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Ebato Y, Nobukawa S, Sakemi Y, Nishimura H, Kanamaru T, Sviridova N, Aihara K. Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks. Sci Rep 2024; 14:8631. [PMID: 38622178 PMCID: PMC11018609 DOI: 10.1038/s41598-024-59143-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/08/2024] [Indexed: 04/17/2024] Open
Abstract
The echo state network (ESN) is an excellent machine learning model for processing time-series data. This model, utilising the response of a recurrent neural network, called a reservoir, to input signals, achieves high training efficiency. Introducing time-history terms into the neuron model of the reservoir is known to improve the time-series prediction performance of ESN, yet the reasons for this improvement have not been quantitatively explained in terms of reservoir dynamics characteristics. Therefore, we hypothesised that the performance enhancement brought about by time-history terms could be explained by delay capacity, a recently proposed metric for assessing the memory performance of reservoirs. To test this hypothesis, we conducted comparative experiments using ESN models with time-history terms, namely leaky integrator ESNs (LI-ESN) and chaotic echo state networks (ChESN). The results suggest that compared with ESNs without time-history terms, the reservoir dynamics of LI-ESN and ChESN can maintain diversity and stability while possessing higher delay capacity, leading to their superior performance. Explaining ESN performance through dynamical metrics are crucial for evaluating the numerous ESN architectures recently proposed from a general perspective and for the development of more sophisticated architectures, and this study contributes to such efforts.
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Affiliation(s)
- Yudai Ebato
- Graduate School of Information and Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan.
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8551, Japan
- Research Center for Mathematical Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan
| | - Yusuke Sakemi
- Research Center for Mathematical Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, 7 choume 3-1 Hongou, Bunkyu ku, Tokyo, 113-8654, Japan
| | - Haruhiko Nishimura
- Faculty of Informatics, Yamato University, 2-5-1 Katanama chou, Suita, Osaka, 564-0082, Japan
| | - Takashi Kanamaru
- Department of Mechanical Science and Engineering, School of Advanced Engineering, Kogakuin University, 2665-1 Nakano chou, Hachioji, Tokyo, 192-0015, Japan
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, 7 choume 3-1 Hongou, Bunkyu ku, Tokyo, 113-8654, Japan
| | - Nina Sviridova
- Department of Intelligent Systems, Tokyo City University, 1 choume 28-1 Tamazutsumi, Setagaya, Tokyo, 158-8557, Japan
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, 7 choume 3-1 Hongou, Bunkyu ku, Tokyo, 113-8654, Japan
| | - Kazuyuki Aihara
- Research Center for Mathematical Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, 7 choume 3-1 Hongou, Bunkyu ku, Tokyo, 113-8654, Japan
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Nobukawa S, Ikeda T, Kikuchi M, Takahashi T. Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease. Sci Rep 2024; 14:88. [PMID: 38167950 PMCID: PMC10761722 DOI: 10.1038/s41598-023-50265-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive functions produced by large-scale neural integrations are the most representative 'emergence phenomena' in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer's disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Research Center for Mathematical Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, 187-8661, Tokyo, Japan.
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Psychiatry and Behavioral Science, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuoka, Yoshida, 910-1193, Fukui, Japan
- Uozu Shinkei Sanatorium, 1784-1 Eguchi, Uozu, 937-0017, Toyama, Japan
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Nobukawa S, Takahashi T. Editorial: Perspectives in brain-network dynamics in computational psychiatry. Front Comput Neurosci 2023; 17:1290089. [PMID: 37808339 PMCID: PMC10556857 DOI: 10.3389/fncom.2023.1290089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023] Open
Affiliation(s)
- Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
- Research Centre for Mathematical Engineering, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Medicine Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Centre for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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Doho H, Nishimura H, Nobukawa S. Dynamic Pattern Recognition Model Based on Neural Network Response to Signal Fluctuation. JACIII 2023. [DOI: 10.20965/jaciii.2023.p0044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
We have proposed a model of dynamic retrieval in associative memory based on temporal input/output correlations under a stimulus-response open scheme of neural networks. This mechanism is different from that of the conventional stationary Hopfield model in which the input signal is used only as information for the initial state of the network. Building upon the fundamental properties of the proposed model, in this paper, we newly evaluate the dependence of identification performance on the signal fluctuation level and on the number of stored patterns by introducing an accuracy rate for known (stored) and unknown (non-stored) patterns, based on the network correlation to the input signal with fluctuation. The results indicate that the dynamic scheme of network response to a fluctuating signal leads to increased efficacy and usefulness.
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Affiliation(s)
- Hirotaka Doho
- Faculty of Education, Kochi University, 2-5-1 Akebono-cho, Kochi 780-8520, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Minatojima-Minami-cho, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan
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Tobe M, Nobukawa S, Mizukami K, Kawaguchi M, Higashima M, Tanaka Y, Yamanishi T, Takahashi T. Hub structure in functional network of EEG signals supporting high cognitive functions in older individuals. Front Aging Neurosci 2023; 15:1130428. [PMID: 37139091 PMCID: PMC10149684 DOI: 10.3389/fnagi.2023.1130428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/15/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction Maintaining high cognitive functions is desirable for "wellbeing" in old age and is particularly relevant to a super-aging society. According to their individual cognitive functions, optimal intervention for older individuals facilitates the maintenance of cognitive functions. Cognitive function is a result of whole-brain interactions. These interactions are reflected in several measures in graph theory analysis for the topological characteristics of functional connectivity. Betweenness centrality (BC), which can identify the "hub" node, i.e., the most important node affecting whole-brain network activity, may be appropriate for capturing whole-brain interactions. During the past decade, BC has been applied to capture changes in brain networks related to cognitive deficits arising from pathological conditions. In this study, we hypothesized that the hub structure of functional networks would reflect cognitive function, even in healthy elderly individuals. Method To test this hypothesis, based on the BC value of the functional connectivity obtained using the phase lag index from the electroencephalogram under the eyes closed resting state, we examined the relationship between the BC value and cognitive function measured using the Five Cognitive Functions test total score. Results We found a significant positive correlation of BC with cognitive functioning and a significant enhancement in the BC value of individuals with high cognitive functioning, particularly in the frontal theta network. Discussion The hub structure may reflect the sophisticated integration and transmission of information in whole-brain networks to support high-level cognitive function. Our findings may contribute to the development of biomarkers for assessing cognitive function, enabling optimal interventions for maintaining cognitive function in older individuals.
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Affiliation(s)
- Mayuna Tobe
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
- Research Center for Mathematical Engineering, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- *Correspondence: Sou Nobukawa
| | - Kimiko Mizukami
- Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Megumi Kawaguchi
- Department of Nursing, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
| | | | | | | | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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Wagatsuma N, Shimomura H, Nobukawa S. Disinhibitory circuit mediated by connections from vasoactive intestinal polypeptide to somatostatin interneurons underlies the paradoxical decrease in spike synchrony with increased border ownership selective neuron firing rate. Front Comput Neurosci 2022; 16:988715. [PMID: 36405781 PMCID: PMC9672816 DOI: 10.3389/fncom.2022.988715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
The activity of border ownership selective (BOS) neurons in intermediate-level visual areas indicates which side of a contour owns a border relative to its classical receptive field and provides a fundamental component of figure-ground segregation. A physiological study reported that selective attention facilitates the activity of BOS neurons with a consistent border ownership preference, defined as two neurons tuned to respond to the same visual object. However, spike synchrony between this pair is significantly suppressed by selective attention. These neurophysiological findings are derived from a biologically-plausible microcircuit model consisting of spiking neurons including two subtypes of inhibitory interneurons, somatostatin (SOM) and vasoactive intestinal polypeptide (VIP) interneurons, and excitatory BOS model neurons. In our proposed model, BOS neurons and SOM interneurons cooperate and interact with each other. VIP interneurons not only suppress SOM interneuron responses but also are activated by feedback signals mediating selective attention, which leads to disinhibition of BOS neurons when they are directing selective attention toward an object. Our results suggest that disinhibition arising from the synaptic connections from VIP to SOM interneurons plays a critical role in attentional modulation of neurons in intermediate-level visual areas.
