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Lewis CM, Hoffmann A, Helmchen F. Linking brain activity across scales with simultaneous opto- and electrophysiology. NEUROPHOTONICS 2024; 11:033403. [PMID: 37662552 PMCID: PMC10472193 DOI: 10.1117/1.nph.11.3.033403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 09/05/2023]
Abstract
The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.
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Affiliation(s)
| | - Adrian Hoffmann
- University of Zurich, Brain Research Institute, Zurich, Switzerland
- University of Zurich, Neuroscience Center Zurich, Zurich, Switzerland
| | - Fritjof Helmchen
- University of Zurich, Brain Research Institute, Zurich, Switzerland
- University of Zurich, Neuroscience Center Zurich, Zurich, Switzerland
- University of Zurich, University Research Priority Program, Adaptive Brain Circuits in Development and Learning, Zurich, Switzerland
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2
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Iwase M, Diba K, Pastalkova E, Mizuseki K. Dynamics of spike transmission and suppression between principal cells and interneurons in the hippocampus and entorhinal cortex. Hippocampus 2024; 34:393-421. [PMID: 38874439 DOI: 10.1002/hipo.23612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/29/2024] [Accepted: 05/07/2024] [Indexed: 06/15/2024]
Abstract
Synaptic excitation and inhibition are essential for neuronal communication. However, the variables that regulate synaptic excitation and inhibition in the intact brain remain largely unknown. Here, we examined how spike transmission and suppression between principal cells (PCs) and interneurons (INTs) are modulated by activity history, brain state, cell type, and somatic distance between presynaptic and postsynaptic neurons by applying cross-correlogram analyses to datasets recorded from the dorsal hippocampus and medial entorhinal cortex (MEC) of 11 male behaving and sleeping Long Evans rats. The strength, temporal delay, and brain-state dependency of the spike transmission and suppression depended on the subregions/layers. The spike transmission probability of PC-INT excitatory pairs that showed short-term depression versus short-term facilitation was higher in CA1 and lower in CA3. Likewise, the intersomatic distance affected the proportion of PC-INT excitatory pairs that showed short-term depression and facilitation in the opposite manner in CA1 compared with CA3. The time constant of depression was longer, while that of facilitation was shorter in MEC than in CA1 and CA3. During sharp-wave ripples, spike transmission showed a larger gain in the MEC than in CA1 and CA3. The intersomatic distance affected the spike transmission gain during sharp-wave ripples differently in CA1 versus CA3. A subgroup of MEC layer 3 (EC3) INTs preferentially received excitatory inputs from and inhibited MEC layer 2 (EC2) PCs. The EC2 PC-EC3 INT excitatory pairs, most of which showed short-term depression, exhibited higher spike transmission probabilities than the EC2 PC-EC2 INT and EC3 PC-EC3 INT excitatory pairs. EC2 putative stellate cells exhibited stronger spike transmission to and received weaker spike suppression from EC3 INTs than EC2 putative pyramidal cells. This study provides detailed comparisons of monosynaptic interaction dynamics in the hippocampal-entorhinal loop, which may help to elucidate circuit operations.
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Affiliation(s)
- Motosada Iwase
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
- Department of Physiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Kamran Diba
- Department of Anesthesiology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Eva Pastalkova
- The William Alanson White Institute of Psychiatry, Psychoanalysis & Psychology, New York, New York, USA
| | - Kenji Mizuseki
- Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
- Department of Physiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
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3
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Lee K, Barradas V, Schweighofer N. Self-organizing recruitment of compensatory areas maximizes residual motor performance post-stroke. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.28.601213. [PMID: 39005333 PMCID: PMC11244868 DOI: 10.1101/2024.06.28.601213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Whereas the orderly recruitment of compensatory motor cortical areas after stroke depends on the size of the motor cortex lesion affecting arm and hand movements, the mechanisms underlying this reorganization are unknown. Here, we hypothesized that the recruitment of compensatory areas results from the motor system's goal to optimize performance given the anatomical constraints before and after the lesion. This optimization is achieved through two complementary plastic processes: a homeostatic regulation process, which maximizes information transfer in sensory-motor networks, and a reinforcement learning process, which minimizes movement error and effort. To test this hypothesis, we developed a neuro-musculoskeletal model that controls a 7-muscle planar arm via a cortical network that includes a primary motor cortex and a premotor cortex that directly project to spinal motor neurons, and a contra-lesional primary motor cortex that projects to spinal motor neurons via the reticular formation. Synapses in the cortical areas are updated via reinforcement learning and the activity of spinal motor neurons is adjusted through homeostatic regulation. The model replicated neural, muscular, and behavioral outcomes in both non-lesioned and lesioned brains. With increasing lesion sizes, the model demonstrated systematic recruitment of the remaining primary motor cortex, premotor cortex, and contra-lesional cortex. The premotor cortex acted as a reserve area for fine motor control recovery, while the contra-lesional cortex helped avoid paralysis at the cost of poor joint control. Plasticity in spinal motor neurons enabled force generation after large cortical lesions despite weak corticospinal inputs. Compensatory activity in the premotor and contra-lesional motor cortex was more prominent in the early recovery period, gradually decreasing as the network minimized effort. Thus, the orderly recruitment of compensatory areas following strokes of varying sizes results from biologically plausible local plastic processes that maximize performance, whether the brain is intact or lesioned.
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Affiliation(s)
- Kevin Lee
- Computer Science, University of Southern California, Los Angeles, USA
| | - Victor Barradas
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Nicolas Schweighofer
- Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, USA
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4
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Honcamp H, Schwartze M, Amorim M, Linden DEJ, Pinheiro AP, Kotz SA. Revisiting alpha resting state dynamics underlying hallucinatory vulnerability: Insights from Hidden semi-Markov Modeling. J Neurosci Methods 2024; 407:110138. [PMID: 38648892 DOI: 10.1016/j.jneumeth.2024.110138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/22/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Resting state (RS) brain activity is inherently non-stationary. Hidden semi-Markov Models (HsMM) can characterize continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics. NEW METHOD Recent explorations suggest that HsMM state dynamics in the alpha frequency band link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aimed to replicate these findings to elucidate robust neural correlates of hallucinatory vulnerability. Specifically, we aimed to investigate the reproducibility of HsMM states across different data sets and within-data set variants as well as the replicability of the association between alpha brain state dynamics and HP. RESULTS We found that most brain states are reproducible in different data sets, confirming that the HsMM characterized robust and generalizable EEG RS dynamics on a sub-second timescale. Brain state topographies and temporal dynamics of different within-data set variants showed substantial similarities and were robust against reduced data length and number of electrodes. However, the association with HP was not directly reproducible across data sets. COMPARISON WITH EXISTING METHODS The HsMM optimally leverages the high temporal resolution of EEG data and overcomes time-domain restrictions of other state allocation methods. CONCLUSION The results indicate that the sensitivity of brain state dynamics to capture individual variability in HP may depend on the data recording characteristics and individual variability in RS cognition, such as mind wandering. Future studies should consider that the order in which eyes-open and eyes-closed RS data are acquired directly influences an individual's attentional state and generation of spontaneous thoughts, and thereby might mediate the link to hallucinatory vulnerability.
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Affiliation(s)
- Hanna Honcamp
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.
| | - Michael Schwartze
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Maria Amorim
- Centro de Investigação em Ciência Psicológica, Faculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal
| | - David E J Linden
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Ana P Pinheiro
- Centro de Investigação em Ciência Psicológica, Faculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal
| | - Sonja A Kotz
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
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5
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Fan WY, Chen YM, Wang YF, Wang YQ, Hu JQ, Tang WX, Feng Y, Cheng Q, Xue L. L-Type Calcium Channel Modulates Low-Intensity Pulsed Ultrasound-Induced Excitation in Cultured Hippocampal Neurons. Neurosci Bull 2024; 40:921-936. [PMID: 38498092 PMCID: PMC11250733 DOI: 10.1007/s12264-024-01186-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 12/06/2023] [Indexed: 03/19/2024] Open
Abstract
As a noninvasive technique, ultrasound stimulation is known to modulate neuronal activity both in vitro and in vivo. The latest explanation of this phenomenon is that the acoustic wave can activate the ion channels and further impact the electrophysiological properties of targeted neurons. However, the underlying mechanism of low-intensity pulsed ultrasound (LIPUS)-induced neuro-modulation effects is still unclear. Here, we characterize the excitatory effects of LIPUS on spontaneous activity and the intracellular Ca2+ homeostasis in cultured hippocampal neurons. By whole-cell patch clamp recording, we found that 15 min of 1-MHz LIPUS boosts the frequency of both spontaneous action potentials and spontaneous excitatory synaptic currents (sEPSCs) and also increases the amplitude of sEPSCs in hippocampal neurons. This phenomenon lasts for > 10 min after LIPUS exposure. Together with Ca2+ imaging, we clarified that LIPUS increases the [Ca2+]cyto level by facilitating L-type Ca2+ channels (LTCCs). In addition, due to the [Ca2+]cyto elevation by LIPUS exposure, the Ca2+-dependent CaMKII-CREB pathway can be activated within 30 min to further regulate the gene transcription and protein expression. Our work suggests that LIPUS regulates neuronal activity in a Ca2+-dependent manner via LTCCs. This may also explain the multi-activation effects of LIPUS beyond neurons. LIPUS stimulation potentiates spontaneous neuronal activity by increasing Ca2+ influx.
