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Khanra P, Nakuci J, Muldoon S, Watanabe T, Masuda N. Reliability of energy landscape analysis of resting-state functional MRI data. Eur J Neurosci 2024. [PMID: 38837814 DOI: 10.1111/ejn.16390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 04/05/2024] [Accepted: 04/25/2024] [Indexed: 06/07/2024]
Abstract
Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.
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Affiliation(s)
- Pitambar Khanra
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Sarah Muldoon
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
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Foster M, Scheinost D. Brain states as wave-like motifs. Trends Cogn Sci 2024; 28:492-503. [PMID: 38582654 DOI: 10.1016/j.tics.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.
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Affiliation(s)
- Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
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Xing L, Guo Z, Long Z. Energy landscape analysis of brain network dynamics in Alzheimer's disease. Front Aging Neurosci 2024; 16:1375091. [PMID: 38813531 PMCID: PMC11133694 DOI: 10.3389/fnagi.2024.1375091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024] Open
Abstract
Background Alzheimer's disease (AD) is a common neurodegenerative dementia, characterized by abnormal dynamic functional connectivity (DFC). Traditional DFC analysis, assuming linear brain dynamics, may neglect the complexity of the brain's nonlinear interactions. Energy landscape analysis offers a holistic, nonlinear perspective to investigate brain network attractor dynamics, which was applied to resting-state fMRI data for AD in this study. Methods This study utilized resting-state fMRI data from 60 individuals, comparing 30 Alzheimer's patients with 30 controls, from the Alzheimer's Disease Neuroimaging Initiative. Energy landscape analysis was applied to the data to characterize the aberrant brain network dynamics of AD patients. Results The AD group stayed in the co-activation state for less time than the healthy control (HC) group, and a positive correlation was identified between the transition frequency of the co-activation state and behavior performance. Furthermore, the AD group showed a higher occurrence frequency and transition frequency of the cognitive control state and sensory integration state than the HC group. The transition between the two states was positively correlated with behavior performance. Conclusion The results suggest that the co-activation state could be important to cognitive processing and that the AD group possibly raised cognitive ability by increasing the occurrence and transition between the impaired cognitive control and sensory integration states.
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Affiliation(s)
- Le Xing
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhitao Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhiying Long
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
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Miyata J, Sasamoto A, Ezaki T, Isobe M, Kochiyama T, Masuda N, Mori Y, Sakai Y, Sawamoto N, Tei S, Ubukata S, Aso T, Murai T, Takahashi H. Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network. Psychiatry Clin Neurosci 2024; 78:322-331. [PMID: 38414202 DOI: 10.1111/pcn.13652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 12/15/2023] [Accepted: 01/21/2024] [Indexed: 02/29/2024]
Abstract
AIM While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear. METHODS Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter. RESULTS We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions. CONCLUSION Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.
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Grants
- Kyoto University
- JP18dm0307008 Japan Agency for Medical Research and Development
- JP21uk1024002 Japan Agency for Medical Research and Development
- JPMJMS2021 Japan Science and Technology Agency
- Novartis Pharma Research Grant
- SENSHIN Medical Research Foundation
- JP17H04248 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP18H05130 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP19H03583 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP20H05064 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP20K21567 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP21K07544 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP26461767 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- Takeda Science Foundation
- Uehara Memorial Foundation
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Affiliation(s)
- Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Psychiatry, Aichi Medical University, Aichi, Japan
| | - Akihiko Sasamoto
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takahiro Ezaki
- PRESTO, Japan Science and Technology Agency, Saitama, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Masanori Isobe
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York, USA
| | - Yasuo Mori
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuki Sakai
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
| | - Nobukatsu Sawamoto
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shisei Tei
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- School of Human and Social Sciences, Tokyo International University, Tokyo, Japan
| | - Shiho Ubukata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Islam S, Khanra P, Nakuci J, Muldoon SF, Watanabe T, Masuda N. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis. BMC Neurosci 2024; 25:14. [PMID: 38438838 PMCID: PMC10913599 DOI: 10.1186/s12868-024-00854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
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Affiliation(s)
- Saiful Islam
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
| | - Pitambar Khanra
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Sarah F Muldoon
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
- Neuroscience Program, University at Buffalo, State University of New York at Buffalo, 955 Main Street, Buffalo, 14203, NY, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 731 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Naoki Masuda
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA.
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA.
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Ishida T, Yamada S, Yasuda K, Uenishi S, Tamaki A, Tabata M, Ikeda N, Takahashi S, Kimoto S. Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder. Neuroimage Clin 2024; 41:103574. [PMID: 38346380 PMCID: PMC10944194 DOI: 10.1016/j.nicl.2024.103574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/05/2024] [Accepted: 02/05/2024] [Indexed: 03/16/2024]
Abstract
INTRODUCTION The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders. METHODS We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition. RESULTS We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD. CONCLUSIONS Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.
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Affiliation(s)
- Takuya Ishida
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan.
| | - Shinichi Yamada
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
| | - Kasumi Yasuda
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Hanwa Izumi Hospital, Osaka 594-1157, Japan
| | - Shinya Uenishi
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Hidaka Hospital, Wakayama 644-0002, Japan
| | - Atsushi Tamaki
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Wakayama Prefectural Mental Health Care Center, Wakayama 643-0811, Japan
| | - Michiyo Tabata
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Nokamikosei Hospital, Wakayama 640-1141, Japan
| | - Natsuko Ikeda
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
| | - Shun Takahashi
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan; Clinical Research and Education Center, Asakayama General Hospital, Osaka 590-0018, Japan; Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - Sohei Kimoto
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
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Yonezawa S, Haruki T, Koizumi K, Taketani A, Oshima Y, Oku M, Wada A, Sato T, Masuda N, Tahara J, Fujisawa N, Koshiyama S, Kadowaki M, Kitajima I, Saito S. Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis. Int J Mol Sci 2024; 25:1570. [PMID: 38338848 PMCID: PMC10855579 DOI: 10.3390/ijms25031570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages-834 NL, 711 MGUS, and 970 MM spectra-and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527-1530 cm-1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.
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Affiliation(s)
- Shota Yonezawa
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Takayuki Haruki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Akinori Taketani
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Yusuke Oshima
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Makito Oku
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Akinori Wada
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Tsutomu Sato
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, NY 14260-2200, USA
| | - Jun Tahara
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Noritaka Fujisawa
- Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Shota Koshiyama
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Isao Kitajima
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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Kindler J, Ishida T, Michel C, Klaassen AL, Stüble M, Zimmermann N, Wiest R, Kaess M, Morishima Y. Aberrant brain dynamics in individuals with clinical high risk of psychosis. SCHIZOPHRENIA BULLETIN OPEN 2024; 5:sgae002. [PMID: 38605980 PMCID: PMC7615822 DOI: 10.1093/schizbullopen/sgae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Background Resting-state network (RSN) functional connectivity analyses have profoundly influenced our understanding of the pathophysiology of psychoses and their clinical high risk (CHR) states. However, conventional RSN analyses address the static nature of large-scale brain networks. In contrast, novel methodological approaches aim to assess the momentum state and temporal dynamics of brain network interactions. Methods Fifty CHR individuals and 33 healthy controls (HC) completed a resting-state functional MRI scan. We performed an Energy Landscape analysis, a data-driven method using the pairwise maximum entropy model, to describe large-scale brain network dynamics such as duration and frequency of, and transition between, different brain states. We compared those measures between CHR and HC, and examined the association between neuropsychological measures and neural dynamics in CHR. Results Our main finding is a significantly increased duration, frequency, and higher transition rates to an infrequent brain state with coactivation of the salience, limbic, default mode and somatomotor RSNs in CHR as compared to HC. Transition of brain dynamics from this brain state was significantly correlated with processing speed in CHR. Conclusion In CHR, temporal brain dynamics are attracted to an infrequent brain state, reflecting more frequent and longer occurrence of aberrant interactions of default mode, salience, and limbic networks. Concurrently, more frequent and longer occurrence of the brain state is associated with core cognitive dysfunctions, predictors of future onset of full-blown psychosis.
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Affiliation(s)
- Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Takuya Ishida
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Kimiidera, Japan
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Arndt-Lukas Klaassen
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Miriam Stüble
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Nadja Zimmermann
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Roland Wiest
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Yosuke Morishima
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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Carlson BM, Mitchell BA, Dougherty K, Westerberg JA, Cox MA, Maier A. Does V1 response suppression initiate binocular rivalry? iScience 2023; 26:107359. [PMID: 37520732 PMCID: PMC10382945 DOI: 10.1016/j.isci.2023.107359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/02/2023] [Accepted: 07/07/2023] [Indexed: 08/01/2023] Open
Abstract
During binocular rivalry (BR) only one eye's view is perceived. Neural underpinnings of BR are debated. Recent studies suggest that primary visual cortex (V1) initiates BR. One trigger might be response suppression across most V1 neurons at the onset of BR. Here, we utilize a variant of BR called binocular rivalry flash suppression (BRFS) to test this hypothesis. BRFS is identical to BR, except stimuli are shown with a ∼1s delay. If V1 response suppression was required to initiate BR, it should occur during BRFS as well. To test this, we compared V1 spiking in two macaques observing BRFS. We found that BRFS resulted in response facilitation rather than response suppression across V1 neurons. However, BRFS still reduces responses in a subset of V1 neurons due to the adaptive effects of asynchronous stimulus presentation. We argue that this selective response suppression could serve as an alternate initiator of BR.
