [PMID: 35901723 DOI: 10.1088/1741-2552/ac8502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 07/28/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored. APPROACH Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e., elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance. MAIN RESULTS Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another thirty-subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network. SIGNIFICANCE Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.
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Affiliation(s)
- Zhiwei Li
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Chanlin Yi
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Chunli Chen
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Chen Liu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Shu Zhang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Shunchang Li
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Dongrui Gao
- Chengdu University of Information Technology, No.24 Block 1, Xuefu Road, Chengdu, Sichuan, 610225, CHINA
| | - Liang Cheng
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Xiabing Zhang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
| | - Junzhi Sun
- Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Ying He
- Small Ball Department of Physical Education and Sport Sciences, Chengdu Sport University, No.2, Tiyuan Road, Wuhou District, Chengdu, 610041, CHINA
| | - Peng Xu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, CHINA
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163
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Wang R, Fan Y, Wu Y, Zang YF, Zhou C. Lifespan associations of resting-state brain functional networks with ADHD symptoms. iScience 2022; 25:104673. [PMID: 35832890 PMCID: PMC9272385 DOI: 10.1016/j.isci.2022.104673] [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: 01/11/2022] [Revised: 04/26/2022] [Accepted: 06/21/2022] [Indexed: 12/04/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks of ADHD patients (n = 97) and healthy controls (HCs, n = 97) across the lifespan (7-50 years). Compared to the linear lifespan associations of brain segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole-brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD and provide potential shared neural bases of distinct ADHD symptoms in children and adults.
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Affiliation(s)
- Rong Wang
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- College of Science, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Yongchen Fan
- College of Science, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Ying Wu
- College of Science, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- Department of Physics, Zhejiang University, Hangzhou 310027, China
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164
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Taylor NL, D'Souza A, Munn BR, Lv J, Zaborszky L, Müller EJ, Wainstein G, Calamante F, Shine JM. Structural connections between the noradrenergic and cholinergic system shape the dynamics of functional brain networks. Neuroimage 2022; 260:119455. [PMID: 35809888 PMCID: PMC10114918 DOI: 10.1016/j.neuroimage.2022.119455] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 10/17/2022] Open
Abstract
Complex cognitive abilities are thought to arise from the ability of the brain to adaptively reconfigure its internal network structure as a function of task demands. Recent work has suggested that this inherent flexibility may in part be conferred by the widespread projections of the ascending arousal systems. While the different components of the ascending arousal system are often studied in isolation, there are anatomical connections between neuromodulatory hubs that we hypothesise are crucial for mediating key features of adaptive network dynamics, such as the balance between integration and segregation. To test this hypothesis, we estimated the strength of structural connectivity between key hubs of the noradrenergic and cholinergic arousal systems (the locus coeruleus [LC] and nucleus basalis of Meynert [nbM], respectively). We then asked whether the strength of structural LC and nbM inter-connectivity was related to individual differences in the emergent, dynamical signatures of functional integration measured from resting state fMRI data, such as network and attractor topography. We observed a significant positive relationship between the strength of white-matter connections between the LC and nbM and the extent of network-level integration following BOLD signal peaks in LC relative to nbM activity. In addition, individuals with denser white-matter streamlines interconnecting neuromodulatory hubs also demonstrated a heightened ability to shift to novel brain states. These results suggest that individuals with stronger structural connectivity between the noradrenergic and cholinergic systems have a greater capacity to mediate the flexible network dynamics required to support complex, adaptive behaviour. Furthermore, our results highlight the underlying static features of the neuromodulatory hubs can impose some constraints on the dynamic features of the brain.
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Affiliation(s)
- N L Taylor
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - A D'Souza
- Brain and Mind Centre, The University of Sydney, Sydney, Australia; Sydney School of Medicine, Central Clinical School, The University of Sydney, Australia
| | - B R Munn
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - J Lv
- Brain and Mind Centre, The University of Sydney, Sydney, Australia; School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - L Zaborszky
- School of Arts and Sciences, Rutgers University, New Jersey, USA
| | - E J Müller
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - G Wainstein
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - F Calamante
- Brain and Mind Centre, The University of Sydney, Sydney, Australia; School of Biomedical Engineering, The University of Sydney, Sydney, Australia; Sydney Imaging, The University of Sydney, Sydney, Australia
| | - J M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, Australia.
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165
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Jensen KHR, McCulloch DEW, Olsen AS, Bruzzone SEP, Larsen SV, Fisher PM, Frokjaer VG. Effects of an Oral Contraceptive on Dynamic Brain States and Network Modularity in a Serial Single-Subject Study. Front Neurosci 2022; 16:855582. [PMID: 35774557 PMCID: PMC9237452 DOI: 10.3389/fnins.2022.855582] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/25/2022] [Indexed: 12/03/2022] Open
Abstract
Hormonal contraceptive drugs are used by adolescent and adult women worldwide. Increasing evidence from human neuroimaging research indicates that oral contraceptives can alter regional functional brain connectivity and brain chemistry. However, questions remain regarding static whole-brain and dynamic network-wise functional connectivity changes. A healthy woman (23 years old) was scanned every day over 30 consecutive days during a naturally occurring menstrual cycle and again a year later while using a combined hormonal contraceptive. Here we calculated graph theory-derived, whole-brain, network-level measures (modularity and system segregation) and global brain connectivity (characteristic path length) as well as dynamic functional brain connectivity using Leading Eigenvector Dynamic Analysis and diametrical clustering. These metrics were calculated for each scan session during the serial sampling periods to compare metrics between the subject's natural and contraceptive cycles. Modularity, system segregation, and characteristic path length were statistically significantly higher across the natural compared to contraceptive cycle scans. We also observed a shift in the prevalence of two discrete brain states when using the contraceptive. Our results suggest a more network-structured brain connectivity architecture during the natural cycle, whereas oral contraceptive use is associated with a generally increased connectivity structure evidenced by lower characteristic path length. The results of this repeated, single-subject analysis allude to the possible effects of oral contraceptives on brain-wide connectivity, which should be evaluated in a cohort to resolve the extent to which these effects generalize across the population and the possible impact of a year-long period between conditions.
