1
|
Turegano-Lopez M, de Las Pozas F, Santuy A, Rodriguez JR, DeFelipe J, Merchan-Perez A. Tracing nerve fibers with volume electron microscopy to quantitatively analyze brain connectivity. Commun Biol 2024; 7:796. [PMID: 38951162 PMCID: PMC11217374 DOI: 10.1038/s42003-024-06491-0] [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: 12/18/2023] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
The highly complex structure of the brain requires an approach that can unravel its connectivity. Using volume electron microscopy and a dedicated software we can trace and measure all nerve fibers present within different samples of brain tissue. With this software tool, individual dendrites and axons are traced, obtaining a simplified "skeleton" of each fiber, which is linked to its corresponding synaptic contacts. The result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. To test this methodology, we apply it to the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex of the mouse. We find that nerve fibers are densely packed in the neuropil, reaching up to 9 kilometers per cubic mm. We obtain the number of synapses, the number and lengths of dendrites and axons, the linear densities of synapses established by dendrites and axons, and their location on dendritic spines and shafts. The quantitative data obtained through this method enable us to identify subtle traits and differences in the synaptic organization of the samples, which might have been overlooked in a qualitative analysis.
Collapse
Affiliation(s)
- Marta Turegano-Lopez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain
| | - Felix de Las Pozas
- Visualization & Graphics Lab (VG-Lab), Universidad Rey Juan Carlos, C/Tulipán S/N, Móstoles, 28933, Madrid, Spain
| | - Andrea Santuy
- Department of Basic Sciences, Universitat Internacional de Catalunya (UIC), San Cugat del Vallès, 08195, Barcelona, Spain
| | - Jose-Rodrigo Rodriguez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda. Doctor Arce, 37, 28002, Madrid, Spain
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda. Doctor Arce, 37, 28002, Madrid, Spain
| | - Angel Merchan-Perez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain.
- Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain.
| |
Collapse
|
2
|
Catacuzzeno L, Michelucci A, Franciolini F. The Long Journey from Animal Electricity to the Discovery of Ion Channels and the Modelling of the Human Brain. Biomolecules 2024; 14:684. [PMID: 38927086 PMCID: PMC11202063 DOI: 10.3390/biom14060684] [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: 05/14/2024] [Revised: 06/05/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
This retrospective begins with Galvani's experiments on frogs at the end of the 18th century and his discovery of 'animal electricity'. It goes on to illustrate the numerous contributions to the field of physical chemistry in the second half of the 19th century (Nernst's equilibrium potential, based on the work of Wilhelm Ostwald, Max Planck's ion electrodiffusion, Einstein's studies of Brownian motion) which led Bernstein to propose his membrane theory in the early 1900s as an explanation of Galvani's findings and cell excitability. These processes were fully elucidated by Hodgkin and Huxley in 1952 who detailed the ionic basis of resting and action potentials, but without addressing the question of where these ions passed. The emerging question of the existence of ion channels, widely debated over the next two decades, was finally accepted and, a decade later, many of them began to be cloned. This led to the possibility of modelling the activity of individual neurons in the brain and then that of simple circuits. Taking advantage of the remarkable advances in computer science in the new millennium, together with a much deeper understanding of brain architecture, more ambitious scientific goals were dreamed of to understand the brain and how it works. The retrospective concludes by reviewing the main efforts in this direction, namely the construction of a digital brain, an in silico copy of the brain that would run on supercomputers and behave just like a real brain.
