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Ahmadi A, Saadatmand M, Wallois F. Evaluation of potential alterations related to ADHD in the effective connectivity between the default mode network and cerebellum, hippocampus, thalamus, and primary visual cortex. Cereb Cortex 2024; 34:bhae335. [PMID: 39147392 DOI: 10.1093/cercor/bhae335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/18/2024] [Accepted: 07/31/2024] [Indexed: 08/17/2024] Open
Abstract
Hyperactivity in children with attention-deficit/hyperactivity disorder (ADHD) leads to restlessness and impulse-control impairments. Nevertheless, the relation between ADHD symptoms and brain regions interactions remains unclear. We focused on dynamic causal modeling to study the effective connectivity in a fully connected network comprised of four regions of the default mode network (DMN) (linked to response control behaviors) and four other regions with previously-reported structural alterations due to ADHD. Then, via the parametric empirical Bayes analysis, the most significant connections, with the highest correlation to the covariates ADHD/control, age, and sex were extracted. Our results demonstrated a positive correlation between ADHD and effective connectivity between the right cerebellum and three DMN nodes (intrinsically inhibitory connections). Therefore, an increase in the effective connectivity leads to more inhibition imposition from the right cerebellum to DMN that reduces this network activation. The lower DMN activity makes leaving the resting-state easier, which may be involved in the restlessness symptom. Furthermore, our results indicated a negative correlation between age and these connections. We showed that the difference between the average of effective connectivities of ADHD and control groups in the age-range of 7-11 years disappeared after 14 years-old. Therefore, aging tends to alleviate ADHD-specific symptoms.
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Affiliation(s)
- Amirhossein Ahmadi
- Ferdowsi Cognitive Science and Technology Center & Medical Imaging Lab, Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Vakil-Abad Blv., Bahonar St., Mashhad, 9177948974, Iran
| | - Mahdi Saadatmand
- Ferdowsi Cognitive Science and Technology Center & Medical Imaging Lab, Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Vakil-Abad Blv., Bahonar St., Mashhad, 9177948974, Iran
| | - Fabrice Wallois
- INSERM U1105, Université de Picardie, CURS, Avenue Laennec, 80054, Amiens, France
- INSERM U1105, Unit Exploration Fonctionnelles du Système Nerveux Pèdiatrique, South University Hospital, Avenue Laennec, 80054, Amiens, France
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Chu C, Zhang Z, Wang J, Wang L, Shen X, Bai L, Li Z, Dong M, Liu C, Yi G, Zhu X. Evolution of brain network dynamics in early Parkinson's disease with mild cognitive impairment. Cogn Neurodyn 2023; 17:681-694. [PMID: 37265660 PMCID: PMC10229513 DOI: 10.1007/s11571-022-09868-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/13/2022] [Accepted: 07/26/2022] [Indexed: 11/03/2022] Open
Abstract
How mild cognitive impairment (MCI) is instantiated in dynamically interacting and spatially distributed functional brain networks remains an unexplored mystery in early Parkinson's disease (PD). We applied a machine-learning technology based on personalized sliding-window algorithm to track continuously time-varying and overlapping subnetworks under the functional brain networks calculated form resting state electroencephalogram data within a sample of 33 early PD patients (13 early PD patients with MCI and 20 early PD patients without MCI). We decoded a set of subnetworks that captured surprisingly dynamically varying and integrated interactions among certain brain lobes. We observed that the master expressed subnetworks were particularly transient, and flexibly switching between high and low expression during integration into a dynamic brain network. This transience was particularly salient in a subnetwork predominantly linking temporal-parietal-occipital lobes, which decreases in both expression and flexibility in early PD patients with MCI and expresses their degree of cognitive impairment. Moreover, MCI induced a regularly interrupted, slow evolution of subnetworks in functional brain network dynamics in early PD at the individual level, and the dynamic expression characteristics of subnetworks also reflected the degree of cognitive impairment in patients with early PD. Collectively, these results provide novel and deeper insights regarding MCI-induced abnormal dynamical interaction and large-scale changes in functional brain network of early PD.