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Affiliation(s)
- Brock M. Carlson
- Department of Psychology, College of Arts and Science, Vanderbilt Vision Research Center, Center for Integrative and Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37235, USA
| | - Blake A. Mitchell
- Department of Psychology, College of Arts and Science, Vanderbilt Vision Research Center, Center for Integrative and Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37235, USA
| | - Kacie Dougherty
- Department of Psychology, College of Arts and Science, Vanderbilt Vision Research Center, Center for Integrative and Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37235, USA
- Department of Psychology, Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Jacob A. Westerberg
- Department of Psychology, College of Arts and Science, Vanderbilt Vision Research Center, Center for Integrative and Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37235, USA
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam 1105 BA, the Netherlands
| | - Michele A. Cox
- Department of Psychology, College of Arts and Science, Vanderbilt Vision Research Center, Center for Integrative and Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37235, USA
- Center for Visual Science, University of Rochester, Rochester, NY 14627, USA
| | - Alexander Maier
- Department of Psychology, College of Arts and Science, Vanderbilt Vision Research Center, Center for Integrative and Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37235, USA
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Fan L, Li Y, Huang ZG, Zhang W, Wu X, Liu T, Wang J. Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape. Cereb Cortex 2023; 33:9583-9598. [PMID: 37376783 DOI: 10.1093/cercor/bhad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics. Using Topological Data Analysis based Mapper approach, we construct the precise dynamic mapping (PDM) for each participant. To reveal the relationship between PDM and canonical functional representation of the resting brain, we annotated the graph using relative activation proportion of a set of large-scale resting-state networks (RSNs) and assigned the single brain volume to corresponding RSN-dominant or a hub state (not any RSN was dominant). Our results show that (i) low-frequency rTMS could induce changed temporal evolution of brain states; (ii) rTMS didn't alter the hub-periphery configurations underlined resting-state brain dynamics; and (iii) the rTMS effects on brain dynamics differ across the left frontal and occipital lobe. In conclusion, low-frequency rTMS significantly alters the individual temporo-spatial dynamics, and our finding further suggested a potential target-dependent alteration of brain dynamics. This work provides a new perspective to comprehend the heterogeneous effect of rTMS.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Wenlong Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, China
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11
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Kloucek MB, Machon T, Kajimura S, Royall CP, Masuda N, Turci F. Biases in inverse Ising estimates of near-critical behavior. Phys Rev E 2023; 108:014109. [PMID: 37583208 DOI: 10.1103/physreve.108.014109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/27/2023] [Indexed: 08/17/2023]
Abstract
Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.
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Affiliation(s)
- Maximilian B Kloucek
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
- Bristol Centre for Functional Nanomaterials, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Thomas Machon
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Shogo Kajimura
- Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto 606-8585, Japan
| | - C Patrick Royall
- Gulliver UMR CNRS 7083, ESPCI Paris, Université PSL, 75005 Paris, France
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York 14260-2900, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York 14260-5030, USA
| | - Francesco Turci
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
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12
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Li Y, An S, Zhou T, Su C, Zhang S, Li C, Jiang J, Mu Y, Yao N, Huang ZG. Triple-network analysis of Alzheimer's disease based on the energy landscape. Front Neurosci 2023; 17:1171549. [PMID: 37287802 PMCID: PMC10242117 DOI: 10.3389/fnins.2023.1171549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/13/2023] [Indexed: 06/09/2023] Open
Abstract
Introduction Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state. Methods Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. Results AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index. Discussion The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.
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Affiliation(s)
- Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Simeng An
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Tianlin Zhou
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chunwang Su
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Siping Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Junjie Jiang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yunfeng Mu
- Department of Gynecological Oncology, Shaanxi Provincial Cancer Hospital, Xi'an, China
| | - Nan Yao
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Department of Applied Physics, Xi'an University of Technology, Xi'an, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- The State Key Laboratory of Congnitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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13
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Fujita H, Ushio M, Suzuki K, Abe MS, Yamamichi M, Iwayama K, Canarini A, Hayashi I, Fukushima K, Fukuda S, Kiers ET, Toju H. Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics. MICROBIOME 2023; 11:63. [PMID: 36978146 PMCID: PMC10052866 DOI: 10.1186/s40168-023-01474-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as "dysbiosis" in human microbiomes. METHODS We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. RESULTS We confirmed that the abrupt community changes observed through the time-series could be described as shifts between "alternative stable states" or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the "energy landscape" analysis of statistical physics or that of a stability index of nonlinear mechanics. CONCLUSIONS The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. Video Abstract.
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Affiliation(s)
- Hiroaki Fujita
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.
| | - Masayuki Ushio
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
- Department of Ocean Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Kenta Suzuki
- Integrated Bioresource Information Division, BioResource Research Center, RIKEN, Tsukuba, Ibaraki, 305-0074, Japan
| | - Masato S Abe
- Faculty of Culture and Information Science, Doshisha University, Kyotanabe, Kyoto, 610-0321, Japan
| | - Masato Yamamichi
- School of Biological Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
- Department of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, 852-8523, Japan
| | - Koji Iwayama
- Faculty of Data Science, Shiga University, Hikone, 522-8522, Japan
| | - Alberto Canarini
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
| | - Ibuki Hayashi
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
| | - Keitaro Fukushima
- Faculty of Food and Agricultural Sciences, Fukushima University, Kanayagawa 1, Fukushima, Fukushima, 960-1296, Japan
| | - Shinji Fukuda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Kanagawa, 210-0821, Japan
- Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan
| | - E Toby Kiers
- Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hirokazu Toju
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.
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14
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Chen Y, Rasool MA, Hussain S, Meng S, Yao Y, Wang X, Liu Y. Bird community structure is driven by urbanization level, blue-green infrastructure configuration and precision farming in Taizhou, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160096. [PMID: 36372169 DOI: 10.1016/j.scitotenv.2022.160096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 10/24/2022] [Accepted: 11/05/2022] [Indexed: 06/16/2023]
Abstract
Land use/land cover (LULC) changes and high urbanization rates are the main drivers of avian habitat loss in developing countries. However, few studies have examined the effects of urbanization intensity on avian diversity distribution and its importance in guiding eco-friendly urban planning. We surveyed bird distribution (n = 67 species) in different seasons using local ecological knowledge (LEK) and transect line methods in Jiangyan District from July 2018 to May 2019. One-way analysis of variance (ANOVA) was used to assess the effects of urbanization levels on birds relative density and richness during spring-summer (breeding season) and autumn-winter seasons (non-breeding season). Generalized linear models (GLM) were identified for the landscape composition and configuration that drive relative density and richness in native bird communities. Using redundancy analysis (RDA), we identified the landscape composition and configuration factors affecting bird foraging and roosting at urbanization levels. The results showed high dependency of waders and granivores on paddy fields and dry arable land respectively during the breeding season. During non-breeding season, wetland abundance, land cover, connectivity and total area of BGI were important habitat factors in attracting birds. Moreover, the landscape composition and configuration factors of BGI: wetlands as well as farmland habitats, are the main environmental cues that influence bird foraging. Therefore, to increase habitat suitability over landscape matrix, we propose creation of multiple waterbodies and green corridors of variable types and sizes on natural patches to improve the connectivity of ecological network. We also recommend land management interventions in farmland ecosystems, which could contribute to natural habitat restoration and improve bird biodiversity in urban areas.
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Affiliation(s)
- Yixue Chen
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, Hohai University, Nanjing 210098, China
| | | | - Sarfraz Hussain
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, Hohai University, Nanjing 210098, China
| | - Shuang Meng
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, Hohai University, Nanjing 210098, China
| | - Yipeng Yao
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, Hohai University, Nanjing 210098, China
| | - Xue Wang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, Hohai University, Nanjing 210098, China
| | - Yuhong Liu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, Hohai University, Nanjing 210098, China; College of Environment, Hohai University, Nanjing 210098, China.
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15
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John YJ, Sawyer KS, Srinivasan K, Müller EJ, Munn BR, Shine JM. It's about time: Linking dynamical systems with human neuroimaging to understand the brain. Netw Neurosci 2022; 6:960-979. [PMID: 36875012 PMCID: PMC9976648 DOI: 10.1162/netn_a_00230] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/04/2022] [Indexed: 11/04/2022] Open
Abstract
Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.
