1
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Yang F, Xu Y, Ma J. A memristive neuron and its adaptability to external electric field. CHAOS (WOODBURY, N.Y.) 2023; 33:023110. [PMID: 36859211 DOI: 10.1063/5.0136195] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Connecting memristors into any neural circuit can enhance its potential controllability under external physical stimuli. Memristive current along a magnetic flux-controlled memristor can estimate the effect of electromagnetic induction on neural circuits and neurons. Here, a charge-controlled memristor is incorporated into one branch circuit of a simple neural circuit to estimate the effect of an external electric field. The field energy kept in each electric component is respectively calculated, and equivalent dimensionless energy function H is obtained to discern the firing mode dependence on the energy from capacitive, inductive, and memristive channels. The electric field energy HM in a memristive channel occupies the highest proportion of Hamilton energy H, and neurons can present chaotic/periodic firing modes because of large energy injection from an external electric field, while bursting and spiking behaviors emerge when magnetic field energy HL holds maximal proportion of Hamilton energy H. The memristive current is modified to control the firing modes in this memristive neuron accompanying with a parameter shift and shape deformation resulting from energy accommodation in the memristive channel. In the presence of noisy disturbance from an external electric field, stochastic resonance is induced in the memristive neuron. Exposed to stronger electromagnetic field, the memristive component can absorb more energy and behave as a signal source for energy shunting, and negative Hamilton energy is obtained for this neuron. The new memristive neuron model can address the main physical properties of biophysical neurons, and it can further be used to explore the collective behaviors and self-organization in networks under energy flow and noisy disturbance.
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Affiliation(s)
- Feifei Yang
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Ying Xu
- School of Mathematics and Statistics, Shandong Normal University, Ji'nan 250014, China
| | - Jun Ma
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
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2
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Li X, Fang F, Li R, Zhang Y. Functional Brain Controllability Alterations in Stroke. Front Bioeng Biotechnol 2022; 10:925970. [PMID: 35832411 PMCID: PMC9271898 DOI: 10.3389/fbioe.2022.925970] [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: 04/22/2022] [Accepted: 06/01/2022] [Indexed: 11/17/2022] Open
Abstract
Motor control deficits are very common in stroke survivors and often lead to disability. Current clinical measures for profiling motor control impairments are largely subjective and lack precise interpretation in a “control” perspective. This study aims to provide an accurate interpretation and assessment of the underlying “motor control” deficits caused by stroke, using a recently developed novel technique, i.e., the functional brain controllability analysis. The electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were simultaneously recorded from 16 stroke patients and 11 healthy subjects during a hand-clenching task. A high spatiotemporal resolution fNIRS-informed EEG source imaging approach was then employed to estimate the cortical activity and construct the functional brain network. Subsequently, network control theory was applied to evaluate the modal controllability of some key motor regions, including primary motor cortex (M1), premotor cortex (PMC), and supplementary motor cortex (SMA), and also the executive control network (ECN). Results indicated that the modal controllability of ECN in stroke patients was significantly lower than healthy subjects (p = 0.03). Besides, the modal controllability of SMA in stroke patients was also significant smaller than healthy subjects (p = 0.02). Finally, the baseline modal controllability of M1 was found to be significantly correlated with the baseline FM-UL clinical scores (r = 0.58, p = 0.01). In conclusion, our results provide a new perspective to better understand the motor control deficits caused by stroke. We expect such an analytical methodology can be extended to investigate the other neurological or psychiatric diseases caused by cognitive control or motor control impairment.
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Affiliation(s)
- Xuhong Li
- Department of Rehabilitation Medicine, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
- *Correspondence: Feng Fang, , Yingchun Zhang,
| | - Rihui Li
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
- *Correspondence: Feng Fang, , Yingchun Zhang,
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3
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Deng S, Li J, Thomas Yeo BT, Gu S. Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity. Commun Biol 2022; 5:295. [PMID: 35365757 PMCID: PMC8975837 DOI: 10.1038/s42003-022-03196-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 02/22/2022] [Indexed: 11/09/2022] Open
Abstract
The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.