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Affiliation(s)
- Chunguang Chu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Zhen Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Liufang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Xiao Shen
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Lipeng Bai
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Zhuo Li
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Mengmeng Dong
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Guosheng Yi
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Xiaodong Zhu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
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Porta-Casteràs D, Cano M, Navarra-Ventura G, Serra-Blasco M, Vicent-Gil M, Solé B, Montejo L, Torrent C, Martinez-Aran A, Harrison BJ, Palao D, Vieta E, Cardoner N. Disrupted network switching in euthymic bipolar disorder: Working memory and self-referential paradigms. J Affect Disord 2023; 320:552-560. [PMID: 36202301 DOI: 10.1016/j.jad.2022.09.152] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/21/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Patients with bipolar disorder (BD) frequently suffer from neurocognitive deficits that can persist during periods of clinical stability. Specifically, impairments in executive functioning such as working memory and in self-processing have been identified as the main components of the neurocognitive profile observed in euthymic BD patients. The study of the neurobiological correlates of these state-independent alterations may be a prerequisite to develop reliable biomarkers in BD. METHODS A sample of 27 euthymic BD patients and 25 healthy participants (HC) completed working memory and self-referential functional Magnetic Resonance Imaging (fMRI) tasks. Activation maps obtained for each group and contrast images (i.e., 2-back > 1-back/self > control) were used for comparisons between patients and HC. RESULTS Euthymic BD patients, in comparison to HC, showed a higher ventromedial prefrontal cortex activation during working memory, a result driven by the lack of deactivation in BD patients. In addition, euthymic BD patients displayed a greater dorsomedial and dorsolateral prefrontal cortex activation during self-reference processing. LIMITATIONS Pharmacotherapy was described but not included as a confounder in our models. Sample size was modest. CONCLUSION Our findings revealed a lack of deactivation in the anterior default mode network (aDMN) during a working memory task, a finding consistent with prior research in BD patients, but also a higher activation in frontal regions within the central executive network (CEN) during self-processing. These results suggest that an imbalance of neural network dynamics underlying external/internal oriented cognition (the CEN and the aDMN, respectively) may be one of the first reliable biomarkers in euthymic bipolar patients.
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Affiliation(s)
- D Porta-Casteràs
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Mental Health Department, Unitat de Neurociència Traslacional, Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Barcelona, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - M Cano
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain.
| | - G Navarra-Ventura
- Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain; Institut Universitari d'Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, Palma, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - M Serra-Blasco
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Programa eHealth ICOnnecta't, Institut Català d'Oncologia, Barcelona, Spain
| | - M Vicent-Gil
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain
| | - B Solé
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, Barcelona, Catalonia, Spain
| | - L Montejo
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, Barcelona, Catalonia, Spain
| | - C Torrent
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, Barcelona, Catalonia, Spain
| | - A Martinez-Aran
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, Barcelona, Catalonia, Spain
| | - B J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - D Palao
- Mental Health Department, Unitat de Neurociència Traslacional, Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Barcelona, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain
| | - E Vieta
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, Barcelona, Catalonia, Spain
| | - N Cardoner
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain
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Zhang W, Guo L, Liu D. Transcerebral information coordination in directional hippocampus-prefrontal cortex network during working memory based on bimodal neural electrical signals. Cogn Neurodyn 2022; 16:1409-1425. [PMID: 36408070 PMCID: PMC9666613 DOI: 10.1007/s11571-022-09792-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/12/2022] [Accepted: 02/17/2022] [Indexed: 11/03/2022] Open
Abstract
Working memory (WM) is a kind of advanced cognitive function, which requires the participation and cooperation of multiple brain regions. Hippocampus and prefrontal cortex are the main responsible brain regions for WM. Exploring information coordination between hippocampus and prefrontal cortex during WM is a frontier problem in cognitive neuroscience. In this paper, an advanced information theory analysis based on bimodal neural electrical signals (local field potentials, LFPs and spikes) was employed to characterize the transcerebral information coordination across the two brain regions. Firstly, LFPs and spikes were recorded simultaneously from rat hippocampus and prefrontal cortex during the WM task by using multi-channel in vivo recording technique. Then, from the perspective of information theory, directional hippocampus-prefrontal cortex networks were constructed by using transfer entropy algorithm based on spectral coherence between LFPs and spikes. Finally, transcerebral coordination of bimodal information at the brain-network level was investigated during acquisition and performance of the WM task. The results show that the transfer entropy in directional hippocampus-prefrontal cortex networks is related to the acquisition and performance of WM. During the acquisition of WM, the information flow, local information transmission ability and information transmission efficiency of the directional hippocampus-prefrontal networks increase over learning days. During the performance of WM, the transfer entropy from the hippocampus to prefrontal cortex plays a leading role for bimodal information coordination across brain regions and hippocampus has a driving effect on prefrontal cortex. Furthermore, bimodal information coordination in the hippocampus → prefrontal cortex network could predict WM during the successful performance of WM.