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Affiliation(s)
- Yohan J. John
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, USA
| | - Kayle S. Sawyer
- Departments of Anatomy and Neurobiology, Boston University, Boston University, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Boston VA Healthcare System, Boston, MA, USA
- Sawyer Scientific, LLC, Boston, MA, USA
| | - Karthik Srinivasan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eli J. Müller
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - Brandon R. Munn
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
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16
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Fan L, Li C, Huang ZG, Zhao J, Wu X, Liu T, Li Y, Wang J. The longitudinal neural dynamics changes of whole brain connectome during natural recovery from poststroke aphasia. NEUROIMAGE: CLINICAL 2022; 36:103190. [PMID: 36174256 PMCID: PMC9668607 DOI: 10.1016/j.nicl.2022.103190] [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: 02/25/2022] [Revised: 07/24/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce. Here we characterize large-scale brain dynamics in left-frontal-stroke aphasia through energy landscape analysis. Using fMRI during an auditory comprehension task, we find that aphasia patients suffer serious whole-brain dynamics perturbation in the acute and subacute stages after stroke, in which the brains were restricted into two major activity patterns. Following spontaneous recovery process, the brain flexibility improved in the chronic stage. Critically, we demonstrated that the abnormal neural dynamics are correlated with the aberrant brain network coordination. Taken together, the energy landscape analysis exhibited that the acute poststroke aphasia has a constrained, low dimensional brain dynamics, which were replaced by less constrained and high dimensional dynamics at chronic aphasia. Our study provides a new perspective to profoundly understand the pathological mechanisms of poststroke aphasia.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Chenxi Li
- Department of the Psychology of Military Medicine, Air Force Medical University, Xi’an, Shaanxi 710032, PR China
| | - Zi-gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Jie Zhao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China,Corresponding authors at: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, PR China.
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, Shaanxi 710049, PR China,Corresponding authors at: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, PR China.
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17
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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: 0] [Impact Index Per Article: 0] [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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18
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Kondo HM, Terashima H, Ezaki T, Kochiyama T, Kihara K, Kawahara JI. Dynamic Transitions Between Brain States Predict Auditory Attentional Fluctuations. Front Neurosci 2022; 16:816735. [PMID: 35368290 PMCID: PMC8972573 DOI: 10.3389/fnins.2022.816735] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/23/2022] [Indexed: 11/23/2022] Open
Abstract
Achievement of task performance is required to maintain a constant level of attention. Attentional level fluctuates over the course of daily activities. However, brain dynamics leading to attentional fluctuation are still unknown. We investigated the underlying mechanisms of sustained attention using functional magnetic resonance imaging (fMRI). Participants were scanned with fMRI while performing an auditory, gradual-onset, continuous performance task (gradCPT). In this task, narrations gradually changed from one to the next. Participants pressed a button for frequent Go trials (i.e., male voices) as quickly as possible and withheld responses to infrequent No-go trials (i.e., female voices). Event-related analysis revealed that frontal and temporal areas, including the auditory cortex, were activated during successful and unsuccessful inhibition of predominant responses. Reaction-time (RT) variability throughout the auditory gradCPT was positively correlated with signal changes in regions of the dorsal attention network: superior frontal gyrus and superior parietal lobule. Energy landscape analysis showed that task-related activations could be clustered into different attractors: regions of the dorsal attention network and default mode network. The number of alternations between RT-stable and erratic periods increased with an increase in transitions between attractors in the brain. Therefore, we conclude that dynamic transitions between brain states are closely linked to auditory attentional fluctuations.
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Affiliation(s)
- Hirohito M. Kondo
- School of Psychology, Chukyo University, Nagoya, Japan
- *Correspondence: Hirohito M. Kondo,
| | - Hiroki Terashima
- NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Atsugi, Japan
| | - Takahiro Ezaki
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | | | - Ken Kihara
- Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Jun I. Kawahara
- Department of Psychology, Hokkaido University, Sapporo, Japan
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19
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Devia C, Concha-Miranda M, Rodríguez E. Bi-Stable Perception: Self-Coordinating Brain Regions to Make-Up the Mind. Front Neurosci 2022; 15:805690. [PMID: 35153663 PMCID: PMC8829010 DOI: 10.3389/fnins.2021.805690] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/16/2021] [Indexed: 11/17/2022] Open
Abstract
Bi-stable perception is a strong instance of cognitive self-organization, providing a research model for how ‘the brain makes up its mind.’ The complexity of perceptual bistability prevents a simple attribution of functions to areas, because many cognitive processes, recruiting multiple brain regions, are simultaneously involved. The functional magnetic resonance imaging (fMRI) evidence suggests the activation of a large network of distant brain areas. Concurrently, electroencephalographic and magnetoencephalographic (MEEG) literature shows sub second oscillatory activity and phase synchrony on several frequency bands. Strongly represented are beta and gamma bands, often associated with neural/cognitive integration processes. The spatial extension and short duration of brain activities suggests the need for a fast, large-scale neural coordination mechanism. To address the range of temporo-spatial scales involved, we systematize the current knowledge from mathematical models, cognitive sciences and neuroscience at large, from single-cell- to system-level research, including evidence from human and non-human primates. Surprisingly, despite evidence spanning through different organization levels, models, and experimental approaches, the scarcity of integrative studies is evident. In a final section of the review we dwell on the reasons behind such scarcity and on the need of integration in order to achieve a real understanding of the complexities underlying bi-stable perception processes.
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Affiliation(s)
- Christ Devia
- Departamento de Neurociencia, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Biomedical Neuroscience Institute, Universidad de Chile, Santiago, Chile
| | - Miguel Concha-Miranda
- Departamento de Neurociencia, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Laboratorio de Neurodinámica Básica y Aplicada, Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Eugenio Rodríguez
- Laboratorio de Neurodinámica Básica y Aplicada, Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile
- *Correspondence: Eugenio Rodríguez,
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20
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Yufik Y, Malhotra R. Situational Understanding in the Human and the Machine. Front Syst Neurosci 2021; 15:786252. [PMID: 35002643 PMCID: PMC8733725 DOI: 10.3389/fnsys.2021.786252] [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: 09/30/2021] [Accepted: 11/24/2021] [Indexed: 11/13/2022] Open
Abstract
The Air Force research programs envision developing AI technologies that will ensure battlespace dominance, by radical increases in the speed of battlespace understanding and decision-making. In the last half century, advances in AI have been concentrated in the area of machine learning. Recent experimental findings and insights in systems neuroscience, the biophysics of cognition, and other disciplines provide converging results that set the stage for technologies of machine understanding and machine-augmented Situational Understanding. This paper will review some of the key ideas and results in the literature, and outline new suggestions. We define situational understanding and the distinctions between understanding and awareness, consider examples of how understanding-or lack of it-manifest in performance, and review hypotheses concerning the underlying neuronal mechanisms. Suggestions for further R&D are motivated by these hypotheses and are centered on the notions of Active Inference and Virtual Associative Networks.
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Affiliation(s)
- Yan Yufik
- Virtual Structures Research, Inc., Potomac, MD, United States
| | - Raj Malhotra
- United States Air Force Sensor Directorate, Dayton, OH, United States
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21
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Wu J, Ding W, Ye X, Wei Q, Lv X, Tang Q, Tian Y, Wang K, Jiang Y. Interictal Activity Is Associated With Slower Binocular Rivalry in Idiopathic Generalized Epilepsy. Front Neurol 2021; 12:720126. [PMID: 34867711 PMCID: PMC8634877 DOI: 10.3389/fneur.2021.720126] [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: 06/03/2021] [Accepted: 10/19/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Perceptual alternations evoked by binocular rivalry (BR) reflect cortical dynamics strongly dependent on the excitatory-inhibitory balance, suggesting potential utility as a biomarker for epileptogenesis. Therefore, we investigated the characteristics of BR in patients with idiopathic generalized epilepsy (IGE) and potential associations with clinical variables. Methods: Sixty-two healthy controls (HCs) and 94 IGE patients completed BR task. Perceptual alternation rates were compared between HC and IGE groups as well as among the HC group and IGE patients stratified according to the presence or absence of interictal activity on the ambulatory electroencephalogram (EEG), termed the abnormal ambulatory EEG group (AB-AEEG, n = 64) and normal ambulatory EEG group (N-AEEG, n = 30), respectively. Results: The IGE patients demonstrated a slower rate of BR perceptual alternation than HC subjects (t = -4.364, p < 0.001). The alternation rate also differed among the HC, AB-AEEG, and N-AEEG groups (F = 44.962, df = 2, p < 0.001), and post hoc comparisons indicated a significantly slower alternation rate in the AB-AEEG group compared with the N-AEEG and HC groups (0.28 vs. 0.46, and 0.43 Hz). Stepwise linear regression revealed positive correlations between the BR alternation rate and both the ambulatory EEG status (β, 0.173; standard error, 0.022 p < 0.001) and Montreal Cognitive Assessment score (β, 0.013; standard error, 0.004; p = 0.003). Receiver operating characteristic curve analysis of the BR alternation rate distinguished AB-AEEG from N-AEEG subjects with 90.00% sensitivity and 76.90% specificity (area under the curve = 0.881; 95% confidence interval = 0.801- 0.961, cut-off = 0.319). Alternatively, Montreal Cognitive Assessment score did not accurately distinguish AB-AEEG from N-AEEG subjects and the area under the receiver operating characteristic curve combining the BR alternation rate and Montreal Cognitive Assessment score was not markedly larger than that of the BR alternation rate alone (0.894, 95% confidence interval = 0.822-0.966, p < 0.001). K-fold cross-validation was used to evaluate the predictive performance of BR alternation rate, MoCA score, and the combination of both, which yielded average AUC values of 0.870, 0.584 and 0.847, average sensitivity values of 89.36, 92.73, and 91.28%, and average specificity values of 62.25, 13.42, and 61.78%, respectively. The number of interictal epileptiform discharges was significantly correlated with the alternation rate in IGE patients (r = 0.296, p = 0.018). A forward stepwise linear regression model identified the number of interictal epileptiform discharges (β, 0.001; standard error, 0.001; p = 0.025) as an independent factor associated with BR alternation rate in these patients. Conclusion: These results suggest that interictal epileptiform discharges are associated with disruptions in perceptual awareness, and that the BR may be a useful auxiliary behavioral task to diagnosis and dynamically monitor IGE patients with interictal discharge.