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Affiliation(s)
- Nobuhiko Wagatsuma
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Japan
- *Correspondence: Nobuhiko Wagatsuma,
| | - Haruka Shimomura
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Center of Neurology and Psychiatry, Kodaira, Japan
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Kumano H, Nobukawa S, Shirama A, Takahashi T, Takeda T, Ohta H, Kikuchi M, Iwanami A, Kato N, Toda S. Asymmetric Complexity in a Pupil Control Model with Laterally Imbalanced Neural Activity in the Locus Coeruleus: A Potential Biomarker for Attention-Deficit/Hyperactivity Disorder. Neural Comput 2022; 34:2388-2407. [DOI: 10.1162/neco_a_01545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 07/23/2022] [Indexed: 11/07/2022]
Abstract
Abstract
Locus coeruleus (LC) overactivity, especially in the right hemisphere, is a recognized pathophysiology of attention-deficit/hyperactivity disorder (ADHD) and may be related to inattention. LC activity synchronizes with the kinetics of the pupil diameter and reflects neural activity related to cognitive functions such as attention and arousal. Recent studies highlight the importance of the complexity of the temporal patterns of pupil diameter. Moreover, asymmetrical pupil diameter, which correlates with the severity of inattention, impulsivity, and hyperactivity in ADHD, might be attributed to a left-right imbalance in LC activity. We recently constructed a computational model of pupil diameter based on the newly discovered contralateral projection from the LC to the Edinger–Westphal nucleus (EWN), which demonstrated mechanisms for the complex temporal patterns of pupil kinetics; however, it remains unclear how LC overactivity and its asymmetry affect pupil diameter. We hypothesized that a neural model of pupil diameter control featuring left-right differences in LC activity and projections onto two opponent sides may clarify the role of pupil behavior in ADHD studies. Therefore, we developed a pupil diameter control model reflecting LC overactivity in the right hemisphere by incorporating a contralateral projection from the LC to EWN and evaluated the complexity of the temporal patterns of pupil diameter generated by the model. Upon comparisons with experimentally measured pupil diameters in adult patients with ADHD, the parameter region of interest of the neural model was estimated, which was a region in the two-dimensional plot of complexity versus left-side LC baseline activity and that of the right. A region resulting in relatively high right-side complexity, which corresponded to the pathophysiological indexes, was identified. We anticipate that the discovery of lateralization of complexity in pupil diameter fluctuations will facilitate the development of biomarkers for accurate diagnosis of ADHD.
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Affiliation(s)
- Hiraku Kumano
- Department of Computer Science, Chiba Institute of Technology, Narashino, Chiba 275-0016, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Chiba 275-0016, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center for Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Aya Shirama
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center for Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Ishikawa 187-8551, Japan
- Department of Neuropsychiatry, University of Fukui, Fukui 910-1193, Japan
- Uozu Shinkei Sanatorium, Uozu 937-0017, Japan
| | | | - Haruhisa Ohta
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development and Department of Psychiatry and Behavioral Science, Kanazawa University, Ishikawa 920-8640, Japan
| | - Akira Iwanami
- Department of Psychiatry, School of Medicine, Showa University, Tokyo 157-8577, Japan
| | - Nobumasa Kato
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
| | - Shigenobu Toda
- Department of Psychiatry and Behavioral Science, Kanazawa University, Ishikawa 920-8640, Japan
- Department of Psychiatry, Showa University East Hospital, Showa University, Tokyo 142-0054, Japan
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Iinuma Y, Nobukawa S, Mizukami K, Kawaguchi M, Higashima M, Tanaka Y, Yamanishi T, Takahashi T. Enhanced temporal complexity of EEG signals in older individuals with high cognitive functions. Front Neurosci 2022; 16:878495. [PMID: 36213750 PMCID: PMC9533123 DOI: 10.3389/fnins.2022.878495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Recent studies suggest that the maintenance of cognitive function in the later life of older people is an essential factor contributing to mental wellbeing and physical health. Particularly, the risk of depression, sleep disorders, and Alzheimer's disease significantly increases in patients with mild cognitive impairment. To develop early treatment and prevention strategies for cognitive decline, it is necessary to individually identify the current state of cognitive function since the progression of cognitive decline varies among individuals. Therefore, the development of biomarkers that allow easier measurement of cognitive function in older individuals is relevant for hyperaged societies. One of the methods used to estimate cognitive function focuses on the temporal complexity of electroencephalography (EEG) signals. The characteristics of temporal complexity depend on the time scale, which reflects the range of neuron functional interactions. To capture the dynamics, composed of multiple time scales, multiscale entropy (MSE) analysis is effective for comprehensively assessing the neural activity underlying cognitive function in the brain. Thus, we hypothesized that EEG complexity analysis could serve to assess a wide range of cognitive functions in older adults. To validate our hypothesis, we divided older participants into two groups based on their cognitive function test scores: a high cognitive function group and a low cognitive function group, and applied MSE analysis to the measured EEG data of all participants. The results of the repeated-measures analysis of covariance using age and sex as a covariate in the MSE profile showed a significant difference between the high and low cognitive function groups (F = 10.18, p = 0.003) and the interaction of the group × electrodes (F = 3.93, p = 0.002). Subsequently, the results of the post-hoct-test showed high complexity on a slower time scale in the frontal, parietal, and temporal lobes in the high cognitive function group. This high complexity on a slow time scale reflects the activation of long-distance neural interactions among various brain regions to achieve high cognitive functions. This finding could facilitate the development of a tool for diagnosis of cognitive decline in older individuals.
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Affiliation(s)
- Yuta Iinuma
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Center of Neurology and Psychiatry, National Institute of Mental Health, Tokyo, Japan
- *Correspondence: Sou Nobukawa
| | - Kimiko Mizukami
- Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Megumi Kawaguchi
- Department of Nursing, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
| | | | | | | | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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Wagatsuma N, Nobukawa S, Fukai T. A microcircuit model involving parvalbumin, somatostatin, and vasoactive intestinal polypeptide inhibitory interneurons for the modulation of neuronal oscillation during visual processing. Cereb Cortex 2022; 33:4459-4477. [PMID: 36130096 PMCID: PMC10110453 DOI: 10.1093/cercor/bhac355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/12/2022] Open
Abstract
Various subtypes of inhibitory interneurons contact one another to organize cortical networks. Most cortical inhibitory interneurons express 1 of 3 genes: parvalbumin (PV), somatostatin (SOM), or vasoactive intestinal polypeptide (VIP). This diversity of inhibition allows the flexible regulation of neuronal responses within and between cortical areas. However, the exact roles of these interneuron subtypes and of excitatory pyramidal (Pyr) neurons in regulating neuronal network activity and establishing perception (via interactions between feedforward sensory and feedback attentional signals) remain largely unknown. To explore the regulatory roles of distinct neuronal types in cortical computation, we developed a computational microcircuit model with biologically plausible visual cortex layers 2/3 that combined Pyr neurons and the 3 inhibitory interneuron subtypes to generate network activity. In simulations with our model, inhibitory signals from PV and SOM neurons preferentially induced neuronal firing at gamma (30-80 Hz) and beta (20-30 Hz) frequencies, respectively, in agreement with observed physiological results. Furthermore, our model indicated that rapid inhibition from VIP to SOM subtypes underlies marked attentional modulation for low-gamma frequency (30-50 Hz) in Pyr neuron responses. Our results suggest the distinct but cooperative roles of inhibitory interneuron subtypes in the establishment of visual perception.
