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Choi KH, Heo YJ, Baek HJ, Kim JH, Jang JY. Comparison of Inter-Method Agreement and Reliability for Automatic Brain Volumetry Using Three Different Clinically Available Software Packages. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:727. [PMID: 38792912 PMCID: PMC11122718 DOI: 10.3390/medicina60050727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/21/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024]
Abstract
Background and Objectives: No comparative study has evaluated the inter-method agreement and reliability between Heuron AD and other clinically available brain volumetric software packages. Hence, we aimed to investigate the inter-method agreement and reliability of three clinically available brain volumetric software packages: FreeSurfer (FS), NeuroQuant® (NQ), and Heuron AD (HAD). Materials and Methods: In this study, we retrospectively included 78 patients who underwent conventional three-dimensional (3D) T1-weighed imaging (T1WI) to evaluate their memory impairment, including 21 with normal objective cognitive function, 24 with mild cognitive impairment, and 33 with Alzheimer's disease (AD). All 3D T1WI scans were analyzed using three different volumetric software packages. Repeated-measures analysis of variance, intraclass correlation coefficient, effect size measurements, and Bland-Altman analysis were used to evaluate the inter-method agreement and reliability. Results: The measured volumes demonstrated substantial to almost perfect agreement for most brain regions bilaterally, except for the bilateral globi pallidi. However, the volumes measured using the three software packages showed significant mean differences for most brain regions, with consistent systematic biases and wide limits of agreement in the Bland-Altman analyses. The pallidum showed the largest effect size in the comparisons between NQ and FS (5.20-6.93) and between NQ and HAD (2.01-6.17), while the cortical gray matter showed the largest effect size in the comparisons between FS and HAD (0.79-1.91). These differences and variations between the software packages were also observed in the subset analyses of 45 patients without AD and 33 patients with AD. Conclusions: Despite their favorable reliability, the software-based brain volume measurements showed significant differences and systematic biases in most regions. Thus, these volumetric measurements should be interpreted based on the type of volumetric software used, particularly for smaller structures. Moreover, users should consider the replaceability-related limitations when using these packages in real-world practice.
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Affiliation(s)
- Kwang Ho Choi
- Department of Thoracic and Cardiovascular Surgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea
| | - Young Jin Heo
- Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, 75, Bokji-ro, Busanjin-gu, Busan 47392, Republic of Korea
| | - Hye Jin Baek
- Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, 11 Samjeongja-ro, Seongsan-gu, Changwon 51472, Republic of Korea
- Miracle Radiology Clinic, 201 Songpa-daero, Songpa-gu, Seoul 05854, Republic of Korea
| | - Jun-Ho Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Jeong Yoon Jang
- Division of Cardiology, Department of Internal Medicine, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, 11 Samjeongja-ro, Seongsan-gu, Changwon 51472, Republic of Korea
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Gemo E, Spiga S, Brivio S. SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks. Front Neurosci 2024; 17:1270090. [PMID: 38264497 PMCID: PMC10804805 DOI: 10.3389/fnins.2023.1270090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024] Open
Abstract
Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.
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Affiliation(s)
- Emanuele Gemo
- CNR–IMM, Unit of Agrate Brianza, Agrate Brianza, Italy
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Soplata AE, Adam E, Brown EN, Purdon PL, McCarthy MM, Kopell N. Rapid thalamocortical network switching mediated by cortical synchronization underlies propofol-induced EEG signatures: a biophysical model. J Neurophysiol 2023; 130:86-103. [PMID: 37314079 PMCID: PMC10312318 DOI: 10.1152/jn.00068.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/15/2023] Open
Abstract
Propofol-mediated unconsciousness elicits strong alpha/low-beta and slow oscillations in the electroencephalogram (EEG) of patients. As anesthetic dose increases, the EEG signal changes in ways that give clues to the level of unconsciousness; the network mechanisms of these changes are only partially understood. Here, we construct a biophysical thalamocortical network involving brain stem influences that reproduces transitions in dynamics seen in the EEG involving the evolution of the power and frequency of alpha/low-beta and slow rhythm, as well as their interactions. Our model suggests that propofol engages thalamic spindle and cortical sleep mechanisms to elicit persistent alpha/low-beta and slow rhythms, respectively. The thalamocortical network fluctuates between two mutually exclusive states on the timescale of seconds. One state is characterized by continuous alpha/low-beta-frequency spiking in thalamus (C-state), whereas in the other, thalamic alpha spiking is interrupted by periods of co-occurring thalamic and cortical silence (I-state). In the I-state, alpha colocalizes to the peak of the slow oscillation; in the C-state, there is a variable relationship between an alpha/beta rhythm and the slow oscillation. The C-state predominates near loss of consciousness; with increasing dose, the proportion of time spent in the I-state increases, recapitulating EEG phenomenology. Cortical synchrony drives the switch to the I-state by changing the nature of the thalamocortical feedback. Brain stem influence on the strength of thalamocortical feedback mediates the amount of cortical synchrony. Our model implicates loss of low-beta, cortical synchrony, and coordinated thalamocortical silent periods as contributing to the unconscious state.NEW & NOTEWORTHY GABAergic anesthetics induce alpha/low-beta and slow oscillations in the EEG, which interact in dose-dependent ways. We constructed a thalamocortical model to investigate how these interdependent oscillations change with propofol dose. We find two dynamic states of thalamocortical coordination, which change on the timescale of seconds and dose-dependently mirror known changes in EEG. Thalamocortical feedback determines the oscillatory coupling and power seen in each state, and this is primarily driven by cortical synchrony and brain stem neuromodulation.