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Affiliation(s)
- Jiaonan Wu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.,Department of Neurology, Anhui Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Ding
- Department of Nephrology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xing Ye
- Department of Neurology, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qiang Wei
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Xinyi Lv
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Qiqiang Tang
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.,Department of Neurology, Anhui Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Yubao Jiang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
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22
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Jeong SO, Kang J, Pae C, Eo J, Park SM, Son J, Park HJ. Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics. Neuroimage 2021; 244:118618. [PMID: 34571159 DOI: 10.1016/j.neuroimage.2021.118618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.
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Affiliation(s)
- Seok-Oh Jeong
- Department of Statistics, Hankuk University of Foreign Studies, Yong-In, Republic of Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinseok Eo
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Sung Min Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Junho Son
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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23
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Lehmann SJ, Corneil BD. Completing the puzzle: Why studies in non-human primates are needed to better understand the effects of non-invasive brain stimulation. Neurosci Biobehav Rev 2021; 132:1074-1085. [PMID: 34742722 DOI: 10.1016/j.neubiorev.2021.10.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/29/2021] [Accepted: 10/31/2021] [Indexed: 11/27/2022]
Abstract
Brain stimulation is a core method in neuroscience. Numerous non-invasive brain stimulation (NIBS) techniques are currently in use in basic and clinical research, and recent advances promise the ability to non-invasively access deep brain structures. While encouraging, there is a surprising gap in our understanding of precisely how NIBS perturbs neural activity throughout an interconnected network, and how such perturbed neural activity ultimately links to behaviour. In this review, we will consider why non-human primate (NHP) models of NIBS are ideally situated to address this gap in knowledge, and why the oculomotor network that moves our line of sight offers a particularly valuable platform in which to empirically test hypothesis regarding NIBS-induced changes in brain and behaviour. NHP models of NIBS will enable investigation of the complex, dynamic effects of brain stimulation across multiple hierarchically interconnected brain areas, networks, and effectors. By establishing such links between brain and behavioural output, work in NHPs can help optimize experimental and therapeutic approaches, improve NIBS efficacy, and reduce side-effects of NIBS.
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Affiliation(s)
- Sebastian J Lehmann
- Department of Physiology and Pharmacology, Western University, London, Ontario, N6A 5B7, Canada.
| | - Brian D Corneil
- Department of Physiology and Pharmacology, Western University, London, Ontario, N6A 5B7, Canada; Department of Psychology, Western University, London, Ontario, N6A 5B7, Canada; Robarts Research Institute, London, Ontario, N6A 5B7, Canada.
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24
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Long-term priors influence visual perception through recruitment of long-range feedback. Nat Commun 2021; 12:6288. [PMID: 34725348 PMCID: PMC8560909 DOI: 10.1038/s41467-021-26544-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/08/2021] [Indexed: 11/10/2022] Open
Abstract
Perception results from the interplay of sensory input and prior knowledge. Despite behavioral evidence that long-term priors powerfully shape perception, the neural mechanisms underlying these interactions remain poorly understood. We obtained direct cortical recordings in neurosurgical patients as they viewed ambiguous images that elicit constant perceptual switching. We observe top-down influences from the temporal to occipital cortex, during the preferred percept that is congruent with the long-term prior. By contrast, stronger feedforward drive is observed during the non-preferred percept, consistent with a prediction error signal. A computational model based on hierarchical predictive coding and attractor networks reproduces all key experimental findings. These results suggest a pattern of large-scale information flow change underlying long-term priors’ influence on perception and provide constraints on theories about long-term priors’ influence on perception. Priors learnt from lifetime experiences influence perception. The authors show that when perception is congruent with a long-term prior, there is increased top-down input in the ventral visual stream, whereas bottom-up input is enhanced when perception is incongruent with prior.
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25
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Watanabe T. Causal roles of prefrontal cortex during spontaneous perceptual switching are determined by brain state dynamics. eLife 2021; 10:69079. [PMID: 34713803 PMCID: PMC8631941 DOI: 10.7554/elife.69079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 10/28/2021] [Indexed: 12/23/2022] Open
Abstract
The prefrontal cortex (PFC) is thought to orchestrate cognitive dynamics. However, in tests of bistable visual perception, no direct evidence supporting such presumable causal roles of the PFC has been reported except for a recent work. Here, using a novel brain-state-dependent neural stimulation system, we identified causal effects on percept dynamics in three PFC activities—right frontal eye fields, dorsolateral PFC (DLPFC), and inferior frontal cortex (IFC). The causality is behaviourally detectable only when we track brain state dynamics and modulate the PFC activity in brain-state-/state-history-dependent manners. The behavioural effects are underpinned by transient neural changes in the brain state dynamics, and such neural effects are quantitatively explainable by structural transformations of the hypothetical energy landscapes. Moreover, these findings indicate distinct functions of the three PFC areas: in particular, the DLPFC enhances the integration of two PFC-active brain states, whereas IFC promotes the functional segregation between them. This work resolves the controversy over the PFC roles in spontaneous perceptual switching and underlines brain state dynamics in fine investigations of brain-behaviour causality. A cube that seems to shift its spatial arrangement as you keep looking; the elegant silhouette of a pirouetting dancer, which starts to spin in the opposite direction the more you stare at it; an illustration that shows two profiles – or is it a vase? These optical illusions are examples of bistable visual perception. Beyond their entertaining aspect, they provide a way for scientists to explore the dynamics of human consciousness, and the neural regions involved in this process. Some studies show that bistable visual perception is associated with the activation of the prefrontal cortex, a brain area involved in complex cognitive processes. However, it is unclear whether this region is required for the illusions to emerge. Some research has showed that even if sections of the prefrontal cortex are temporally deactivated, participants can still experience the illusions. Instead, Takamitsu Watanabe proposes that bistable visual perception is a process tied to dynamic brain states – that is, that distinct regions of the prefontal cortex are required for this fluctuating visual awareness, depending on the state of the whole brain. Such causal link cannot be observed if brain activity is not tracked closely. To investigate this, the brain states of 65 participants were recorded as individuals were experiencing the optical illusions; the activity of their various brain regions could therefore be mapped, and then areas of the prefrontal cortex could precisely be inhibited at the right time using transcranial magnetic stimulation. This revealed that, indeed, prefrontal cortex regions were necessary for bistable visual perception, but not in a simple way. Instead, which ones were required and when depended on activity dynamics taking place in the whole brain. Overall, these results indicate that monitoring brain states is necessary to better understand – and ultimately, control – the neural pathways underlying perception and behaviour.
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Affiliation(s)
- Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, Tokyo, Japan.,RIKEN Centre for Brain Science, Saitama, Japan
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26
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Ezaki T, Himeno Y, Watanabe T, Masuda N. Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models. Eur J Neurosci 2021; 54:5404-5416. [PMID: 34250639 PMCID: PMC9291560 DOI: 10.1111/ejn.15386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022]
Abstract
Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting‐state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data are relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden‐state dynamics under this condition.
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Affiliation(s)
- Takahiro Ezaki
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,PRESTO, Japan Science and Technology Agency, Saitama, Japan
| | - Yu Himeno
- Department of Aeronautics and Astronautics, The University of Tokyo, Tokyo, Japan
| | - Takamitsu Watanabe
- Laboratory for Cognition Circuit Dynamics, RIKEN Centre for Brain Science, Saitama, Japan.,International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA.,Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York, USA
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27
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Variable rather than extreme slow reaction times distinguish brain states during sustained attention. Sci Rep 2021; 11:14883. [PMID: 34290318 PMCID: PMC8295386 DOI: 10.1038/s41598-021-94161-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/07/2021] [Indexed: 02/03/2023] Open
Abstract
A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.