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Affiliation(s)
- Nobuhiko Wagatsuma
- Faculty of Science, Toho University, 2-2-1 Miyama, Funabashi, Chiba 274-8510, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan.,Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8502, Japan
| | - Tomoki Fukai
- Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa 904-0495, Japan
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Iinuma Y, Nobukawa S, Nishimura H, Takahashi T. Dynamic Characteristics of State Transitions Composed of Neural Activity in the Brain by Circadian Rhythms. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:152-157. [PMID: 36085992 DOI: 10.1109/embc48229.2022.9871057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In recent years, as a treatment for mental disorders in addition to drug treatment, a non-drug treatment called chronotherapy has been attracting attention. However, the achievement of optimized chronotherapy for each subject's condition requires that the disturbance of the patient's circadian rhythm must be captured over a long duration. Therefore, it is necessary to develop biomarkers that are easy to measure, quantitative, and continuously measured. Complexity analysis of electroencephalograms revealed specific patterns related to circadian rhythms. However, such complexity analysis cannot capture variability in spatial patterns, although moment-to-moment temporal dynamic characteristics can be captured. Therefore, it is necessary to evaluate the dynamic characteristics of the interaction of neural activity throughout the brain. To evaluate the dynamic whole-brain interaction, we proposed a new microstate approach based on the instantaneous frequency distribution. In this context, we hypothesized that it would be possible to detect circadian rhythms using the microstate approach. In this study, to clarify the dynamic interactions of the entire neural network of the brain by circadian rhythms, we measured EEG data at day and night, and detected dynamic state transitions based on the instantaneous frequency distribution of the whole brain from EEG. The results showed the probability of transition among region-specific phase-leading states related to circadian rhythms. This finding might be widely utilized to detect circadian rhythms in healthy and pathological conditions.
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11
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Ando M, Nobukawa S, Kikuchi M, Takahashi T. Alteration of Neural Network Activity With Aging Focusing on Temporal Complexity and Functional Connectivity Within Electroencephalography. Front Aging Neurosci 2022; 14:793298. [PMID: 35185527 PMCID: PMC8855040 DOI: 10.3389/fnagi.2022.793298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022] Open
Abstract
With the aging process, brain functions, such as attention, memory, and cognitive functions, degrade over time. In a super-aging society, the alteration of neural activity owing to aging is considered crucial for interventions for the prevention of brain dysfunction. The complexity of temporal neural fluctuations with temporal scale dependency plays an important role in optimal brain information processing, such as perception and thinking. Complexity analysis is a useful approach for detecting cortical alteration in healthy individuals, as well as in pathological conditions, such as senile psychiatric disorders, resulting in changes in neural activity interactions among a wide range of brain regions. Multi-fractal (MF) and multi-scale entropy (MSE) analyses are known methods for capturing the complexity of temporal scale dependency of neural activity in the brain. MF and MSE analyses exhibit high accuracy in detecting changes in neural activity and are superior with regard to complexity detection when compared with other methods. In addition to complex temporal fluctuations, functional connectivity reflects the integration of information of brain processes in each region, described as mutual interactions of neural activity among brain regions. Thus, we hypothesized that the complementary relationship between functional connectivity and complexity could improve the ability to detect the alteration of spatiotemporal patterns observed on electroencephalography (EEG) with respect to aging. To prove this hypothesis, this study investigated the relationship between the complexity of neural activity and functional connectivity in aging based on EEG findings. Concretely, MF and MSE analyses were performed to evaluate the temporal complexity profiles, and phase lag index analyses assessing the unique profile of functional connectivity were performed based on the EEGs conducted for young and older participants. Subsequently, these profiles were combined through machine learning. We found that the complementary relationship between complexity and functional connectivity improves the classification accuracy among aging participants. Thus, the outcome of this study could be beneficial in formulating interventions for the prevention of age-related brain dysfunction.
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Affiliation(s)
- Momo Ando
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- *Correspondence: Sou Nobukawa
| | - Mitsuru Kikuchi
- Department of Psychiatry and Behavioral Science, Kanazawa University, Ishikawa, Japan
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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12
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Nobukawa S, Wagatsuma N, Ikeda T, Hasegawa C, Kikuchi M, Takahashi T. Effect of steady-state response versus excitatory/inhibitory balance on spiking synchronization in neural networks with log-normal synaptic weight distribution. Cogn Neurodyn 2021; 16:871-885. [PMID: 35847535 PMCID: PMC9279535 DOI: 10.1007/s11571-021-09757-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 10/22/2021] [Accepted: 11/14/2021] [Indexed: 01/18/2023] Open
Abstract
AbstractSynchronization of neural activity, especially at the gamma band, contributes to perceptual functions. In several psychiatric disorders, deficits of perceptual functions are reflected in synchronization abnormalities. Plausible cause of this impairment is an alteration in the balance between excitation and inhibition (E/I balance); a disruption in the E/I balance leads to abnormal neural interactions reminiscent of pathological states. Moreover, the local lateral excitatory-excitatory synaptic connections in the cortex exhibit excitatory postsynaptic potentials (EPSPs) that follow a log-normal amplitude distribution. This long-tailed distribution is considered an important factor for the emergence of spatiotemporal neural activity. In this context, we hypothesized that manipulating the EPSP distribution under abnormal E/I balance conditions would provide insights into psychiatric disorders characterized by deficits in perceptual functions, potentially revealing the mechanisms underlying pathological neural behaviors. In this study, we evaluated the synchronization of neural activity with external periodic stimuli in spiking neural networks in cases of both E/I balance and imbalance with or without a long-tailed EPSP amplitude distribution. The results showed that external stimuli of a high frequency lead to a decrease in the degree of synchronization with an increasing ratio of excitatory to inhibitory neurons in the presence, but not in the absence, of high-amplitude EPSPs. This monotonic reduction can be interpreted as an autonomous, strong-EPSP-dependent spiking activity selectively interfering with the responses to external stimuli. This observation is consistent with pathological findings. Thus, our modeling approach has potential to improve the understanding of the steady-state response in both healthy and pathological states.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2–17–1 Tsudanuma, Narashino, Chiba 275–0016 Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8551 Japan
| | - Nobuhiko Wagatsuma
- Faculty of Science, Department of Information Science, Toho University, Chiba, Japan
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Osaka, Japan
| | - Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Osaka, Japan
- Department of Psychiatry and Behavioral Science, Kanazawa University, Kanazawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Toyama, Japan
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13
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Inagaki K, Wagatsuma N, Nobukawa S. The Effects of Driving Experience on the P300 Event-Related Potential during the Perception of Traffic Scenes. Int J Environ Res Public Health 2021; 18:10396. [PMID: 34639696 PMCID: PMC8507739 DOI: 10.3390/ijerph181910396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/18/2021] [Accepted: 09/29/2021] [Indexed: 11/30/2022]
Abstract
The incidence of human-error-related traffic collisions is markedly reduced among drivers who have few years of driving experience compared with those with little driving experience or fewer driving opportunities, even if they have a driver's license. This study analyzes the effect of driving experience on the perception of the traffic scenes through electroencephalograms (EEGs). Primarily, we focused on visual attention during driving, the essential visual function in the visual search and human gaze, and evaluated the P300, which is involved in attention, to explore the effect of driving experience on the visual attention of traffic scenes, not for improving visual ability. In the results, the P300 response was observed in both experienced and beginner drivers when they paid visual attention to the visual target. Furthermore, the latency for the peak amplitude of the P300 response among experienced drivers was markedly faster than that in beginner drivers, suggesting that the P300 latency is a piece of crucial information for driving experience on visual attention.