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Affiliation(s)
- Shikuang Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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4
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Gabrieli D, Schumm SN, Vigilante NF, Meaney DF. NMDA Receptor Alterations After Mild Traumatic Brain Injury Induce Deficits in Memory Acquisition and Recall. Neural Comput 2020; 33:67-95. [PMID: 33253030 DOI: 10.1162/neco_a_01343] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Mild traumatic brain injury (mTBI) presents a significant health concern with potential persisting deficits that can last decades. Although a growing body of literature improves our understanding of the brain network response and corresponding underlying cellular alterations after injury, the effects of cellular disruptions on local circuitry after mTBI are poorly understood. Our group recently reported how mTBI in neuronal networks affects the functional wiring of neural circuits and how neuronal inactivation influences the synchrony of coupled microcircuits. Here, we utilized a computational neural network model to investigate the circuit-level effects of N-methyl D-aspartate receptor dysfunction. The initial increase in activity in injured neurons spreads to downstream neurons, but this increase was partially reduced by restructuring the network with spike-timing-dependent plasticity. As a model of network-based learning, we also investigated how injury alters pattern acquisition, recall, and maintenance of a conditioned response to stimulus. Although pattern acquisition and maintenance were impaired in injured networks, the greatest deficits arose in recall of previously trained patterns. These results demonstrate how one specific mechanism of cellular-level damage in mTBI affects the overall function of a neural network and point to the importance of reversing cellular-level changes to recover important properties of learning and memory in a microcircuit.
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Affiliation(s)
- David Gabrieli
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
| | - Samantha N Schumm
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
| | - Nicholas F Vigilante
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
| | - David F Meaney
- Department of Bioengineering, School of Engineering and Applied Sciences, and Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
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5
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Protachevicz PR, Iarosz KC, Caldas IL, Antonopoulos CG, Batista AM, Kurths J. Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses. Front Syst Neurosci 2020; 14:604563. [PMID: 33328913 PMCID: PMC7734146 DOI: 10.3389/fnsys.2020.604563] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/05/2020] [Indexed: 11/29/2022] Open
Abstract
A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.
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Affiliation(s)
| | - Kelly C Iarosz
- Faculdade de Telêmaco Borba, FATEB, Telêmaco Borba, Brazil.,Graduate Program in Chemical Engineering, Federal University of Technology Paraná, Ponta Grossa, Brazil
| | - Iberê L Caldas
- Institute of Physics, University of São Paulo, São Paulo, Brazil
| | - Chris G Antonopoulos
- Department of Mathematical Sciences, University of Essex, Colchester, United Kingdom
| | - Antonio M Batista
- Institute of Physics, University of São Paulo, São Paulo, Brazil.,Department of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Jurgen Kurths
- Department Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Department of Physics, Humboldt University, Berlin, Germany.,Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University, Moscow, Russia
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6
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Yan L, Zhang H, Sun Z, Shen Z. Control analysis of electrical stimulation for epilepsy waveforms in a thalamocortical network. J Theor Biol 2020; 504:110391. [PMID: 32640272 DOI: 10.1016/j.jtbi.2020.110391] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 06/20/2020] [Accepted: 06/24/2020] [Indexed: 11/28/2022]
Abstract
Physiological experiments and computational models both show that the thalamic reticular nucleus (RE) participates in inducing various firing patterns of cortex. Absence seizure, featured by 2-4 Hz spike-wave discharges (SWD) oscillation, is a high incidence of disease in children. Lots of electrophysiological experiments have verified the correlation between absence seizures and RE, however, the dynamical mechanisms are not well understood. Based on previous Taylor model, we firstly study the effects of external input and self-inhibition of RE on epilepsy transition. We show that increasing external input and self-inhibition of RE can lead the system from epileptic state to normal state, and vice versa. Next, we explore two stimulus strategies added in RE and various transition behaviors can be induced, such as high saturated state to clonic. Meanwhile, as the intensity of stimulation increasing, they can not only suppress the SWD, but also produce tonic-clonic oscillation. Finally, the control of DBS on single neuron cluster and two neuron clusters are compared and we find stimulating RE and TC simultaneously is a superior mode to stimulate anyone of RE or TC. It is hoped that the results we obtained will have an enlightenment on clinical treatment.