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28
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Suzuki K, Nakaoka S, Fukuda S, Masuya H. Energy landscape analysis elucidates the multistability of ecological communities across environmental gradients. ECOL MONOGR 2021. [DOI: 10.1002/ecm.1469] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Kenta Suzuki
- Integrated Bioresource Information Division BioResource Research Center RIKEN 3‐1‐1 Koyadai Tsukuba Ibaraki 305‐0074 Japan
| | - Shinji Nakaoka
- Laboratory of Mathematical Biology Faculty of Advanced Life Science Hokkaido University Kita‐10 Nishi‐8Kita‐ku Sapporo Hokkaido 060‐0819 Japan
- PRESTO Japan Science and Technology Agency 4‐1‐8 Honcho Kawaguchi Saitama 332‐0012 Japan
| | - Shinji Fukuda
- PRESTO Japan Science and Technology Agency 4‐1‐8 Honcho Kawaguchi Saitama 332‐0012 Japan
- Institute for Advanced Biosciences Keio University 246‐2 MizukamiKakuganji Tsuruoka Yamagata 997‐0052 Japan
- Intestinal Microbiota Project Kanagawa Institute of Industrial Science and Technology 3‐25‐13 TonomachiKawasaki‐ku Kawasaki Kanagawa 210‐0821 Japan
- Transborder Medical Research Center University of Tsukuba 1‐1‐1 Tennodai Tsukuba Ibaraki 305‐8575 Japan
| | - Hiroshi Masuya
- Integrated Bioresource Information Division BioResource Research Center RIKEN 3‐1‐1 Koyadai Tsukuba Ibaraki 305‐0074 Japan
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29
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Nakano T, Ichiki A, Fujikado T. Pupil constriction via the parasympathetic pathway precedes perceptual switch of ambiguous stimuli. Int J Psychophysiol 2021; 167:15-21. [PMID: 34144076 DOI: 10.1016/j.ijpsycho.2021.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/19/2021] [Accepted: 06/11/2021] [Indexed: 10/21/2022]
Abstract
Perceptual rivalry of ambiguous stimuli reflects the interaction of neural activity among multiple cortical regions. However, it remains unclear what drives a spontaneous perceptual alteration. We hypothesized that increased fluctuations in spontaneous neural activity due to arousal reduction drive the perceptual switch. Here, we show that the pupils shrank a few seconds prior to the onset of the spontaneous perceptual switch. Such pupil constriction was not observed before the exogenous perceptual switch. Pharmacological experiments confirmed that the pupil constriction disappeared when the peripheral parasympathetic pathway (pupil sphincter muscle) was blocked, but it remained intact when the peripheral sympathetic pathway (pupil dilator muscle) was manipulated. Furthermore, rapid pupil dilations with behavioral response are also mediated by the peripheral parasympathetic pathway. The present findings suggested that transient arousal drops, as denoted by the autonomic nervous modulation of pupil size, are involved in inducing the spontaneous perceptual switch of bistable stimuli.
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Affiliation(s)
- Tamami Nakano
- Graduate School of Frontiers Bioscience, Osaka University, Osaka 565-0871, Japan; Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan; Center for Information and Neural Networks (CiNet), Suita, Osaka 565-0871, Japan.
| | - Arata Ichiki
- Graduate School of Frontiers Bioscience, Osaka University, Osaka 565-0871, Japan
| | - Takashi Fujikado
- Graduate School of Frontiers Bioscience, Osaka University, Osaka 565-0871, Japan; Center for Information and Neural Networks (CiNet), Suita, Osaka 565-0871, Japan
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30
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Parisot K, Zozor S, Guérin-Dugué A, Phlypo R, Chauvin A. Micro-pursuit: A class of fixational eye movements correlating with smooth, predictable, small-scale target trajectories. J Vis 2021; 21:9. [PMID: 33444434 PMCID: PMC7838552 DOI: 10.1167/jov.21.1.9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Humans generate ocular pursuit movements when a moving target is tracked throughout the visual field. In this article, we show that pursuit can be generated and measured at small amplitudes, at the scale of fixational eye movements, and tag these eye movements as micro-pursuits. During micro-pursuits, gaze direction correlates with a target's smooth, predictable target trajectory. We measure similarity between gaze and target trajectories using a so-called maximally projected correlation and provide results in three experimental data sets. A first observation of micro-pursuit is provided in an implicit pursuit task, where observers were tasked to maintain their gaze fixed on a static cross at the center of screen, while reporting changes in perception of an ambiguous, moving (Necker) cube. We then provide two experimental paradigms and their corresponding data sets: a first replicating micro-pursuits in an explicit pursuit task, where observers had to follow a moving fixation cross (Cross), and a second with an unambiguous square (Square). Individual and group analyses provide evidence that micro-pursuits exist in both the Necker and Cross experiments but not in the Square experiment. The interexperiment analysis results suggest that the manipulation of stimulus target motion, task, and/or the nature of the stimulus may play a role in the generation of micro-pursuits.
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Affiliation(s)
- Kevin Parisot
- CNRS, Institute of Engineering, GIPSA-lab & LPNC, University of Grenoble Alpes, Grenoble, France., https://scholar.google.fr/citations?user=WjGkMmIAAAAJ&hl=fr&oi=ao
| | - Steeve Zozor
- CNRS, Institute of Engineering, GIPSA-lab, University of Grenoble Alpes, Grenoble, France., http://www.gipsa-lab.grenoble-inp.fr/page_pro.php?vid=86
| | - Anne Guérin-Dugué
- CNRS, Institute of Engineering, GIPSA-lab, University of Grenoble Alpes, Grenoble, France., http://www.gipsa-lab.grenoble-inp.fr/page_pro.php?vid=71
| | - Ronald Phlypo
- CNRS, Institute of Engineering, GIPSA-lab, University of Grenoble Alpes, Grenoble, France., http://www.gipsa-lab.grenoble-inp.fr/page_pro.php?vid=2173
| | - Alan Chauvin
- CNRS, LPNC, University of Grenoble Alpes, Grenoble, France., https://lpnc.univ-grenoble-alpes.fr/Alan-Chauvin
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31
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Kang J, Jeong S, Pae C, Park H. Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics. Hum Brain Mapp 2021; 42:3411-3428. [PMID: 33934421 PMCID: PMC8249903 DOI: 10.1002/hbm.25442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/25/2021] [Accepted: 03/29/2021] [Indexed: 11/24/2022] Open
Abstract
The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.
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Affiliation(s)
- Jiyoung Kang
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Seok‐Oh Jeong
- Department of StatisticsHankuk University of Foreign StudiesYong‐In, SeoulSouth Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Hae‐Jeong Park
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
- Graduate School of Medical Science, Brain Korea 21 ProjectYonsei University College of MedicineSeoulSouth Korea
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32
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Yamashita A, Rothlein D, Kucyi A, Valera EM, Esterman M. Brain state-based detection of attentional fluctuations and their modulation. Neuroimage 2021; 236:118072. [PMID: 33882346 DOI: 10.1016/j.neuroimage.2021.118072] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 03/30/2021] [Accepted: 04/09/2021] [Indexed: 01/05/2023] Open
Abstract
In the search for brain markers of optimal attentional focus, the mainstream approach has been to first define attentional states based on behavioral performance, and to subsequently investigate "neural correlates" associated with these performance variations. However, this approach constrains the range of contexts in which attentional states can be operationalized by relying on overt behavior, and assumes a one-to-one correspondence between behavior and brain state. Here, we reversed the logic of these previous studies and sought to identify behaviorally-relevant brain states based solely on brain activity, agnostic to behavioral performance. In four independent datasets, we found that the same two brain states were dominant during a sustained attention task. One state was behaviorally optimal, with higher accuracy and stability, but a greater tendency to mind wander (State1). The second state was behaviorally suboptimal, with lower accuracy and instability (State2). We further demonstrate how these brain states were impacted by motivation and attention-deficit/hyperactivity disorder (ADHD). Individuals with ADHD spent more time in suboptimal State2 and less time in optimal State1 than healthy controls. Motivation overcame the suboptimal behavior associated with State2. Our study provides compelling evidence for the existence of two attentional states from the sole viewpoint of brain activity.
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Affiliation(s)
- Ayumu Yamashita
- Department of Psychiatry, Boston University School of Medicine, Massachusetts, 02118. United States; Boston Attention and Learning Laboratory, VA Boston Healthcare System, Massachusetts,02130. United States; Overseas Research Fellow, Japan Society for the Promotion of Science, Tokyo,102-0083. Japan.
| | - David Rothlein
- Department of Psychiatry, Boston University School of Medicine, Massachusetts, 02118. United States; Boston Attention and Learning Laboratory, VA Boston Healthcare System, Massachusetts,02130. United States; National Center for PTSD, VA Boston Healthcare System, Massachusetts, 02130. United States
| | - Aaron Kucyi
- Department of Psychology, Northeastern University, Massachusetts, 02115. United States
| | - Eve M Valera
- Department of Psychiatry, Harvard Medical School, Massachusetts, 02215. United States; Department of Psychiatry, Massachusetts General Hospital, Massachusetts, 02125, United States
| | - Michael Esterman
- Department of Psychiatry, Boston University School of Medicine, Massachusetts, 02118. United States; Boston Attention and Learning Laboratory, VA Boston Healthcare System, Massachusetts,02130. United States; National Center for PTSD, VA Boston Healthcare System, Massachusetts, 02130. United States
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33
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Park HJ, Kang J. A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles. Front Comput Neurosci 2021; 15:590019. [PMID: 33935674 PMCID: PMC8079648 DOI: 10.3389/fncom.2021.590019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.
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Affiliation(s)
- Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
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34
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Ashourvan A, Shah P, Pines A, Gu S, Lynn CW, Bassett DS, Davis KA, Litt B. Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states. Commun Biol 2021; 4:210. [PMID: 33594239 PMCID: PMC7887247 DOI: 10.1038/s42003-021-01700-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/06/2021] [Indexed: 01/30/2023] Open
Abstract
A major challenge in neuroscience is determining a quantitative relationship between the brain's white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes' activation patterns' probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM's interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions' distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain's structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.