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Affiliation(s)
- Keiichiro Inagaki
- College of Engineering, Chubu University, 1200 Matsumoto, Kasugai 487-8501, Japan
| | - Nobuhiko Wagatsuma
- Faculty of Science, Toho University, Miyama 2-2-1, Funabashi 274-8510, Japan;
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Tsudanuma 2-17-1, Narashino 275-0016, Japan;
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14
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Nobukawa S, Wagatsuma N, Nishimura H, Doho H, Takahashi T. An Approach for Stabilizing Abnormal Neural Activity in ADHD Using Chaotic Resonance. Front Comput Neurosci 2021; 15:726641. [PMID: 34539367 PMCID: PMC8442914 DOI: 10.3389/fncom.2021.726641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/09/2021] [Indexed: 12/02/2022] Open
Abstract
Reduced integrity of neural pathways from frontal to sensory cortices has been suggested as a potential neurobiological basis of attention-deficit hyperactivity disorder. Neurofeedback has been widely applied to enhance reduced neural pathways in attention-deficit hyperactivity disorder by repeated training on a daily temporal scale. Clinical and model-based studies have demonstrated that fluctuations in neural activity underpin sustained attention deficits in attention-deficit hyperactivity disorder. These aberrant neural fluctuations may be caused by the chaos–chaos intermittency state in frontal-sensory neural systems. Therefore, shifting the neural state from an aberrant chaos–chaos intermittency state to a normal stable state with an optimal external sensory stimulus, termed chaotic resonance, may be applied in neurofeedback for attention-deficit hyperactivity disorder. In this study, we applied a neurofeedback method based on chaotic resonance induced by “reduced region of orbit” feedback signals in the Baghdadi model for attention-deficit hyperactivity disorder. We evaluated the stabilizing effect of reduced region of orbit feedback and its robustness against noise from errors in estimation of neural activity. The effect of chaotic resonance successfully shifted the abnormal chaos-chaos intermittency of neural activity to the intended stable activity. Additionally, evaluation of the influence of noise due to measurement errors revealed that the efficiency of chaotic resonance induced by reduced region of orbit feedback signals was maintained over a range of certain noise strengths. In conclusion, applying chaotic resonance induced by reduced region of orbit feedback signals to neurofeedback methods may provide a promising treatment option for attention-deficit hyperactivity disorder.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Chiba, Japan
| | - Nobuhiko Wagatsuma
- Department of Information Science, Faculty of Science, Toho University, Chiba, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Hirotaka Doho
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan.,Faculty of Education, Teacher Training Division, Kochi University, Kochi, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.,Department of Neuropsychiatry, University of Fukui, Fukui, Japan.,Uozu Shinkei Sanatorium, Uozu, Japan
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15
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Nobukawa S, Nishimura H, Wagatsuma N, Ando S, Yamanishi T. Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution. IEEE Trans Neural Netw Learn Syst 2021; 32:3525-3537. [PMID: 32822305 DOI: 10.1109/tnnls.2020.3015208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Studies of structural connectivity at the synaptic level show that in synaptic connections of the cerebral cortex, the excitatory postsynaptic potential (EPSP) in most synapses exhibits sub-mV values, while a small number of synapses exhibit large EPSPs ( >~1.0 [mV]). This means that the distribution of EPSP fits a log-normal distribution. While not restricting structural connectivity, skewed and long-tailed distributions have been widely observed in neural activities, such as the occurrences of spiking rates and the size of a synchronously spiking population. Many studies have been modeled this long-tailed EPSP neural activity distribution; however, its causal factors remain controversial. This study focused on the long-tailed EPSP distributions and interlateral synaptic connections primarily observed in the cortical network structures, thereby having constructed a spiking neural network consistent with these features. Especially, we constructed two coupled modules of spiking neural networks with excitatory and inhibitory neural populations with a log-normal EPSP distribution. We evaluated the spiking activities for different input frequencies and with/without strong synaptic connections. These coupled modules exhibited intermittent intermodule-alternative behavior, given moderate input frequency and the existence of strong synaptic and intermodule connections. Moreover, the power analysis, multiscale entropy analysis, and surrogate data analysis revealed that the long-tailed EPSP distribution and intermodule connections enhanced the complexity of spiking activity at large temporal scales and induced nonlinear dynamics and neural activity that followed the long-tailed distribution.
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16
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Hasegawa C, Takahashi T, Ikeda T, Yoshimura Y, Hiraishi H, Nobukawa S, Saito DN, Kumazaki H, Yaoi K, Hirata M, Asada M, Kikuchi M. Effects of familiarity on child brain networks when listening to a storybook reading: A magneto-encephalographic study. Neuroimage 2021; 241:118389. [PMID: 34265420 DOI: 10.1016/j.neuroimage.2021.118389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 03/23/2021] [Accepted: 07/10/2021] [Indexed: 10/20/2022] Open
Abstract
Parent-child book reading is important for fostering the development of various lifelong cognitive and social abilities in young children. Despite numerous reports describing the effects of familiarity on shared reading for children, the exact neural basis of the functional network architecture remains unclear. We conducted Magnet-Encephalographic (MEG) experiments using graph theory to elucidate the role of familiarity in shared reading in a child's brain network and to measure the connectivity dynamics of a child while Listening to Storybook Reading (LSBR), which represents the daily activity of shared book reading between the child and caregiver. The LSBR task was performed with normally developing preschool- and school-age children (N = 15) under two conditions: reading by their own mother (familiar condition) vs. an experimenter (unfamiliar condition). We used the phase lag index (PLI), which captures synchronization of MEG signals, to estimate functional connectivity. For the whole brain network topology, an undirected weighted graph was produced using 68 brain regions as nodes and interregional PLI values as edges for five frequency bands. Behavioral data (i.e., the degree of attention and facial expressions) were evaluated from video images of the child's face during the two conditions. Our results showed enhanced widespread functional connectivity in the alpha band during the mother condition. In the mother condition, the whole brain network in the alpha band exhibited topographically high local segregation with high global integration, indicating an increased small-world property. Results of the behavioral analysis revealed that children were more attentive and showed more positive facial expressions in the mother condition than in the experimenter condition. Behavioral data were significantly correlated with graph metrics in the mother condition but not in the experimenter condition. In this study, we identified the neural correlates of a familiarity effect in children's brain connectivity dynamics during LSBR. Furthermore, these familiarity-related brain dynamics were closely linked to the child's behavior. Graph theory applied to MEG data may provide useful insight into the familiarity-related child brain response in a naturalistic setting and its relevance to child attitudes.