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Affiliation(s)
- Wei Zhang
- School of Information Engineering, Tianjin University of Commerce, Tianjin, 300134 China
| | - Lei Guo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300130 China
| | - Dongzhao Liu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300130 China
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5
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Cheng P, Liu Z, Yang J, Sun F, Fan Z, Yang J. Decreased integration of default-mode network during a working memory task in schizophrenia with severe attention deficits. Front Cell Neurosci 2022; 16:1006797. [PMID: 36425664 PMCID: PMC9679280 DOI: 10.3389/fncel.2022.1006797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/18/2022] [Indexed: 11/10/2022] Open
Abstract
Background Working memory (WM) and attention deficits are both important features of schizophrenia. WM is closely related to attention, for it acted as an important characteristic in activating and manipulating WM. However, the knowledge of neural mechanisms underlying the relationship between WM and attention deficits in schizophrenia is poorly investigated. Methods Graph theory was used to examine the network topology at the whole-brain and large-scale network levels among 125 schizophrenia patients with different severity of attention deficits (65 mild attention deficits; 46 moderate attention deficits; and 14 severe attention deficits) and 53 healthy controls (HCs) during an N-back WM task. These analyses were repeated in the same participants during the resting state. Results In the WM task, there were omnibus differences in small-worldness and normalized clustering coefficient at a whole-brain level and normalized characterized path length of the default-mode network (DMN) among all groups. Post hoc analysis further indicated that all patient groups showed increased small-worldness and normalized clustering coefficient of the whole brain compared with HCs, and schizophrenia with severe attention deficits showed increased normalized characterized path length of the DMN compared with schizophrenia with mild attention deficits and HCs. However, these observations were not persisted under the resting state. Further correlation analyses indicated that the increased normalized characterized path length of the DMN was correlated with more severe attentional deficits and poorer accuracy of the WM task. Conclusion Our research demonstrated that, compared with the schizophrenia patients with less attention deficits, disrupted integration of the DMN may more particularly underlie the WM deficits in schizophrenia patients with severe attention deficits.
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Affiliation(s)
- Peng Cheng
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- *Correspondence: Zhening Liu,
| | - Jun Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Fuping Sun
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Zebin Fan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- Zebin Fan,
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- Jie Yang,
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6
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Gallucci J, Tan T, Schifani C, Dickie EW, Voineskos AN, Hawco C. Greater individual variability in functional brain activity during working memory performance in Schizophrenia Spectrum Disorders (SSD). Schizophr Res 2022; 248:21-31. [PMID: 35908378 DOI: 10.1016/j.schres.2022.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 06/14/2022] [Accepted: 07/17/2022] [Indexed: 10/16/2022]
Abstract
Heterogeneity has been a persistent challenge in understanding Schizophrenia Spectrum Disorders (SSD). Traditional case-control comparisons often show variable results, and may not map well onto individuals. To better understand heterogeneity and group differences in SSD compared to typically developing controls (TDC), we examined variability in functional brain activity during a working memory (WM) task with known deficits in SSD. Neuroimaging and behavioural data were extracted from two datasets collectively providing 34 TDC and 56 individuals with SSD (n = 90). Functional activity in response to an N-Back WM task (3-Back vs 1-Back) was examined between and within groups. Individual variability was calculated via the correlational distance of fMRI activity maps between participants; mean correlational distance from one participant to all others was defined as a 'variability score'. Greater individual variability in functional activity was found in SSD compared to TDC (p = 0.00090). At the group level, a case-control comparison suggested SSD had reduced activity in task positive and task negative networks. However, when SSD were divided into high and low variability subgroups, the low variability groups showed no differences relative to TDC while the high variability group showed little activity at the group level. Our results imply prior case-control differences may be driven by a subgroup of SSD who do not show specific impairments but instead show more 'idiosyncratic' activity patterns. In SSD but not TDC, variability was also related to cognitive performance and age. This novel approach focusing on individual variability has important implications for understanding the neurobiology of SSD.