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Affiliation(s)
- Wen-Yong Fan
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
- Department of Physiology and Neurobiology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yi-Ming Chen
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Tongji University, Shanghai, 200070, China
| | - Yi-Fan Wang
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Tongji University, Shanghai, 200070, China
| | - Yu-Qi Wang
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
- Department of Physiology and Neurobiology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Jia-Qi Hu
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
- Department of Physiology and Neurobiology, School of Life Sciences, Fudan University, Shanghai, 200438, China
- Center for Rehabilitation Medicine, Department of Pain Management, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, China
| | - Wen-Xu Tang
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
- Department of Physiology and Neurobiology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yi Feng
- Department of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200080, China
| | - Qian Cheng
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Tongji University, Shanghai, 200070, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, 201210, China.
| | - Lei Xue
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- Department of Physiology and Neurobiology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.
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Rößler N, Smilovic D, Vuksic M, Jedlicka P, Deller T. Maintenance of Lognormal-Like Skewed Dendritic Spine Size Distributions in Dentate Granule Cells of TNF, TNF-R1, TNF-R2, and TNF-R1/2-Deficient Mice. J Comp Neurol 2024; 532:e25645. [PMID: 38943486 DOI: 10.1002/cne.25645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/25/2024] [Accepted: 05/30/2024] [Indexed: 07/01/2024]
Abstract
Dendritic spines are sites of synaptic plasticity and their head size correlates with the strength of the corresponding synapse. We recently showed that the distribution of spine head sizes follows a lognormal-like distribution even after blockage of activity or plasticity induction. As the cytokine tumor necrosis factor (TNF) influences synaptic transmission and constitutive TNF and receptor (TNF-R)-deficiencies cause changes in spine head size distributions, we tested whether these genetic alterations disrupt the lognormality of spine head sizes. Furthermore, we distinguished between spines containing the actin-modulating protein synaptopodin (SP-positive), which is present in large, strong and stable spines and those lacking it (SP-negative). Our analysis revealed that neither TNF-deficiency nor the absence of TNF-R1, TNF-R2 or TNF-R 1 and 2 (TNF-R1/R2) degrades the general lognormal-like, skewed distribution of spine head sizes (all spines, SP-positive spines, SP-negative spines). However, TNF, TNF-R1 and TNF-R2-deficiency affected the width of the lognormal distribution, and TNF-R1/2-deficiency shifted the distribution to the left. Our findings demonstrate the robustness of the lognormal-like, skewed distribution, which is maintained even in the face of genetic manipulations that alter the distribution of spine head sizes. Our observations are in line with homeostatic adaptation mechanisms of neurons regulating the distribution of spines and their head sizes.
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MESH Headings
- Animals
- Dendritic Spines/metabolism
- Mice
- Receptors, Tumor Necrosis Factor, Type I/deficiency
- Receptors, Tumor Necrosis Factor, Type I/metabolism
- Receptors, Tumor Necrosis Factor, Type I/genetics
- Mice, Knockout
- Dentate Gyrus/metabolism
- Dentate Gyrus/cytology
- Tumor Necrosis Factor-alpha/metabolism
- Mice, Inbred C57BL
- Receptors, Tumor Necrosis Factor, Type II/deficiency
- Receptors, Tumor Necrosis Factor, Type II/metabolism
- Receptors, Tumor Necrosis Factor, Type II/genetics
- Neurons/metabolism
- Male
- Microfilament Proteins/metabolism
- Microfilament Proteins/genetics
- Microfilament Proteins/deficiency
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Affiliation(s)
- Nina Rößler
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, Frankfurt, Germany
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Computer-Based Modelling, Faculty of Medicine, Justus-Liebig-University, Giessen, Germany
| | - Dinko Smilovic
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, Frankfurt, Germany
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Mario Vuksic
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, Frankfurt, Germany
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Peter Jedlicka
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, Frankfurt, Germany
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Computer-Based Modelling, Faculty of Medicine, Justus-Liebig-University, Giessen, Germany
| | - Thomas Deller
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, Frankfurt, Germany
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Tsubo Y, Shinomoto S. Nondifferentiable activity in the brain. PNAS NEXUS 2024; 3:pgae261. [PMID: 38994500 PMCID: PMC11238849 DOI: 10.1093/pnasnexus/pgae261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/05/2024] [Indexed: 07/13/2024]
Abstract
Spike raster plots of numerous neurons show vertical stripes, indicating that neurons exhibit synchronous activity in the brain. We seek to determine whether these coherent dynamics are caused by smooth brainwave activity or by something else. By analyzing biological data, we find that their cross-correlograms exhibit not only slow undulation but also a cusp at the origin, in addition to possible signs of monosynaptic connectivity. Here we show that undulation emerges if neurons are subject to smooth brainwave oscillations while a cusp results from nondifferentiable fluctuations. While modern analysis methods have achieved good connectivity estimation by adapting the models to slow undulation, they still make false inferences due to the cusp. We devise a new analysis method that may solve both problems. We also demonstrate that oscillations and nondifferentiable fluctuations may emerge in simulations of large-scale neural networks.
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Affiliation(s)
- Yasuhiro Tsubo
- College of Information Science and Engineering, Ritsumeikan University, Osaka 567-8570, Japan
| | - Shigeru Shinomoto
- Research Organization of Open Innovation and Collaboration, Ritsumeikan University, Osaka 567-8570, Japan
- Graduate School of Biostudies, Kyoto University, Kyoto 606-8501, Japan
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8
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Abdul-Rahman A, Morgan W, Vukmirovic A, Yu DY. Probability density and information entropy of machine learning derived intracranial pressure predictions. PLoS One 2024; 19:e0306028. [PMID: 38950055 PMCID: PMC11216561 DOI: 10.1371/journal.pone.0306028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 06/10/2024] [Indexed: 07/03/2024] Open
Abstract
Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event's probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p< 0.004) with the absolute difference between the measured and predicted median intracranial pressure (DiffICPmd). The concordance between the arterial and venous probability density functions was estimated using the two-sample Kolmogorov-Smirnov statistic, extending the distribution agreement across all data points. This parameter showed a statistically significant and positive correlation (0.4942±0.18, p< 0.001) with DiffICPmd. Two cautionary subset cases (Case 8 and Case 9), where disagreement was observed between measured and predicted intracranial pressure, were compared to the seven hold-out test cases. Arterial predictions from both cautionary subset cases converged on a uniform distribution in contrast to all other cases where distributions converged on either log-normal or closely related skewed distributions (gamma, logistic, beta). The mean±standard error of the arterial DIICP from cases 8 and 9 (3.83±0.56%) was lower compared to that of the hold-out test cases (14.14±1.07%) the between group difference was statistically significant (p<0.03). Although the sample size in this analysis was limited, these results support a dual and complementary analysis approach from independently derived retinal arterial and venous non-invasive intracranial pressure predictions. Results suggest that plotting the PDF and calculating the lower order moments, arterial DIICP, and the two sample Kolmogorov-Smirnov statistic may provide individualized predictive accuracy parameters.
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Affiliation(s)
- Anmar Abdul-Rahman
- Department of Ophthalmology, Counties Manukau District Health Board, Auckland, New Zealand
| | - William Morgan
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
- Lions Eye Institute, University of Western Australia, Perth, Australia
| | - Aleksandar Vukmirovic
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
- Lions Eye Institute, University of Western Australia, Perth, Australia
| | - Dao-Yi Yu
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
- Lions Eye Institute, University of Western Australia, Perth, Australia
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9
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Chini M, Hnida M, Kostka JK, Chen YN, Hanganu-Opatz IL. Preconfigured architecture of the developing mouse brain. Cell Rep 2024; 43:114267. [PMID: 38795344 DOI: 10.1016/j.celrep.2024.114267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/13/2024] [Accepted: 05/08/2024] [Indexed: 05/27/2024] Open
Abstract
In the adult brain, structural and functional parameters, such as synaptic sizes and neuronal firing rates, follow right-skewed and heavy-tailed distributions. While this organization is thought to have significant implications, its development is still largely unknown. Here, we address this knowledge gap by investigating a large-scale dataset recorded from the prefrontal cortex and the olfactory bulb of mice aged 4-60 postnatal days. We show that firing rates and spike train interactions have a largely stable distribution shape throughout the first 60 postnatal days and that the prefrontal cortex displays a functional small-world architecture. Moreover, early brain activity exhibits an oligarchical organization, where high-firing neurons have hub-like properties. In a neural network model, we show that analogously right-skewed and heavy-tailed synaptic parameters are instrumental to consistently recapitulate the experimental data. Thus, functional and structural parameters in the developing brain are already extremely distributed, suggesting that this organization is preconfigured and not experience dependent.
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Affiliation(s)
- Mattia Chini
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Marilena Hnida
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Johanna K Kostka
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yu-Nan Chen
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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10
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Jahani Yekta MM. Biological direct-shortcut deep residual learning for sparse visual processing. Sci Rep 2024; 14:14066. [PMID: 38890361 PMCID: PMC11189431 DOI: 10.1038/s41598-024-62756-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
We show, based on the following three grounds, that the primary visual cortex (V1) is a biological direct-shortcut deep residual learning neural network (ResNet) for sparse visual processing: (1) We first highlight that Gabor-like sets of basis functions, which are similar to the receptive fields of simple cells in the primary visual cortex (V1), are excellent candidates for sparse representation of natural images; i.e., images from the natural world, affirming the brain to be optimized for this. (2) We then prove that the intra-layer synaptic weight matrices of this region can be reasonably first-order approximated by identity mappings, and are thus sparse themselves. (3) Finally, we point out that intra-layer weight matrices having identity mapping as their initial approximation, irrespective of this approximation being also a reasonable first-order one or not, resemble the building blocks of direct-shortcut digital ResNets, which completes the grounds. This biological ResNet interconnects the sparsity of the final representation of the image to that of its intra-layer weights. Further exploration of this ResNet, and understanding the joint effects of its architecture and learning rules, e.g. on its inductive bias, could lead to major advancements in the area of bio-inspired digital ResNets. One immediate line of research in this context, for instance, is to study the impact of forcing the direct-shortcuts to be good first-order approximations of each building block. For this, along with the ℓ 1 -minimization posed on the basis function coefficients the ResNet finally provides at its output, another parallel one could e.g. also be posed on the weights of its residual layers.