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Affiliation(s)
- Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8640, Japan; JSPS Oversea Research Fellow RRA, Visiting Fellow, Department of Cognitive Science, Macquarie University, Tokyo 102-0083, Japan.
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8640, Japan; Uozu Shinkei Sanatorium, Uozu 937-0017, Japan; Department of Neuropsychiatry, University of Fukui, Fukui 910-1193, Japan.
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8640, Japan; United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka/Kanazawa/Hamamatsu/Chiba/Fukui, Japan.
| | - Yuko Yoshimura
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8640, Japan; United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka/Kanazawa/Hamamatsu/Chiba/Fukui, Japan; Institute of Human and Social Sciences, Kanazawa University, Kanazawa 921-1192, Japan.
| | - Hirotoshi Hiraishi
- Department of Biofunctional Imaging, Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino 275-0016, Japan.
| | - Daisuke N Saito
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8640, Japan; United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka/Kanazawa/Hamamatsu/Chiba/Fukui, Japan; Department of Psychology, Faculty of Psychology, Yasuda Woman's University, Hiroshima 731-0153, Japan.
| | - Hirokazu Kumazaki
- National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo 187-8553, Japan.
| | - Ken Yaoi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8640, Japan; United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka/Kanazawa/Hamamatsu/Chiba/Fukui, Japan.
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Endowed Research Department of Clinical Neuroengineering Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan.
| | - Minoru Asada
- International Professional University of Technology in Osaka, Kita-ku 530-0001, Japan; Symbiotic Intelligent System Research Center, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan.
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8640, Japan; United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka/Kanazawa/Hamamatsu/Chiba/Fukui, Japan; Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-8641, Japan.
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17
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Ando M, Nobukawa S, Kikuchi M, Takahashi T. Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis. Front Neurosci 2021; 15:667614. [PMID: 34262427 PMCID: PMC8273283 DOI: 10.3389/fnins.2021.667614] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are emphasized. As a diagnostic index for AD patients, evaluating the complexity of the dependence of the electroencephalography (EEG) signal on the temporal scale of Alzheimer's disease (AD) patients is effective. Multiscale entropy analysis and multifractal analysis have been performed individually, and their usefulness as diagnostic indicators has been confirmed, but the complemental relationship between these analyses, which may enhance diagnostic accuracy, has not been investigated. We hypothesize that combining multiscale entropy and fractal analyses may add another dimension to understanding the alteration of EEG dynamics in AD. In this study, we performed both multiscale entropy and multifractal analyses on EEGs from AD patients and healthy subjects. We found that the classification accuracy was improved using both techniques. These findings suggest that the use of multiscale entropy analysis and multifractal analysis may lead to the development of AD diagnostic tools.
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Affiliation(s)
- Momo Ando
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan.,Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Behavioral Science, Kanazawa University, Ishikawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Ishikawa, Japan.,Department of Neuropsychiatry, University of Fukui, Fukui, Japan.,Uozu Shinkei Sanatorium, Uozu, Japan
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18
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Nobukawa S, Shirama A, Takahashi T, Takeda T, Ohta H, Kikuchi M, Iwanami A, Kato N, Toda S. Pupillometric Complexity and Symmetricity Follow Inverted-U Curves Against Baseline Diameter Due to Crossed Locus Coeruleus Projections to the Edinger-Westphal Nucleus. Front Physiol 2021; 12:614479. [PMID: 33643064 PMCID: PMC7905168 DOI: 10.3389/fphys.2021.614479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 01/19/2021] [Indexed: 11/13/2022] Open
Abstract
In addition to photic reflex function, the temporal behavior of the pupil diameter reflects levels of arousal and attention and thus internal cognitive neural activity. Recent studies have reported that these behaviors are characterized by baseline activity, temporal complexity, and symmetricity (i.e., degree of symmetry) between the right and left pupil diameters. We hypothesized that experimental analysis to reveal relationships among these characteristics and model-based analysis focusing on the newly discovered contralateral projection from the locus coeruleus (LC) to the Edinger-Westphal nucleus (EWN) within the neural system for controlling pupil diameter could contribute to another dimension of understanding of complex pupil dynamics. In this study, we aimed to validate our hypothesis by analyzing the pupillary hippus in the healthy resting state in terms of sample entropy (SampEn), to capture complexity, and transfer entropy (TranEn), to capture symmetricity. We also constructed a neural model embedded with the new findings on neural pathways. The following results were observed: first, according to surrogate data analysis, the complexity and symmetricity of pupil diameter changes reflect a non-linear deterministic process. Second, both the complexity and the symmetricity are unimodal, peaking at intermediate pupil diameters. Third, according to simulation results, the neural network that controls pupil diameter has an inverted U-shaped profile of complexity and symmetricity vs. baseline LC activity; this tendency is enhanced by the contralateral synaptic projections from the LCs to the EWNs. Thus, we characterized the typical relationships between the baseline activity and the complexity and symmetricity of the pupillometric data in terms of SampEn and TranEn. Our evaluation method and findings may facilitate the development of estimation and diagnostic tools for exploring states of the healthy brain and psychiatric disorders based on measurements of pupil diameter.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Chiba, Japan
| | - Aya Shirama
- National Center of Neurology and Psychiatry, Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Ishikawa, Japan.,Department of Neuropsychiatry, University of Fukui, Fukui, Japan.,Uozu Shinkei Sanatorium, Uozu, Japan
| | | | - Haruhisa Ohta
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry & Behavioral Science, Kanazawa University, Ishikawa, Japan
| | - Akira Iwanami
- Department of Psychiatry, School of Medicine, Showa University, Tokyo, Japan
| | - Nobumasa Kato
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Shigenobu Toda
- Department of Psychiatry & Behavioral Science, Kanazawa University, Ishikawa, Japan.,Department of Psychiatry, Showa University East Hospital, Showa University, Tokyo, Japan
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19
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Soma D, Hirosawa T, Hasegawa C, An KM, Kameya M, Hino S, Yoshimura Y, Nobukawa S, Iwasaki S, Tanaka S, Yaoi K, Sano M, Shiota Y, Naito N, Kikuchi M. Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach. Front Psychiatry 2021; 12:790234. [PMID: 34970170 PMCID: PMC8712628 DOI: 10.3389/fpsyt.2021.790234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/19/2021] [Indexed: 12/17/2022] Open
Abstract
Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their relation with social impairment severity. Magnetoencephalographic (MEG) data were recorded for 21 children with ASD (7 girls, 60-89 months old) and for 25 typically developing (TD) control children (10 girls, 60-91 months old) in a resting state while gazing at a fixation cross. After signal sources were localized onto the Desikan-Killiany brain atlas, statistical relations between localized activities were found and evaluated in terms of the phase lag index. After brain networks were constructed and after matching with intelligence using a coarsened exact matching algorithm, ASD and TD graph theoretical measures were compared. We measured autism symptoms severity using the Social Responsiveness Scale and investigated its relation with altered small-worldness using linear regression models. Children with ASD were found to have significantly lower small-worldness in the beta band (p = 0.007) than TD children had. Lower small-worldness in the beta band of children with ASD was associated with higher Social Responsiveness Scale total t-scores (p = 0.047). Significant relations were also inferred for the Social Awareness (p = 0.008) and Social Cognition (p = 0.015) sub-scales. Results obtained using graph theory demonstrate a difference between children with and without ASD in MEG-derived resting-state functional brain networks, and the relation of that difference with social impairment. Combining graph theory and MEG might be a promising approach to establish a biological marker for ASD.