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Affiliation(s)
- Kristian Høj Reveles Jensen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Anders Stevnhoved Olsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, DTU Compute, Kongens Lyngby, Denmark
| | - Silvia Elisabetta Portis Bruzzone
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Vinther Larsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Vibe Gedsoe Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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166
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DiNuzzo M, Mascali D, Bussu G, Moraschi M, Guidi M, Macaluso E, Mangia S, Giove F. Hemispheric functional segregation facilitates target detection during sustained visuospatial attention. Hum Brain Mapp 2022; 43:4529-4539. [PMID: 35695003 PMCID: PMC9491284 DOI: 10.1002/hbm.25970] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/05/2022] [Accepted: 05/22/2022] [Indexed: 11/10/2022] Open
Abstract
Visuospatial attention is strongly lateralized, with the right hemisphere commonly exhibiting stronger activation and connectivity patterns than the left hemisphere during attentive processes. However, whether such asymmetry influences inter‐hemispheric information transfer and behavioral performance is not known. Here we used a region of interest (ROI) and network‐based approach to determine steady‐state fMRI functional connectivity (FC) in the whole cerebral cortex during a leftward/rightward covert visuospatial attention task. We found that the global FC topology between either ROIs or networks was independent on the attended side. The side of attention significantly modulated FC strength between brain networks, with leftward attention primarily involving the connections of the right visual network with dorsal and ventral attention networks in both the left and right hemisphere. High hemispheric functional segregation significantly correlated with faster target detection response times (i.e., better performance). Our findings suggest that the dominance of the right hemisphere in visuospatial attention is associated with an hemispheric functional segregation that is beneficial for behavioral performance.
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Affiliation(s)
- Mauro DiNuzzo
- Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy
| | - Daniele Mascali
- Dipartimento di Neuroscienze, Imaging e Scienze Cliniche, Università Gabriele D'Annunzio, Chieti, Italy
| | - Giorgia Bussu
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Marta Moraschi
- Unità Operativa di Radioterapia Oncologica, Università Campus Bio-Medico, Rome, Italy
| | - Maria Guidi
- Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy
| | | | - Silvia Mangia
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Federico Giove
- Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy.,Fondazione Santa Lucia IRCCS, Rome, Italy
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167
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Yoo K, Rosenberg MD, Kwon YH, Lin Q, Avery EW, Sheinost D, Constable RT, Chun MM. A brain-based general measure of attention. Nat Hum Behav 2022; 6:782-795. [PMID: 35241793 PMCID: PMC9232838 DOI: 10.1038/s41562-022-01301-1] [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: 06/17/2021] [Accepted: 01/14/2022] [Indexed: 11/15/2022]
Abstract
Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual's task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.
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Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA,Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Qi Lin
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Dustin Sheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA,Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT, USA. .,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA. .,Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA. .,Wu Tsai Institute, Yale University, New Haven, CT, USA.
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168
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Riedel L, van den Heuvel MP, Markett S. Trajectory of rich club properties in structural brain networks. Hum Brain Mapp 2022; 43:4239-4253. [PMID: 35620874 PMCID: PMC9435005 DOI: 10.1002/hbm.25950] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 11/06/2022] Open
Abstract
Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8066 participants aged 5-80 years. Across all brain regions, structural connectivity strength followed an "inverted-U"-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a "first come, last served" model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.
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Affiliation(s)
- Levin Riedel
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, Germany
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
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169
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Xiang J, Fan C, Wei J, Li Y, Wang B, Niu Y, Yang L, Lv J, Cui X. The Task Pre-Configuration Is Associated With Cognitive Performance Evidence From the Brain Synchrony. Front Comput Neurosci 2022; 16:883660. [PMID: 35603133 PMCID: PMC9120823 DOI: 10.3389/fncom.2022.883660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Although many resting state and task state characteristics have been studied, it is still unclear how the brain network switches from the resting state during tasks. The current theory shows that the brain is a complex dynamic system and synchrony is defined to measure brain activity. The study compared the changes of synchrony between the resting state and different task states in healthy young participants (N = 954). It also examined the ability to switch from the resting state to the task-general architecture of synchrony. We found that the synchrony increased significantly during the tasks. And the results showed that the brain has a task-general architecture of synchrony during different tasks. The main feature of task-based reasoning is that the increase in synchrony of high-order cognitive networks is significant, while the increase in synchrony of sensorimotor networks is relatively low. In addition, the high synchrony of high-order cognitive networks in the resting state can promote task switching effectively and the pre-configured participants have better cognitive performance, which shows that spontaneous brain activity and cognitive ability are closely related. These results revealed changes in the brain network configuration for switching between the resting state and task state, highlighting the consistent changes in the brain network between different tasks. Also, there was an important relationship between the switching ability and the cognitive performance.
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170
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Krendl AC, Betzel RF. Social cognitive network neuroscience. Soc Cogn Affect Neurosci 2022; 17:510-529. [PMID: 35352125 PMCID: PMC9071476 DOI: 10.1093/scan/nsac020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/27/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
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Affiliation(s)
- Anne C Krendl
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
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171
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Jones JS, Astle DE. Segregation and integration of the functional connectome in neurodevelopmentally 'at risk' children. Dev Sci 2022; 25:e13209. [PMID: 34873798 PMCID: PMC7613070 DOI: 10.1111/desc.13209] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/08/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023]
Abstract
Functional connectivity within and between Intrinsic Connectivity Networks (ICNs) transforms over development and is thought to support high order cognitive functions. But how variable is this process, and does it diverge with altered cognitive development? We investigated age-related changes in integration and segregation within and between ICNs in neurodevelopmentally 'at-risk' children, identified by practitioners as experiencing cognitive difficulties in attention, learning, language, or memory. In our analysis we used performance on a battery of 10 cognitive tasks alongside resting-state functional magnetic resonance imaging in 175 at-risk children and 62 comparison children aged 5-16. We observed significant age-by-group interactions in functional connectivity between two network pairs. Integration between the ventral attention and visual networks and segregation of the limbic and fronto-parietal networks increased with age in our comparison sample, relative to at-risk children. Furthermore, functional connectivity between the ventral attention and visual networks in comparison children significantly mediated age-related improvements in executive function, compared to at-risk children. We conclude that integration between ICNs show divergent neurodevelopmental trends in the broad population of children experiencing cognitive difficulties, and that these differences in functional brain organisation may partly explain the pervasive cognitive difficulties within this group over childhood and adolescence.