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Affiliation(s)
- Austin E Soplata
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States
| | - Elie Adam
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Michelle M McCarthy
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States
| | - Nancy Kopell
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States
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Zhang Z, Li K, Hu X. Mapping nonlinear brain dynamics by phase space embedding with fMRI data. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Jawale D, Khandibharad S, Singh S. Decoding systems immunological model of sphingolipids with IL-6/IL-17/IL-23 axes in L. major infection. Biochim Biophys Acta Mol Cell Biol Lipids 2023; 1868:159261. [PMID: 36494028 DOI: 10.1016/j.bbalip.2022.159261] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022]
Abstract
IL-6, IL-17, IL-23 and IL-1β are the crucial cytokines controlling inflammatory and immune response during L. major infection. During cutaneous leishmaniasis, an important T helper cell type CD4+ Th17 subset plays a deterministic role in lesion formation through channelling infected macrophages and production of IL-1β, IL-6, IL-23 and IFN-γ. Ceramide derived sphingosine precursors may assist in pro-inflammatory cytokine response. However, the role of these metabolites in inflammation with pleiotropic pro-inflammatory cytokines in L. major infection is unknown. The present study indicates IL-6/IL-17/IL-23 and SPHK1-S1P-S1PRs signaling axes with the overexpression of SATB1 aiding in disease progression. Targeting SATB1 might modulate the secretion of pro-inflammatory cytokines and abnormal immune functioning, thereby killing the intracellular parasite. Systems immunological methods assisted in a step towards identifying the key to the mystery of crucial components and serving as an approach for therapeutic intervention in L. major infection.
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Affiliation(s)
- Diksha Jawale
- National Centre for Cell Science, NCCS Complex, Ganeshkhind, SP Pune University Campus, Pune 411007, India
| | - Shweta Khandibharad
- National Centre for Cell Science, NCCS Complex, Ganeshkhind, SP Pune University Campus, Pune 411007, India
| | - Shailza Singh
- National Centre for Cell Science, NCCS Complex, Ganeshkhind, SP Pune University Campus, Pune 411007, India.
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Emotional Memory Processing during REM Sleep with Implications for Post-Traumatic Stress Disorder. J Neurosci 2023; 43:433-446. [PMID: 36639913 PMCID: PMC9864570 DOI: 10.1523/jneurosci.1020-22.2022] [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: 05/26/2022] [Revised: 11/15/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022] Open
Abstract
REM sleep is important for the processing of emotional memories, including fear memories. Rhythmic interactions, especially in the theta band, between the medial prefrontal cortex (mPFC) and limbic structures are thought to play an important role, but the ways in which memory processing occurs at a mechanistic and circuits level are largely unknown. To investigate how rhythmic interactions lead to fear extinction during REM sleep, we used a biophysically based model that included the infralimbic cortex (IL), a part of the mPFC with a critical role in suppressing fear memories. Theta frequency (4-12 Hz) inputs to a given cell assembly in IL, representing an emotional memory, resulted in the strengthening of connections from the IL to the amygdala and the weakening of connections from the amygdala to the IL, resulting in the suppression of the activity of fear expression cells for the associated memory. Lower frequency (4 Hz) theta inputs effected these changes over a wider range of input strengths. In contrast, inputs at other frequencies were ineffective at causing these synaptic changes and did not suppress fear memories. Under post-traumatic stress disorder (PTSD) REM sleep conditions, rhythmic activity dissipated, and 4 Hz theta inputs to IL were ineffective, but higher-frequency (10 Hz) theta inputs to IL induced changes similar to those seen with 4 Hz inputs under normal REM sleep conditions, resulting in the suppression of fear expression cells. These results suggest why PTSD patients may repeatedly experience the same emotionally charged dreams and suggest potential neuromodulatory therapies for the amelioration of PTSD symptoms.SIGNIFICANCE STATEMENT Rhythmic interactions in the theta band between the mPFC and limbic structures are thought to play an important role in processing emotional memories, including fear memories, during REM sleep. The infralimbic cortex (IL) in the mPFC is thought to play a critical role in suppressing fear memories. We show that theta inputs to the IL, unlike other frequency inputs, are effective in producing synaptic changes that suppress the activity of fear expression cells associated with a given memory. Under PTSD REM sleep conditions, lower-frequency (4 Hz) theta inputs to the IL do not suppress the activity of fear expression cells associated with the given memory but, surprisingly, 10 Hz inputs do. These results suggest potential neuromodulatory therapies for PTSD.