Collapse
Affiliation(s)
- Luigi Catacuzzeno
- Dipartimento di Chimica, Biologia e Biotecnologie, Universita’ di Perugia, 06123 Perugia, Italy;
| | | | - Fabio Franciolini
- Dipartimento di Chimica, Biologia e Biotecnologie, Universita’ di Perugia, 06123 Perugia, Italy;
| |
Collapse
|
3
|
Qi Z, Noetscher GM, Miles A, Weise K, Knosche T, Cadman CR, Potashinsky AR, Liu K, Wartman WA, Nunez Ponasso GC, Bikson M, Lu H, Deng ZD, Nummenmaa A, Makaroff SN. Enabling Electric Field Modeling of Microscopically Realistic Brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.04.588004. [PMID: 38645100 PMCID: PMC11030228 DOI: 10.1101/2024.04.04.588004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Across all domains of brain stimulation (neuromodulation), conventional analysis of neuron activation involves two discrete steps: i) prediction of macroscopic electric field, ignoring presence of cells and; ii) prediction of cell activation from tissue electric fields. The first step assumes that current flow is not distorted by the dense tortuous network of cell structures. The deficiencies of this assumption have long been recognized, but - except for trivial geometries - ignored, because it presented intractable computation hurdles. This study introduces a novel approach for analyzing electric fields within a microscopically realistic brain volume. Our pipeline overcomes the technical intractability that prevented such analysis while also showing significant implications for brain stimulation. Contrary to the standard finite element method (FEM), we suggest using a nested iterative boundary element method (BEM) coupled with the fast multipole method (FMM). This approach allows for solving problems with multiple length scales more efficiently. A target application is a subvolume of the L2/3 P36 mouse primary visual cortex containing approximately 400 detailed densely packed neuronal cells at a resolution of 100 nm, which is obtained from scanning electron microscopy data. Our immediate result is a reduction of the stimulation field strength necessary for neuron activation by a factor of 0.85-0.55 (by 15%-45%) as compared to macroscopic predictions. This is in line with modern experimental data stating that existing macroscopic theories substantially overestimate electric field levels necessary for brain stimulation.
Collapse
|
4
|
Buccino AP, Damart T, Bartram J, Mandge D, Xue X, Zbili M, Gänswein T, Jaquier A, Emmenegger V, Markram H, Hierlemann A, Van Geit W. A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays. Neural Comput 2024; 36:1286-1331. [PMID: 38776965 DOI: 10.1162/neco_a_01672] [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: 03/15/2023] [Accepted: 02/20/2024] [Indexed: 05/25/2024]
Abstract
In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.
Collapse
Affiliation(s)
- Alessio Paolo Buccino
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Tanguy Damart
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Julian Bartram
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Darshan Mandge
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Xiaohan Xue
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Mickael Zbili
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Tobias Gänswein
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Aurélien Jaquier
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Vishalini Emmenegger
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Andreas Hierlemann
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland Present address: Foundation for Research on Information Technologies in Society (IT'IS), Zurich 8004, Switzerland
| |
Collapse
|
5
|
Shore AN, Li K, Safari M, Qunies AM, Spitznagel BD, Weaver CD, Emmitte KA, Frankel WN, Weston MC. Heterozygous expression of a Kcnt1 gain-of-function variant has differential effects on SST- and PV-expressing cortical GABAergic neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.11.561953. [PMID: 37873369 PMCID: PMC10592778 DOI: 10.1101/2023.10.11.561953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
More than twenty recurrent missense gain-of-function (GOF) mutations have been identified in the sodium-activated potassium (KNa) channel gene KCNT1 in patients with severe developmental and epileptic encephalopathies (DEEs), most of which are resistant to current therapies. Defining the neuron types most vulnerable to KCNT1 GOF will advance our understanding of disease mechanisms and provide refined targets for precision therapy efforts. Here, we assessed the effects of heterozygous expression of a Kcnt1 GOF variant (Y777H) on KNa currents and neuronal physiology among cortical glutamatergic and GABAergic neurons in mice, including those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), to identify and model the pathogenic mechanisms of autosomal dominant KCNT1 GOF variants in DEEs. Although the Kcnt1-Y777H variant had no effects on glutamatergic or VIP neuron function, it increased subthreshold KNa currents in both SST and PV neurons but with opposite effects on neuronal output; SST neurons became hypoexcitable with a higher rheobase current and lower action potential (AP) firing frequency, whereas PV neurons became hyperexcitable with a lower rheobase current and higher AP firing frequency. Further neurophysiological and computational modeling experiments showed that the differential effects of the Y777H variant on SST and PV neurons are not likely due to inherent differences in these neuron types, but to an increased persistent sodium current in PV, but not SST, neurons. The Y777H variant also increased excitatory input onto, and chemical and electrical synaptic connectivity between, SST neurons. Together, these data suggest differential pathogenic mechanisms, both direct and compensatory, contribute to disease phenotypes, and provide a salient example of how a pathogenic ion channel variant can cause opposite functional effects in closely related neuron subtypes due to interactions with other ionic conductances.