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Affiliation(s)
- Julia Gallucci
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Thomas Tan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Christin Schifani
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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7
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Zheng Q, Shen J, Xu Y, Pandey V, Guan L. Pattern mechanism in stochastic SIR networks with ER connectivity. PHYSICA A 2022; 603:127765. [PMID: 35757185 PMCID: PMC9212650 DOI: 10.1016/j.physa.2022.127765] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/13/2022] [Indexed: 05/04/2023]
Abstract
The diffusion of the susceptible and infected is a vital factor in spreading infectious diseases. However, the previous SIR networks cannot explain the dynamical mechanism of periodic behavior and endemic diseases. Here, we incorporate the diffusion and network effect into the SIR model and describes the mechanism of periodic behavior and endemic diseases through wavenumber and saddle-node bifurcation. We also introduce the standard network structured entropy (NSE) and demonstrate diffusion effect could induce the saddle-node bifurcation and Turing instability. Then we reveal the mechanism of the periodic outbreak and endemic diseases by the mean-field method. We provide the Turing instability condition through wavenumber in this network-organized SIR model. In the end, the data from COVID-19 authenticated the theoretical results.
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Affiliation(s)
- Qianqian Zheng
- School of Science, Xuchang University, Xuchang, Henan 461000, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China
| | - Jianwei Shen
- School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Yong Xu
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China
| | - Vikas Pandey
- School of Medicine, University of California, Los Angeles, CA 90095, USA
- NIPS, National Institute of Natural Science, Okazaki 4448585, Japan
| | - Linan Guan
- School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
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A new patterns of self-organization activity of brain: Neural energy coding. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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9
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Li Y, Zhang B, Pan X, Wang Y, Xu X, Wang R, Liu Z. Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models. Front Cell Neurosci 2022; 16:923039. [PMID: 35966208 PMCID: PMC9373714 DOI: 10.3389/fncel.2022.923039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/06/2022] [Indexed: 12/01/2022] Open
Abstract
Major depressive disorder (MDD) is a serious psychiatric disorder, with an increasing incidence in recent years. The abnormal dopaminergic pathways of the midbrain cortical and limbic system are the key pathological regions of MDD, particularly the ventral tegmental area- nucleus accumbens- medial prefrontal cortex (VTA-NAc-mPFC) neural circuit. MDD usually occurs with the dysfunction of dopaminergic neurons in VTA, which decreases the dopamine concentration and metabolic rate in NAc/mPFC brain regions. However, it has not been fully explained how abnormal dopamine concentration levels affect this neural circuit dynamically through the modulations of ion channels and synaptic activities. We used Hodgkin-Huxley and dynamical receptor binding model to establish this network, which can quantitatively explain neural activity patterns observed in MDD with different dopamine concentrations by changing the kinetics of some ion channels. The simulation replicated some important pathological patterns of MDD at the level of neurons and circuits with low dopamine concentration, such as the decreased action potential frequency in pyramidal neurons of mPFC with significantly reduced burst firing frequency. The calculation results also revealed that NaP and KS channels of mPFC pyramidal neurons played key roles in the functional regulation of this neural circuit. In addition, we analyzed the synaptic currents and local field potentials to explain the mechanism of MDD from the perspective of dysfunction of excitation-inhibition balance, especially the disinhibition effect in the network. The significance of this article is that we built the first computational model to illuminate the effect of dopamine concentrations for the NAc-mPFC-VTA circuit between MDD and normal groups, which can be used to quantitatively explain the results of existing physiological experiments, predict the results for unperformed experiments and screen possible drug targets.