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Human brain structural connectivity matrices-ready for modelling. Sci Data 2022; 9:486. [PMID: 35945231 PMCID: PMC9363436 DOI: 10.1038/s41597-022-01596-9] [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: 12/14/2021] [Accepted: 07/25/2022] [Indexed: 11/10/2022] Open
Abstract
The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects. Measurement(s) | Diffusion Weighted Imaging | Technology Type(s) | Magnetic Resonance Imaging | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | Czech Republic |
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Jiang J, Zhao Y, Liu J, Yang Y, Liang P, Huang H, Wu Y, Kang Y, Zhu T, Zhou C. Signatures of Thalamocortical Alpha Oscillations and Synchronization With Increased Anesthetic Depths Under Isoflurane. Front Pharmacol 2022; 13:887981. [PMID: 35721144 PMCID: PMC9204038 DOI: 10.3389/fphar.2022.887981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Electroencephalography (EEG) recordings under propofol exhibit an increase in slow and alpha oscillation power and dose-dependent phase–amplitude coupling (PAC), which underlie GABAA potentiation and the central role of thalamocortical entrainment. However, the exact EEG signatures elicited by volatile anesthetics and the possible neurophysiological mechanisms remain unclear.Methods: Cortical EEG signals and thalamic local field potential (LFP) were recorded in a mouse model to detect EEG signatures induced by 0.9%, 1.5%, and 2.0% isoflurane. Then, the power of the EEG spectrum, thalamocortical coherence, and slow–alpha phase–amplitude coupling were analyzed. A computational model based on the thalamic network was used to determine the primary neurophysiological mechanisms of alpha spiking of thalamocortical neurons under isoflurane anesthesia.Results: Isoflurane at 0.9% (light anesthesia) increased the power of slow and delta oscillations both in cortical EEG and in thalamic LFP. Isoflurane at 1.5% (surgery anesthesia) increased the power of alpha oscillations both in cortical EEG and in thalamic LFP. Isoflurane at 2% (deep anesthesia) further increased the power of cortical alpha oscillations, while thalamic alpha oscillations were unchanged. Thalamocortical coherence of alpha oscillation only exhibited a significant increase under 1.5% isoflurane. Isoflurane-induced PAC modulation remained unchanged throughout under various concentrations of isoflurane. By adjusting the parameters in the computational model, isoflurane-induced alpha spiking in thalamocortical neurons was simulated, which revealed the potential molecular targets and the thalamic network involved in isoflurane-induced alpha spiking in thalamocortical neurons.Conclusion: The EEG changes in the cortical alpha oscillation, thalamocortical coherence, and slow–alpha PAC may provide neurophysiological signatures for monitoring isoflurane anesthesia at various depths.
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Affiliation(s)
- Jingyao Jiang
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yi Zhao
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Jin Liu
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yaoxin Yang
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Peng Liang
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Han Huang
- Department of Anesthesiology, West China Second Hospital of Sichuan University, Chengdu, China
| | - Yongkang Wu
- Intelligent Manufacturing Institute, Chengdu Jincheng College, Chengdu, China
| | - Yi Kang
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Tao Zhu
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- *Correspondence: Tao Zhu, ; Cheng Zhou,
| | - Cheng Zhou
- Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- *Correspondence: Tao Zhu, ; Cheng Zhou,
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Layer M, Senk J, Essink S, van Meegen A, Bos H, Helias M. NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models. Front Neuroinform 2022; 16:835657. [PMID: 35712677 PMCID: PMC9196133 DOI: 10.3389/fninf.2022.835657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mean-field theory of neuronal networks has led to numerous advances in our analytical and intuitive understanding of their dynamics during the past decades. In order to make mean-field based analysis tools more accessible, we implemented an extensible, easy-to-use open-source Python toolbox that collects a variety of mean-field methods for the leaky integrate-and-fire neuron model. The Neuronal Network Mean-field Toolbox (NNMT) in its current state allows for estimating properties of large neuronal networks, such as firing rates, power spectra, and dynamical stability in mean-field and linear response approximation, without running simulations. In this article, we describe how the toolbox is implemented, show how it is used to reproduce results of previous studies, and discuss different use-cases, such as parameter space explorations, or mapping different network models. Although the initial version of the toolbox focuses on methods for leaky integrate-and-fire neurons, its structure is designed to be open and extensible. It aims to provide a platform for collecting analytical methods for neuronal network model analysis, such that the neuroscientific community can take maximal advantage of them.