Collapse
Affiliation(s)
- Amy N. Shore
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Center for Neurobiology Research, Roanoke, VA, USA
- Department of Neurological Sciences, University of Vermont, Burlington, VT, USA
| | - Keyong Li
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Center for Neurobiology Research, Roanoke, VA, USA
| | - Mona Safari
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Center for Neurobiology Research, Roanoke, VA, USA
- Translational Biology, Medicine, and Health Graduate Program, Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Alshaima’a M. Qunies
- Department of Pharmaceutical Sciences, UNT System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, TX, USA
- School of Biomedical Sciences, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Brittany D. Spitznagel
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - C. David Weaver
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Kyle A. Emmitte
- Department of Pharmaceutical Sciences, UNT System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Wayne N. Frankel
- Institute for Genomic Medicine, Columbia University, New York, NY, USA
- Department of Neurology, Columbia University, New York, NY, USA
| | - Matthew C. Weston
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Center for Neurobiology Research, Roanoke, VA, USA
- Department of Neurological Sciences, University of Vermont, Burlington, VT, USA
- Translational Biology, Medicine, and Health Graduate Program, Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- School of Neuroscience, Virginia Tech, Blacksburg, VA, USA
| |
Collapse
|
6
|
Storm JF, Klink PC, Aru J, Senn W, Goebel R, Pigorini A, Avanzini P, Vanduffel W, Roelfsema PR, Massimini M, Larkum ME, Pennartz CMA. An integrative, multiscale view on neural theories of consciousness. Neuron 2024; 112:1531-1552. [PMID: 38447578 DOI: 10.1016/j.neuron.2024.02.004] [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: 10/08/2023] [Revised: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 03/08/2024]
Abstract
How is conscious experience related to material brain processes? A variety of theories aiming to answer this age-old question have emerged from the recent surge in consciousness research, and some are now hotly debated. Although most researchers have so far focused on the development and validation of their preferred theory in relative isolation, this article, written by a group of scientists representing different theories, takes an alternative approach. Noting that various theories often try to explain different aspects or mechanistic levels of consciousness, we argue that the theories do not necessarily contradict each other. Instead, several of them may converge on fundamental neuronal mechanisms and be partly compatible and complementary, so that multiple theories can simultaneously contribute to our understanding. Here, we consider unifying, integration-oriented approaches that have so far been largely neglected, seeking to combine valuable elements from various theories.
Collapse
Affiliation(s)
- Johan F Storm
- The Brain Signaling Group, Division of Physiology, IMB, Faculty of Medicine, University of Oslo, Domus Medica, Sognsvannsveien 9, Blindern, 0317 Oslo, Norway.
