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Jia H, Wu X, Zhang X, Guo M, Yang C, Wang E. Resting-state EEG Microstate Features Can Quantitatively Predict Autistic Traits in Typically Developing Individuals. Brain Topogr 2024; 37:410-419. [PMID: 37833486 DOI: 10.1007/s10548-023-01010-6] [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: 04/22/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
Autism spectrum disorder (ASD) is not a discrete disorder and that symptoms of ASD (i.e., so-called "autistic traits") are found to varying degrees in the general population. Typically developing individuals with sub-clinical yet high-level autistic traits have similar abnormities both in behavioral performances and cortical activation patterns to individuals diagnosed with ASD. Thus it's crucial to develop objective and efficient tools that could be used in the assessment of autistic traits. Here, we proposed a novel machine learning-based assessment of the autistic traits using EEG microstate features derived from a brief resting-state EEG recording. The results showed that: (1) through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and correlation analysis, the mean duration of microstate class D, the occurrence rate of microstate class A, the time coverage of microstate class D and the transition rate from microstate class B to D were selected to be crucial microstate features which could be used in autistic traits prediction; (2) in the support vector regression (SVR) model, which was constructed to predict the participants' autistic trait scores using these four microstate features, the out-of-sample predicted autistic trait scores showed a significant and good match with the self-reported scores. These results suggest that the resting-state EEG microstate analysis technique can be used to predict autistic trait to some extent.
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Affiliation(s)
- Huibin Jia
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Xiangci Wu
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Xiaolin Zhang
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Meiling Guo
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Chunying Yang
- School of Special Education, Zhengzhou Normal University, Zhengzhou, 450000, China.
| | - Enguo Wang
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China.
- School of Psychology, Henan University, Kaifeng, 475004, China.
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Yang L, Lu J, Li D, Xiang J, Yan T, Sun J, Wang B. Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models. Brain Sci 2023; 13:1133. [PMID: 37626490 PMCID: PMC10452161 DOI: 10.3390/brainsci13081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.
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Affiliation(s)
- Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Ting Yan
- Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, China;
| | - Jie Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
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Yokoyama H, Kitajo K. Detecting changes in dynamical structures in synchronous neural oscillations using probabilistic inference. Neuroimage 2022; 252:119052. [PMID: 35247547 DOI: 10.1016/j.neuroimage.2022.119052] [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: 07/28/2021] [Revised: 12/06/2021] [Accepted: 03/01/2022] [Indexed: 11/28/2022] Open
Abstract
Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with the posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.
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Affiliation(s)
- Hiroshi Yokoyama
- Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, 444-8585, Japan; Department of Physiological Sciences, School of Life Science, Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, 444-8585, Japan.
| | - Keiichi Kitajo
- Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, 444-8585, Japan; Department of Physiological Sciences, School of Life Science, Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, 444-8585, Japan.
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Takeda Y, Hiroe N, Yamashita O. Whole-brain propagating patterns in human resting-state brain activities. Neuroimage 2021; 245:118711. [PMID: 34793956 DOI: 10.1016/j.neuroimage.2021.118711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/15/2021] [Accepted: 11/04/2021] [Indexed: 11/17/2022] Open
Abstract
Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. "Whole-brain" propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.
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Affiliation(s)
- Yusuke Takeda
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
| | - Nobuo Hiroe
- Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
| | - Okito Yamashita
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
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Onojima T, Kitajo K. A state-informed stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter. J Neural Eng 2021; 18. [PMID: 34644689 DOI: 10.1088/1741-2552/ac2f7b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 10/13/2021] [Indexed: 11/11/2022]
Abstract
Objective.We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for state-informed sensory stimulation in electroencephalography (EEG) experiments.Approach.The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation.Main results.Our method showed higher accuracy in predicting the EEG phase than the conventional autoregressive (AR) model-based method.Significance.A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated AR model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.
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Affiliation(s)
- Takayuki Onojima
- CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako 351-0198, Japan
| | - Keiichi Kitajo
- CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako 351-0198, Japan.,Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8585, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki 444-8585, Japan
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Jia H, Gao F, Yu D. Altered Temporal Structure of Neural Phase Synchrony in Patients With Autism Spectrum Disorder. Front Psychiatry 2021; 12:618573. [PMID: 34899403 PMCID: PMC8660096 DOI: 10.3389/fpsyt.2021.618573] [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: 10/17/2020] [Accepted: 10/20/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity, quantified by phase synchrony, between brain regions is known to be aberrant in patients with autism spectrum disorder (ASD). Here, we evaluated the long-range temporal correlations of time-varying phase synchrony (TV-PS) of electrocortical oscillations in patients with ASD as well as typically developing people using detrended fluctuation analysis (DFA) after validating the scale-invariance of the TV-PS time series. By comparing the DFA exponents between the two groups, we found that those of the TV-PS time series of high-gamma oscillations were significantly attenuated in patients with ASD. Furthermore, the regions involved in aberrant TV-PS time series were mainly within the social ability and cognition-related cortical networks. These results support the notion that abnormal social functions observed in patients with ASD may be caused by the highly volatile phase synchrony states of electrocortical oscillations.
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Affiliation(s)
- Huibin Jia
- Institute of Cognition, Brain and Health, Henan University, Kaifeng, China.,School of Psychology, Henan University, Kaifeng, China.,Institute of Psychology and Behavior, Henan University, Kaifeng, China.,Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - Fei Gao
- Department of Pain Medicine, Peking University People's Hospital, Beijing, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
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