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Affiliation(s)
- Moritz Layer
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Johanna Senk
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Simon Essink
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Alexander van Meegen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Institute of Zoology, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany
| | - Hannah Bos
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Moritz Helias
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
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Tarnaud T, Joseph W, Schoeters R, Martens L, Tanghe E. Improved alpha-beta power reduction via combined electrical and ultrasonic stimulation in a parkinsonian cortex-basal ganglia-thalamus computational model. J Neural Eng 2021; 18. [PMID: 34874304 DOI: 10.1088/1741-2552/ac3f6d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/02/2021] [Indexed: 11/11/2022]
Abstract
Objective. To investigate computationally the interaction of combined electrical and ultrasonic modulation of isolated neurons and of the parkinsonian cortex-basal ganglia-thalamus loop.Approach. Continuous-wave or pulsed electrical and ultrasonic neuromodulation is applied to isolated Otsuka plateau-potential generating subthalamic nucleus (STN) and Pospischil regular, fast and low-threshold spiking cortical cells in a temporally alternating or simultaneous manner. Similar combinations of electrical/ultrasonic waveforms are applied to a parkinsonian biophysical cortex-basal ganglia-thalamus neuronal network. Ultrasound-neuron interaction is modelled respectively for isolated neurons and the neuronal network with the NICE and SONIC implementations of the bilayer sonophore underlying mechanism. Reduction inα-βspectral energy is used as a proxy to express improvement in Parkinson's disease by insonication and electrostimulation.Main results. Simultaneous electro-acoustic stimulation achieves a given level of neuronal activity at lower intensities compared to the separate stimulation modalities. Conversely, temporally alternating stimulation with50 Hzelectrical and ultrasound pulses is capable of eliciting100 HzSTN firing rates. Furthermore, combination of ultrasound with hyperpolarizing currents can alter cortical cell relative spiking regimes. In the parkinsonian neuronal network, continuous-wave and pulsed ultrasound reduce pathological oscillations by different mechanisms. High-frequency pulsed separated electrical and ultrasonic deep brain stimulation (DBS) reduce pathologicalα-βpower by entraining STN-neurons. In contrast, continuous-wave ultrasound reduces pathological oscillations by silencing the STN. Compared to the separated stimulation modalities, temporally simultaneous or alternating electro-acoustic stimulation can achieve higher reductions inα-βpower for the same safety contraints on electrical/ultrasonic intensity.Significance. Focused ultrasound has the potential of becoming a non-invasive alternative of conventional DBS for the treatment of Parkinson's disease. Here, we elaborate on proposed benefits of combined electro-acoustic stimulation in terms of improved dynamic range, efficiency, spatial resolution, and neuronal selectivity.
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Affiliation(s)
- Thomas Tarnaud
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologiepark 126Zwijnaarde, 9052, Belgium
| | - Wout Joseph
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologiepark 126Zwijnaarde, 9052, Belgium
| | - Ruben Schoeters
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologiepark 126Zwijnaarde, 9052, Belgium
| | - Luc Martens
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologiepark 126Zwijnaarde, 9052, Belgium
| | - Emmeric Tanghe
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologiepark 126Zwijnaarde, 9052, Belgium
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AIM: A network model of attention in auditory cortex. PLoS Comput Biol 2021; 17:e1009356. [PMID: 34449761 PMCID: PMC8462696 DOI: 10.1371/journal.pcbi.1009356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 09/24/2021] [Accepted: 08/18/2021] [Indexed: 11/19/2022] Open
Abstract
Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem. Selective attention plays a key role in how we navigate our everyday lives. For example, at a cocktail party, we can attend to friend’s speech amidst other speakers, music, and background noise. In stark contrast, hundreds of millions of people with hearing impairment and other disorders find such environments overwhelming and debilitating. Understanding the mechanisms underlying selective attention may lead to breakthroughs in improving the quality of life for those negatively affected. Here, we propose a mechanistic network model of attention in primary auditory cortex based on attentional inhibitory modulation (AIM). In the AIM model, attention targets specific cortical inhibitory neurons, which then modulate local cortical circuits to emphasize a particular feature of sounds and suppress competing features. We show that the AIM model can account for experimental observations across different species and stimulus domains. We also demonstrate that the same mechanisms can enable listeners to flexibly switch between attending to specific targets sounds and monitoring the environment in complex acoustic scenes, such as a cocktail party. The AIM network provides a theoretical framework which can work in tandem with new experiments to help unravel cortical circuits underlying attention.