| | - P Christiaan Klink
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, 3584 CS Utrecht, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris 75012, France
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Walter Senn
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan 20122, Italy
| | - Pietro Avanzini
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, 43125 Parma, Italy
| | - Wim Vanduffel
- Department of Neurosciences, Laboratory of Neuro and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
| | - Pieter R Roelfsema
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris 75012, France; Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands; Department of Neurosurgery, Academisch Medisch Centrum, Postbus 22660, 1100 DD Amsterdam, the Netherlands
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan 20157, Italy; Istituto di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan 20122, Italy; Azrieli Program in Brain, Mind and Consciousness, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5G 1M1, Canada
| | - Matthew E Larkum
- Institute of Biology, Humboldt University Berlin, Berlin, Germany; Neurocure Center for Excellence, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cyriel M A Pennartz
- Swammerdam Institute for Life Sciences, Center for Neuroscience, Faculty of Science, University of Amsterdam, Sciencepark 904, Amsterdam 1098 XH, the Netherlands; Research Priority Program Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| |
Collapse
|
7
|
Wendling F, Koksal-Ersoz E, Al-Harrach M, Yochum M, Merlet I, Ruffini G, Bartolomei F, Benquet P. Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy. Clin Neurophysiol 2024; 161:198-210. [PMID: 38520800 DOI: 10.1016/j.clinph.2024.03.006] [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: 09/15/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 03/25/2024]
Abstract
OBJECTIVE The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy. METHODS We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome. RESULTS For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves. CONCLUSIONS The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy. SIGNIFICANCE These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology Department, Marseille, France; Univ Aix Marseille, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Nelson AD, Catalfio AM, Gupta JP, Min L, Caballero-Florán RN, Dean KP, Elvira CC, Derderian KD, Kyoung H, Sahagun A, Sanders SJ, Bender KJ, Jenkins PM. Physical and functional convergence of the autism risk genes Scn2a and Ank2 in neocortical pyramidal cell dendrites. Neuron 2024; 112:1133-1149.e6. [PMID: 38290518 PMCID: PMC11097922 DOI: 10.1016/j.neuron.2024.01.003] [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: 06/08/2022] [Revised: 04/26/2023] [Accepted: 01/03/2024] [Indexed: 02/01/2024]
Abstract
Dysfunction in sodium channels and their ankyrin scaffolding partners have both been implicated in neurodevelopmental disorders, including autism spectrum disorder (ASD). In particular, the genes SCN2A, which encodes the sodium channel NaV1.2, and ANK2, which encodes ankyrin-B, have strong ASD association. Recent studies indicate that ASD-associated haploinsufficiency in Scn2a impairs dendritic excitability and synaptic function in neocortical pyramidal cells, but how NaV1.2 is anchored within dendritic regions is unknown. Here, we show that ankyrin-B is essential for scaffolding NaV1.2 to the dendritic membrane of mouse neocortical neurons and that haploinsufficiency of Ank2 phenocopies intrinsic dendritic excitability and synaptic deficits observed in Scn2a+/- conditions. These results establish a direct, convergent link between two major ASD risk genes and reinforce an emerging framework suggesting that neocortical pyramidal cell dendritic dysfunction can contribute to neurodevelopmental disorder pathophysiology.
Collapse
Affiliation(s)
- Andrew D Nelson
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Amanda M Catalfio
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Julie P Gupta
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lia Min
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Kendall P Dean
- Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Carina C Elvira
- Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Kimberly D Derderian
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Henry Kyoung
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Atehsa Sahagun
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Stephan J Sanders
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Kevin J Bender
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Paul M Jenkins
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA.
| |
Collapse
|
10
|
Latifi S, Carmichael ST. The emergence of multiscale connectomics-based approaches in stroke recovery. Trends Neurosci 2024; 47:303-318. [PMID: 38402008 DOI: 10.1016/j.tins.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: 08/22/2023] [Revised: 12/31/2023] [Accepted: 01/21/2024] [Indexed: 02/26/2024]
Abstract
Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.
Collapse
Affiliation(s)
- Shahrzad Latifi
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA; Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV 26506, USA
| | - S Thomas Carmichael
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
| |
Collapse
|
11
|
Ramaswamy S. Data-driven multiscale computational models of cortical and subcortical regions. Curr Opin Neurobiol 2024; 85:102842. [PMID: 38320453 DOI: 10.1016/j.conb.2024.102842] [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: 03/23/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 02/08/2024]
Abstract
Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.