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Affiliation(s)
- Daiki Soma
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Tetsu Hirosawa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Kyung-Min An
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Shoryoku Hino
- Department of Neuropsychiatry, Ishikawa Prefectural Takamatsu Hospital, Kahoku, Japan
| | - Yuko Yoshimura
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.,Faculty of Education, Institute of Human and Social Sciences, Kanazawa University, Kanazawa, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sumie Iwasaki
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Sanae Tanaka
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Ken Yaoi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Masuhiko Sano
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yuka Shiota
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Nobushige Naito
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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20
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Nobukawa S, Yamanishi T, Ueno K, Mizukami K, Nishimura H, Takahashi T. High Phase Synchronization in Alpha Band Activity in Older Subjects With High Creativity. Front Hum Neurosci 2020; 14:583049. [PMID: 33192416 PMCID: PMC7642763 DOI: 10.3389/fnhum.2020.583049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/14/2020] [Indexed: 12/11/2022] Open
Abstract
Despite growing evidence that high creativity leads to mental well-being in older individuals, the neurophysiological bases of creativity remain elusive. Creativity reportedly involves multiple brain areas and their functional interconnections. In particular, functional magnetic resonance imaging (fMRI) is used to investigate the role of patterns of functional connectivity between the default network and other networks in creative activity. These interactions among networks play the role of integrating various neural processes to support creative activity and involve attention, cognitive control, and memory. The electroencephalogram (EEG) enables researchers to capture a pattern of band-specific functional connectivity, as well as moment-to-moment dynamics of brain activity; this can be accomplished even in the resting-state by exploiting the excellent temporal resolution of the EEG. Furthermore, the recent advent of functional connectivity analysis in EEG studies has focused on the phase-difference variable because of its fine spatio-temporal resolution. Therefore, we hypothesized that the combining method of EEG signals having high-temporal resolution and the phase synchronization analysis having high-spatio-temporal resolutions brings a new insight of functional connectivity regarding high creative activity of older participants. In this study, we examined the resting-state EEG signal in 20 healthy older participants and estimated functional connectivities using the phase lag index (PLI), which evaluates the phase synchronization of EEG signals. Individual creativity was assessed using the S-A creativity test in a separate session before the EEG recording. In the analysis of associations of EEG measures with the S-A test scores, the covariate effect of the intelligence quotient was evaluated. As a result, higher individual S-A scores were significantly associated with higher node degrees, defined as the average PLI of a node (electrode) across all links with the remaining nodes, across all nodes at the alpha band. A conventional power spectrum analysis revealed no significant association with S-A scores in any frequency band. Older participants with high creativity exhibited high functional connectivity even in the resting-state, irrespective of intelligence quotient, which supports the theory that creativity entails widespread brain connectivity. Thus, PLIs derived from EEG data may provide new insights into the relationship between functional connectivity and creativity in healthy older people.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Teruya Yamanishi
- AI & IoT Center, Department of Management and Information Sciences, Fukui University of Technology, Fukui, Japan
| | - Kanji Ueno
- Department of Neuropsychiatry, University of Fukui, Fukui, Japan
| | - Kimiko Mizukami
- Faculty of Medicine, Institute of Medical, Pharmaceutical, and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Tetsuya Takahashi
- Department of Neuropsychiatry, University of Fukui, Fukui, Japan
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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21
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Doho H, Nobukawa S, Nishimura H, Wagatsuma N, Takahashi T. Transition of Neural Activity From the Chaotic Bipolar-Disorder State to the Periodic Healthy State Using External Feedback Signals. Front Comput Neurosci 2020; 14:76. [PMID: 32982709 PMCID: PMC7484049 DOI: 10.3389/fncom.2020.00076] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 07/20/2020] [Indexed: 12/21/2022] Open
Abstract
Chronotherapy is a treatment for mood disorders, including major depressive disorder, mania, and bipolar disorder (BD). Neurotransmitters associated with the pathology of mood disorders exhibit circadian rhythms. A functional deficit in the neural circuits related to mood disorders disturbs the circadian rhythm; chronotherapy is an intervention that helps resynchronize the patient's biological clock with the periodic daily cycle, leading to amelioration of symptoms. In previous reports, Hadaeghi et al. proposed a non-linear dynamic model composed of the frontal and sensory cortical neural networks and the hypothalamus to explain the relationship between deficits in neural function in the frontal cortex and the disturbed circadian rhythm/mood transitions in BD (hereinafter referred to as the Hadaeghi model). In this model, neural activity in the frontal and sensory lobes exhibits periodic behavior in the healthy state; while in BD, this neural activity is in a state of chaos-chaos intermittency; this temporal departure from the healthy periodic state disturbs the circadian pacemaker in the hypothalamus. In this study, we propose an intervention based on a feedback method called the “reduced region of orbit” (RRO) method to facilitate the transition of the disturbed frontal cortical neural activity underlying BD to healthy periodic activity. Our simulation was based on the Hadaeghi model. We used an RRO feedback signal based on the return-map structure of the simulated frontal and sensory lobes to induce synchronization with a relatively weak periodic signal corresponding to the healthy condition by applying feedback of appropriate strength. The RRO feedback signal induces chaotic resonance, which facilitates the transition to healthy, periodic frontal neural activity, although this synchronization is restricted to a relatively low frequency of the periodic input signal. Additionally, applying an appropriate strength of the RRO feedback signal lowered the amplitude of the periodic input signal required to induce a synchronous state compared with the periodic signal applied alone. In conclusion, through a chaotic-resonance effect induced by the RRO feedback method, the state of the disturbed frontal neural activity characteristic of BD was transformed into a state close to healthy periodic activity by relatively weak periodic perturbations. Thus, RRO feedback-modulated chronotherapy might be an innovative new type of minimally invasive chronotherapy.
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Affiliation(s)
- Hirotaka Doho
- Faculty of Education, Teacher Training Division, Kochi University, Kochi, Japan.,Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Nobuhiko Wagatsuma
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.,Department of Neuropsychiatry, University of Fukui, Yoshida, Japan
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22
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Nobukawa S, Yamanishi T, Kasakawa S, Nishimura H, Kikuchi M, Takahashi T. Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease. Front Psychiatry 2020; 11:255. [PMID: 32317994 PMCID: PMC7154080 DOI: 10.3389/fpsyt.2020.00255] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/16/2020] [Indexed: 12/22/2022] Open
Abstract
Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among wide-spread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-and-regional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods to neurophysiological data may provide a novel understanding of the neural network processes in both healthy brains and pathological conditions.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Teruya Yamanishi
- AI & IoT Center, Department of Management Information Science, Fukui University of Technology, Fukui, Japan
| | - Shinya Kasakawa
- AI & IoT Center, Department of Management Information Science, Fukui University of Technology, Fukui, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Psychiatry & Behavioral Science, Kanazawa University, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, University of Fukui, Yoshida, Japan
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23
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Nobukawa S, Nishimura H, Yamanishi T. Temporal-specific complexity of spiking patterns in spontaneous activity induced by a dual complex network structure. Sci Rep 2019; 9:12749. [PMID: 31484990 PMCID: PMC6726653 DOI: 10.1038/s41598-019-49286-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/22/2019] [Indexed: 11/08/2022] Open
Abstract
Temporal fluctuation of neural activity in the brain has an important function in optimal information processing. Spontaneous activity is a source of such fluctuation. The distribution of excitatory postsynaptic potentials (EPSPs) between cortical pyramidal neurons can follow a log-normal distribution. Recent studies have shown that networks connected by weak synapses exhibit characteristics of a random network, whereas networks connected by strong synapses have small-world characteristics of small path lengths and large cluster coefficients. To investigate the relationship between temporal complexity spontaneous activity and structural network duality in synaptic connections, we executed a simulation study using the leaky integrate-and-fire spiking neural network with log-normal synaptic weight distribution for the EPSPs and duality of synaptic connectivity, depending on synaptic weight. We conducted multiscale entropy analysis of the temporal spiking activity. Our simulation demonstrated that, when strong synaptic connections approach a small-world network, specific spiking patterns arise during irregular spatio-temporal spiking activity, and the complexity at the large temporal scale (i.e., slow frequency) is enhanced. Moreover, we confirmed through a surrogate data analysis that slow temporal dynamics reflect a deterministic process in the spiking neural networks. This modelling approach may improve the understanding of the spatio-temporal complex neural activity in the brain.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan.