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Affiliation(s)
- Arian Ashourvan
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Preya Shah
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Adam Pines
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Shi Gu
- grid.54549.390000 0004 0369 4060Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Christopher W. Lynn
- grid.25879.310000 0004 1936 8972Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA USA
| | - Danielle S. Bassett
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA ,grid.411115.10000 0004 0435 0884Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
| | - Kathryn A. Davis
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Brian Litt
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA ,grid.411115.10000 0004 0435 0884Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
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35
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Saberi M, Khosrowabadi R, Khatibi A, Misic B, Jafari G. Topological impact of negative links on the stability of resting-state brain network. Sci Rep 2021; 11:2176. [PMID: 33500525 PMCID: PMC7838299 DOI: 10.1038/s41598-021-81767-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 01/12/2021] [Indexed: 11/08/2022] Open
Abstract
Stability is a physical attribute that stands opposite the change. However, it is still unclear how the arrangement of links called topology affects network stability. In this study, we tackled this issue in the resting-state brain network using structural balance. Structural balance theory employs the quality of triadic associations between signed links to determine the network stability. In this study, we showed that negative links of the resting-state network make hubs to reduce balance-energy and push the network into a more stable state compared to null-networks with trivial topologies. In this regard, we created a global measure entitled 'tendency to make hub' to assess the hubness of the network. Besides, we revealed nodal degrees of negative links have an exponential distribution that confirms the existence of negative hubs. Our findings indicate that the arrangement of negative links plays an important role in the balance (stability) of the resting-state brain network.
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Affiliation(s)
- Majid Saberi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C., Evin Sq., Tehran, 19839-63113, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C., Evin Sq., Tehran, 19839-63113, Iran.
| | - Ali Khatibi
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Gholamreza Jafari
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C., Evin Sq., Tehran, 19839-63113, Iran
- Physics Department, Shahid Beheshti University, G.C., Tehran, 1983969411, Iran
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36
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Regonia PR, Takamura M, Nakano T, Ichikawa N, Fermin A, Okada G, Okamoto Y, Yamawaki S, Ikeda K, Yoshimoto J. Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis. Front Psychiatry 2021; 12:780997. [PMID: 34899435 PMCID: PMC8656401 DOI: 10.3389/fpsyt.2021.780997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.
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Affiliation(s)
- Paul Rossener Regonia
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,Department of Computer Science, College of Engineering, University of the Philippines Diliman, Quezon City, Philippines
| | - Masahiro Takamura
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Neurology, Faculty of Medicine, Shimane University, Izumo, Japan
| | - Takashi Nakano
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,School of Medicine, Fujita Health University, Toyoake, Japan
| | - Naho Ichikawa
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Alan Fermin
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yasumasa Okamoto
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazushi Ikeda
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Junichiro Yoshimoto
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
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37
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Zhang B, Wu L, Feng X, Li C, Miao X, Hui Y, Zhao K, Ding J, Jin B, Chen J, Zhu Y, Sun CJ, Chow GM. Tuning Irreversible Magnetoresistance in Pr 0.67Sr 0.33MnO 3 Film via Octahedral Rotation. ACS APPLIED MATERIALS & INTERFACES 2020; 12:43222-43230. [PMID: 32820885 DOI: 10.1021/acsami.0c10402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The oxygen octahedral rotation around the out-of-plane axis is explored to study its effect on metastable status, magnetic cluster glass in manganite. The antiphase rotation around the out-of-plane axis (TiO6 a0a0c-) of SrTiO3 enhances the Mn-O bond anisotropy along in-plane and out-of-plane directions and weakens the ferromagnetic interactions in a 12 nm Pr0.67Sr0.33MnO3 film on the (001) SrTiO3 substrate, which together promote the formation of magnetic cluster-glassiness and enlarges the irreversible magnetoresistance (MR) effect with enhanced relaxation time of charge carriers. The effect of TiO6 a0a0c- in the SrTiO3 substrate on material properties is obvious with a large irreversible MR effect for thin films, which fades away with the increase in film thickness. At 10 K, the irreversible MR is 0.91 for the 12 nm film and 0.22 for the 30 nm film. This work extends current understanding on interfacial coupling to metastable status and could help explore other systems in the perovskite structure with octahedral rotation.
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Affiliation(s)
- Bangmin Zhang
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
| | - Lijun Wu
- Condensed Matter Physics & Materials Science Division, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Xin Feng
- Department of Materials Science & Engineering, National University of Singapore, 117575, Singapore
| | - Chun Li
- Research Institute of Superconductor Electronics (RISE), School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
| | - Xinyang Miao
- Petroleum and Chemical Industry Federation Key Laboratory of Oil and Gas Terahertz Spectroscopy and Photoelectric Detection, China University of Petroleum, Beijing 102249, China
| | - Yajuan Hui
- Department of Materials Science & Engineering, National University of Singapore, 117575, Singapore
| | - Kun Zhao
- Petroleum and Chemical Industry Federation Key Laboratory of Oil and Gas Terahertz Spectroscopy and Photoelectric Detection, China University of Petroleum, Beijing 102249, China
| | - Jun Ding
- Department of Materials Science & Engineering, National University of Singapore, 117575, Singapore
| | - Biaobing Jin
- Research Institute of Superconductor Electronics (RISE), School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
| | - Jingsheng Chen
- Department of Materials Science & Engineering, National University of Singapore, 117575, Singapore
| | - Yimei Zhu
- Condensed Matter Physics & Materials Science Division, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Cheng-Jun Sun
- Advanced Photon Source, Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - Gan Moog Chow
- Department of Materials Science & Engineering, National University of Singapore, 117575, Singapore
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38
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Little DF, Snyder JS, Elhilali M. Ensemble modeling of auditory streaming reveals potential sources of bistability across the perceptual hierarchy. PLoS Comput Biol 2020; 16:e1007746. [PMID: 32275706 PMCID: PMC7185718 DOI: 10.1371/journal.pcbi.1007746] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 04/27/2020] [Accepted: 02/25/2020] [Indexed: 11/19/2022] Open
Abstract
Perceptual bistability-the spontaneous, irregular fluctuation of perception between two interpretations of a stimulus-occurs when observing a large variety of ambiguous stimulus configurations. This phenomenon has the potential to serve as a tool for, among other things, understanding how function varies across individuals due to the large individual differences that manifest during perceptual bistability. Yet it remains difficult to interpret the functional processes at work, without knowing where bistability arises during perception. In this study we explore the hypothesis that bistability originates from multiple sources distributed across the perceptual hierarchy. We develop a hierarchical model of auditory processing comprised of three distinct levels: a Peripheral, tonotopic analysis, a Central analysis computing features found more centrally in the auditory system, and an Object analysis, where sounds are segmented into different streams. We model bistable perception within this system by applying adaptation, inhibition and noise into one or all of the three levels of the hierarchy. We evaluate a large ensemble of variations of this hierarchical model, where each model has a different configuration of adaptation, inhibition and noise. This approach avoids the assumption that a single configuration must be invoked to explain the data. Each model is evaluated based on its ability to replicate two hallmarks of bistability during auditory streaming: the selectivity of bistability to specific stimulus configurations, and the characteristic log-normal pattern of perceptual switches. Consistent with a distributed origin, a broad range of model parameters across this hierarchy lead to a plausible form of perceptual bistability.
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Affiliation(s)
- David F. Little
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Joel S. Snyder
- Department of Psychology, University of Nevada, Las Vegas; Las Vegas, Nevada, United States of America
| | - Mounya Elhilali
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
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39
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Krzemiński D, Masuda N, Hamandi K, Singh KD, Routley B, Zhang J. Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy. Netw Neurosci 2020; 4:374-396. [PMID: 32537532 PMCID: PMC7286306 DOI: 10.1162/netn_a_00125] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 01/07/2020] [Indexed: 12/13/2022] Open
Abstract
Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices.
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Affiliation(s)
- Dominik Krzemiński
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Bethany Routley
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
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40
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Mao Y, Kanai R, Ding C, Bi T, Qiu J. Temporal variability of brain networks predicts individual differences in bistable perception. Neuropsychologia 2020; 142:107426. [PMID: 32147392 DOI: 10.1016/j.neuropsychologia.2020.107426] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 01/23/2020] [Accepted: 03/02/2020] [Indexed: 11/30/2022]
Abstract
When ambiguous visual stimuli are presented to the eyes, conscious perception can spontaneously alternate across the competing interpretations - which was known as bistable perception. The spontaneous alternation of perception might indicate a connection between bistable perception and the dynamic interaction of brain networks. Here, we hypothesized that individual differences in perceptual dynamics may be reflected in dynamics of spontaneous neural activities. To test this idea, we investigated the relationship between the percept duration and the reconfiguration patterns of dynamic brain networks as measured by the functional connectivity (FC) during the resting state. Firstly, we found that individual difference of percept duration is associated with the temporal variability of the brain regions which were previously reported in studies of bistable perception, including anterior cingulate cortex (ACC), dorsal medial prefrontal cortex (DMPFC), dorsal lateral prefrontal cortex (DLPFC), superior parietal lobule (SPL), inferior parietal lobule (IPL), precuneus, insula, and V5. Secondly, there is a positive relationship between the temporal variability within the frontal-parietal network (FPN) and the percept duration. Thirdly, our results indicated that individual difference of bistable perception was related to the dynamic interaction between large-scale functional networks including default mode network (DMN), FPN, cingulo-opercular network (CON), dorsal attention network (DAN), salience network (SN), memory retrieval network (MRN). Altogether, our results demonstrated that inter-individual variability in bistable perception was associated with dynamic coupling of brain regions and networks involved in primary visual processing, spatial attention, and cognitive control.