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Affiliation(s)
- Yuanxi Li
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bing Zhang
- Department of Anesthesiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
- Clinical and Translational Research Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaochuan Pan
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
| | - Yihong Wang
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
| | - Xuying Xu
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China
- *Correspondence: Rubin Wang, ;
| | - Zhiqiang Liu
- Department of Anesthesiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
- Anesthesia and Brain Function Research Institute, Tongji University School of Medicine, Shanghai, China
- Zhiqiang Liu,
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10
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Ursino M, Cesaretti N, Pirazzini G. A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code. Cogn Neurodyn 2022; 17:489-521. [PMID: 37007198 PMCID: PMC10050512 DOI: 10.1007/s11571-022-09836-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 02/25/2022] [Accepted: 05/27/2022] [Indexed: 11/24/2022] Open
Abstract
AbstractRecent experimental evidence suggests that oscillatory activity plays a pivotal role in the maintenance of information in working memory, both in rodents and humans. In particular, cross-frequency coupling between theta and gamma oscillations has been suggested as a core mechanism for multi-item memory. The aim of this work is to present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. We show that this model, with different synapse values, can be used to address different problems, such as the reconstruction of an item from partial information, the maintenance of multiple items simultaneously in memory, without any sequential order, and the reconstruction of an ordered sequence starting from an initial cue. The model consists of four interconnected layers; synapses are trained using Hebbian and anti-Hebbian mechanisms, in order to synchronize features in the same items, and desynchronize features in different items. Simulations show that the trained network is able to desynchronize up to nine items without a fixed order using the gamma rhythm. Moreover, the network can replicate a sequence of items using a gamma rhythm nested inside a theta rhythm. The reduction in some parameters, mainly concerning the strength of GABAergic synapses, induce memory alterations which mimic neurological deficits. Finally, the network, isolated from the external environment (“imagination phase”) and stimulated with high uniform noise, can randomly recover sequences previously learned, and link them together by exploiting the similarity among items.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Campus of Cesena Area di Campus Cesena Via Dell’Università 50, 47521 Cesena, FC Italy
| | - Nicole Cesaretti
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Campus of Cesena Area di Campus Cesena Via Dell’Università 50, 47521 Cesena, FC Italy
| | - Gabriele Pirazzini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Campus of Cesena Area di Campus Cesena Via Dell’Università 50, 47521 Cesena, FC Italy
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11
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Peng X, Lin W. Complex Dynamics of Noise-Perturbed Excitatory-Inhibitory Neural Networks With Intra-Correlative and Inter-Independent Connections. Front Physiol 2022; 13:915511. [PMID: 35812336 PMCID: PMC9263264 DOI: 10.3389/fphys.2022.915511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/09/2022] [Indexed: 11/24/2022] Open
Abstract
Real neural system usually contains two types of neurons, i.e., excitatory neurons and inhibitory ones. Analytical and numerical interpretation of dynamics induced by different types of interactions among the neurons of two types is beneficial to understanding those physiological functions of the brain. Here, we articulate a model of noise-perturbed random neural networks containing both excitatory and inhibitory (E&I) populations. Particularly, both intra-correlatively and inter-independently connected neurons in two populations are taken into account, which is different from the most existing E&I models only considering the independently-connected neurons. By employing the typical mean-field theory, we obtain an equivalent system of two dimensions with an input of stationary Gaussian process. Investigating the stationary autocorrelation functions along the obtained system, we analytically find the parameters’ conditions under which the synchronized behaviors between the two populations are sufficiently emergent. Taking the maximal Lyapunov exponent as an index, we also find different critical values of the coupling strength coefficients for the chaotic excitatory neurons and for the chaotic inhibitory ones. Interestingly, we reveal that the noise is able to suppress chaotic dynamics of the random neural networks having neurons in two populations, while an appropriate amount of correlation coefficient in intra-coupling strengths can enhance chaos occurrence. Finally, we also detect a previously-reported phenomenon where the parameters region corresponds to neither linearly stable nor chaotic dynamics; however, the size of the region area crucially depends on the populations’ parameters.
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Affiliation(s)
- Xiaoxiao Peng
- Shanghai Center for Mathematical Sciences, School of Mathematical Sciences, and LMNS, Fudan University, Shanghai, China
- Research Institute of Intelligent Complex Systemsand Center for Computational Systems Biology, Fudan University, Shanghai, China
- *Correspondence: Xiaoxiao Peng, ; Wei Lin,
| | - Wei Lin
- Shanghai Center for Mathematical Sciences, School of Mathematical Sciences, and LMNS, Fudan University, Shanghai, China
- Research Institute of Intelligent Complex Systemsand Center for Computational Systems Biology, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, and Institutes of Brain Science, Fudan University, Shanghai, China
- *Correspondence: Xiaoxiao Peng, ; Wei Lin,
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Dong T, Huang Q, Huang S, Xin J, Jia Q, Gao Y, Shen H, Tang Y, Zhang H. Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging. Front Psychol 2021; 12:717519. [PMID: 34526937 PMCID: PMC8435858 DOI: 10.3389/fpsyg.2021.717519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/09/2021] [Indexed: 11/19/2022] Open
Abstract
Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.
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Affiliation(s)
- Tingting Dong
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiuping Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Shucai Huang
- The Fourth People’s Hospital of Wuhu, Wuhu, China
| | - Jiang Xin
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiaolan Jia
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Yang Gao
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hongxian Shen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China
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