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo, 650-8588, Japan
| | - Teruya Yamanishi
- AI & IoT Center, Department of Management and Information Sciences, Fukui University of Technology, 3-6-1 Gakuen, Fukui, 910-8505, Japan
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24
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Nobukawa S, Shibata N, Nishimura H, Doho H, Wagatsuma N, Yamanishi T. Resonance phenomena controlled by external feedback signals and additive noise in neural systems. Sci Rep 2019; 9:12630. [PMID: 31477740 PMCID: PMC6718685 DOI: 10.1038/s41598-019-48950-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 08/16/2019] [Indexed: 12/11/2022] Open
Abstract
Chaotic resonance is a phenomenon that can replace the fluctuation source in stochastic resonance from additive noise to chaos. We previously developed a method to control the chaotic state for suitably generating chaotic resonance by external feedback even when the external adjustment of chaos is difficult, establishing a method named reduced region of orbit (RRO) feedback. However, a feedback signal was utilized only for dividing the merged attractor. In addition, the signal sensitivity in chaotic resonance induced by feedback signals and that of stochastic resonance by additive noise have not been compared. To merge the separated attractor, we propose a negative strength of the RRO feedback signal in a discrete neural system which is composed of excitatory and inhibitory neurons. We evaluate the features of chaotic resonance and compare it to stochastic resonance. The RRO feedback signal with negative strength can merge the separated attractor and induce chaotic resonance. We also confirm that additive noise induces stochastic resonance through attractor merging. The comparison of these resonance modalities verifies that chaotic resonance provides more applicability than stochastic resonance given its capability to handle attractor separation and merging.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan.
| | - Natsusaku Shibata
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo, 650-8588, Japan
| | - Hirotaka Doho
- Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo, 650-8588, Japan.,Faculty of Education, Teacher Training Division, Kochi University, 2-5-1 Akebono-cho, Kochi, 780-8520, Japan
| | - Nobuhiko Wagatsuma
- Faculty of Science, Department of Information Science, Toho University, 2-2-1 Miyama, Funabashi, Chiba, 274-8510, Japan
| | - Teruya Yamanishi
- AI & IoT Center, Department of Management and Information Sciences, Fukui University of Technology, 3-6-1 Gakuen, Fukui, Fukui, 910-8505, Japan
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25
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Abstract
Stochastic resonance is a phenomenon in which the signal response of a non-linear system is enhanced by appropriate external noise. Likewise, a similar phenomenon can be caused by deterministic chaos; this is called chaotic resonance. Devices that employ stochastic resonance have been proposed for the purpose of enhancing tactile sensitivity. However, no applications of chaotic resonance have been reported so far, even though chaotic resonance exhibits a higher sensitivity than stochastic resonance. This contrast in applications could be attributed to the fact that chaotic resonance is induced by adjusting internal parameters. In many cases, especially in biological systems, these parameters are difficult to adjust. In this study, by applying our proposed reduced region of orbit method to a neural system consisting of excitatory and inhibitory neurons, we induce chaotic resonance with signal frequency dependency against weak input signals. Furthermore, the external noise exhibits effects for both diminishing and enhancing signal responses in chaotic resonance. The outcome of this study might facilitate the development of devices utilising the mechanism of chaotic resonance.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan.
| | - Natsusaku Shibata
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan
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26
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Nobukawa S, Kikuchi M, Takahashi T. Changes in functional connectivity dynamics with aging: A dynamical phase synchronization approach. Neuroimage 2019; 188:357-368. [DOI: 10.1016/j.neuroimage.2018.12.008] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 10/15/2018] [Accepted: 12/04/2018] [Indexed: 02/06/2023] Open
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27
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Nobukawa S, Yamanishi T, Nishimura H, Wada Y, Kikuchi M, Takahashi T. Atypical temporal-scale-specific fractal changes in Alzheimer's disease EEG and their relevance to cognitive decline. Cogn Neurodyn 2018; 13:1-11. [PMID: 30728867 PMCID: PMC6339858 DOI: 10.1007/s11571-018-9509-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 07/20/2018] [Accepted: 09/29/2018] [Indexed: 12/20/2022] Open
Abstract
Recent advances in nonlinear analytic methods for electroencephalography have clarified the reduced complexity of spatiotemporal dynamics in brain activity observed in Alzheimer’s disease (AD). However, there are far fewer studies exploring temporal scale dependent fractal properties in AD, despite the importance of studying the dynamics of brain activity within physiologically relevant frequency ranges. Higuchi’s fractal dimension is a widely used index for evaluating fractality in brain activity, but temporal-scale-specific characteristics are lost due to its requirement of averaging over the entire range of temporal scales. In this study, we adapted Higuchi’s fractal algorithm into a method for investigating temporal-scale-specific fractal properties. We then compared the values of the temporal-scale-specific fractal dimension between healthy control (HC) and AD patient groups. Our data indicate that relative to the HC group, the AD group demonstrated reduced fractality at both slow and fast temporal scales. Moreover, we confirmed that the fractality at fast temporal scales correlates with cognitive decline. These properties might serve as a basis for a useful approach to characterizing temporal neural dynamics in AD or other neurodegenerative disorders.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2–17–1 Tsudanuma, Narashino, Chiba 275–0016 Japan
| | - Teruya Yamanishi
- Department of Management Information Science, Fukui University of Technology, 3–6–1 Gakuen, Fukui, Fukui 910–8505 Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7–1–28 Chuo-ku, Kobe, Hyogo 650–8588 Japan
| | - Yuji Wada
- Department of Neuropsychiatry, University of Fukui, 23–3 Matsuokashimoaizuki, Eiheiji, Yoshida, Fukui, 910–1193 Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, 13–1 Takaramachi, Kanazawa, Ishikawa 920–8640 Japan
| | - Tetsuya Takahashi
- Department of Neuropsychiatry, University of Fukui, 23–3 Matsuokashimoaizuki, Eiheiji, Yoshida, Fukui, 910–1193 Japan
- Research Center for Child Mental Development, Kanazawa University, 13–1 Takaramachi, Kanazawa, Ishikawa 920–8640 Japan
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28
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Hasegawa C, Takahashi T, Yoshimura Y, Nobukawa S, Ikeda T, Saito DN, Kumazaki H, Minabe Y, Kikuchi M. Developmental Trajectory of Infant Brain Signal Variability: A Longitudinal Pilot Study. Front Neurosci 2018; 12:566. [PMID: 30154695 PMCID: PMC6102372 DOI: 10.3389/fnins.2018.00566] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 07/27/2018] [Indexed: 11/13/2022] Open
Abstract
The infant brain shows rapid neural network development that considerably influences cognitive and behavioral abilities in later life. Reportedly, this neural development process can be indexed by estimating neural signal complexity. However, the precise developmental trajectory of brain signal complexity during infancy remains elusive. This study was conducted to ascertain the trajectory of magnetoencephalography (MEG) signal complexity from 2 months to 3 years of age in five infants using multiscale entropy (MSE), which captures signal complexity at multiple temporal scales. Analyses revealed scale-dependent developmental trajectories. Specifically, signal complexity predominantly increased from 5 to 15 months of age at higher temporal scales, whereas the complexity at lower temporal scales was constant across age, except in one infant who showed decreased complexity. Despite a small sample size limiting this study’s power, this is the first report of a longitudinal investigation of changes in brain signal complexity during early infancy and is unique in its application of MSE analysis of longitudinal MEG data during infancy. The results of this pilot study may serve to further our understanding of the longitudinal changes in the neural dynamics of the developing infant brain.