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11
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Yang SH, Huang CJ, Huang JS. Increasing Robustness of Intracortical Brain-Computer Interfaces for Recording Condition Changes via Data Augmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108208. [PMID: 38754326 DOI: 10.1016/j.cmpb.2024.108208] [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: 02/08/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Intracortical brain-computer interfaces (iBCIs) aim to help paralyzed individuals restore their motor functions by decoding neural activity into intended movement. However, changes in neural recording conditions hinder the decoding performance of iBCIs, mainly because the neural-to-kinematic mappings shift. Conventional approaches involve either training the neural decoders using large datasets before deploying the iBCI or conducting frequent calibrations during its operation. However, collecting data for extended periods can cause user fatigue, negatively impacting the quality and consistency of neural signals. Furthermore, frequent calibration imposes a substantial computational load. METHODS This study proposes a novel approach to increase iBCIs' robustness against changing recording conditions. The approach uses three neural augmentation operators to generate augmented neural activity that mimics common recording conditions. Then, contrastive learning is used to learn latent factors by maximizing the similarity between the augmented neural activities. The learned factors are expected to remain stable despite varying recording conditions and maintain a consistent correlation with the intended movement. RESULTS Experimental results demonstrate that the proposed iBCI outperformed the state-of-the-art iBCIs and was robust to changing recording conditions across days for long-term use on one publicly available nonhuman primate dataset. It achieved satisfactory offline decoding performance, even when a large training dataset was unavailable. CONCLUSIONS This study paves the way for reducing the need for frequent calibration of iBCIs and collecting a large amount of annotated training data. Potential future works aim to improve offline decoding performance with an ultra-small training dataset and improve the iBCIs' robustness to severely disabled electrodes.
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Affiliation(s)
- Shih-Hung Yang
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan.
| | - Chun-Jui Huang
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
| | - Jhih-Siang Huang
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
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12
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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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13
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Liu F, Zheng H, Ma S, Zhang W, Liu X, Chua Y, Shi L, Zhao R. Advancing brain-inspired computing with hybrid neural networks. Natl Sci Rev 2024; 11:nwae066. [PMID: 38577666 PMCID: PMC10989656 DOI: 10.1093/nsr/nwae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 04/06/2024] Open
Abstract
Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.
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Affiliation(s)
- Faqiang Liu
- Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Hao Zheng
- Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Songchen Ma
- Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Weihao Zhang
- Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Xue Liu
- Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Yansong Chua
- Neuromorphic Computing Laboratory, China Nanhu Academy of Electronics and Information Technology, Jiaxing 314001, China
| | - Luping Shi
- Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Rong Zhao
- Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, IDG/McGovern Institute for Brain Research, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
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14
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Uytiepo M, Zhu Y, Bushong E, Polli F, Chou K, Zhao E, Kim C, Luu D, Chang L, Quach T, Haberl M, Patapoutian L, Beutter E, Zhang W, Dong B, McCue E, Ellisman M, Maximov A. Synaptic architecture of a memory engram in the mouse hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590812. [PMID: 38712256 PMCID: PMC11071366 DOI: 10.1101/2024.04.23.590812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Memory engrams are formed through experience-dependent remodeling of neural circuits, but their detailed architectures have remained unresolved. Using 3D electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway following chemogenetic labeling of cellular ensembles with a remote history of correlated excitation during associative learning. Projection neurons involved in memory acquisition expanded their connectomes via multi-synaptic boutons without altering the numbers and spatial arrangements of individual axonal terminals and dendritic spines. This expansion was driven by presynaptic activity elicited by specific negative valence stimuli, regardless of the co-activation state of postsynaptic partners. The rewiring of initial ensembles representing an engram coincided with local, input-specific changes in the shapes and organelle composition of glutamatergic synapses, reflecting their weights and potential for further modifications. Our findings challenge the view that the connectivity among neuronal substrates of memory traces is governed by Hebbian mechanisms, and offer a structural basis for representational drifts.
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15
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Blanco I, Caccavano A, Wu JY, Vicini S, Glasgow E, Conant K. Coupling of Sharp Wave Events between Zebrafish Hippocampal and Amygdala Homologs. J Neurosci 2024; 44:e1467232024. [PMID: 38508712 PMCID: PMC11044098 DOI: 10.1523/jneurosci.1467-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024] Open
Abstract
The mammalian hippocampus exhibits spontaneous sharp wave events (1-30 Hz) with an often-present superimposed fast ripple oscillation (120-220 Hz) to form a sharp wave ripple (SWR) complex. During slow-wave sleep or quiet restfulness, SWRs result from the sequential spiking of hippocampal cell assemblies initially activated during learned or imagined experiences. Additional cortical/subcortical areas exhibit SWR events that are coupled to hippocampal SWRs, and studies in mammals suggest that coupling may be critical for the consolidation and recall of specific memories. In the present study, we have examined juvenile male and female zebrafish and show that SWR events are intrinsically generated and maintained within the telencephalon and that their hippocampal homolog, the anterodorsolateral lobe (ADL), exhibits SW events with ∼9% containing an embedded ripple (SWR). Single-cell calcium imaging coupled to local field potential recordings revealed that ∼10% of active cells in the dorsal telencephalon participate in any given SW event. Furthermore, fluctuations in cholinergic tone modulate SW events consistent with mammalian studies. Moreover, the basolateral amygdala (BLA) homolog exhibits SW events with ∼5% containing an embedded ripple. Computing the SW peak coincidence difference between the ADL and BLA showed bidirectional communication. Simultaneous coupling occurred more frequently within the same hemisphere, and in coupled events across hemispheres, the ADL more commonly preceded BLA. Together, these data suggest conserved mechanisms across species by which SW and SWR events are modulated, and memories may be transferred and consolidated through regional coupling.
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Affiliation(s)
- Ismary Blanco
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Adam Caccavano
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Jian-Young Wu
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC 20057
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Stefano Vicini
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC 20057
- Pharmacology and Physiology, Georgetown University Medical Center, Washington, DC 20057
| | - Eric Glasgow
- Oncology, Georgetown University Medical Center, Washington, DC 20057
| | - Katherine Conant
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC 20057
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
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16
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Waitzmann F, Wu YK, Gjorgjieva J. Top-down modulation in canonical cortical circuits with short-term plasticity. Proc Natl Acad Sci U S A 2024; 121:e2311040121. [PMID: 38593083 PMCID: PMC11032497 DOI: 10.1073/pnas.2311040121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/14/2024] [Indexed: 04/11/2024] Open
Abstract
Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.
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Affiliation(s)
- Felix Waitzmann
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
| | - Yue Kris Wu
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
| | - Julijana Gjorgjieva
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
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17
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Tian ZQK, Chen K, Li S, McLaughlin DW, Zhou D. Causal connectivity measures for pulse-output network reconstruction: Analysis and applications. Proc Natl Acad Sci U S A 2024; 121:e2305297121. [PMID: 38551842 PMCID: PMC10998614 DOI: 10.1073/pnas.2305297121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 03/03/2024] [Indexed: 04/08/2024] Open
Abstract
The causal connectivity of a network is often inferred to understand network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four commonly utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking a simulated Hodgkin-Huxley network and a real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore illustrating a direct link between the causal and structural connectivity. We stress that the structural connectivity of pulse-output networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.
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Affiliation(s)
- Zhong-qi K. Tian
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai200240, China
| | - Kai Chen
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai200240, China
| | - Songting Li
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai200240, China
| | - David W. McLaughlin
- Courant Institute of Mathematical Sciences, New York University, New York, NY10012
- Center for Neural Science, New York University, New York, NY10012
- Institute of Mathematical Sciences, New York University Shanghai, Shanghai200122, China
- Neuroscience Institute of New York University Langone Health, New York University, New York, NY10016
| | - Douglas Zhou
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai200240, China
- Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai200240, China
- Shanghai Frontier Science Center of Modern Analysis, Shanghai Jiao Tong University, Shanghai200240, China
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18
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Politi A, Torcini A. A robust balancing mechanism for spiking neural networks. CHAOS (WOODBURY, N.Y.) 2024; 34:041102. [PMID: 38639569 DOI: 10.1063/5.0199298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/03/2024] [Indexed: 04/20/2024]
Abstract
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons.
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Affiliation(s)
- Antonio Politi
- Institute for Complex Systems and Mathematical Biology and Department of Physics, Aberdeen AB24 3UE, United Kingdom
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
| | - Alessandro Torcini
- CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
- Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS UMR 8089, 95302 Cergy-Pontoise cedex, France
- INFN Sezione di Firenze, Via Sansone 1 50019 Sesto Fiorentino, Italy
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19
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Vareberg AD, Bok I, Eizadi J, Ren X, Hai A. Inference of network connectivity from temporally binned spike trains. J Neurosci Methods 2024; 404:110073. [PMID: 38309313 PMCID: PMC10949361 DOI: 10.1016/j.jneumeth.2024.110073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored. NEW METHOD We infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data. RESULTS Results reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred. COMPARISON WITH EXISTING METHOD(S) Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types. CONCLUSIONS We provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.