Collapse
Affiliation(s)
- Srikanth Ramaswamy
- Neural Circuits Laboratory, Biosciences Institute, Newcastle University, Newcastle Upon Tyne, NE2 4HH, United Kingdom.
| |
Collapse
|
12
|
Tian Y, Murphy MJH, Steiner LA, Kalia SK, Hodaie M, Lozano AM, Hutchison WD, Popovic MR, Milosevic L, Lankarany M. Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus. Neuromodulation 2024; 27:464-475. [PMID: 37140523 DOI: 10.1016/j.neurom.2023.03.012] [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: 11/13/2022] [Revised: 01/27/2023] [Accepted: 03/02/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei-including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)-across different DBS frequencies. MATERIALS AND METHODS Both synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim-across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. RESULTS Our model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. CONCLUSIONS The result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.
Collapse
Affiliation(s)
- Yupeng Tian
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | | | - Leon A Steiner
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Berlin Institute of Health, Berlin, Germany; Department of Surgery, University of Toronto, Toronto, ON, Canada; Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Suneil K Kalia
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Mojgan Hodaie
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Andres M Lozano
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - William D Hutchison
- CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Luka Milosevic
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milad Lankarany
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
13
|
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.
Collapse
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.
| |
Collapse
|
14
|
Marasco A, Tribuzi C, Iuorio A, Migliore M. Mathematical generation of data-driven hippocampal CA1 pyramidal neurons and interneurons copies via A-GLIF models for large-scale networks covering the experimental variability range. Math Biosci 2024; 371:109179. [PMID: 38521453 DOI: 10.1016/j.mbs.2024.109179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 11/10/2023] [Accepted: 03/13/2024] [Indexed: 03/25/2024]
Abstract
Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational efficiency is achieved using simplified neurons, whereas there are no practical solutions available to solve the problem of reproducing in a large-scale network the experimentally observed heterogeneity of the intrinsic properties of neurons. This is important, because the use of identical nodes in a network can generate artifacts which can hinder an adequate representation of the properties of a real network. To this aim, we introduce a mathematical procedure to generate an arbitrary large number of copies of simplified hippocampal CA1 pyramidal neurons and interneurons models, which exhibit the full range of firing dynamics observed in these cells - including adapting, non-adapting and bursting. For this purpose, we rely on a recently published adaptive generalized leaky integrate-and-fire (A-GLIF) modeling approach, leveraging on its ability to reproduce the rich set of electrophysiological behaviors of these types of neurons under a variety of different stimulation currents. The generation procedure is based on a perturbation of model's parameters related to the initial data, firing block, and internal dynamics, and suitably validated against experimental data to ensure that the firing dynamics of any given cell copy remains within the experimental range. A classification procedure confirmed that the firing behavior of most of the pyramidal/interneuron copies was consistent with the experimental data. This approach allows to obtain heterogeneous copies with mathematically controlled firing properties. A full set of heterogeneous neurons composing the CA1 region of a rat hippocampus (approximately 1.2 million neurons), are provided in a database freely available in the live paper section of the EBRAINS platform. By adapting the underlying A-GLIF framework, it will be possible to extend the numerical approach presented here to create, in a mathematically controlled manner, an arbitrarily large number of non-identical copies of cell populations with firing properties related to other brain areas.
Collapse
Affiliation(s)
- A Marasco
- Department of Mathematics and Applications, University of Naples Federico II, Naples, Italy; Institute of Biophysics, National Research Council, Palermo, Italy.
| | - C Tribuzi
- Department of Mathematics and Applications, University of Naples Federico II, Naples, Italy
| | - A Iuorio
- University of Vienna, Faculty of Mathematics, Vienna, Austria; Department of Engineering, Parthenope University of Naples, Naples, Italy
| | - M Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| |
Collapse
|
15
|
Venkadesh S, Shaikh A, Shakeri H, Barreto E, Van Horn JD. Biophysical modulation and robustness of itinerant complexity in neuronal networks. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1302499. [PMID: 38516614 PMCID: PMC10954887 DOI: 10.3389/fnetp.2024.1302499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.