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Affiliation(s)
- Jonathan S Jones
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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172
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Jun S, Alderson TH, Altmann A, Sadaghiani S. Dynamic trajectories of connectome state transitions are heritable. Neuroimage 2022; 256:119274. [PMID: 35504564 PMCID: PMC9223440 DOI: 10.1016/j.neuroimage.2022.119274] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 04/23/2022] [Accepted: 04/29/2022] [Indexed: 11/09/2022] Open
Abstract
The brain’s functional connectome is dynamic, constantly reconfiguring in an individual-specific manner. However, which characteristics of such reconfigurations are subject to genetic effects, and to what extent, is largely unknown. Here, we identified heritable dynamic features, quantified their heritability, and determined their association with cognitive phenotypes. In resting-state fMRI, we obtained multivariate features, each describing a temporal or spatial characteristic of connectome dynamics jointly over a set of connectome states. We found strong evidence for heritability of temporal features, particularly, Fractional Occupancy (FO) and Transition Probability (TP), representing the duration spent in each connectivity configuration and the frequency of shifting between configurations, respectively. These effects were robust against methodological choices of number of states and global signal regression. Genetic effects explained a substantial proportion of phenotypic variance of these features (h2 = 0.39, 95% CI = [.24,.54] for FO; h2 = 0. 43, 95% CI = [.29,.57] for TP). Moreover, these temporal phenotypes were associated with cognitive performance. Contrarily, we found no robust evidence for heritability of spatial features of the dynamic states (i.e., states’ Modularity and connectivity pattern). Genetic effects may therefore primarily contribute to how the connectome transitions across states, rather than the precise spatial instantiation of the states in individuals. In sum, genetic effects impact the dynamic trajectory of state transitions (captured by FO and TP), and such temporal features may act as endophenotypes for cognitive abilities.
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Affiliation(s)
- Suhnyoung Jun
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 618201; Psychology Department, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Thomas H Alderson
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 618201
| | - Andre Altmann
- Centre for Medical Image Computing (CMIC), Department of Medical Physics, University College London, London, UK
| | - Sepideh Sadaghiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 618201; Psychology Department, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801; Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801.
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173
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Chen J, Tam A, Kebets V, Orban C, Ooi LQR, Asplund CL, Marek S, Dosenbach NUF, Eickhoff SB, Bzdok D, Holmes AJ, Yeo BTT. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nat Commun 2022; 13:2217. [PMID: 35468875 PMCID: PMC9038754 DOI: 10.1038/s41467-022-29766-8] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 03/18/2022] [Indexed: 12/30/2022] Open
Abstract
How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.
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Affiliation(s)
- Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Christopher L Asplund
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore.,Division of Social Sciences, Yale-NUS College, Singapore, Singapore.,Department of Psychology, National University of Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA.,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviours (INM-7), Research Center Jülich, Jülich, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore. .,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore. .,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore. .,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore. .,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore. .,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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174
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Trujillo JP, Özyürek A, Kan CC, Sheftel-Simanova I, Bekkering H. Differences in functional brain organization during gesture recognition between autistic and neurotypical individuals. Soc Cogn Affect Neurosci 2022; 17:1021-1034. [PMID: 35428885 PMCID: PMC9629468 DOI: 10.1093/scan/nsac026] [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: 09/17/2021] [Revised: 03/19/2022] [Accepted: 04/15/2022] [Indexed: 01/12/2023] Open
Abstract
Persons with and without autism process sensory information differently. Differences in sensory processing are directly relevant to social functioning and communicative abilities, which are known to be hampered in persons with autism. We collected functional magnetic resonance imaging data from 25 autistic individuals and 25 neurotypical individuals while they performed a silent gesture recognition task. We exploited brain network topology, a holistic quantification of how networks within the brain are organized to provide new insights into how visual communicative signals are processed in autistic and neurotypical individuals. Performing graph theoretical analysis, we calculated two network properties of the action observation network: 'local efficiency', as a measure of network segregation, and 'global efficiency', as a measure of network integration. We found that persons with autism and neurotypical persons differ in how the action observation network is organized. Persons with autism utilize a more clustered, local-processing-oriented network configuration (i.e. higher local efficiency) rather than the more integrative network organization seen in neurotypicals (i.e. higher global efficiency). These results shed new light on the complex interplay between social and sensory processing in autism.
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Affiliation(s)
- James P Trujillo
- Correspondence should be addressed to James P. Trujillo, Radboud University, Donders Centre for Cognition, Maria Montessori Building, Thomas van Aquinostraat 4, Nijmegen 6525 GD, The Netherlands. E-mail:
| | - Asli Özyürek
- Donders Institute for Brain, Cognition, and Behavior, Donders Centre for Cognition, Nijmegen, GD 6525, The Netherlands,Max Planck Institute for Psycholinguistics, Nijmegen, XD 6525, The Netherlands
| | - Cornelis C Kan
- Department of Psychiatry, Radboud University Medical Centre, Radboudumc, Nijmegen, GA 6525, The Netherlands
| | - Irina Sheftel-Simanova
- One Planet Research Centre, Radboud University Medical Centre, Radboudumc, Nijmegen, GA 6525, The Netherlands
| | - Harold Bekkering
- Donders Institute for Brain, Cognition, and Behavior, Donders Centre for Cognition, Nijmegen, GD 6525, The Netherlands
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175
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Kim WSH, Luciw NJ, Atwi S, Shirzadi Z, Dolui S, Detre JA, Nasrallah IM, Swardfager W, Bryan RN, Launer LJ, MacIntosh BJ. Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy. Hum Brain Mapp 2022; 43:3680-3693. [PMID: 35429100 PMCID: PMC9294299 DOI: 10.1002/hbm.25876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 12/02/2022] Open
Abstract
White matter hyperintensities (WMHs) are emblematic of cerebral small vessel disease, yet effects on the brain have not been well characterized at midlife. Here, we investigated whether WMH volume is associated with brain network alterations in midlife adults. Two hundred and fifty‐four participants from the Coronary Artery Risk Development in Young Adults study were selected and stratified by WMH burden into Lo‐WMH (mean age = 50 ± 3.5 years) and Hi‐WMH (mean age = 51 ± 3.7 years) groups of equal size. We constructed group‐level covariance networks based on cerebral blood flow (CBF) and gray matter volume (GMV) maps across 74 gray matter regions. Through consensus clustering, we found that both CBF and GMV covariance networks partitioned into modules that were largely consistent between groups. Next, CBF and GMV covariance network topologies were compared between Lo‐ and Hi‐WMH groups at global (clustering coefficient, characteristic path length, global efficiency) and regional (degree, betweenness centrality, local efficiency) levels. At the global level, there were no between‐group differences in either CBF or GMV covariance networks. In contrast, we found between‐group differences in the regional degree, betweenness centrality, and local efficiency of several brain regions in both CBF and GMV covariance networks. Overall, CBF and GMV covariance analyses provide evidence that WMH‐related network alterations are present at midlife.