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Schoeters R, Tarnaud T, Martens L, Joseph W, Raedt R, Tanghe E. Double Two-State Opsin Model With Autonomous Parameter Inference. Front Comput Neurosci 2021; 15:688331. [PMID: 34220478 PMCID: PMC8243001 DOI: 10.3389/fncom.2021.688331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/21/2021] [Indexed: 11/13/2022] Open
Abstract
Optogenetics has a lot of potential to become an effective neuromodulative therapy for clinical applications. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin's non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a double two-state opsin model as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for autonomous model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins (ChR2(H134R) and MerMAID). Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013). Finally, a computational speed gain was observed with the proposed model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.
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Affiliation(s)
- Ruben Schoeters
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Thomas Tarnaud
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Luc Martens
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Wout Joseph
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Robrecht Raedt
- 4BRAIN, Department of Neurology, Institute for Neuroscience, Ghent University, Ghent, Belgium
| | - Emmeric Tanghe
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
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13
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Saponati M, Garcia-Ojalvo J, Cataldo E, Mazzoni A. Thalamocortical Spectral Transmission Relies on Balanced Input Strengths. Brain Topogr 2021; 35:4-18. [PMID: 34089121 PMCID: PMC8813837 DOI: 10.1007/s10548-021-00851-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 05/05/2021] [Indexed: 12/27/2022]
Abstract
The thalamus is a key element of sensory transmission in the brain, as it gates and selects sensory streams through a modulation of its internal activity. A preponderant role in these functions is played by its internal activity in the alpha range ([8–14] Hz), but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus driven information selectively over the back-ground of thalamic internally generated activity? Here we investigate this issue with a spiking network model of feedforward connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found that in a feedforward network, thalamic oscillations in the alpha range do not entrain cortical activity for two reasons: (i) alpha range oscillations are weaker in neurons projecting to the cortex, (ii) the gamma resonance dynamics of cortical networks hampers oscillations over the 10–20 Hz range thus weakening alpha range oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results highlight the relevance of corticothalamic feedback to sustain alpha range oscillations and pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.
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Affiliation(s)
- Matteo Saponati
- The Biorobotics Institute, Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Viale Rinaldo Piaggio 34, 56025, Pontedera, IT, Italy.,Dipartimento di Fisica "E. Fermi", Largo Bruno Pontecorvo 3, 56127, Pisa, IT, Italy
| | - Jordi Garcia-Ojalvo
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park Dr. Aiguader 88, 08003, Barcelona, ES, Spain
| | - Enrico Cataldo
- Dipartimento di Fisica "E. Fermi", Largo Bruno Pontecorvo 3, 56127, Pisa, IT, Italy
| | - Alberto Mazzoni
- The Biorobotics Institute, Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Viale Rinaldo Piaggio 34, 56025, Pontedera, IT, Italy.
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14
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Differential contributions of synaptic and intrinsic inhibitory currents to speech segmentation via flexible phase-locking in neural oscillators. PLoS Comput Biol 2021; 17:e1008783. [PMID: 33852573 PMCID: PMC8104450 DOI: 10.1371/journal.pcbi.1008783] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 05/07/2021] [Accepted: 02/05/2021] [Indexed: 01/07/2023] Open
Abstract
Current hypotheses suggest that speech segmentation—the initial division and grouping of the speech stream into candidate phrases, syllables, and phonemes for further linguistic processing—is executed by a hierarchy of oscillators in auditory cortex. Theta (∼3-12 Hz) rhythms play a key role by phase-locking to recurring acoustic features marking syllable boundaries. Reliable synchronization to quasi-rhythmic inputs, whose variable frequency can dip below cortical theta frequencies (down to ∼1 Hz), requires “flexible” theta oscillators whose underlying neuronal mechanisms remain unknown. Using biophysical computational models, we found that the flexibility of phase-locking in neural oscillators depended on the types of hyperpolarizing currents that paced them. Simulated cortical theta oscillators flexibly phase-locked to slow inputs when these inputs caused both (i) spiking and (ii) the subsequent buildup of outward current sufficient to delay further spiking until the next input. The greatest flexibility in phase-locking arose from a synergistic interaction between intrinsic currents that was not replicated by synaptic currents at similar timescales. Flexibility in phase-locking enabled improved entrainment to speech input, optimal at mid-vocalic channels, which in turn supported syllabic-timescale segmentation through identification of vocalic nuclei. Our results suggest that synaptic and intrinsic inhibition contribute to frequency-restricted and -flexible phase-locking in neural oscillators, respectively. Their differential deployment may enable neural oscillators to play diverse roles, from reliable internal clocking to adaptive segmentation of quasi-regular sensory inputs like speech. Oscillatory activity in auditory cortex is believed to play an important role in auditory and speech processing. One suggested function of these rhythms is to divide the speech stream into candidate phonemes, syllables, words, and phrases, to be matched with learned linguistic templates. This requires brain rhythms to flexibly synchronize with regular acoustic features of the speech stream. How neuronal circuits implement this task remains unknown. In this study, we explored the contribution of inhibitory currents to flexible phase-locking in neuronal theta oscillators, believed to perform initial syllabic segmentation. We found that a combination of specific intrinsic inhibitory currents at multiple timescales, present in a large class of cortical neurons, enabled exceptionally flexible phase-locking, which could be used to precisely segment speech by identifying vowels at mid-syllable. This suggests that the cells exhibiting these currents are a key component in the brain’s auditory and speech processing architecture.