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Affiliation(s)
- Adam D Vareberg
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Ilhan Bok
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Jenna Eizadi
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Xiaoxuan Ren
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States
| | - Aviad Hai
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States.
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20
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Kopsick JD, Kilgore JA, Adam GC, Ascoli GA. Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.586909. [PMID: 38585941 PMCID: PMC10996657 DOI: 10.1101/2024.03.27.586909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.
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Affiliation(s)
- Jeffrey D. Kopsick
- Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason University, Fairfax, VA, United States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason University, Fairfax, VA, United States
| | - Joseph A. Kilgore
- Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., United States
| | - Gina C. Adam
- Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., United States
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason University, Fairfax, VA, United States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason University, Fairfax, VA, United States
- Bioengineering Department, College of Engineering and Computing, George Mason University, Fairfax, VA, United States
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21
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Shi J, Nutkovich B, Kushinsky D, Rao BY, Herrlinger SA, Mihaila TS, Malina KCK, O’Toole CK, Conde Paredes ME, Yong HC, Varol E, Losonczy A, Spiegel I. 2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586118. [PMID: 38585980 PMCID: PMC10996538 DOI: 10.1101/2024.03.21.586118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the functional properties of transcriptionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.
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Affiliation(s)
- Jingcheng Shi
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
- Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY, United States
| | - Boaz Nutkovich
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Dahlia Kushinsky
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Bovey Y. Rao
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
- Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY, United States
| | - Stephanie A. Herrlinger
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Tiberiu S. Mihaila
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Katayun Cohen-Kashi Malina
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Cliodhna K. O’Toole
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Margaret E. Conde Paredes
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
- Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY, United States
- Tandon School of Engineering, New York University, New York, NY, United States
| | - Hyun Choong Yong
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Erdem Varol
- Tandon School of Engineering, New York University, New York, NY, United States
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Ivo Spiegel
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
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22
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Búzás A, Makai A, Groma GI, Dancsházy Z, Szendi I, Kish LB, Santa-Maria AR, Dér A. Hierarchical organization of human physical activity. Sci Rep 2024; 14:5981. [PMID: 38472275 DOI: 10.1038/s41598-024-56185-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 03/14/2024] Open
Abstract
Human physical activity (HPA), a fundamental physiological signal characteristic of bodily motion is of rapidly growing interest in multidisciplinary research. Here we report the existence of hitherto unidentified hierarchical levels in the temporal organization of HPA on the ultradian scale: on the minute's scale, passive periods are followed by activity bursts of similar intensity ('quanta') that are organized into superstructures on the hours- and on the daily scale. The time course of HPA can be considered a stochastic, quasi-binary process, where quanta, assigned to task-oriented actions are organized into work packages on higher levels of hierarchy. In order to grasp the essence of this complex dynamic behaviour, we established a stochastic mathematical model which could reproduce the main statistical features of real activity time series. The results are expected to provide important data for developing novel behavioural models and advancing the diagnostics of neurological or psychiatric diseases.
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Affiliation(s)
- András Búzás
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - András Makai
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - Géza I Groma
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - Zsolt Dancsházy
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary
| | - István Szendi
- Department of Psychiatry, Kiskunhalas Semmelweis Hospital, 1 Dr. Monszpart László Street, Kiskunhalas, 6400, Hungary
| | - Laszlo B Kish
- Department of Electrical and Computer Engineering, Texas A&M University, TAMUS 3128, College Station, TX, 77843-3128, USA
| | - Ana Raquel Santa-Maria
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
| | - András Dér
- Institute of Biophysics, HUN-REN Biological Research Centre, Temesvári Krt. 62, P.O.B. 521, Szeged, 6701, Hungary.
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23
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Liu Y, Seguin C, Betzel RF, Akarca D, Di Biase MA, Zalesky A. A generative model of the connectome with dynamic axon growth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.23.581824. [PMID: 38464116 PMCID: PMC10925171 DOI: 10.1101/2024.02.23.581824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization - axonal growth. Emulating the chemoaffinity guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones. This simple dynamic growth mechanism, despite being solely geometry-dependent, is shown to generate axonal fiber bundles with brain-like geometry and features of complex network architecture consistent with the human brain, including lognormally distributed connectivity weights, scale-free nodal degrees, small-worldness, and modularity. We demonstrate that our model parameters can be fitted to individual connectomes, enabling connectome dimensionality reduction and comparison of parameters between groups. Our work offers an opportunity to bridge studies of axon guidance and connectome development, providing new avenues for understanding neural development from a computational perspective.
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Affiliation(s)
- Yuanzhe Liu
- Department of Biomedical Engineering, Faculty of Engineering & Information Technology, The University of Melbourne, Melbourne, VIC, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Danyal Akarca
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Maria A. Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew Zalesky
- Department of Biomedical Engineering, Faculty of Engineering & Information Technology, The University of Melbourne, Melbourne, VIC, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
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24
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Azizpour Lindi S, Mallet NP, Leblois A. Synaptic Changes in Pallidostriatal Circuits Observed in the Parkinsonian Model Triggers Abnormal Beta Synchrony with Accurate Spatio-temporal Properties across the Basal Ganglia. J Neurosci 2024; 44:e0419232023. [PMID: 38123981 PMCID: PMC10903930 DOI: 10.1523/jneurosci.0419-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 11/27/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Excessive oscillatory activity across basal ganglia (BG) nuclei in the β frequencies (12-30 Hz) is a hallmark of Parkinson's disease (PD). While the link between oscillations and symptoms remains debated, exaggerated β oscillations constitute an important biomarker for therapeutic effectiveness in PD. The neuronal mechanisms of β-oscillation generation however remain unknown. Many existing models rely on a central role of the subthalamic nucleus (STN) or cortical inputs to BG. Contrarily, neural recordings and optogenetic manipulations in normal and parkinsonian rats recently highlighted the central role of the external pallidum (GPe) in abnormal β oscillations, while showing that the integrity of STN or motor cortex is not required. Here, we evaluate the mechanisms for the generation of abnormal β oscillations in a BG network model where neuronal and synaptic time constants, connectivity, and firing rate distributions are strongly constrained by experimental data. Guided by a mean-field approach, we show in a spiking neural network that several BG sub-circuits can drive oscillations. Strong recurrent STN-GPe connections or collateral intra-GPe connections drive γ oscillations (>40 Hz), whereas strong pallidostriatal loops drive low-β (10-15 Hz) oscillations. We show that pathophysiological strengthening of striatal and pallidal synapses following dopamine depletion leads to the emergence of synchronized oscillatory activity in the mid-β range with spike-phase relationships between BG neuronal populations in-line with experiments. Furthermore, inhibition of GPe, contrary to STN, abolishes oscillations. Our modeling study uncovers the neural mechanisms underlying PD β oscillations and may thereby guide the future development of therapeutic strategies.
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Affiliation(s)
- Shiva Azizpour Lindi
- CNRS, Institut des Maladies Neurodégénératives (IMN), UMR 5293, Université de Bordeaux, Bordeaux F-33000, France
| | - Nicolas P Mallet
- CNRS, Institut des Maladies Neurodégénératives (IMN), UMR 5293, Université de Bordeaux, Bordeaux F-33000, France
| | - Arthur Leblois
- CNRS, Institut des Maladies Neurodégénératives (IMN), UMR 5293, Université de Bordeaux, Bordeaux F-33000, France
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25
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Pattadkal JJ, Zemelman BV, Fiete I, Priebe NJ. Primate neocortex performs balanced sensory amplification. Neuron 2024; 112:661-675.e7. [PMID: 38091984 PMCID: PMC10922204 DOI: 10.1016/j.neuron.2023.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 05/08/2023] [Accepted: 11/07/2023] [Indexed: 01/25/2024]
Abstract
The sensory cortex amplifies relevant features of external stimuli. This sensitivity and selectivity arise through the transformation of inputs by cortical circuitry. We characterize the circuit mechanisms and dynamics of cortical amplification by making large-scale simultaneous measurements of single cells in awake primates and testing computational models. By comparing network activity in both driven and spontaneous states with models, we identify the circuit as operating in a regime of non-normal balanced amplification. Incoming inputs are strongly but transiently amplified by strong recurrent feedback from the disruption of excitatory-inhibitory balance in the network. Strong inhibition rapidly quenches responses, thereby permitting the tracking of time-varying stimuli.
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Affiliation(s)
- Jagruti J Pattadkal
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Boris V Zemelman
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ila Fiete
- Department of Brain and Cognitive Sciences, MIT, Boston, MA 02139, USA
| | - Nicholas J Priebe
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA.
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26
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Sammons RP, Vezir M, Moreno-Velasquez L, Cano G, Orlando M, Sievers M, Grasso E, Metodieva VD, Kempter R, Schmidt H, Schmitz D. Structure and function of the hippocampal CA3 module. Proc Natl Acad Sci U S A 2024; 121:e2312281120. [PMID: 38289953 PMCID: PMC10861929 DOI: 10.1073/pnas.2312281120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/01/2023] [Indexed: 02/01/2024] Open
Abstract
The hippocampal formation is crucial for learning and memory, with submodule CA3 thought to be the substrate of pattern completion. However, the underlying synaptic and computational mechanisms of this network are not well understood. Here, we perform circuit reconstruction of a CA3 module using three dimensional (3D) electron microscopy data and combine this with functional connectivity recordings and computational simulations to determine possible CA3 network mechanisms. Direct measurements of connectivity schemes with both physiological measurements and structural 3D EM revealed a high connectivity rate, multi-fold higher than previously assumed. Mathematical modelling indicated that such CA3 networks can robustly generate pattern completion and replay memory sequences. In conclusion, our data demonstrate that the connectivity scheme of the hippocampal submodule is well suited for efficient memory storage and retrieval.