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Affiliation(s)
- William S. H. Kim
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Nicholas J. Luciw
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Sarah Atwi
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Zahra Shirzadi
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Sudipto Dolui
- Center for Functional Neuroimaging University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Neurology University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA
| | - John A. Detre
- Center for Functional Neuroimaging University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Neurology University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA
| | - Ilya M. Nasrallah
- Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA
| | - Walter Swardfager
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
- Canadian Partnership for Stroke Recovery Sunnybrook Research Institute Toronto Ontario Canada
- Department of Pharmacology and Toxicology University of Toronto Toronto Ontario Canada
- Toronto Rehabilitation Institute, University Health Network Toronto Ontario Canada
- Dr. Sandra Black Centre for Brain Resilience & Recovery Sunnybrook Research Institute Toronto Ontario Canada
| | - Robert Nick Bryan
- Department of Diagnostic Medicine University of Texas Austin Texas USA
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Science National Institute on Aging Bethesda Maryland USA
| | - Bradley J. MacIntosh
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
- Canadian Partnership for Stroke Recovery Sunnybrook Research Institute Toronto Ontario Canada
- Dr. Sandra Black Centre for Brain Resilience & Recovery Sunnybrook Research Institute Toronto Ontario Canada
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176
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Cortico-subcortical interactions in overlapping communities of edge functional connectivity. Neuroimage 2022; 250:118971. [PMID: 35131435 PMCID: PMC9903436 DOI: 10.1016/j.neuroimage.2022.118971] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/25/2022] [Accepted: 02/03/2022] [Indexed: 02/01/2023] Open
Abstract
Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.
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177
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Williams KA, Numssen O, Hartwigsen G. Task-specific network interactions across key cognitive domains. Cereb Cortex 2022; 32:5050-5071. [PMID: 35158372 PMCID: PMC9667178 DOI: 10.1093/cercor/bhab531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 12/27/2022] Open
Abstract
Human cognition is organized in distributed networks in the brain. Although distinct specialized networks have been identified for different cognitive functions, previous work also emphasizes the overlap of key cognitive domains in higher level association areas. The majority of previous studies focused on network overlap and dissociation during resting states whereas task-related network interactions across cognitive domains remain largely unexplored. A better understanding of network overlap and dissociation during different cognitive tasks may elucidate flexible (re-)distribution of resources during human cognition. The present study addresses this issue by providing a broad characterization of large-scale network dynamics in three key cognitive domains. Combining prototypical tasks of the larger domains of attention, language, and social cognition with whole-brain multivariate activity and connectivity approaches, we provide a spatiotemporal characterization of multiple large-scale, overlapping networks that differentially interact across cognitive domains. We show that network activity and interactions increase with increased cognitive complexity across domains. Interaction patterns reveal a common core structure across domains as well as dissociable domain-specific network activity. The observed patterns of activation and deactivation of overlapping and strongly coupled networks provide insight beyond region-specific activity within a particular cognitive domain toward a network perspective approach across diverse key cognitive functions.
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Affiliation(s)
- Kathleen A Williams
- Address correspondence to Kathleen A. Williams, Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.
| | - Ole Numssen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
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178
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Maliske L, Kanske P. The Social Connectome - Moving Toward Complexity in the Study of Brain Networks and Their Interactions in Social Cognitive and Affective Neuroscience. Front Psychiatry 2022; 13:845492. [PMID: 35449570 PMCID: PMC9016142 DOI: 10.3389/fpsyt.2022.845492] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.
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Affiliation(s)
- Lara Maliske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
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179
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Saberi A, Aldenkamp AP, Kurniawan NA, Bouten CVC. In-vitro engineered human cerebral tissues mimic pathological circuit disturbances in 3D. Commun Biol 2022; 5:254. [PMID: 35322168 PMCID: PMC8943047 DOI: 10.1038/s42003-022-03203-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/01/2022] [Indexed: 12/30/2022] Open
Abstract
In-vitro modeling of brain network disorders such as epilepsy remains a major challenge. A critical step is to develop an experimental approach that enables recapitulation of in-vivo-like three-dimensional functional complexity while allowing local modulation of the neuronal networks. Here, by promoting matrix-supported active cell reaggregation, we engineered multiregional cerebral tissues with intact 3D neuronal networks and functional interconnectivity characteristic of brain networks. Furthermore, using a multi-chambered tissue-culture chip, we show that our separated but interconnected cerebral tissues can mimic neuropathological signatures such as the propagation of epileptiform discharges. A method is developed to engineer cerebral tissues with intact 3D neuronal networks, mimicking neuropathological signatures such as the propagation of epileptiform discharges, using a multi-chambered tissue culture chip.
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Affiliation(s)
- Aref Saberi
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands. .,Institute for Complex Molecular Systems, Eindhoven, the Netherlands.
| | - Albert P Aldenkamp
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands.,Department of Behavioral Sciences, Epilepsy Center Kempenhaeghe, Heeze, the Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Nicholas A Kurniawan
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands. .,Institute for Complex Molecular Systems, Eindhoven, the Netherlands.
| | - Carlijn V C Bouten
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Institute for Complex Molecular Systems, Eindhoven, the Netherlands
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180
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The overlapping modular organization of human brain functional networks across the adult lifespan. Neuroimage 2022; 253:119125. [PMID: 35331872 DOI: 10.1016/j.neuroimage.2022.119125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 03/02/2022] [Accepted: 03/19/2022] [Indexed: 01/06/2023] Open
Abstract
Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.