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15
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Desai NV, Varela C. Distinct burst properties contribute to the functional diversity of thalamic nuclei. J Comp Neurol 2021; 529:3726-3750. [PMID: 33723858 PMCID: PMC8440663 DOI: 10.1002/cne.25141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 03/10/2021] [Accepted: 03/12/2021] [Indexed: 12/21/2022]
Abstract
Thalamic neurons fire spikes in two modes, burst and tonic. The function of burst firing is unclear, but the evidence suggests that bursts are more effective at activating cortical cells, and that postinhibition rebound bursting contributes to thalamocortical oscillations during sleep. Bursts are considered stereotyped signals; however, there is limited evidence regarding how burst properties compare across thalamic nuclei of different functional or anatomical organization. Here, we used whole-cell patch clamp recordings and compartmental modeling to investigate the properties of bursts in six sensory thalamic nuclei, to study the mechanisms that can lead to different burst properties, and to assess the implications of different burst properties for thalamocortical transmission and oscillatory functions. We found that bursts in higher-order cells on average had higher number of spikes and longer latency to the first spike. Additionally, burst features in first-order neurons were determined by sensory modality. Shifting the voltage-dependence and density of the T-channel conductance in a compartmental model replicates the burst properties from the intracellular recordings, pointing to molecular mechanisms that can generate burst diversity. Furthermore, the model predicts that bursts with higher number of spikes will drastically reduce the effectiveness of thalamocortical transmission. In addition, the latency to burst limited the rebound oscillatory frequency in modeled cells. These results demonstrate that burst properties vary according to the thalamocortical hierarchy and with sensory modality. The findings imply that, while in burst mode, thalamocortical transmission and firing frequency will be determined by the number of spikes and latency to burst.
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Affiliation(s)
- Nidhi Vasant Desai
- Psychology Department, Jupiter Life Sciences Initiative, Florida Atlantic University, Boca Raton, Florida, USA
| | - Carmen Varela
- Psychology Department, Jupiter Life Sciences Initiative, Florida Atlantic University, Boca Raton, Florida, USA
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16
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Lazar AA, Liu T, Turkcan MK, Zhou Y. Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era. eLife 2021; 10:e62362. [PMID: 33616035 PMCID: PMC8016480 DOI: 10.7554/elife.62362] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 02/21/2021] [Indexed: 11/25/2022] Open
Abstract
In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.
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Affiliation(s)
- Aurel A Lazar
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
| | - Tingkai Liu
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
| | | | - Yiyin Zhou
- Department of Electrical Engineering, Columbia UniversityNew YorkUnited States
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17
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Xiao J, Chen Z, Yu B. A Potential Mechanism of Sodium Channel Mediating the General Anesthesia Induced by Propofol. Front Cell Neurosci 2020; 14:593050. [PMID: 33343303 PMCID: PMC7746837 DOI: 10.3389/fncel.2020.593050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 11/10/2020] [Indexed: 12/19/2022] Open
Abstract
General anesthesia has revolutionized healthcare over the past 200 years and continues to show advancements. However, many phenomena induced by general anesthetics including paradoxical excitation are still poorly understood. Voltage-gated sodium channels (NaV) were believed to be one of the proteins targeted during general anesthesia. Based on electrophysiological measurements before and after propofol treatments of different concentrations, we mathematically modified the Hodgkin–Huxley sodium channel formulations and constructed a thalamocortical model to investigate the potential roles of NaV. The ion channels of individual neurons were modeled using the Hodgkin–Huxley type equations. The enhancement of propofol-induced GABAa current was simulated by increasing the maximal conductance and the time-constant of decay. Electroencephalogram (EEG) was evaluated as the post-synaptic potential from pyramidal (PY) cells. We found that a left shift in activation of NaV was induced primarily by a low concentration of propofol (0.3–10 μM), while a left shift in inactivation of NaV was induced by an increasing concentration (0.3–30 μM). Mathematical simulation indicated that a left shift of NaV activation produced a Hopf bifurcation, leading to cell oscillations. Left shift of NaV activation around a value of 5.5 mV in the thalamocortical models suppressed normal bursting of thalamocortical (TC) cells by triggering its chaotic oscillations. This led to irregular spiking of PY cells and an increased frequency in EEG readings. This observation suggests a mechanism leading to paradoxical excitation during general anesthesia. While a left shift in inactivation led to light hyperpolarization in individual cells, it inhibited the activity of the thalamocortical model after a certain depth of anesthesia. This finding implies that high doses of propofol inhibit the network partly by accelerating NaV toward inactivation. Additionally, this result explains why the application of sodium channel blockers decreases the requirement for general anesthetics. Our study provides an insight into the roles that NaV plays in the mechanism of general anesthesia. Since the activation and inactivation of NaV are structurally independent, it should be possible to avoid side effects by state-dependent binding to the NaV to achieve precision medicine in the future.
