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Kaboodvand N, Karimi H, Iravani B. Preparatory activity of anterior insula predicts conflict errors: integrating convolutional neural networks and neural mass models. Sci Rep 2024; 14:16682. [PMID: 39030222 PMCID: PMC11271609 DOI: 10.1038/s41598-024-67034-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024] Open
Abstract
Preparatory brain activity is a cornerstone of proactive cognitive control, a top-down process optimizing attention, perception, and inhibition, fostering cognitive flexibility and adaptive attention control in the human brain. In this study, we proposed a neuroimaging-informed convolutional neural network model to predict cognitive control performance from the baseline pre-stimulus preparatory electrophysiological activity of core cognitive control regions. Particularly, combined with perturbation-based occlusion sensitivity analysis, we pinpointed regions with the most predictive preparatory activity for proactive cognitive control. We found that preparatory arrhythmic broadband neural dynamics in the right anterior insula, right precentral gyrus, and the right opercular part of inferior frontal gyrus (posterior ventrolateral prefrontal cortex), are highly predictive of prospective cognitive control performance. The pre-stimulus preparatory activity in these regions corresponds to readiness for conflict detection, inhibitory control, and overall elaborate attentional processing. We integrated the convolutional neural network with biologically inspired Jansen-Rit neural mass model to investigate neurostimulation effects on cognitive control. High-frequency stimulation (130 Hz) of the left anterior insula provides significant cognitive enhancement, especially in reducing conflict errors, despite the right anterior insula's higher predictive value for prospective cognitive control performance. Thus, effective neurostimulation targets may differ from regions showing biomarker activity. Finally, we validated our theoretical finding by evaluating intrinsic neuromodulation through neurofeedback-guided volitional control in an independent dataset. We found that left anterior insula was intrinsically modulated in real-time by volitional control of emotional valence, but not arousal. Our findings further highlight central role of anterior insula in orchestrating proactive cognitive control processes, positioning it at the top of hierarchy for cognitive control.
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Affiliation(s)
- Neda Kaboodvand
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Hanie Karimi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Behzad Iravani
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.
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Wiafe SL, Asante NO, Calhoun VD, Faghiri A. Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598720. [PMID: 38915498 PMCID: PMC11195172 DOI: 10.1101/2024.06.12.598720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.
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Affiliation(s)
- Sir-Lord Wiafe
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Nana O. Asante
- ETH Zürich, Zürich, Rämistrasse 101, Switzerland
- Ashesi University, 1 University Avenue Berekuso, Ghana
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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Shavikloo M, Esmaeili A, Valizadeh A, Madadi Asl M. Synchronization of delayed coupled neurons with multiple synaptic connections. Cogn Neurodyn 2024; 18:631-643. [PMID: 38699603 PMCID: PMC11061096 DOI: 10.1007/s11571-023-10013-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/16/2023] [Accepted: 09/16/2023] [Indexed: 05/05/2024] Open
Abstract
Synchronization is a key feature of the brain dynamics and is necessary for information transmission across brain regions and in higher brain functions like cognition, learning and memory. Experimental findings demonstrated that in cortical microcircuits there are multiple synapses between pairs of connected neurons. Synchronization of neurons in the presence of multiple synaptic connections may be relevant for optimal learning and memory, however, its effect on the dynamics of the neurons is not adequately studied. Here, we address the question that how changes in the strength of the synaptic connections and transmission delays between neurons impact synchronization in a two-neuron system with multiple synapses. To this end, we analytically and computationally investigated synchronization dynamics by considering both phase oscillator model and conductance-based Hodgkin-Huxley (HH) model. Our results show that symmetry/asymmetry of feedforward and feedback connections crucially determines stability of the phase locking of the system based on the strength of connections and delays. In both models, the two-neuron system with multiple synapses achieves in-phase synchrony in the presence of small and large delays, whereas an anti-phase synchronization state is favored for median delays. Our findings can expand the understanding of the functional role of multisynaptic contacts in neuronal synchronization and may shed light on the dynamical consequences of pathological multisynaptic connectivity in a number of brain disorders.