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Affiliation(s)
- Rosanna P. Sammons
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Neuroscience Research Center, Berlin10117, Germany
| | - Mourat Vezir
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main60528, Germany
| | - Laura Moreno-Velasquez
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Neuroscience Research Center, Berlin10117, Germany
| | - Gaspar Cano
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin10115, Germany
| | - Marta Orlando
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Neuroscience Research Center, Berlin10117, Germany
| | - Meike Sievers
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
| | - Eleonora Grasso
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main60528, Germany
| | - Verjinia D. Metodieva
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Neuroscience Research Center, Berlin10117, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin10115, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin10115, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Einstein Center for Neurosciences Berlin, Berlin10117, Germany
| | - Helene Schmidt
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main60528, Germany
| | - Dietmar Schmitz
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Neuroscience Research Center, Berlin10117, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin10115, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Einstein Center for Neurosciences Berlin, Berlin10117, Germany
- German Center for Neurodegenerative Diseases Berlin, Berlin10117, Germany
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin13125, Germany
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27
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Yamaguchi YY, Terada Y. Reconstruction of phase dynamics from macroscopic observations based on linear and nonlinear response theories. Phys Rev E 2024; 109:024217. [PMID: 38491619 DOI: 10.1103/physreve.109.024217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/22/2024] [Indexed: 03/18/2024]
Abstract
We propose a method to reconstruct the phase dynamics in rhythmical interacting systems from macroscopic responses to weak inputs by developing linear and nonlinear response theories, which predict the responses in a given system. By solving an inverse problem, the method infers an unknown system: the natural frequency distribution, the coupling function, and the time delay which is inevitable in real systems. In contrast to previous methods, our method requires neither strong invasiveness nor microscopic observations. We demonstrate that the method reconstructs two phase systems from observed responses accurately. The qualitative methodological advantages demonstrated by our quantitative numerical examinations suggest its broad applicability in various fields, including brain systems, which are often observed through macroscopic signals such as electroencephalograms and functional magnetic response imaging.
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Affiliation(s)
| | - Yu Terada
- Department of Neurobiology, University of California San Diego, La Jolla, California 92093, USA
- Institute for Physics of Intelligence, Department of Physics, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
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28
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Wu S, Wardak A, Khan MM, Chen CH, Regehr WG. Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs. eLife 2024; 13:e89095. [PMID: 38241596 PMCID: PMC10798666 DOI: 10.7554/elife.89095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 12/27/2023] [Indexed: 01/21/2024] Open
Abstract
Purkinje cell (PC) synapses onto cerebellar nuclei (CbN) neurons allow signals from the cerebellar cortex to influence the rest of the brain. PCs are inhibitory neurons that spontaneously fire at high rates, and many PC inputs are thought to converge onto each CbN neuron to suppress its firing. It has been proposed that PCs convey information using a rate code, a synchrony and timing code, or both. The influence of PCs on CbN neuron firing was primarily examined for the combined effects of many PC inputs with comparable strengths, and the influence of individual PC inputs has not been extensively studied. Here, we find that single PC to CbN synapses are highly variable in size, and using dynamic clamp and modeling we reveal that this has important implications for PC-CbN transmission. Individual PC inputs regulate both the rate and timing of CbN firing. Large PC inputs strongly influence CbN firing rates and transiently eliminate CbN firing for several milliseconds. Remarkably, the refractory period of PCs leads to a brief elevation of CbN firing prior to suppression. Thus, individual PC-CbN synapses are suited to concurrently convey rate codes and generate precisely timed responses in CbN neurons. Either synchronous firing or synchronous pauses of PCs promote CbN neuron firing on rapid time scales for nonuniform inputs, but less effectively than for uniform inputs. This is a secondary consequence of variable input sizes elevating the baseline firing rates of CbN neurons by increasing the variability of the inhibitory conductance. These findings may generalize to other brain regions with highly variable inhibitory synapse sizes.
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Affiliation(s)
- Shuting Wu
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Asem Wardak
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Mehak M Khan
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Christopher H Chen
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
- Department of Neural and Behavioral Sciences, Pennsylvania State University College of MedicineHersheyUnited States
| | - Wade G Regehr
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
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29
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Schoeters R, Tarnaud T, Martens L, Tanghe E. Simulation study on high spatio-temporal resolution acousto-electrophysiological neuroimaging. J Neural Eng 2024; 20:066039. [PMID: 38109769 DOI: 10.1088/1741-2552/ad169c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 12/18/2023] [Indexed: 12/20/2023]
Abstract
Objective.Acousto-electrophysiological neuroimaging (AENI) is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will be modulated onto the ultrasonic frequency. The region's electrical activity can subsequently be retrieved via demodulation of the measured signal. In this study, the feasibility of this hypothesized technique is tested.Approach.This is done by calculating the forward electroencephalography response under quasi-static assumptions. The head is simplified as a set of concentric spheres. Two sizes are evaluated representing human and mouse brains. Moreover, feasibility is assessed for wet and dry transcranial, and for cortically placed electrodes. The activity sources are modeled by dipoles, with their current intensity profile drawn from a power-law power spectral density.Results.It is shown that mechanical vibration modulates the endogenous activity onto the ultrasonic frequency. The signal strength depends non-linearly on the alignment between dipole orientation, vibration direction and recording point. The strongest signal is measured when these three dependencies are perfectly aligned. The signal strengths are in the pV-range for a dipole moment of 5 nAm and ultrasonic pressures within Food and Drug Administration (FDA)-limits. The endogenous activity can then be accurately reconstructed via demodulation. Two interference types are investigated: vibrational and static. Depending on the vibrational interference, it is shown that millimeter resolution signal detection is possible also for deep brain regions. Subsequently, successful demodulation depends on the static interference, that at MHz-range has to be sub-picovolt.Significance.Our results show that mechanical vibration is a possible underlying mechanism of acousto-electrophyisological neuroimaging. This paper is a first step towards improved understanding of the conditions under which AENI is feasible.
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Affiliation(s)
- Ruben Schoeters
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Thomas Tarnaud
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Luc Martens
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Emmeric Tanghe
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
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30
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Loison V, Voskobiynyk Y, Lindquist B, Necula D, Longrois D, Paz J, Holcman D. Mapping general anesthesia states based on electro-encephalogram transition phases. Neuroimage 2024; 285:120498. [PMID: 38135170 PMCID: PMC10792552 DOI: 10.1016/j.neuroimage.2023.120498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Cortical electro-encephalography (EEG) served as the clinical reference for monitoring unconsciousness during general anesthesia. The existing EEG-based monitors classified general anesthesia states as underdosed, adequate, or overdosed, lacking predictive power due to the absence of transition phases among these states. In response to this limitation, we undertook an analysis of the EEG signal during isoflurane-induced general anesthesia in mice. Adopting a data-driven approach, we applied signal processing techniques to track θ- and δ-band dynamics, along with iso-electric suppressions. Combining this approach with machine learning, we successfully developed an automated algorithm. The findings of our study revealed that the dampening of the δ-band occurred several minutes before the onset of significant iso-electric suppression episodes. Furthermore, a distinct γ-frequency oscillation was observed, persisting for several minutes during the recovery phase subsequent to isoflurane-induced overdose. As a result of our research, we generated a map summarizing multiple brain states and their transitions, offering a tool for predicting and preventing overdose during general anesthesia. The transition phases identified, along with the developed algorithm, have the potential to be generalized, enabling clinicians to prevent inadequate anesthesia and, consequently, tailor anesthetic regimens to individual patients.
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Affiliation(s)
- V Loison
- Group of Data Modeling and Computational Biology, Institut de Biologie (IBENS), École Normale Supérieure CNRS, Université PSL Paris, France
| | - Y Voskobiynyk
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - B Lindquist
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - D Necula
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - D Longrois
- Département d'Anesthésie-Réanimation, Hôpital Bichat-Claude Bernard, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - J Paz
- Gladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA
| | - D Holcman
- Group of Data Modeling and Computational Biology, Institut de Biologie (IBENS), École Normale Supérieure CNRS, Université PSL Paris, France; DAMPT, University of Cambridge and Churchill College, CB30DS, Cambridge, UK.
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31
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic Inputs. PHYSICAL REVIEW. X 2024; 14:011021. [PMID: 38911939 PMCID: PMC11194039 DOI: 10.1103/physrevx.14.011021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4-9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Baowang Li
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychology, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Nicholas J. Priebe
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Eyal Seidemann
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychology, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, Texas 78712, USA
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32
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Chwiłka M, Karbowski J. Explicit mutual information for simple networks and neurons with lognormal activities. Phys Rev E 2024; 109:014117. [PMID: 38366499 DOI: 10.1103/physreve.109.014117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/13/2023] [Indexed: 02/18/2024]
Abstract
Networks with stochastic variables described by heavy-tailed lognormal distribution are ubiquitous in nature, and hence they deserve an exact information-theoretic characterization. We derive analytical formulas for mutual information between elements of different networks with correlated lognormally distributed activities. In a special case, we find an explicit expression for mutual information between neurons when neural activities and synaptic weights are lognormally distributed, as suggested by experimental data. Comparison of this expression with the case when these two variables have short tails reveals that mutual information with heavy tails for neurons and synapses is generally larger and can diverge for some finite variances in presynaptic firing rates and synaptic weights. This result suggests that evolution might prefer brains with heterogeneous dynamics to optimize information processing.