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Affiliation(s)
- Luyao Yan
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Honghui Zhang
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Zhongkui Sun
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.
| | - Zhuan Shen
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
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7
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Ju H, Kim JZ, Beggs JM, Bassett DS. Network structure of cascading neural systems predicts stimulus propagation and recovery. J Neural Eng 2020; 17:056045. [PMID: 33036007 PMCID: PMC11191848 DOI: 10.1088/1741-2552/abbff1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network's local and global connectivity to these patterns and information processing remains largely unknown. APPROACH Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory. MAIN RESULTS In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks. SIGNIFICANCE Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.
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Affiliation(s)
- Harang Ju
- Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Jason Z Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John M Beggs
- Department of Physics, Indiana University, Bloomington, IN 47405, United States of America
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, United States of America
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8
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Hattori K, Hayakawa T, Nakanishi A, Ishida M, Yamamoto H, Hirano-Iwata A, Tanii T. Contribution of AMPA and NMDA receptors in the spontaneous firing patterns of single neurons in autaptic culture. Biosystems 2020; 198:104278. [PMID: 33075473 DOI: 10.1016/j.biosystems.2020.104278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 10/11/2020] [Accepted: 10/14/2020] [Indexed: 10/23/2022]
Abstract
Single neurons in an autaptic culture exhibit various types of firing pattern with different firing durations and rhythms. However, a neuron with autapses has often been modeled as an oscillator providing a monotonic firing pattern with a constant periodicity because of the lack of a mathematical model. In the work described in this study, we use computational simulation and whole-cell patch-clamp recording to elucidate and model the mechanism by which such neurons generate various firing pattens. In the computational simulation, three types of spontaneous firing pattern, i.e., short, long-lasting, and periodic burst firing patterns are realized by changing the combination ratio of N-methyl-d-aspartate (NMDA) to α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) conductance. These three types of firing patterns are also observed in the experiments where neurons are cultured in isolation on micropatterned substrates. Using the AMPA and NMDA current models, we discuss that, in principle, autapses can regulate rhythmicity and information selection in neuronal networks.
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Affiliation(s)
- Kouhei Hattori
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan.
| | - Takeshi Hayakawa
- Tohoku University, Research Institute of Electrical Communication, 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan
| | - Akira Nakanishi
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
| | - Mihoko Ishida
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
| | - Hideaki Yamamoto
- Tohoku University, Research Institute of Electrical Communication, 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan
| | - Ayumi Hirano-Iwata
- Tohoku University, Research Institute of Electrical Communication, 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan; Tohoku University, WPI-Advanced Institute for Materials Research (WPI-AIMR), 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan
| | - Takashi Tanii
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
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9
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Gabrieli D, Schumm SN, Vigilante NF, Parvesse B, Meaney DF. Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks. PLoS One 2020; 15:e0234749. [PMID: 32966291 PMCID: PMC7510994 DOI: 10.1371/journal.pone.0234749] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/01/2020] [Indexed: 12/26/2022] Open
Abstract
Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.
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Affiliation(s)
- David Gabrieli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Samantha N. Schumm
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Nicholas F. Vigilante
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Brandon Parvesse
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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10
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Bekkers JM. Autaptic Cultures: Methods and Applications. Front Synaptic Neurosci 2020; 12:18. [PMID: 32425765 PMCID: PMC7203343 DOI: 10.3389/fnsyn.2020.00018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 04/01/2020] [Indexed: 11/13/2022] Open
Abstract
Neurons typically form daisy chains of synaptic connections with other neurons, but they can also form synapses with themselves. Although such self-synapses, or autapses, are comparatively rare in vivo, they are surprisingly common in dissociated neuronal cultures. At first glance, autapses in culture seem like a mere curiosity. However, by providing a simple model system in which a single recording electrode gives simultaneous access to the pre- and postsynaptic compartments, autaptic cultures have proven to be invaluable in facilitating important and elegant experiments in the area of synaptic neuroscience. Here, I provide detailed protocols for preparing and recording from autaptic cultures (also called micro-island or microdot cultures). Variations on the basic procedure are presented, as well as practical tips for optimizing the outcomes. I also illustrate the utility of autaptic cultures by reviewing the types of experiments that have used them over the past three decades. These examples serve to highlight the power and elegance of this simple model system, and will hopefully inspire new experiments for the interrogation of synaptic function.