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Affiliation(s)
- Yu Mao
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China; Department of Psychology, Southwest University, Chongqing, 400715, China
| | - Ryota Kanai
- Araya, Inc., Tokyo, Japan; Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK
| | - Cody Ding
- Department of Psychology, Southwest University, Chongqing, 400715, China; Education Science & Professional Programs, University of Missouri-St. Louis, United States
| | - Taiyong Bi
- School of Management, Zunyi Medical University, Zunyi, 563000, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China; Department of Psychology, Southwest University, Chongqing, 400715, China.
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41
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Ezaki T, Fonseca Dos Reis E, Watanabe T, Sakaki M, Masuda N. Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence. Commun Biol 2020; 3:52. [PMID: 32015402 PMCID: PMC6997374 DOI: 10.1038/s42003-020-0774-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/13/2020] [Indexed: 01/05/2023] Open
Abstract
According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of "edge-of-chaos" neural computation.
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Affiliation(s)
- Takahiro Ezaki
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Meguro-ku, Tokyo, Japan
| | | | - Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AZ, UK
- RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Michiko Sakaki
- School of Psychology and Clinical Language Sciences, University of Reading, Earley Gate, Whiteknights Road, Reading, UK
- Research Institute, Kochi University of Technology, Kami, Kochi, Japan
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Clifton, Bristol, UK.
- Department of Mathematics, University at Buffalo, State University of New York, Buffalo, New York, USA.
- Computational and Data-Enabled Science and Engineering Program, University at Buffalo, State University of New York, Buffalo, New York, USA.
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42
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Kang J, Pae C, Park HJ. Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex. PLoS One 2019; 14:e0222161. [PMID: 31498822 PMCID: PMC6733463 DOI: 10.1371/journal.pone.0222161] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/22/2019] [Indexed: 11/19/2022] Open
Abstract
The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state transition processes of the human cerebral cortex system at rest by introducing a graph-theoretical analysis of the state transition network. The energy landscape analysis of brain state occurrences, estimated using the pairwise maximum entropy model for resting-state fMRI data, identified multiple local minima, some of which mediate multi-step transitions toward the global minimum. The state transition among local minima is clustered into two groups according to state transition rates and most inter-group state transitions were mediated by a hub transition state. The distance to the hub transition state determined the path length of the inter-group transition. The cortical system appeared to have redundancy in inter-group transitions when the hub transition state was removed. Such a hub-like organization of transition processes disappeared when the connectivity of the cortical system was altered from the resting-state configuration. In the state transition, the default mode network acts as a transition hub, while coactivation of the prefrontal cortex and default mode network is captured as the global minimum. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by a graph-theoretical analysis of the nonlinear state transition network of the brain.
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Affiliation(s)
- Jiyoung Kang
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
- * E-mail:
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43
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A Neuroanatomical Substrate Linking Perceptual Stability to Cognitive Rigidity in Autism. J Neurosci 2019; 39:6540-6554. [PMID: 31213484 PMCID: PMC6697400 DOI: 10.1523/jneurosci.2831-18.2019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 04/15/2019] [Accepted: 05/21/2019] [Indexed: 01/30/2023] Open
Abstract
Overly stable visual perception seen in individuals with autism spectrum disorder (ASD) is related to higher-order core symptoms of the condition. However, the neural basis by which these seemingly different symptoms are simultaneously observed in individuals with ASD remains unclear. Here, we aimed to identify such a neuroanatomical substrate linking perceptual stability to autistic cognitive rigidity, a part of core restricted, repetitive behaviors (RRBs). First, using a bistable visual perception test, we measured the perceptual stability of 22 high-functioning adults with ASD and 22 age-, IQ-, and sex-matched typically developing human individuals and confirmed overstable visual perception in autism. Next, using a spontaneous task-switching (TS) test, we showed that the individuals with ASD were more likely to repeat the same task voluntarily and spontaneously, and such rigid TS behavior was associated with the severity of their RRB symptoms. We then compared these perceptual and cognitive behaviors and found a significant correlation between them for individuals with ASD. Finally, we found that this behavioral link was supported by a smaller gray matter volume (GMV) of the posterior superior parietal lobule (pSPL) in individuals with ASD. Moreover, this smaller GMV in the pSPL was also associated with the RRB symptoms and replicated in two independent datasets. Our findings suggest that the pSPL could be one of the neuroanatomical mediators of cognitive and perceptual inflexibility in autism, which could help a unified biological understanding of the mechanisms underpinning diverse symptoms of this developmental disorder.SIGNIFICANCE STATEMENT Behavioral studies show perceptual overstability in autism spectrum disorder (ASD). However, the neural mechanisms by which such sensory symptoms can coexist and often correlate with seemingly separate core symptoms remain unknown. Here, we have identified such a key neuroanatomical substrate. We have revealed that overstable sensory perception of individuals with ASD is linked with their cognitive rigidity, a part of core restricted, repetitive behavior symptoms, and such a behavioral link is underpinned by a smaller gray matter volume in the posterior superior parietal lobule in autism. These findings uncover a key neuroanatomical mediator of autistic perceptual and cognitive inflexibility and would ignite future studies on how the core symptoms of ASD interact with its unique sensory perception.
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44
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Ye X, Zhu RL, Zhou XQ, He S, Wang K. Slower and Less Variable Binocular Rivalry Rates in Patients With Bipolar Disorder, OCD, Major Depression, and Schizophrenia. Front Neurosci 2019; 13:514. [PMID: 31164802 PMCID: PMC6536692 DOI: 10.3389/fnins.2019.00514] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 05/06/2019] [Indexed: 01/12/2023] Open
Abstract
When two different images are presented to the two eyes dichoptically, observers usually experience a perceptual alternation between the two images. This phenomenon, known as binocular rivalry, has been used as a powerful tool to investigate mechanisms of visual awareness. It was also found that the rates of perceptual alternation are slower in patients with bipolar disorder than in healthy controls (Pettigrew and Miller, 1998; Miller et al., 2003). To investigate the broader clinical relevance of binocular rivalry in psychiatric disorders, we measured the perceptual alternation rates during rivalry in healthy controls (n = 39) and in patients with different types of psychiatric disorders, including bipolar disorder type I (BD, n = 28), obsessive-compulsive disorder (OCD, n = 22), major depression (MD, n = 50), schizophrenia (SCZ, n = 44), and first-degree relatives (FDRs) of SCZ patients (n = 32). Participants viewed competing red-green images on a computer monitor through red-green anaglyph glasses and pressed buttons to record their perceptual alternations. The distributions of the rivalry rates were well described by a lognormal function in all groups. Critically, the median rate of perceptual alternation was 0.27 Hz for BD patients, 0.26 Hz for the OCD patients, 0.25 Hz for the MD patients, and 0.23 Hz and 0.27 Hz for the SCZ patients and their FDRs, respectively. All of which were significantly slower than the rate of 0.41 Hz obtained for the healthy controls, suggesting there may be shared genotypes between these different disorders. While rivalry alternations were generally slower in different types of psychiatric disorders compared to healthy controls, adding variance of rivalry rates in the analysis helped to partially separate among the different patient groups. Our results suggest that the slowing of binocular rivalry is likely due to certain common factors among the patient groups, but more subtle differences between different patient groups could be revealed when additional properties of rivalry dynamics are considered.
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Affiliation(s)
- Xing Ye
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Department of Neurology, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Ruo-Lin Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | | | - Sheng He
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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45
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Katyal S, He S, He B, Engel SA. Frequency of alpha oscillation predicts individual differences in perceptual stability during binocular rivalry. Hum Brain Mapp 2019; 40:2422-2433. [PMID: 30702190 DOI: 10.1002/hbm.24533] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 12/14/2018] [Accepted: 01/20/2019] [Indexed: 12/14/2022] Open
Abstract
When ambiguous visual stimuli have multiple interpretations, human perception can alternate between them, producing perceptual multistability. There is a large variation between individuals in how long stable percepts endure, on average, between switches, but the underlying neural basis of this individual difference in perceptual dynamics remains obscure. Here, we show that in one widely studied multistable paradigm-binocular rivalry-perceptual stability in individuals is predicted by the frequency of their neural oscillations within the alpha range (7-13 Hz). Our results suggest revising models of rivalry to incorporate effects of neural oscillations on perceptual alternations, and raise the possibility that a common factor may influence dynamics in many neural processes.
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Affiliation(s)
- Sucharit Katyal
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota
| | - Sheng He
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Stephen A Engel
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota
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46
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Kondo HM, Pressnitzer D, Shimada Y, Kochiyama T, Kashino M. Inhibition-excitation balance in the parietal cortex modulates volitional control for auditory and visual multistability. Sci Rep 2018; 8:14548. [PMID: 30267021 PMCID: PMC6162284 DOI: 10.1038/s41598-018-32892-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 09/18/2018] [Indexed: 11/25/2022] Open
Abstract
Perceptual organisation must select one interpretation from several alternatives to guide behaviour. Computational models suggest that this could be achieved through an interplay between inhibition and excitation across competing types of neural population coding for each interpretation. Here, to test for such models, we used magnetic resonance spectroscopy to measure non-invasively the concentrations of inhibitory γ-aminobutyric acid (GABA) and excitatory glutamate-glutamine (Glx) in several brain regions. Human participants first performed auditory and visual multistability tasks that produced spontaneous switching between percepts. Then, we observed that longer percept durations during behaviour were associated with higher GABA/Glx ratios in the sensory area coding for each modality. When participants were asked to voluntarily modulate their perception, a common factor across modalities emerged: the GABA/Glx ratio in the posterior parietal cortex tended to be positively correlated with the amount of effective volitional control. Our results provide direct evidence implicating that the balance between neural inhibition and excitation within sensory regions resolves perceptual competition. This powerful computational principle appears to be leveraged by both audition and vision, implemented independently across modalities, but modulated by an integrated control process.