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Affiliation(s)
- Maurycy Chwiłka
- Department of Mathematics, Informatics, and Mechanics, Institute of Applied Mathematics and Mechanics, University of Warsaw, Ulica Banacha 2, 02-097 Warsaw, Poland
| | - Jan Karbowski
- Department of Mathematics, Informatics, and Mechanics, Institute of Applied Mathematics and Mechanics, University of Warsaw, Ulica Banacha 2, 02-097 Warsaw, Poland
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33
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van der Molen T, Spaeth A, Chini M, Bartram J, Dendukuri A, Zhang Z, Bhaskaran-Nair K, Blauvelt LJ, Petzold LR, Hansma PK, Teodorescu M, Hierlemann A, Hengen KB, Hanganu-Opatz IL, Kosik KS, Sharf T. Protosequences in human cortical organoids model intrinsic states in the developing cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.29.573646. [PMID: 38234832 PMCID: PMC10793448 DOI: 10.1101/2023.12.29.573646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.
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Affiliation(s)
- Tjitse van der Molen
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Alex Spaeth
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Mattia Chini
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Julian Bartram
- Department of Biosystems Science and Engineering, ETH Zürich, Klingelbergstrasse 48, 4056 Basel, Switzerland
| | - Aditya Dendukuri
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Zongren Zhang
- Department of Physics, University of California Santa Barbara, Santa Barbara, CA 93106
| | - Kiran Bhaskaran-Nair
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Lon J. Blauvelt
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Linda R. Petzold
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Paul K. Hansma
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Physics, University of California Santa Barbara, Santa Barbara, CA 93106
| | - Mircea Teodorescu
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zürich, Klingelbergstrasse 48, 4056 Basel, Switzerland
| | - Keith B. Hengen
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Ileana L. Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Kenneth S. Kosik
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Tal Sharf
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Institute for the Biology of Stem Cells, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs. ARXIV 2023:arXiv:2304.09280v3. [PMID: 37131877 PMCID: PMC10153295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance ≃ 4 - 9 m V 2 ) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
| | - Baowang Li
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Center for Learning and Memory, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
| | - Nicholas J. Priebe
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Learning and Memory, The University of Texas at Austin
| | - Eyal Seidemann
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Department of Mathematics, The University of Texas at Austin
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35
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Gajwani M, Oldham S, Pang JC, Arnatkevičiūtė A, Tiego J, Bellgrove MA, Fornito A. Can hubs of the human connectome be identified consistently with diffusion MRI? Netw Neurosci 2023; 7:1326-1350. [PMID: 38144690 PMCID: PMC10631793 DOI: 10.1162/netn_a_00324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/17/2023] [Indexed: 12/26/2023] Open
Abstract
Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
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Affiliation(s)
- Mehul Gajwani
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Victoria, Australia
| | - James C. Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Aurina Arnatkevičiūtė
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Mark A. Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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Hoy CW, Quiroga-Martinez DR, Sandoval E, King-Stephens D, Laxer KD, Weber P, Lin JJ, Knight RT. Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex. Nat Commun 2023; 14:8520. [PMID: 38129440 PMCID: PMC10739882 DOI: 10.1038/s41467-023-44248-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
The signed value and unsigned salience of reward prediction errors (RPEs) are critical to understanding reinforcement learning (RL) and cognitive control. Dorsomedial prefrontal cortex (dMPFC) and insula (INS) are key regions for integrating reward and surprise information, but conflicting evidence for both signed and unsigned activity has led to multiple proposals for the nature of RPE representations in these brain areas. Recently developed RL models allow neurons to respond differently to positive and negative RPEs. Here, we use intracranially recorded high frequency activity (HFA) to test whether this flexible asymmetric coding strategy captures RPE coding diversity in human INS and dMPFC. At the region level, we found a bias towards positive RPEs in both areas which paralleled behavioral adaptation. At the local level, we found spatially interleaved neural populations responding to unsigned RPE salience and valence-specific positive and negative RPEs. Furthermore, directional connectivity estimates revealed a leading role of INS in communicating positive and unsigned RPEs to dMPFC. These findings support asymmetric coding across distinct but intermingled neural populations as a core principle of RPE processing and inform theories of the role of dMPFC and INS in RL and cognitive control.
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Affiliation(s)
- Colin W Hoy
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
| | - David R Quiroga-Martinez
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Center for Music in the Brain, Aarhus University & The Royal Academy of Music, Aarhus, Denmark
| | - Eduardo Sandoval
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - David King-Stephens
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kenneth D Laxer
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
| | - Peter Weber
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
| | - Jack J Lin
- Department of Neurology, University of California, Davis, Davis, CA, USA
- Center for Mind and Brain, University of California, Davis, Davis, CA, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
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37
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Sanzeni A, Palmigiano A, Nguyen TH, Luo J, Nassi JJ, Reynolds JH, Histed MH, Miller KD, Brunel N. Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys. Neuron 2023; 111:4102-4115.e9. [PMID: 37865082 PMCID: PMC10841937 DOI: 10.1016/j.neuron.2023.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 05/05/2023] [Accepted: 09/15/2023] [Indexed: 10/23/2023]
Abstract
The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low-/high-rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.
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Affiliation(s)
- Alessandro Sanzeni
- Department of Computing Sciences, Bocconi University, 20100 Milan, Italy; Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neurobiology, Duke University, Durham, NC 27710, USA
| | - Agostina Palmigiano
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Tuan H Nguyen
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Physics, Columbia University, New York, NY 10027, USA
| | - Junxiang Luo
- Systems Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jonathan J Nassi
- Systems Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - John H Reynolds
- Systems Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Mark H Histed
- National Institute of Mental Health Intramural Program, NIH, Bethesda, MD 20814, USA
| | - Kenneth D Miller
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027, USA.
| | - Nicolas Brunel
- Department of Neurobiology, Duke University, Durham, NC 27710, USA; Department of Physics, Duke University, Durham, NC 27710, USA.
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Ohki T, Kunii N, Chao ZC. Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0. Rev Neurosci 2023; 34:839-868. [PMID: 36960579 DOI: 10.1515/revneuro-2022-0137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/26/2023] [Indexed: 03/25/2023]
Abstract
There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.
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Affiliation(s)
- Takefumi Ohki
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
| | - Naoto Kunii
- Department of Neurosurgery, The University of Tokyo, Tokyo 113-0033, Japan
| | - Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
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39
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Hernandez AR, Barrett ME, Lubke KN, Maurer AP, Burke SN. A long-term ketogenic diet in young and aged rats has dissociable effects on prelimbic cortex and CA3 ensemble activity. Front Aging Neurosci 2023; 15:1274624. [PMID: 38155737 PMCID: PMC10753023 DOI: 10.3389/fnagi.2023.1274624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/20/2023] [Indexed: 12/30/2023] Open
Abstract
Introduction Age-related cognitive decline has been linked to distinct patterns of cellular dysfunction in the prelimbic cortex (PL) and the CA3 subregion of the hippocampus. Because higher cognitive functions require both structures, selectively targeting a neurobiological change in one region, at the expense of the other, is not likely to restore normal behavior in older animals. One change with age that both the PL and CA3 share, however, is a reduced ability to utilize glucose, which can produce aberrant neural activity patterns. Methods The current study used a ketogenic diet (KD) intervention, which reduces the brain's reliance on glucose, and has been shown to improve cognition, as a metabolic treatment for restoring neural ensemble dynamics in aged rats. Expression of the immediate-early genes Arc and Homer1a were used to quantify the neural ensembles that were active in the home cage prior to behavior, during a working memory/biconditional association task, and a continuous spatial alternation task. Results Aged rats on the control diet had increased activity in CA3 and less ensemble overlap in PL between different task conditions than did the young animals. In the PL, the KD was associated with increased activation of neurons in the superficial cortical layers, establishing a clear link between dietary macronutrient content and frontal cortical activity. The KD did not lead to any significant changes in CA3 activity. Discussion These observations suggest that the availability of ketone bodies may permit the engagement of compensatory mechanisms in the frontal cortices that produce better cognitive outcomes.
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Affiliation(s)
- Abbi R. Hernandez
- Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Maya E. Barrett
- Department of Psychology, The University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Katelyn N. Lubke
- Department of Neuroscience, McKnight Brain Institute, and Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, United States
| | - Andrew P. Maurer
- Department of Neuroscience, McKnight Brain Institute, and Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, United States
| | - Sara N. Burke
- Department of Neuroscience, McKnight Brain Institute, and Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, United States
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40
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Ju C, Guan C. Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:10955-10969. [PMID: 35749326 DOI: 10.1109/tnnls.2022.3172108] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imagery (MI) classification in the past five years. The mainstream DL methodology for the MI-EEG classification exploits the temporospatial patterns of EEG signals using convolutional neural networks (CNNs), which have been particularly successful in visual images. However, since the statistical characteristics of visual images depart radically from EEG signals, a natural question arises whether an alternative network architecture exists apart from CNNs. To address this question, we propose a novel geometric DL (GDL) framework called Tensor-CSPNet, which characterizes spatial covariance matrices derived from EEG signals on symmetric positive definite (SPD) manifolds and fully captures the temporospatiofrequency patterns using existing deep neural networks on SPD manifolds, integrating with experiences from many successful MI-EEG classifiers to optimize the framework. In the experiments, Tensor-CSPNet attains or slightly outperforms the current state-of-the-art performance on the cross-validation and holdout scenarios in two commonly used MI-EEG datasets. Moreover, the visualization and interpretability analyses also exhibit the validity of Tensor-CSPNet for the MI-EEG classification. To conclude, in this study, we provide a feasible answer to the question by generalizing the DL methodologies on SPD manifolds, which indicates the start of a specific GDL methodology for the MI-EEG classification.