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Affiliation(s)
- Masoumeh Shavikloo
- Department of Physics, Faculty of Science, Urmia University, Urmia, Iran
| | - Asghar Esmaeili
- Department of Physics, Faculty of Science, Urmia University, Urmia, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
| | - Mojtaba Madadi Asl
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
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Madadi Asl M, Ramezani Akbarabadi S. Delay-dependent transitions of phase synchronization and coupling symmetry between neurons shaped by spike-timing-dependent plasticity. Cogn Neurodyn 2023; 17:523-536. [PMID: 37007192 PMCID: PMC10050303 DOI: 10.1007/s11571-022-09850-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 05/24/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022] Open
Abstract
Synchronization plays a key role in learning and memory by facilitating the communication between neurons promoted by synaptic plasticity. Spike-timing-dependent plasticity (STDP) is a form of synaptic plasticity that modifies the strength of synaptic connections between neurons based on the coincidence of pre- and postsynaptic spikes. In this way, STDP simultaneously shapes the neuronal activity and synaptic connectivity in a feedback loop. However, transmission delays due to the physical distance between neurons affect neuronal synchronization and the symmetry of synaptic coupling. To address the question that how transmission delays and STDP can jointly determine the emergent pairwise activity-connectivity patterns, we studied phase synchronization properties and coupling symmetry between two bidirectionally coupled neurons using both phase oscillator and conductance-based neuron models. We show that depending on the range of transmission delays, the activity of the two-neuron motif can achieve an in-phase/anti-phase synchronized state and its connectivity can attain a symmetric/asymmetric coupling regime. The coevolutionary dynamics of the neuronal system and the synaptic weights due to STDP stabilizes the motif in either one of these states by transitions between in-phase/anti-phase synchronization states and symmetric/asymmetric coupling regimes at particular transmission delays. These transitions crucially depend on the phase response curve (PRC) of the neurons, but they are relatively robust to the heterogeneity of transmission delays and potentiation-depression imbalance of the STDP profile.
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Affiliation(s)
- Mojtaba Madadi Asl
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5531 Iran
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Xue J, Yao R, Cui X, Wang B, Wei J, Wu X, Sun J, Yang Y, Xiang J, Liu Y. Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer’s disease. Cereb Cortex 2022; 33:4230-4247. [PMID: 36104855 DOI: 10.1093/cercor/bhac339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/14/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.
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Affiliation(s)
- Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Rong Yao
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jing Wei
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xubin Wu
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Sun
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital , No. 3, Workers New Street, Taiyuan, Shanxi, 030013 , China
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Madadi Asl M, Vahabie AH, Valizadeh A, Tass PA. Spike-Timing-Dependent Plasticity Mediated by Dopamine and its Role in Parkinson's Disease Pathophysiology. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:817524. [PMID: 36926058 PMCID: PMC10013044 DOI: 10.3389/fnetp.2022.817524] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/08/2022] [Indexed: 01/05/2023]
Abstract
Parkinson's disease (PD) is a multi-systemic neurodegenerative brain disorder. Motor symptoms of PD are linked to the significant dopamine (DA) loss in substantia nigra pars compacta (SNc) followed by basal ganglia (BG) circuit dysfunction. Increasing experimental and computational evidence indicates that (synaptic) plasticity plays a key role in the emergence of PD-related pathological changes following DA loss. Spike-timing-dependent plasticity (STDP) mediated by DA provides a mechanistic model for synaptic plasticity to modify synaptic connections within the BG according to the neuronal activity. To shed light on how DA-mediated STDP can shape neuronal activity and synaptic connectivity in the PD condition, we reviewed experimental and computational findings addressing the modulatory effect of DA on STDP as well as other plasticity mechanisms and discussed their potential role in PD pathophysiology and related network dynamics and connectivity. In particular, reshaping of STDP profiles together with other plasticity-mediated processes following DA loss may abnormally modify synaptic connections in competing pathways of the BG. The cascade of plasticity-induced maladaptive or compensatory changes can impair the excitation-inhibition balance towards the BG output nuclei, leading to the emergence of pathological activity-connectivity patterns in PD. Pre-clinical, clinical as well as computational studies reviewed here provide an understanding of the impact of synaptic plasticity and other plasticity mechanisms on PD pathophysiology, especially PD-related network activity and connectivity, after DA loss. This review may provide further insights into the abnormal structure-function relationship within the BG contributing to the emergence of pathological states in PD. Specifically, this review is intended to provide detailed information for the development of computational network models for PD, serving as testbeds for the development and optimization of invasive and non-invasive brain stimulation techniques. Computationally derived hypotheses may accelerate the development of therapeutic stimulation techniques and potentially reduce the number of related animal experiments.
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Affiliation(s)
- Mojtaba Madadi Asl
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Abdol-Hossein Vahabie
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,Department of Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Peter A Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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