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Affiliation(s)
- Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | | | - Yuko Yoshimura
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.,Faculty of Education, Kanazawa University, Kanazawa, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Daisuke N Saito
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Hirokazu Kumazaki
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Yoshio Minabe
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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29
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Takahashi T, Goto T, Nobukawa S, Tanaka Y, Kikuchi M, Higashima M, Wada Y. Abnormal functional connectivity of high-frequency rhythms in drug-naïve schizophrenia. Clin Neurophysiol 2018; 129:222-231. [DOI: 10.1016/j.clinph.2017.11.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 11/07/2017] [Accepted: 11/09/2017] [Indexed: 01/15/2023]
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30
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Takahashi T, Yamanishi T, Nobukawa S, Kasakawa S, Yoshimura Y, Hiraishi H, Hasegawa C, Ikeda T, Hirosawa T, Munesue T, Higashida H, Minabe Y, Kikuchi M. Band-specific atypical functional connectivity pattern in childhood autism spectrum disorder. Clin Neurophysiol 2017. [DOI: 10.1016/j.clinph.2017.05.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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31
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Nobukawa S, Nishimura H, Yamanishi T. Chaotic Resonance in Typical Routes to Chaos in the Izhikevich Neuron Model. Sci Rep 2017; 7:1331. [PMID: 28465524 PMCID: PMC5430992 DOI: 10.1038/s41598-017-01511-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 03/29/2017] [Indexed: 11/09/2022] Open
Abstract
Chaotic resonance (CR), in which a system responds to a weak signal through the effects of chaotic activities, is a known function of chaos in neural systems. The current belief suggests that chaotic states are induced by different routes to chaos in spiking neural systems. However, few studies have compared the efficiency of signal responses in CR across the different chaotic states in spiking neural systems. We focused herein on the Izhikevich neuron model, comparing the characteristics of CR in the chaotic states arising through the period-doubling or tangent bifurcation routes. We found that the signal response in CR had a unimodal maximum with respect to the stability of chaotic orbits in the tested chaotic states. Furthermore, the efficiency of signal responses at the edge of chaos became especially high as a result of synchronization between the input signal and the periodic component in chaotic spiking activity.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Japan.
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, 650-8588, Japan
| | - Teruya Yamanishi
- Department of Management Information Science, Fukui University of Technology, 3-6-1 Gakuen, Fukui, 910-8505, Japan
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32
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Affiliation(s)
- Sou Nobukawa
- Department of Management Information Science, Fukui University of Technology
| | | | - Teruya Yamanishi
- Department of Management Information Science, Fukui University of Technology
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33
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Abstract
It is well known that cerebellar motor control is fine-tuned by the learning process adjusted according to rich error signals from inferior olive (IO) neurons. Schweighofer and colleagues proposed that these signals can be produced by chaotic irregular firing in the IO neuron assembly; such chaotic resonance (CR) was replicated in their computer demonstration of a Hodgkin-Huxley (HH)-type compartment model. In this study, we examined the response of CR to a periodic signal in the IO neuron assembly comprising the Llinás approach IO neuron model. This system involves empirically observed dynamics of the IO membrane potential and is simpler than the HH-type compartment model. We then clarified its dependence on electrical coupling strength, input signal strength, and frequency. Furthermore, we compared the physiological validity for IO neurons such as low firing rate and sustaining subthreshold oscillation between CR and conventional stochastic resonance (SR) and examined the consistency with asynchronous firings indicated by the previous model-based studies in the cerebellar learning process. In addition, the signal response of CR and SR was investigated in a large neuron assembly. As the result, we confirmed that CR was consistent with the above IO neuron's characteristics, but it was not as easy for SR.
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Affiliation(s)
- Sou Nobukawa
- Department of Management Information Science, Fukui University of Technology, Fukui, Fukui, 910-8505 Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-8588 Japan
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34
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Abstract
Synaptic plasticity is widely recognized to support adaptable information processing in the brain. Spike-timing-dependent plasticity, one subtype of plasticity, can lead to synchronous spike propagation with temporal spiking coding information. Recently, it was reported that in a noisy environment, like the actual brain, the spike-timing-dependent plasticity may be made efficient by the effect of stochastic resonance. In the stochastic resonance, the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. However, previous studies have ignored the full variety of spiking patterns and many relevant factors in neural dynamics. Thus, in order to prove the physiological possibility for the enhancement of spike-timing-dependent plasticity by stochastic resonance, it is necessary to demonstrate that this stochastic resonance arises in realistic cortical neural systems. In this study, we evaluate this stochastic resonance phenomenon in the realistic cortical neural system described by the Izhikevich neuron model and compare the characteristics of typical spiking patterns of regular spiking, intrinsically bursting and chattering experimentally observed in the cortex.
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Affiliation(s)
- Sou Nobukawa
- Department of Management Information Science, Fukui University of Technology, Fukui, Fukui 910-8505, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Hyogo 650-0047, Japan
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35
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Nobukawa S, Nishimura H, Yamanishi T, Liu JQ. Chaotic States Induced By Resetting Process In Izhikevich Neuron Model. Journal of Artificial Intelligence and Soft Computing Research 2015. [DOI: 10.1515/jaiscr-2015-0023] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Several hybrid neuron models, which combine continuous spike-generation mechanisms and discontinuous resetting process after spiking, have been proposed as a simple transition scheme for membrane potential between spike and hyperpolarization. As one of the hybrid spiking neuron models, Izhikevich neuron model can reproduce major spike patterns observed in the cerebral cortex only by tuning a few parameters and also exhibit chaotic states in specific conditions. However, there are a few studies concerning the chaotic states over a large range of parameters due to the difficulty of dealing with the state dependent jump on the resetting process in this model. In this study, we examine the dependence of the system behavior on the resetting parameters by using Lyapunov exponent with saltation matrix and Poincaré section methods, and classify the routes to chaos.
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Affiliation(s)
- Sou Nobukawa
- Department of Management Information Science, Fukui University of Technology, 3-6-1 Gakuen, Fukui, Fukui, 910-8505 Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo, 650-8588 Japan
| | - Teruya Yamanishi
- Department of Management Information Science, Fukui University of Technology, 3-6-1 Gakuen, Fukui, Fukui, 910-8505 Japan
| | - Jian-Qin Liu
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, 588-2 Iwaoka, Iwaoka-cho, Nishi-ku, Kobe, Hyogo, 651-2492 Japan
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