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Affiliation(s)
- John M Bekkers
- Eccles Institute of Neuroscience, The John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
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11
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Karrer TM, Kim JZ, Stiso J, Kahn AE, Pasqualetti F, Habel U, Bassett DS. A practical guide to methodological considerations in the controllability of structural brain networks. J Neural Eng 2020; 17:026031. [PMID: 31968320 PMCID: PMC7734595 DOI: 10.1088/1741-2552/ab6e8b] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Predicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units. APPROACH Given the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 h of scanning from ten healthy young adults. MAIN RESULTS Following a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state-of-the-art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints. SIGNIFICANCE Our hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.
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Affiliation(s)
- Teresa M. Karrer
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jason Z. Kim
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer Stiso
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ari E. Kahn
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
- JARA - Translational Brain Medicine, Aachen, Germany
- Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics and Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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12
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Schumm SN, Gabrieli D, Meaney DF. Neuronal Degeneration Impairs Rhythms Between Connected Microcircuits. Front Comput Neurosci 2020; 14:18. [PMID: 32194390 PMCID: PMC7063469 DOI: 10.3389/fncom.2020.00018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 02/11/2020] [Indexed: 11/23/2022] Open
Abstract
Synchronization of neural activity across brain regions is critical to processes that include perception, learning, and memory. After traumatic brain injury (TBI), neuronal degeneration is one possible effect and can alter communication between neural circuits. Consequently, synchronization between neurons may change and can contribute to both lasting changes in functional brain networks and cognitive impairment in patients. However, fundamental principles relating exactly how TBI at the cellular scale affects synchronization of mesoscale circuits are not well understood. In this work, we use computational networks of Izhikevich integrate-and-fire neurons to study synchronized, oscillatory activity between clusters of neurons, which also adapt according to spike-timing-dependent plasticity (STDP). We study how the connections within and between these neuronal clusters change as unidirectional connections form between the two neuronal populations. In turn, we examine how neuronal deletion, intended to mimic the temporary or permanent loss of neurons in the mesoscale circuit, affects these dynamics. We determine synchronization of two neuronal circuits requires very modest connectivity between these populations; approximately 10% of neurons projecting from one circuit to another circuit will result in high synchronization. In addition, we find that synchronization level inversely affects the strength of connection between neuronal microcircuits - moderately synchronized microcircuits develop stronger intercluster connections than do highly synchronized circuits. Finally, we find that highly synchronized circuits are largely protected against the effects of neuronal deletion but may display changes in frequency properties across circuits with targeted neuronal loss. Together, our results suggest that strongly and weakly connected regions differ in their inherent resilience to damage and may serve different roles in a larger network.
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Affiliation(s)
- Samantha N. Schumm
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - David Gabrieli
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - David F. Meaney
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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13
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Yuan Y, Liu J, Zhao P, Xing F, Huo H, Fang T. Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases. Front Neurosci 2019; 13:892. [PMID: 31507365 PMCID: PMC6714520 DOI: 10.3389/fnins.2019.00892] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 08/08/2019] [Indexed: 11/13/2022] Open
Abstract
The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.
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Affiliation(s)
- Ye Yuan
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Jian Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Peng Zhao
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Fu Xing
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Hong Huo
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Tao Fang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
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14
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Bernhardt BC, Fadaie F, Liu M, Caldairou B, Gu S, Jefferies E, Smallwood J, Bassett DS, Bernasconi A, Bernasconi N. Temporal lobe epilepsy: Hippocampal pathology modulates connectome topology and controllability. Neurology 2019; 92:e2209-e2220. [PMID: 31004070 DOI: 10.1212/wnl.0000000000007447] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 01/08/2019] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE To assess whether hippocampal sclerosis (HS) severity is mirrored at the level of large-scale networks. METHODS We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 temporal lobe epilepsy (TLE) patients with histopathologic diagnosis of HS (n = 25; TLE-HS) and isolated gliosis (n = 19; TLE-G) and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm that simulates functional dynamics from structural network data. RESULTS Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from gray matter atrophy. CONCLUSIONS Severe HS is associated with marked remodeling of connectome topology and structurally governed functional dynamics in TLE, as opposed to isolated gliosis, which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales.