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41
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Moody G, Musco A, Bennett J, Wollmuth LP. An integrated approach to evaluate the functional effects of disease-associated NMDA receptor variants. Neuropharmacology 2023; 240:109703. [PMID: 37689262 DOI: 10.1016/j.neuropharm.2023.109703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/30/2023] [Accepted: 08/31/2023] [Indexed: 09/11/2023]
Abstract
The NMDA receptor (NMDAR) is a ubiquitously expressed glutamate-gated ion channel that plays key roles in brain development and function. Not surprisingly, a variety of disease-associated variants have been identified in genes encoding NMDAR subunits. A critical first step to assess whether these variants contribute to their associated disorder is to characterize their effect on receptor function. However, the complexity of NMDAR function makes this challenging, with many variants typically altering multiple functional properties. At synapses, NMDARs encode pre- and postsynaptic activity to carry a charge transfer that alters membrane excitability and a Ca2+ influx that has both short- and long-term signaling actions. Here, we characterized epilepsy-associated variants in GluN1 and GluN2A subunits with various phenotypic severity in HEK293 cells. To capture the complexity of NMDAR gating, we applied 10 glutamate pulses at 10 Hz to derive a charge integral. This assay is advantageous since it incorporates multiple gating parameters - activation, deactivation, and desensitization - into a single value. We then integrated this gating parameter with Mg2+ block and Ca2+ influx using fractional Ca2+ currents to generate indices of charge transfer and Ca2+ transfer over wide voltage ranges. This approach yields consolidated parameters that can be used as a reference to normalize channel block and allosteric modulation to better define potential patient treatment. This is especially true for variants in the transmembrane domain that affect not only receptor gating but also often Mg2+ block and Ca2+ permeation.
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Affiliation(s)
- Gabrielle Moody
- Graduate Program in Molecular & Cellular Pharmacology, Stony Brook University, Stony Brook, NY, 11794-5230, United States
| | - Angela Musco
- Stony Brook Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794-5230, United States
| | - Joseph Bennett
- Department of Neurobiology & Behavior, Stony Brook University, Stony Brook, NY, 11794-5230, United States
| | - Lonnie P Wollmuth
- Department of Neurobiology & Behavior, Stony Brook University, Stony Brook, NY, 11794-5230, United States; Department of Biochemistry & Cell Biology, Stony Brook University, Stony Brook, NY, 11794-5230, United States; Center for Nervous System Disorders, Stony Brook University, Stony Brook, NY, 11794-5230, United States.
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42
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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43
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Joechner AK, Hahn MA, Gruber G, Hoedlmoser K, Werkle-Bergner M. Sleep spindle maturity promotes slow oscillation-spindle coupling across child and adolescent development. eLife 2023; 12:e83565. [PMID: 37999945 PMCID: PMC10672804 DOI: 10.7554/elife.83565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 10/18/2023] [Indexed: 11/25/2023] Open
Abstract
The synchronization of canonical fast sleep spindle activity (12.5-16 Hz, adult-like) precisely during the slow oscillation (0.5-1 Hz) up peak is considered an essential feature of adult non-rapid eye movement sleep. However, there is little knowledge on how this well-known coalescence between slow oscillations and sleep spindles develops. Leveraging individualized detection of single events, we first provide a detailed cross-sectional characterization of age-specific patterns of slow and fast sleep spindles, slow oscillations, and their coupling in children and adolescents aged 5-6, 8-11, and 14-18 years, and an adult sample of 20- to 26-year-olds. Critically, based on this, we then investigated how spindle and slow oscillation maturity substantiate age-related differences in their precise orchestration. While the predominant type of fast spindles was development-specific in that it was still nested in a frequency range below the canonical fast spindle range for the majority of children, the well-known slow oscillation-spindle coupling pattern was evident for sleep spindles in the adult-like canonical fast spindle range in all four age groups-but notably less precise in children. To corroborate these findings, we linked personalized measures of fast spindle maturity, which indicate the similarity between the prevailing development-specific and adult-like canonical fast spindles, and slow oscillation maturity, which reflects the extent to which slow oscillations show frontal dominance, with individual slow oscillation-spindle coupling patterns. Importantly, we found that fast spindle maturity was uniquely associated with enhanced slow oscillation-spindle coupling strength and temporal precision across the four age groups. Taken together, our results suggest that the increasing ability to generate adult-like canonical fast sleep spindles actuates precise slow oscillation-spindle coupling patterns from childhood through adolescence and into young adulthood.
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Affiliation(s)
- Ann-Kathrin Joechner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Michael A Hahn
- Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
- Hertie-Institute for Clinical Brain Research, University Medical Center Tuebingen, Tuebingen, Germany
| | - Georg Gruber
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- The Siesta Group, Vienna, Austria
| | - Kerstin Hoedlmoser
- Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
| | - Markus Werkle-Bergner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
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44
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Plitt MH, Kaganovsky K, Südhof TC, Giocomo LM. Hippocampal place code plasticity in CA1 requires postsynaptic membrane fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.20.567978. [PMID: 38045362 PMCID: PMC10690209 DOI: 10.1101/2023.11.20.567978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Rapid delivery of glutamate receptors to the postsynaptic membrane via vesicle fusion is a central component of synaptic plasticity. However, it is unknown how this process supports specific neural computations during behavior. To bridge this gap, we combined conditional genetic deletion of a component of the postsynaptic membrane fusion machinery, Syntaxin3 (Stx3), in hippocampal CA1 neurons of mice with population in vivo calcium imaging. This approach revealed that Stx3 is necessary for forming the neural dynamics that support novelty processing, spatial reward memory and offline memory consolidation. In contrast, CA1 Stx3 was dispensable for maintaining aspects of the neural code that exist presynaptic to CA1 such as representations of context and space. Thus, manipulating postsynaptic membrane fusion identified computations that specifically require synaptic restructuring via membrane trafficking in CA1 and distinguished them from neural representation that could be inherited from upstream brain regions or learned through other mechanisms.
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Sheroziya M, Khazipov R. Synaptic Origin of Early Sensory-evoked Oscillations in the Immature Thalamus. Neuroscience 2023; 532:50-64. [PMID: 37769898 DOI: 10.1016/j.neuroscience.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/22/2023] [Accepted: 09/08/2023] [Indexed: 10/03/2023]
Abstract
During the critical period of postnatal development, brain maturation is extremely sensitive to external stimuli. Newborn rodents already have functional somatosensory pathways and the thalamus, but the cortex is still forming. Immature thalamic synapses may produce large postsynaptic potentials in immature neurons, while non-synaptic membrane currents remain relatively weak and slow. The thalamocortical system generates spontaneous and evoked early gamma and spindle-burst oscillations in newborn rodents. How relatively strong synapses and weak intrinsic currents interact with each other and how they contribute to early thalamic activities remains largely unknown. Here, we performed local field potential (LFP), juxtacellular, and patch-clamp recordings in the somatosensory thalamus of urethane-anesthetized rat pups at postnatal days 6-7 with one whisker stimulation. We removed the overlying cortex and hippocampus to reach the thalamus with electrodes. Deflection of only one (the principal) whisker induced spikes in a particular thalamic cell. Whisker deflection evoked a group of large-amplitude excitatory events, likely originating from lemniscal synapses and multiple inhibitory postsynaptic events in thalamocortical cells. Large-amplitude excitatory events produced a group of spike bursts and could evoke a depolarization block. Juxtacellular recordings confirmed the partial inactivation of spikes. Inhibitory events prevented inactivation of action potentials and gamma-modulated neuronal firing. We conclude that the interplay of strong excitatory and inhibitory synapses and relatively weak intrinsic currents produces sensory-evoked early gamma oscillations in thalamocortical cells. We also propose that sensory-evoked large-amplitude excitatory events contribute to evoked spindle-bursts.
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Affiliation(s)
- Maxim Sheroziya
- Laboratory of Neurobiology, Kazan Federal University, Kazan, Russia.
| | - Roustem Khazipov
- Laboratory of Neurobiology, Kazan Federal University, Kazan, Russia; Aix Marseille University, INSERM, INMED, Marseille, France
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Lv S, He E, Luo J, Liu Y, Liang W, Xu S, Zhang K, Yang Y, Wang M, Song Y, Wu Y, Cai X. Using Human-Induced Pluripotent Stem Cell Derived Neurons on Microelectrode Arrays to Model Neurological Disease: A Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301828. [PMID: 37863819 PMCID: PMC10667858 DOI: 10.1002/advs.202301828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/04/2023] [Indexed: 10/22/2023]
Abstract
In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human-induced pluripotent stem cell (hiPSC) derived neurons (hiPSC-DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two-dimensional (2D), and more recently, three-dimensional (3D) neural stem cell-derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real-time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC-DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC-DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC-DNs. The paper also highlights the application of MEAs combined with hiPSC-DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.