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Affiliation(s)
- Hirohito M Kondo
- School of Psychology, Chukyo University, Nagoya, Aichi, Japan.
- Human Information Science Laboratory, NTT Communication Science Laboratories, NTT Corporation, Atsugi, Kanagawa, Japan.
| | - Daniel Pressnitzer
- Laboratoire des Systèmes Perceptifs, CNRS UMR 8248, Paris, France
- Département d'Études Cognitive, École Normale Supérieure, Paris, France
| | - Yasuhiro Shimada
- Brain Activity Imaging Center, ATR-Promotions, Seika-cho, Kyoto, Japan
| | - Takanori Kochiyama
- Brain Activity Imaging Center, ATR-Promotions, Seika-cho, Kyoto, Japan
- Department of Cognitive Neuroscience, Advanced Telecommunications Research Institute International, Seika-cho, Kyoto, Japan
| | - Makio Kashino
- Sports Brain Science Project, NTT Communication Science Laboratories, NTT Corporation, Atsugi, Kanagawa, Japan
- School of Engineering, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
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47
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Genomic Analyses of Visual Cognition: Perceptual Rivalry and Top-Down Control. J Neurosci 2018; 38:9668-9678. [PMID: 30242048 DOI: 10.1523/jneurosci.1970-17.2018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 09/10/2018] [Accepted: 09/11/2018] [Indexed: 11/21/2022] Open
Abstract
Visual cognition in humans has traditionally been studied with cognitive behavioral methods and brain imaging, but much less with genetic methods. Perceptual rivalry, an important phenomenon in visual cognition, is the spontaneous perceptual alternation that occurs between two distinct interpretations of a physically constant visual stimulus (e.g., binocular rivalry stimuli) or a perceptually ambiguous stimulus (e.g., the Necker cube). The switching rate varies dramatically across individuals and can be voluntarily modulated by observers. Here, we adopted a genomic approach to systematically investigate the genetics underlying binocular rivalry, Necker cube rivalry and voluntary modulation of Necker cube rivalry in young Chinese adults (Homo sapiens, 81% female, 20 ± 1 years old) at multiple levels, including common single nucleotide polymorphism (SNP)-based heritability estimation, SNP-based genome-wide association study (GWAS), gene-based analysis, and pathway analysis. We performed a pilot GWAS in 2441 individuals and replicated it in an independent cohort of 943 individuals. Common SNP-based heritability was estimated to be 25% for spontaneous perceptual rivalry. SNPs rs184765639 and rs75595941 were associated with voluntary modulation, and imaging data suggested genotypic difference of rs184765639 in the surface area of the left caudal-middle frontal cortex. Additionally, converging evidence from multilevel analyses associated genes such as PRMT1 with perceptual switching rate, and MIR1178 with voluntary modulation strength. In summary, this study discovered specific genetic contributions to perceptual rivalry and its voluntary modulation in human beings. These findings may promote our understanding of psychiatric disorders, as perceptual rivalry is a potential psychiatric biomarker.SIGNIFICANCE STATEMENT Perceptual rivalry is an important visual phenomenon in which our perception of a physically constant visual input spontaneously switches between two different states. There are individual variations in perceptual switching rate and voluntary modulation strength. Our genomic analyses reveal several loci associated with these two kinds of variation. Because perceptual rivalry is thought to be relevant to and potentially an endophenotype for psychiatric disorders, these results may help understand not only visual cognition, but also psychiatric disorders.
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48
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Tozzi A, Peters JF, Çankaya MN. The informational entropy endowed in cortical oscillations. Cogn Neurodyn 2018; 12:501-507. [PMID: 30250628 DOI: 10.1007/s11571-018-9491-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 05/31/2018] [Accepted: 06/14/2018] [Indexed: 12/20/2022] Open
Abstract
A two-dimensional shadow may encompass more information than its corresponding three-dimensional object. Indeed, if we rotate the object, we achieve a pool of observed shadows from different angulations, gradients, shapes and variable length contours that make it possible for us to increase our available information. Starting from this simple observation, we show how informational entropies might turn out to be useful in the evaluation of scale-free dynamics in the brain. Indeed, brain activity exhibits a scale-free distribution that leads to the variations in the power law exponent typical of different functional neurophysiological states. Here we show that modifications in scaling slope are associated with variations in Rényi entropy, a generalization of Shannon informational entropy. From a three-dimensional object's perspective, by changing its orientation (standing for the cortical scale-free exponent), we detect different two-dimensional shadows from different perception angles (standing for Rényi entropy in different brain areas). We show how, starting from known values of Rényi entropy (easily detectable in brain fMRIs or EEG traces), it is feasible to calculate the scaling slope in a given moment and in a given brain area. Because changes in scale-free cortical dynamics modify brain activity, this issue points towards novel approaches to mind reading and description of the forces required for transcranial stimulation.
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Affiliation(s)
- Arturo Tozzi
- 1Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB R3T 5V6 Canada
| | - James F Peters
- 2Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB R3T 5V6 Canada
- 3Department of Mathematics, Faculty of Arts and Sciences, Adıyaman University, 02040 Adıyaman, Turkey
| | - Mehmet Niyazi Çankaya
- 4Applied Sciences School, Department of International Trading, Department of Statistics, Faculty of Arts and Science, Usak University, Usak, Turkey
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49
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Ezaki T, Sakaki M, Watanabe T, Masuda N. Age-related changes in the ease of dynamical transitions in human brain activity. Hum Brain Mapp 2018; 39:2673-2688. [PMID: 29524289 PMCID: PMC6619404 DOI: 10.1002/hbm.24033] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Revised: 02/27/2018] [Accepted: 02/27/2018] [Indexed: 01/07/2023] Open
Abstract
Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural systems, the age-related decline in executive functions should be underpinned by alteration of large-scale neural dynamics. However, the effects of age on brain dynamics have not been firmly formulated. Here, we investigate such age-related changes in brain dynamics by applying "energy landscape analysis" to publicly available functional magnetic resonance imaging data from healthy younger and older human adults. We quantified the ease of dynamical transitions between different major patterns of brain activity, and estimated it for the default mode network (DMN) and the cingulo-opercular network (CON) separately. We found that the two age groups shared qualitatively the same trajectories of brain dynamics in both the DMN and CON. However, in both of networks, the ease of transitions was significantly smaller in the older than the younger group. Moreover, the ease of transitions was associated with the performance in executive function tasks in a doubly dissociated manner: for the younger adults, the ability of executive functions was mainly correlated with the ease of transitions in the CON, whereas that for the older adults was specifically associated with the ease of transitions in the DMN. These results provide direct biological evidence for age-related changes in macroscopic brain dynamics and suggest that such neural dynamics play key roles when individuals carry out cognitively demanding tasks.
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Affiliation(s)
- Takahiro Ezaki
- PRESTO, JST, 4‐1‐8 HonchoKawaguchiSaitamaJapan
- National Institute of Informatics, HitotsubashiChiyoda‐kuTokyoJapan
- Kawarabayashi Large Graph Project, ERARO, JST, c/o Global Research Center for Big Data Mathematics, NIIChiyoda‐kuTokyoJapan
| | - Michiko Sakaki
- School of Psychology and Clinical Language SciencesUniversity of Reading, Earley Gate, Whiteknights RoadReadingUnited Kingdom
- Research Institute, Kochi University of TechnologyKamiKochiJapan
| | - Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, 17 Queen SquareLondonWC1N 3AZUnited Kingdom
| | - Naoki Masuda
- Department of Engineering MathematicsUniversity of BristolCliftonBristolUnited Kingdom
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50
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Gu S, Cieslak M, Baird B, Muldoon SF, Grafton ST, Pasqualetti F, Bassett DS. The Energy Landscape of Neurophysiological Activity Implicit in Brain Network Structure. Sci Rep 2018; 8:2507. [PMID: 29410486 PMCID: PMC5802783 DOI: 10.1038/s41598-018-20123-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 01/08/2018] [Indexed: 01/03/2023] Open
Abstract
A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of the brain. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states - characterized by minimal energy - display common activation profiles across brain areas: local spatially-contiguous sets of brain regions reminiscent of cognitive systems are co-activated frequently. The predicted activation rate of these systems is highly correlated with the observed activation rate measured in a separate resting state fMRI data set, validating the utility of the maximum entropy model in describing neurophysiological dynamics. This approach also offers a formal notion of the energy of activity within a system, and the energy of activity shared between systems. We observe that within- and between-system energies cleanly separate cognitive systems into distinct categories, optimized for differential contributions to integrated versus segregated function. These results support the notion that energetic and structural constraints circumscribe brain dynamics, offering insights into the roles that cognitive systems play in driving whole-brain activation patterns.
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Affiliation(s)
- Shi Gu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Benjamin Baird
- Center for Sleep and Consciousness, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Sarah F Muldoon
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA, 92521, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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