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181
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Coppola P, Spindler LRB, Luppi AI, Adapa R, Naci L, Allanson J, Finoia P, Williams GB, Pickard JD, Owen AM, Menon DK, Stamatakis EA. Network dynamics scale with levels of awareness. Neuroimage 2022; 254:119128. [PMID: 35331869 DOI: 10.1016/j.neuroimage.2022.119128] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 02/10/2022] [Accepted: 03/20/2022] [Indexed: 02/04/2023] Open
Abstract
Small world topologies are thought to provide a valuable insight into human brain organisation and consciousness. However, functional magnetic resonance imaging studies in consciousness have not yielded consistent results. Given the importance of dynamics for both consciousness and cognition, here we investigate how the diversity of small world dynamics (quantified by sample entropy; dSW-E1) scales with decreasing levels of awareness (i.e., sedation and disorders of consciousness). Paying particular attention to result reproducibility, we show that dSW-E is a consistent predictor of levels of awareness even when controlling for the underlying functional connectivity dynamics. We find that dSW-E of subcortical and cortical areas are predictive, with the former showing higher and more robust effect sizes across analyses. We find that the network dynamics of intermodular communication in the cerebellum also have unique predictive power for levels of awareness. Consequently, we propose that the dynamic reorganisation of the functional information architecture, in particular of the subcortex, is a characteristic that emerges with awareness and has explanatory power beyond that of the complexity of dynamic functional connectivity.
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Affiliation(s)
- Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Lennart R B Spindler
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Ram Adapa
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Hills Rd., Cambridge, CB2 0QQ, UK
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - Adrian M Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, University of Western Ontario, London, ON N6A 5B7, Canada
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK.
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182
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Chen Q, Turnbull A, Cole M, Zhang Z, Lin FV. Enhancing Cortical Network-level Participation Coefficient as a Potential Mechanism for Transfer in Cognitive Training in aMCI. Neuroimage 2022; 254:119124. [PMID: 35331866 PMCID: PMC9199485 DOI: 10.1016/j.neuroimage.2022.119124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/19/2022] [Indexed: 02/06/2023] Open
Abstract
Effective cognitive training must improve cognition beyond the trained domain (show a transfer effect) and be applicable to dementia-risk populations, e.g., amnesic mild cognitive impairment (aMCI). Theories suggest training should target processes that 1) show robust engagement, 2) are domain-general, and 3) reflect long-lasting changes in brain organization. Brain regions that connect to many different networks (i.e., show high participation coefficient; PC) are known to support integration. This capacity is 1) relatively preserved in aMCI, 2) required across a wide range of cognitive domains, and 3) trait-like. In 49 individuals with aMCI that completed a 6-week visual speed of processing training (VSOP) and 28 active controls, enhancement in PC was significantly more related to transfer to working memory at global and network levels in VSOP compared to controls, particularly in networks with many high-PC nodes. This suggests that enhancing brain integration may provide a target for developing effective cognitive training.
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Affiliation(s)
- Quanjing Chen
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, United States
| | - Adam Turnbull
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, United States; School of Nursing, University of Rochester, United States.
| | - Martin Cole
- Department of Biostatics and Computational Biology, University of Rochester, United States
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, UNC-Chapel Hill, United States
| | - Feng V Lin
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, United States; The Wu Tsai Neuroscience Institute, Stanford University, University of Rochester, United States
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183
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Peng Y, Li C, Chen Q, Zhu Y, Sun L. Functional Connectivity Analysis and Detection of Mental Fatigue Induced by Different Tasks Using Functional Near-Infrared Spectroscopy. Front Neurosci 2022; 15:771056. [PMID: 35368967 PMCID: PMC8964790 DOI: 10.3389/fnins.2021.771056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives The objective of this study was to investigate common functional near-infrared spectroscopy (fNIRS) features of mental fatigue induced by different tasks. In addition to distinguishing fatigue from non-fatigue state, the early signs of fatigue were also studied so as to give an early warning of fatigue. Methods fNIRS data from 36 participants were used to investigate the common character of functional connectivity network corresponding to mental fatigue, which was induced by psychomotor vigilance test (PVT), cognitive work, or simulated driving. To analyze the network reorganizations quantitatively, clustering coefficient, characteristic path length, and small worldness were calculated in five sub-bands (0.6-2.0, 0.145-0.600, 0.052-0.145, 0.021-0.052, and 0.005-0.021 Hz). Moreover, we applied a random forest method to classify three fatigue states. Results In a moderate fatigue state: the functional connectivity strength between brain regions increased overall in 0.021-0.052 Hz, and an asymmetrical pattern of connectivity (right hemisphere > left hemisphere) was presented. In 0.052-0.145 Hz, the connectivity strength decreased overall, the clustering coefficient decreased, and the characteristic path length increased significantly. In severe fatigue state: in 0.021-0.052 Hz, the brain network began to deviate from a small-world pattern. The classification accuracy of fatigue and non-fatigue was 85.4%. The classification accuracy of moderate fatigue and severe fatigue was 82.8%. Conclusion The preliminary research demonstrates the feasibility of detecting mental fatigue induced by different tasks, by applying the functional network features of cerebral hemoglobin signal. This universal and robust method has the potential to detect early signs of mental fatigue and prevent relative human error in various working environments.
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Affiliation(s)
- Yaoxing Peng
- The Key Laboratory of Robotics System of Jiangsu Province School of Mechanical Electric Engineering Soochow University, Suzhou, China
| | - Chunguang Li
- The Key Laboratory of Robotics System of Jiangsu Province School of Mechanical Electric Engineering Soochow University, Suzhou, China
| | - Qu Chen
- Mathematics Teaching and Research Section, Basic Course Department, Communication Sergeant School of Army Engineering University, Chongqing, China
| | - Yufei Zhu
- The Key Laboratory of Robotics System of Jiangsu Province School of Mechanical Electric Engineering Soochow University, Suzhou, China
| | - Lining Sun
- The Key Laboratory of Robotics System of Jiangsu Province School of Mechanical Electric Engineering Soochow University, Suzhou, China
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184
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Shorten DP, Priesemann V, Wibral M, Lizier JT. Early lock-in of structured and specialised information flows during neural development. eLife 2022; 11:74651. [PMID: 35286256 PMCID: PMC9064303 DOI: 10.7554/elife.74651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/13/2022] [Indexed: 11/13/2022] Open
Abstract
The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for spiking data. Here, we leverage recent advances in this estimation task in order to quantify the changes in transfer entropy during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows exhibits a tendency to lock-in at the point when they arise. We also characterise the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes tend to undertake specialised computational roles as either transmitters, mediators, or receivers of information, with these roles tending to align with their average spike ordering. Further, we find that these roles are regularly locked-in when the information flows are established. Finally, we compare these results to information flows in a model network developing according to a spike-timing-dependent plasticity learning rule. Similar temporal patterns in the development of information flows were observed in these networks, hinting at the broader generality of these phenomena.