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Affiliation(s)
- Boris C Bernhardt
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Fatemeh Fadaie
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Min Liu
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Benoit Caldairou
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Shi Gu
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Elizabeth Jefferies
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Jonathan Smallwood
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Danielle S Bassett
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Andrea Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Neda Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK.
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15
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Aguilar-Velázquez D, Guzmán-Vargas L. Critical synchronization and 1/f noise in inhibitory/excitatory rich-club neural networks. Sci Rep 2019; 9:1258. [PMID: 30718817 PMCID: PMC6361933 DOI: 10.1038/s41598-018-37920-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 12/17/2018] [Indexed: 12/16/2022] Open
Abstract
In recent years, diverse studies have reported that different brain regions, which are internally densely connected, are also highly connected to each other. This configuration seems to play a key role in integrating and interchanging information between brain areas. Also, changes in the rich-club connectivity and the shift from inhibitory to excitatory behavior of hub neurons have been associated with several diseases. However, there is not a clear understanding about the role of the proportion of inhibitory/excitatory hub neurons, the dynamic consequences of rich-club disconnection, and hub inhibitory/excitatory shifts. Here, we study the synchronization and temporal correlations in the neural Izhikevich model, which comprises excitatory and inhibitory neurons located in a scale-free hierarchical network with rich-club connectivity. We evaluated the temporal autocorrelations and global synchronization dynamics displayed by the system in terms of rich-club connectivity and hub inhibitory/excitatory population. We evaluated the synchrony between pairs of sets of neurons by means of the global lability synchronization, based on the rate of change in the total number of synchronized signals. The results show that for a wide range of excitatory/inhibitory hub ratios the network displays 1/f dynamics with critical synchronization that is concordant with numerous health brain registers, while a network configuration with a vast majority of excitatory hubs mostly exhibits short-term autocorrelations with numerous large avalanches. Furthermore, rich-club connectivity promotes the increase of the global lability of synchrony and the temporal persistence of the system.
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Affiliation(s)
- Daniel Aguilar-Velázquez
- Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Av. IPN No. 2580, L. Ticomán, Ciudad de México, 07340, Mexico
| | - Lev Guzmán-Vargas
- Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Av. IPN No. 2580, L. Ticomán, Ciudad de México, 07340, Mexico.
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16
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De Vico Fallani F, Bassett DS. Network neuroscience for optimizing brain-computer interfaces. Phys Life Rev 2019; 31:304-309. [PMID: 30642781 DOI: 10.1016/j.plrev.2018.10.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/29/2018] [Accepted: 10/10/2018] [Indexed: 01/30/2023]
Abstract
Human-machine interactions are being increasingly explored to create alternative ways of communication and to improve our daily life. Based on a classification of the user's intention from the user's underlying neural activity, brain-computer interfaces (BCIs) allow direct interactions with the external environment while bypassing the traditional effector of the musculoskeletal system. Despite the enormous potential of BCIs, there are still a number of challenges that limit their societal impact, ranging from the correct decoding of a human's thoughts, to the application of effective learning strategies. Despite several important engineering advances, the basic neuroscience behind these challenges remains poorly explored. Indeed, BCIs involve complex dynamic changes related to neural plasticity at a diverse range of spatiotemporal scales. One promising antidote to this complexity lies in network science, which provides a natural language in which to model the organizational principles of brain architecture and function as manifest in its interconnectivity. Here, we briefly review the main limitations currently affecting BCIs, and we offer our perspective on how they can be addressed by means of network theoretic approaches. We posit that the emerging field of network neuroscience will prove to be an effective tool to unlock human-machine interactions.
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Affiliation(s)
- Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France.