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Affiliation(s)
- Jinglei Xiao
- Department of Anesthesiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhengguo Chen
- College of Computer, National University of Defence Technology, Changsha, China
| | - Buwei Yu
- Department of Anesthesiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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18
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Lee JS, Briguglio JJ, Cohen JD, Romani S, Lee AK. The Statistical Structure of the Hippocampal Code for Space as a Function of Time, Context, and Value. Cell 2020; 183:620-635.e22. [DOI: 10.1016/j.cell.2020.09.024] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 05/25/2020] [Accepted: 09/09/2020] [Indexed: 01/22/2023]
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19
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Sherfey J, Ardid S, Miller EK, Hasselmo ME, Kopell NJ. Prefrontal oscillations modulate the propagation of neuronal activity required for working memory. Neurobiol Learn Mem 2020; 173:107228. [PMID: 32561459 DOI: 10.1016/j.nlm.2020.107228] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 02/01/2020] [Accepted: 04/01/2020] [Indexed: 01/11/2023]
Abstract
Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma-frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.
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Affiliation(s)
- Jason Sherfey
- Center for Systems Neuroscience, Department of Psychological and Brain Sciences, Boston University, MA 02215, United States; The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States.
| | - Salva Ardid
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT 06510, United States
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Department of Psychological and Brain Sciences, Boston University, MA 02215, United States
| | - Nancy J Kopell
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States.
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20
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Chartove JAK, McCarthy MM, Pittman-Polletta BR, Kopell NJ. A biophysical model of striatal microcircuits suggests gamma and beta oscillations interleaved at delta/theta frequencies mediate periodicity in motor control. PLoS Comput Biol 2020; 16:e1007300. [PMID: 32097404 PMCID: PMC7059970 DOI: 10.1371/journal.pcbi.1007300] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 03/06/2020] [Accepted: 12/19/2019] [Indexed: 01/02/2023] Open
Abstract
Striatal oscillatory activity is associated with movement, reward, and decision-making, and observed in several interacting frequency bands. Local field potential recordings in rodent striatum show dopamine- and reward-dependent transitions between two states: a "spontaneous" state involving β (∼15-30 Hz) and low γ (∼40-60 Hz), and a state involving θ (∼4-8 Hz) and high γ (∼60-100 Hz) in response to dopaminergic agonism and reward. The mechanisms underlying these rhythmic dynamics, their interactions, and their functional consequences are not well understood. In this paper, we propose a biophysical model of striatal microcircuits that comprehensively describes the generation and interaction of these rhythms, as well as their modulation by dopamine. Building on previous modeling and experimental work suggesting that striatal projection neurons (SPNs) are capable of generating β oscillations, we show that networks of striatal fast-spiking interneurons (FSIs) are capable of generating δ/θ (ie, 2 to 6 Hz) and γ rhythms. Under simulated low dopaminergic tone our model FSI network produces low γ band oscillations, while under high dopaminergic tone the FSI network produces high γ band activity nested within a δ/θ oscillation. SPN networks produce β rhythms in both conditions, but under high dopaminergic tone, this β oscillation is interrupted by δ/θ-periodic bursts of γ-frequency FSI inhibition. Thus, in the high dopamine state, packets of FSI γ and SPN β alternate at a δ/θ timescale. In addition to a mechanistic explanation for previously observed rhythmic interactions and transitions, our model suggests a hypothesis as to how the relationship between dopamine and rhythmicity impacts motor function. We hypothesize that high dopamine-induced periodic FSI γ-rhythmic inhibition enables switching between β-rhythmic SPN cell assemblies representing the currently active motor program, and thus that dopamine facilitates movement in part by allowing for rapid, periodic shifts in motor program execution.
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Affiliation(s)
- Julia A. K. Chartove
- Graduate program in Neuroscience, Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
| | - Michelle M. McCarthy
- Department of Mathematics & Statistics, Boston University, Boston, Massachusetts, United States of America
| | | | - Nancy J. Kopell
- Department of Mathematics & Statistics, Boston University, Boston, Massachusetts, United States of America
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21
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Stimberg M, Brette R, Goodman DFM. Brian 2, an intuitive and efficient neural simulator. eLife 2019; 8:e47314. [PMID: 31429824 PMCID: PMC6786860 DOI: 10.7554/elife.47314] [Citation(s) in RCA: 184] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 08/19/2019] [Indexed: 01/20/2023] Open
Abstract
Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.