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Affiliation(s)
- David P Shorten
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Michael Wibral
- Campus Institute for Dynamics of Biological Networks, Georg August University, Göttingen, Germany
| | - Joseph T Lizier
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
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185
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Gupta S, Lim M, Rajapakse JC. Decoding task specific and task general functional architectures of the brain. Hum Brain Mapp 2022; 43:2801-2816. [PMID: 35224817 PMCID: PMC9120557 DOI: 10.1002/hbm.25817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 02/01/2022] [Accepted: 02/13/2022] [Indexed: 11/06/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the brain have been classified as task specific and task general, depending on whether they are particular to a task or common across multiple tasks. Using recent attempts in interpreting deep learning models, we propose an approach to determine both task specific and task general architectures of the functional brain. We demonstrate our methods with a reference‐based decoder on deep learning classifiers trained on 12,500 rest and task fMRI samples from the Human Connectome Project (HCP). The decoded task general and task specific motor and language architectures were validated with findings from previous studies. We found that unlike intersubject variability that is characteristic of functional pathology of neurological diseases, a small set of connections are sufficient to delineate the rest and task states. The nodes and connections in the task general architecture could serve as potential disease biomarkers as alterations in task general brain modulations are known to be implicated in several neuropsychiatric disorders.
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Affiliation(s)
- Sukrit Gupta
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Marcus Lim
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Jagath C. Rajapakse
- School of Computer Science and Engineering Nanyang Technological University Singapore
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186
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Matar E, Ehgoetz Martens KA, Phillips JR, Wainstein G, Halliday GM, Lewis SJG, Shine JM. Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia. NPJ Parkinsons Dis 2022; 8:16. [PMID: 35177652 PMCID: PMC8854384 DOI: 10.1038/s41531-022-00279-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 01/12/2022] [Indexed: 11/10/2022] Open
Abstract
Cognitive fluctuations are a characteristic and distressing disturbance of attention and consciousness seen in patients with Dementia with Lewy bodies and Parkinson's disease dementia. It has been proposed that fluctuations result from disruption of key neuromodulatory systems supporting states of attention and wakefulness which are normally characterised by temporally variable and highly integrated functional network architectures. In this study, patients with DLB (n = 25) and age-matched controls (n = 49) were assessed using dynamic resting state fMRI. A dynamic network signature of reduced temporal variability and integration was identified in DLB patients compared to controls. Reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task. A less integrated (more segregated) functional network architecture was seen in DLB patients compared to the control group, with regions of reduced integration observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Reduced network integration correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments significantly matched areas of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Correlating topological measures with maps of neurotransmitter/neuromodulator receptor gene expression, we found that regions of reduced integration and unstable modular assignments correlated significantly with the pattern of expression of subclasses of noradrenergic and cholinergic receptors across the cerebral cortex. Altogether, these findings demonstrate that cognitive fluctuations are associated with an imaging signature of dynamic network impairment linked to specific neurotransmitters/neuromodulators within the ascending arousal system, highlighting novel potential diagnostic and therapeutic approaches for this troubling symptom.
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Affiliation(s)
- Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia. .,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia. .,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
| | - Kaylena A Ehgoetz Martens
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada
| | - Joseph R Phillips
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,School of Social Sciences and Psychology, Western Sydney University, Sydney, NSW, Australia
| | - Gabriel Wainstein
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Centro de Investigaciones Médicas, Pontifical Catholic University of Chile, Santiago, Chile
| | - Glenda M Halliday
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Simon J G Lewis
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
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187
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Chen Y, Bukhari Q, Lin TW, Sejnowski TJ. Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times. Netw Neurosci 2022; 6:614-633. [PMID: 35733425 PMCID: PMC9207998 DOI: 10.1162/netn_a_00239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 02/10/2022] [Indexed: 11/04/2022] Open
Abstract
Abstract
Recordings from resting state functional Magnetic Resonance Imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of “ground truth” has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. When we applied dCov to rs-fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose dCov-FCs were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration significantly correlated with behavior.
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Affiliation(s)
- Yusi Chen
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA, USA
- Division of Biological Studies, University of California San Diego, La Jolla, CA, USA
| | - Qasim Bukhari
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tiger W. Lin
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA, USA
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Terrence J. Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA, USA
- Division of Biological Studies, University of California San Diego, La Jolla, CA, USA
- Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
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188
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Improving Functional Connectivity in Developmental Dyslexia through Combined Neurofeedback and Visual Training. Symmetry (Basel) 2022. [DOI: 10.3390/sym14020369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study examined the effects of combined neurofeedback (NF) and visual training (VT) on children with developmental dyslexia (DD). Although NF is the first noninvasive approach to support neurological disorders, the mechanisms of its effects on the brain functional connectivity are still unclear. A key question is whether the functional connectivities of the EEG frequency networks change after the combined NF–VT training of DD children (postD). NF sessions of voluntary α/θ rhythm control were applied in a low-spatial-frequency (LSF) illusion contrast discrimination, which provides feedback with visual cues to improve the brain signals and cognitive abilities in DD children. The measures of connectivity, which are defined by small-world propensity, were sensitive to the properties of the brain electrical oscillations in the quantitative EEG-NF training. In the high-contrast LSF illusion, the z-NF reduced the α/θ scores in the frontal areas, and in the right ventral temporal, occipital–temporal, and middle occipital areas in the postD (vs. the preD) because of their suppression in the local hub θ-network and the altered global characteristics of the functional θ-frequency network. In the low-contrast condition, the z-NF stimulated increases in the α/θ scores, which induced hubs in the left-side α-frequency network of the postD, and changes in the global characteristics of the functional α-frequency network. Because of the anterior, superior, and middle temporal deficits affecting the ventral and occipital–temporal pathways, the z-NF–VT compensated for the more ventral brain regions, mainly in the left hemispheres of the postD group in the low-contrast LSF illusion. Compared to pretraining, the NF–VT increased the segregation of the α, β (low-contrast), and θ networks (high-contrast), as well as the γ2-network integration (both contrasts) after the termination of the training of the children with developmental dyslexia. The remediation compensated more for the dorsal (prefrontal, premotor, occipital–parietal connectivities) dysfunction of the θ network in the developmental dyslexia in the high-contrast LSF illusion. Our findings provide neurobehavioral evidence for the exquisite brain functional plasticity and direct effect of NF–VT on cognitive disabilities in DD children.