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Affiliation(s)
- Shiya Lv
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Enhui He
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
- The State Key Lab of Brain‐Machine IntelligenceZhejiang UniversityHangzhou321100China
| | - Jinping Luo
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yaoyao Liu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Wei Liang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Shihong Xu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Kui Zhang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yan Yang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Mixia Wang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yilin Song
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yirong Wu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Xinxia Cai
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
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Ceanga M, Rahmati V, Haselmann H, Schmidl L, Hunter D, Brauer AK, Liebscher S, Kreye J, Prüss H, Groc L, Hallermann S, Dalmau J, Ori A, Heckmann M, Geis C. Human NMDAR autoantibodies disrupt excitatory-inhibitory balance, leading to hippocampal network hypersynchrony. Cell Rep 2023; 42:113166. [PMID: 37768823 DOI: 10.1016/j.celrep.2023.113166] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/30/2023] [Accepted: 09/07/2023] [Indexed: 09/30/2023] Open
Abstract
Anti-NMDA receptor autoantibodies (NMDAR-Abs) in patients with NMDAR encephalitis cause severe disease symptoms resembling psychosis and cause cognitive dysfunction. After passive transfer of patients' cerebrospinal fluid or human monoclonal anti-GluN1-autoantibodies in mice, we find a disrupted excitatory-inhibitory balance resulting from CA1 neuronal hypoexcitability, reduced AMPA receptor (AMPAR) signaling, and faster synaptic inhibition in acute hippocampal slices. Functional alterations are also reflected in widespread remodeling of the hippocampal proteome, including changes in glutamatergic and GABAergic neurotransmission. NMDAR-Abs amplify network γ oscillations and disrupt θ-γ coupling. A data-informed network model reveals that lower AMPAR strength and faster GABAA receptor current kinetics chiefly account for these abnormal oscillations. As predicted in silico and evidenced ex vivo, positive allosteric modulation of AMPARs alleviates aberrant γ activity, reinforcing the causative effects of the excitatory-inhibitory imbalance. Collectively, NMDAR-Ab-induced aberrant synaptic, cellular, and network dynamics provide conceptual insights into NMDAR-Ab-mediated pathomechanisms and reveal promising therapeutic targets that merit future in vivo validation.
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Affiliation(s)
- Mihai Ceanga
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747 Jena, Germany
| | - Vahid Rahmati
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747 Jena, Germany
| | - Holger Haselmann
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747 Jena, Germany
| | - Lars Schmidl
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747 Jena, Germany
| | - Daniel Hunter
- Université de Bordeaux, CNRS, Interdisciplinary Institute for Neuroscience, UMR 5297, 33000 Bordeaux, France
| | - Anna-Katherina Brauer
- Institute of Clinical Neuroimmunology, Klinikum der Universität München, Ludwig Maximilians University Munich, Martinsried, Germany; Biomedical Center, Ludwig Maximilians University Munich, Martinsried, Germany
| | - Sabine Liebscher
- Institute of Clinical Neuroimmunology, Klinikum der Universität München, Ludwig Maximilians University Munich, Martinsried, Germany; Biomedical Center, Ludwig Maximilians University Munich, Martinsried, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Jakob Kreye
- Department of Neurology and Experimental Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany; Department of Pediatric Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Harald Prüss
- Department of Neurology and Experimental Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany
| | - Laurent Groc
- Université de Bordeaux, CNRS, Interdisciplinary Institute for Neuroscience, UMR 5297, 33000 Bordeaux, France
| | - Stefan Hallermann
- Carl Ludwig Institute for Physiology, Faculty of Medicine, Leipzig University, 04103 Leipzig, Germany
| | - Josep Dalmau
- Catalan Institution for Research and Advanced Studies (ICREA) and IDIBAPS-Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
| | - Alessandro Ori
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), 07745 Jena, Germany
| | - Manfred Heckmann
- Department of Neurophysiology, Institute of Physiology, University of Würzburg, 97070 Würzburg, Germany
| | - Christian Geis
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747 Jena, Germany.
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48
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Dearnley B, Jones M, Dervinis M, Okun M. Brain state transitions primarily impact the spontaneous rate of slow-firing neurons. Cell Rep 2023; 42:113185. [PMID: 37773749 DOI: 10.1016/j.celrep.2023.113185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/02/2023] [Accepted: 09/12/2023] [Indexed: 10/01/2023] Open
Abstract
The spontaneous firing of neurons is modulated by brain state. Here, we examine how such modulation impacts the overall distribution of firing rates in neuronal populations of neocortical, hippocampal, and thalamic areas across natural and pharmacologically driven brain state transitions. We report that across all the examined combinations of brain area and state transition category, the structure of rate modulation is similar, with almost all fast-firing neurons experiencing proportionally weak modulation, while slow-firing neurons exhibit high inter-neuron variability in the modulation magnitude, leading to a stronger modulation on average. We further demonstrate that this modulation structure is linked to the left-skewed distribution of firing rates on the logarithmic scale and is recapitulated by bivariate log-gamma, but not Gaussian, distributions. Our findings indicate that a preconfigured log-rate distribution with rigid fast-firing neurons and a long left tail of malleable slow-firing neurons is a generic property of forebrain neuronal circuits.
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Affiliation(s)
- Bradley Dearnley
- Department of Psychology and Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, UK; School of Biological Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Melissa Jones
- Department of Psychology and Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, UK; School of Biological Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Martynas Dervinis
- Department of Psychology and Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, UK; School of Biological Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Michael Okun
- Department of Psychology and Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, UK; School of Biological Sciences, University of Leicester, Leicester LE1 7RH, UK; School of Life Sciences, Queen's Medical Centre, University of Nottingham, Nottingham NG7 2UH, UK.
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49
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Berndt M, Trusel M, Roberts TF, Pfeiffer BE, Volk LJ. Bidirectional synaptic changes in deep and superficial hippocampal neurons following in vivo activity. Neuron 2023; 111:2984-2994.e4. [PMID: 37689058 PMCID: PMC10958998 DOI: 10.1016/j.neuron.2023.08.014] [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: 10/01/2022] [Revised: 07/06/2023] [Accepted: 08/15/2023] [Indexed: 09/11/2023]
Abstract
Neuronal activity during experience is thought to induce plastic changes within the hippocampal network that underlie memory formation, although the extent and details of such changes in vivo remain unclear. Here, we employed a temporally precise marker of neuronal activity, CaMPARI2, to label active CA1 hippocampal neurons in vivo, followed by immediate acute slice preparation and electrophysiological quantification of synaptic properties. Recently active neurons in the superficial sublayer of stratum pyramidale displayed larger post-synaptic responses at excitatory synapses from area CA3, with no change in pre-synaptic release probability. In contrast, in vivo activity correlated with weaker pre- and post-synaptic excitatory weights onto pyramidal cells in the deep sublayer. In vivo activity of deep and superficial neurons within sharp-wave/ripples was bidirectionally changed across experience, consistent with the observed changes in synaptic weights. These findings reveal novel, fundamental mechanisms through which the hippocampal network is modified by experience to store information.
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Affiliation(s)
- Marcus Berndt
- UT Southwestern Medical Center Neuroscience Graduate Program, Dallas, TX 75390, USA; UT Southwestern Medical Center Department of Neuroscience, Dallas, TX 75390, USA
| | - Massimo Trusel
- UT Southwestern Medical Center Department of Neuroscience, Dallas, TX 75390, USA
| | - Todd F Roberts
- UT Southwestern Medical Center Neuroscience Graduate Program, Dallas, TX 75390, USA; UT Southwestern Medical Center Department of Neuroscience, Dallas, TX 75390, USA; Peter O'Donnell Brain Institute, Dallas, TX 75390, USA
| | - Brad E Pfeiffer
- UT Southwestern Medical Center Neuroscience Graduate Program, Dallas, TX 75390, USA; UT Southwestern Medical Center Department of Neuroscience, Dallas, TX 75390, USA; Peter O'Donnell Brain Institute, Dallas, TX 75390, USA.
| | - Lenora J Volk
- UT Southwestern Medical Center Neuroscience Graduate Program, Dallas, TX 75390, USA; UT Southwestern Medical Center Department of Neuroscience, Dallas, TX 75390, USA; UT Southwestern Medical Center Department of Psychiatry, Dallas, TX 75390, USA; Peter O'Donnell Brain Institute, Dallas, TX 75390, USA.
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50
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Emery BA, Hu X, Khanzada S, Kempermann G, Amin H. High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity. Biosens Bioelectron 2023; 237:115471. [PMID: 37379793 DOI: 10.1016/j.bios.2023.115471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/17/2023] [Accepted: 06/10/2023] [Indexed: 06/30/2023]
Abstract
Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different scales, the precise impact of experience on network-wide computational dynamics remains inaccessible due to the lack of applicable large-scale recording methodology. We here demonstrate a large-scale multi-site biohybrid brain circuity on-CMOS-based biosensor with an unprecedented spatiotemporal resolution of 4096 microelectrodes, which allows simultaneous electrophysiological assessment across the entire hippocampal-cortical subnetworks from mice living in an enriched environment (ENR) and standard-housed (SD) conditions. Our platform, empowered with various computational analyses, reveals environmental enrichment's impacts on local and global spatiotemporal neural dynamics, firing synchrony, topological network complexity, and large-scale connectome. Our results delineate the distinct role of prior experience in enhancing multiplexed dimensional coding formed by neuronal ensembles and error tolerance and resilience to random failures compared to standard conditions. The scope and depth of these effects highlight the critical role of high-density, large-scale biosensors to provide a new understanding of the computational dynamics and information processing in multimodal physiological and experience-dependent plasticity conditions and their role in higher brain functions. Knowledge of these large-scale dynamics can inspire the development of biologically plausible computational models and computational artificial intelligence networks and expand the reach of neuromorphic brain-inspired computing into new applications.
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Affiliation(s)
- Brett Addison Emery
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Xin Hu
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Shahrukh Khanzada
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Gerd Kempermann
- Research Group "Adult Neurogenesis", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany; Center for Regenerative Therapies TU Dresden (CRTD), Fetscherstraße 105, 01307, Dresden, Germany
| | - Hayder Amin
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany; TU Dresden, Faculty of Medicine Carl Gustav Carus, Bergstraße 53, 01069, Dresden, Germany.
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