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Affiliation(s)
- Marcel Stimberg
- Sorbonne Université, INSERM, CNRS, Institut de la VisionParisFrance
| | - Romain Brette
- Sorbonne Université, INSERM, CNRS, Institut de la VisionParisFrance
| | - Dan FM Goodman
- Department of Electrical and Electronic EngineeringImperial College LondonLondonUnited Kingdom
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22
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Biased competition in the absence of input bias revealed through corticostriatal computation. Proc Natl Acad Sci U S A 2019; 116:8564-8569. [PMID: 30962383 DOI: 10.1073/pnas.1812535116] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Classical accounts of biased competition require an input bias to resolve the competition between neuronal ensembles driving downstream processing. However, flexible and reliable selection of behaviorally relevant ensembles can occur with unbiased stimulation: striatal D1 and D2 spiny projection neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We here present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded biased competition in the presence of balanced inputs. First, tonic input strength determines which one of the two SPN phenotypes exhibits a higher mean firing rate. Second, low-strength oscillatory inputs induce higher firing rate in D2 SPNs but higher coherence between D1 SPNs. Third, high-strength inputs oscillating at distinct frequencies can preferentially activate D1 or D2 SPN populations. Of these mechanisms, only the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortex.
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23
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Chatzikonstantis G, Sidiropoulos H, Strydis C, Negrello M, Smaragdos G, De Zeeuw C, Soudris D. Multinode implementation of an extended Hodgkin–Huxley simulator. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.10.062] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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24
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Gorur-Shandilya S, Hoyland A, Marder E. Xolotl: An Intuitive and Approachable Neuron and Network Simulator for Research and Teaching. Front Neuroinform 2018; 12:87. [PMID: 30534067 PMCID: PMC6275287 DOI: 10.3389/fninf.2018.00087] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 11/05/2018] [Indexed: 11/13/2022] Open
Abstract
Conductance-based models of neurons are used extensively in computational neuroscience. Working with these models can be challenging due to their high dimensionality and large number of parameters. Here, we present a neuron and network simulator built on a novel automatic type system that binds object-oriented code written in C++ to objects in MATLAB. Our approach builds on the tradition of uniting the speed of languages like C++ with the ease-of-use and feature-set of scientific programming languages like MATLAB. Xolotl allows for the creation and manipulation of hierarchical models with components that are named and searchable, permitting intuitive high-level programmatic control over all parts of the model. The simulator's architecture allows for the interactive manipulation of any parameter in any model, and for visualizing the effects of changing that parameter immediately. Xolotl is fully featured with hundreds of ion channel models from the electrophysiological literature, and can be extended to include arbitrary conductances, synapses, and mechanisms. Several core features like bookmarking of parameters and automatic hashing of source code facilitate reproducible and auditable research. Its ease of use and rich visualization capabilities make it an attractive option in teaching environments. Finally, xolotl is written in a modular fashion, includes detailed tutorials and worked examples, and is freely available at https://github.com/sg-s/xolotl, enabling seamless integration into the workflows of other researchers.
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Affiliation(s)
- Srinivas Gorur-Shandilya
- Volen National Center for Complex Systems and Biology Department, Brandeis University, Waltham, MA, United States
| | - Alec Hoyland
- Volen National Center for Complex Systems and Biology Department, Brandeis University, Waltham, MA, United States
| | - Eve Marder
- Volen National Center for Complex Systems and Biology Department, Brandeis University, Waltham, MA, United States
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25
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Sherfey JS, Ardid S, Hass J, Hasselmo ME, Kopell NJ. Flexible resonance in prefrontal networks with strong feedback inhibition. PLoS Comput Biol 2018; 14:e1006357. [PMID: 30091975 PMCID: PMC6103521 DOI: 10.1371/journal.pcbi.1006357] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 08/21/2018] [Accepted: 07/11/2018] [Indexed: 11/24/2022] Open
Abstract
Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.
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Affiliation(s)
- Jason S. Sherfey
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
- Department of Psychological and Brain Sciences, Center for Systems Neuroscience, Boston University, Massachusetts, United States of America
| | - Salva Ardid
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
| | - Joachim Hass
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
- Faculty of Applied Psychology, SRH University for Applied Sciences Heidelberg, Heidelberg, Germany
| | - Michael E. Hasselmo
- Department of Psychological and Brain Sciences, Center for Systems Neuroscience, Boston University, Massachusetts, United States of America
| | - Nancy J. Kopell
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
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