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189
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Gutierrez-Barragan D, Singh NA, Alvino FG, Coletta L, Rocchi F, De Guzman E, Galbusera A, Uboldi M, Panzeri S, Gozzi A. Unique spatiotemporal fMRI dynamics in the awake mouse brain. Curr Biol 2022; 32:631-644.e6. [PMID: 34998465 PMCID: PMC8837277 DOI: 10.1016/j.cub.2021.12.015] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 12/15/2022]
Abstract
Human imaging studies have shown that spontaneous brain activity exhibits stereotypic spatiotemporal reorganization in awake, conscious conditions with respect to minimally conscious states. However, whether and how this phenomenon can be generalized to lower mammalian species remains unclear. Leveraging a robust protocol for resting-state fMRI (rsfMRI) mapping in non-anesthetized, head-fixed mice, we investigated functional network topography and dynamic structure of spontaneous brain activity in wakeful animals. We found that rsfMRI networks in the awake state, while anatomically comparable to those observed under anesthesia, are topologically configured to maximize interregional communication, departing from the underlying community structure of the mouse axonal connectome. We further report that rsfMRI activity in wakeful animals exhibits unique spatiotemporal dynamics characterized by a state-dependent, dominant occurrence of coactivation patterns encompassing a prominent participation of arousal-related forebrain nuclei and functional anti-coordination between visual-auditory and polymodal cortical areas. We finally show that rsfMRI dynamics in awake mice exhibits a stereotypical temporal structure, in which state-dominant coactivation patterns are configured as network attractors. These findings suggest that spontaneous brain activity in awake mice is critically shaped by state-specific involvement of basal forebrain arousal systems and document that its dynamic structure recapitulates distinctive, evolutionarily relevant principles that are predictive of conscious states in higher mammalian species. fMRI networks in awake mice depart from underlying anatomical structure fMRI dynamics in wakeful mice is critically shaped by arousal-related nuclei Occurrence and topography of rsfMRI coactivation patterns define conscious states fMRI coactivation dynamics defines a signature of consciousness in the mouse brain
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Affiliation(s)
- Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Neha Atulkumar Singh
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Filomena Grazia Alvino
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy; Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - Federico Rocchi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy; Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - Elizabeth De Guzman
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alberto Galbusera
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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190
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Cong S, Yao X, Xie L, Yan J, Shen L. Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts. Front Genet 2022; 12:782953. [PMID: 35237294 PMCID: PMC8884108 DOI: 10.3389/fgene.2021.782953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.
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Affiliation(s)
- Shan Cong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Linhui Xie
- Department of Electrical and Computer Engineering, School of Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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191
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Alvarez GM, Rudolph MD, Cohen JR, Muscatell KA. Lower Socioeconomic Position Is Associated with Greater Activity in and Integration within an Allostatic-Interoceptive Brain Network in Response to Affective Stimuli. J Cogn Neurosci 2022; 34:1906-1927. [PMID: 35139207 DOI: 10.1162/jocn_a_01830] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Socioeconomic inequities shape physical health and emotional well-being. As such, recent work has examined the neural mechanisms through which socioeconomic position (SEP) may influence health. However, there remain critical gaps in knowledge regarding the relationships between SEP and brain function. These gaps include a lack of research on: (1) the association between SEP and brain functioning in later life, (2) relationships between SEP and functioning of the whole brain beyond specific regions of interest, and (3) how neural responses to positive affective stimuli differ by SEP. The current study addressed these gaps by examining the association between SEP (i.e., education, income) and neural responses to affective stimuli among 122 mid- to late-life adults. During MRI scanning, participants viewed 30 positive, 30 negative, and 30 neutral images; activation and network connectivity analyses explored associations between SEP and neural responses to these affective stimuli. Analyses revealed that those with lower SEP showed greater neural activity to both positive and negative images in regions within the allostatic-interoceptive network, a system of regions implicated in representing and regulating physiological states of the body and the external environment. There were no positive associations between SEP and neural responses to negative or positive images. In addition, graph-theory network analyses showed that individuals with lower SEP demonstrated greater global efficiency within the allostatic-interoceptive network and executive control network, across all task conditions. The findings suggest that lower SEP is associated with enhanced neural sensitivity to affective cues that may be metabolically costly to maintain over time and suggest a mechanism by which SEP might get "under the skull" to influence mental and physical well-being.
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Affiliation(s)
| | | | - Jessica R Cohen
- University of North Carolina at Chapel Hill.,Carolina Institute for Developmental Disabilities, Carrboro, NC
| | - Keely A Muscatell
- University of North Carolina at Chapel Hill.,Carolina Institute for Developmental Disabilities, Carrboro, NC
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192
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Gu S, Fotiadis P, Parkes L, Xia CH, Gur RC, Gur RE, Roalf DR, Satterthwaite TD, Bassett DS. Network controllability mediates the relationship between rigid structure and flexible dynamics. Netw Neurosci 2022; 6:275-297. [PMID: 36605890 PMCID: PMC9810281 DOI: 10.1162/netn_a_00225] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 12/15/2021] [Indexed: 01/07/2023] Open
Abstract
Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid interareal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain's structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region's functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes' boundary controllability, suggesting that a region's strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.
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Affiliation(s)
- Shi Gu
- Brain and Intelligence Group, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Cedric H. Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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193
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A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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194
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von Schwanenflug N, Krohn S, Heine J, Paul F, Prüss H, Finke C. State-dependent signatures of anti- N-methyl-d-aspartate receptor encephalitis. Brain Commun 2022; 4:fcab298. [PMID: 35169701 PMCID: PMC8833311 DOI: 10.1093/braincomms/fcab298] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/13/2021] [Accepted: 01/03/2022] [Indexed: 11/21/2022] Open
Abstract
Traditional static functional connectivity analyses have shown distinct functional network alterations in patients with anti-N-methyl-d-aspartate receptor encephalitis. Here, we use a dynamic functional connectivity approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in anti-N-methyl-d-aspartate receptor encephalitis and assess the discriminatory power of functional brain states in a supervised classification approach. We included resting-state functional magnetic resonance imaging data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in functional connectivity, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. Compared to controls, patients exhibited diverging dynamic functional connectivity patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic functional connectivity measures were associated with disease severity and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic functional connectivity models and outperformed static analyses, reaching up to 78.6% classification accuracy. By applying time-resolved analyses, we disentangle state-specific functional connectivity impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on the functional reorganization underlying anti-N-methyl-d-aspartate receptor encephalitis. Finally, the correlation of dynamic functional connectivity measures with disease symptoms and severity demonstrates a clinical relevance of spatiotemporal connectivity dynamics in anti-N-methyl-d-aspartate receptor encephalitis.