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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17
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Khambhati AN, Sizemore AE, Betzel RF, Bassett DS. Modeling and interpreting mesoscale network dynamics. Neuroimage 2018; 180:337-349. [PMID: 28645844 PMCID: PMC5738302 DOI: 10.1016/j.neuroimage.2017.06.029] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 06/12/2017] [Accepted: 06/14/2017] [Indexed: 11/28/2022] Open
Abstract
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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18
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Gu S, Cieslak M, Baird B, Muldoon SF, Grafton ST, Pasqualetti F, Bassett DS. The Energy Landscape of Neurophysiological Activity Implicit in Brain Network Structure. Sci Rep 2018; 8:2507. [PMID: 29410486 PMCID: PMC5802783 DOI: 10.1038/s41598-018-20123-8] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 01/08/2018] [Indexed: 01/03/2023] Open
Abstract
A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of the brain. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states - characterized by minimal energy - display common activation profiles across brain areas: local spatially-contiguous sets of brain regions reminiscent of cognitive systems are co-activated frequently. The predicted activation rate of these systems is highly correlated with the observed activation rate measured in a separate resting state fMRI data set, validating the utility of the maximum entropy model in describing neurophysiological dynamics. This approach also offers a formal notion of the energy of activity within a system, and the energy of activity shared between systems. We observe that within- and between-system energies cleanly separate cognitive systems into distinct categories, optimized for differential contributions to integrated versus segregated function. These results support the notion that energetic and structural constraints circumscribe brain dynamics, offering insights into the roles that cognitive systems play in driving whole-brain activation patterns.
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Affiliation(s)
- Shi Gu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Benjamin Baird
- Center for Sleep and Consciousness, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Sarah F Muldoon
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA, 92521, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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19
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Romeira B, Figueiredo JML, Javaloyes J. Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications. CHAOS (WOODBURY, N.Y.) 2017; 27:114323. [PMID: 29195310 DOI: 10.1063/1.5008888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.
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Affiliation(s)
- Bruno Romeira
- Centro de Electrónica, Optoelectrónica e Telecomunicações (CEOT), Departmento de Física, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
| | - José M L Figueiredo
- Centro de Electrónica, Optoelectrónica e Telecomunicações (CEOT), Departmento de Física, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
| | - Julien Javaloyes
- Departament de Física, Universitat de les Illes Balears, C/Valldemossa km 7.5, 07122 Palma de Mallorca, Spain
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20
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Betzel RF, Bassett DS. Generative models for network neuroscience: prospects and promise. J R Soc Interface 2017; 14:20170623. [PMID: 29187640 PMCID: PMC5721166 DOI: 10.1098/rsif.2017.0623] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/06/2017] [Indexed: 12/22/2022] Open
Abstract
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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21
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Navlakha S, Bar-Joseph Z, Barth AL. Network Design and the Brain. Trends Cogn Sci 2017; 22:64-78. [PMID: 29054336 DOI: 10.1016/j.tics.2017.09.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/18/2017] [Accepted: 09/25/2017] [Indexed: 12/30/2022]
Abstract
Neural circuits have evolved to accommodate similar information processing challenges as those faced by engineered systems. Here, we compare neural versus engineering strategies for constructing networks. During circuit development, synapses are overproduced and then pruned back over time, whereas in engineered networks, connections are initially sparse and are then added over time. We provide a computational perspective on these two different approaches, including discussion of how and why they are used, insights that one can provide the other, and areas for future joint investigation. By thinking algorithmically about the goals, constraints, and optimization principles used by neural circuits, we can develop brain-derived strategies for enhancing network design, while also stimulating experimental hypotheses about circuit development and function.
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Affiliation(s)
- Saket Navlakha
- The Salk Institute for Biological Studies, Integrative Biology Laboratory, La Jolla, CA 92037, USA.
| | - Ziv Bar-Joseph
- Carnegie Mellon University, Machine Learning Department, Computational Biology Department, Pittsburgh, PA 15213, USA
| | - Alison L Barth
- Carnegie Mellon University, Center for the Neural Basis of Cognition, Department of Biological Sciences, Pittsburgh, PA 15213, USA
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