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Affiliation(s)
- Nina von Schwanenflug
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stephan Krohn
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Josephine Heine
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Friedemann Paul
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité—Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Clinical Research Center, Charité—Universitätsmedizin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Harald Prüss
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Centre for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Carsten Finke
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
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195
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Individualized event structure drives individual differences in whole-brain functional connectivity. Neuroimage 2022; 252:118993. [DOI: 10.1016/j.neuroimage.2022.118993] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/25/2021] [Accepted: 02/10/2022] [Indexed: 01/04/2023] Open
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196
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Elsheikh SSM, Chimusa ER, Mulder NJ, Crimi A. Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease. Front Hum Neurosci 2022; 15:761424. [PMID: 35002653 PMCID: PMC8734427 DOI: 10.3389/fnhum.2021.761424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/25/2021] [Indexed: 01/01/2023] Open
Abstract
Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations.
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Affiliation(s)
- Samar S M Elsheikh
- Pharmacogenetic Research Clinic, Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada.,Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | | | - Nicola J Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Alessandro Crimi
- Computer Vision Group, Sano Centre for Computational Medicine, Kraków, Poland.,Institute for Neuropathology, University Hospital of Zurich, Zurich, Switzerland.,Department of Mathematics, African Institute for Mathematical Sciences, Cape Coast, Ghana
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197
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Focal neural perturbations reshape low-dimensional trajectories of brain activity supporting cognitive performance. Nat Commun 2022; 13:4. [PMID: 35013147 PMCID: PMC8749005 DOI: 10.1038/s41467-021-26978-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/26/2021] [Indexed: 11/11/2022] Open
Abstract
The emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain's low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.
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198
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Kastrati G, Thompson WH, Schiffler B, Fransson P, Jensen KB. Brain Network Segregation and Integration during Painful Thermal Stimulation. Cereb Cortex 2022; 32:4039-4049. [PMID: 34997959 PMCID: PMC9476629 DOI: 10.1093/cercor/bhab464] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 11/13/2022] Open
Abstract
The present study aimed to determine changes in brain network integration/segregation during thermal pain using methods optimized for network connectivity events with high temporal resolution. Participants (n = 33) actively judged whether thermal stimuli applied to the volar forearm were painful or not and then rated the warmth/pain intensity after each trial. We show that the temporal evolution of integration/segregation within trials correlates with the subjective ratings of pain. Specifically, the brain shifts from a segregated state to an integrated state when processing painful stimuli. The association with subjective pain ratings occurred at different time points for all networks. However, the degree of association between ratings and integration/segregation vanished for several brain networks when time-varying functional connectivity was measured at lower temporal resolution. Moreover, the increased integration associated with pain is explained to some degree by relative increases in between-network connectivity. Our results highlight the importance of investigating the relationship between pain and brain network connectivity at a single time point scale, since commonly used temporal aggregations of connectivity data may result in that fine-scale changes in network connectivity may go unnoticed. The interplay between integration/segregation reflects shifting demands of information processing between brain networks and this adaptation occurs both for cognitive tasks and nociceptive processing.
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Affiliation(s)
- Gránit Kastrati
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - William H Thompson
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - Björn Schiffler
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - Karin B Jensen
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
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199
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Muscatell KA, Merritt CC, Cohen JR, Chang L, Lindquist KA. The Stressed Brain: Neural Underpinnings of Social Stress Processing in Humans. Curr Top Behav Neurosci 2022; 54:373-392. [PMID: 34796448 DOI: 10.1007/7854_2021_281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
As humans, we face a variety of social stressors on a regular basis. Given the established role of social stress in influencing physical and psychological functioning, researchers have focused immense efforts on understanding the psychological and physiological changes induced by exposure to acute social stressors. With the advancement of functional magnetic resonance imaging (fMRI), more recent work has sought to identify the neural correlates of processing acute social stress. In this review, we provide an overview of research on the neural underpinnings of social stress processing to date. Specifically, we summarize research that has examined the neural underpinnings of three types of social stressors commonly studied in the literature: social rejection, social evaluation, and racism-related stress. Within our discussion of each type of social stressor, we describe the methods used to induce stress, the brain regions commonly activated among studies investigating that type of stress, and recommendations for future work. This review of the current literature identifies activity in midline regions in both prefrontal and parietal cortices, as well as lateral prefrontal regions, as being associated with processing social rejection. Activity in the insula, thalamus, and inferior frontal gyrus is often found in studies using social evaluation tasks. Finally, racism-related stress is associated with activity in the ventrolateral prefrontal cortex and rostral anterior cingulate cortex. We conclude by taking a "30,000-foot view" of this area of research to provide suggestions for the future of research on the neuroscience of social stress.
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Affiliation(s)
| | | | - Jessica R Cohen
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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200
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The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology 2022; 47:90-103. [PMID: 34408276 PMCID: PMC8616903 DOI: 10.1038/s41386-021-01152-w] [Citation(s) in RCA: 167] [Impact Index Per Article: 83.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 01/03/2023]
Abstract
Systems neuroscience approaches with a focus on large-scale brain organization and network analysis are advancing foundational knowledge of how cognitive control processes are implemented in the brain. Over the past decade, technological and computational innovations in the study of brain connectivity have led to advances in our understanding of how brain networks function, inspiring new conceptualizations of the role of prefrontal cortex (PFC) networks in the coordination of cognitive control. In this review, we describe six key PFC networks involved in cognitive control and elucidate key principles relevant for understanding how these networks implement cognitive control. Implementation of cognitive control in a constantly changing environment depends on the dynamic and flexible organization of PFC networks. In this context, we describe major empirical and theoretical models that have emerged in recent years and describe how their functional architecture and dynamic organization supports flexible cognitive control. We take an overarching view of advances made in the past few decades and consider fundamental issues regarding PFC network function, global brain dynamics, and cognition that still need to be resolved. We conclude by clarifying important future directions for research on cognitive control and their implications for advancing our understanding of PFC networks in brain disorders.
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