Collapse
Affiliation(s)
- Siva Venkadesh
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
| | - Asmir Shaikh
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Heman Shakeri
- School of Data Science, University of Virginia, Charlottesville, VA, United States
- Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
| | - Ernest Barreto
- Department of Physics and Astronomy and the Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, United States
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
- School of Data Science, University of Virginia, Charlottesville, VA, United States
| |
Collapse
|
16
|
Galván Fraile J, Scherr F, Ramasco JJ, Arkhipov A, Maass W, Mirasso CR. Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations. PLoS Comput Biol 2024; 20:e1011921. [PMID: 38452057 PMCID: PMC10950248 DOI: 10.1371/journal.pcbi.1011921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/19/2024] [Accepted: 02/18/2024] [Indexed: 03/09/2024] Open
Abstract
In an ever-changing visual world, animals' survival depends on their ability to perceive and respond to rapidly changing motion cues. The primary visual cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses to perturbations in visual flow. However, the underlying neural mechanisms that lead to distinct cortical responses to such perturbations remain enigmatic. In this study, our objective was to uncover the neural dynamics that govern V1 neurons' responses to visual flow perturbations using a biologically realistic computational model. By subjecting the model to sudden changes in visual input, we observed opposing cortical responses in excitatory layer 2/3 (L2/3) neurons, namely, depolarizing and hyperpolarizing responses. We found that this segregation was primarily driven by the competition between external visual input and recurrent inhibition, particularly within L2/3 and L4. This division was not observed in excitatory L5/6 neurons, suggesting a more prominent role for inhibitory mechanisms in the visual processing of the upper cortical layers. Our findings share similarities with recent experimental studies focusing on the opposing influence of top-down and bottom-up inputs in the mouse primary visual cortex during visual flow perturbations.
Collapse
Affiliation(s)
- J. Galván Fraile
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB-CSIC, Palma de Mallorca, Spain
| | - Franz Scherr
- Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - José J. Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB-CSIC, Palma de Mallorca, Spain
| | - Anton Arkhipov
- Allen Institute, Seattle, Washington, United States of America
| | - Wolfgang Maass
- Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Claudio R. Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB-CSIC, Palma de Mallorca, Spain
| |
Collapse
|
17
|
Kumaravelu K, Grill WM. Neural mechanisms of the temporal response of cortical neurons to intracortical microstimulation. Brain Stimul 2024; 17:365-381. [PMID: 38492885 PMCID: PMC11090107 DOI: 10.1016/j.brs.2024.03.012] [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: 10/12/2023] [Revised: 02/20/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Intracortical microstimulation (ICMS) is used to map neuronal circuitry in the brain and restore lost sensory function, including vision, hearing, and somatosensation. The temporal response of cortical neurons to single pulse ICMS is remarkably stereotyped and comprises short latency excitation followed by prolonged inhibition and, in some cases, rebound excitation. However, the neural origin of the different response components to ICMS are poorly understood, and the interactions between the three response components during trains of ICMS pulses remains unclear. OBJECTIVE We used computational modeling to determine the mechanisms contributing to the temporal response to ICMS in model cortical neurons. METHODS We implemented a biophysically based computational model of a cortical column comprising neurons with realistic morphology and synapses and quantified the temporal response of cortical neurons to different ICMS protocols. We characterized the temporal responses to single pulse ICMS across stimulation intensities and inhibitory (GABA-B/GABA-A) synaptic strengths. To probe interactions between response components, we quantified the response to paired pulse ICMS at different inter-pulse intervals and the response to short trains at different stimulation frequencies. Finally, we evaluated the performance of biomimetic ICMS trains in evoking sustained neural responses. RESULTS Single pulse ICMS evoked short latency excitation followed by a period of inhibition, but model neurons did not exhibit post-inhibitory excitation. The strength of short latency excitation increased and the duration of inhibition increased with increased stimulation amplitude. Prolonged inhibition resulted from both after-hyperpolarization currents and GABA-B synaptic transmission. During the paired pulse protocol, the strength of short latency excitation evoked by a test pulse decreased marginally compared to those evoked by a single pulse for interpulse intervals (IPI) < 100 m s. Further, the duration of inhibition evoked by the test pulse was prolonged compared to single pulse for IPIs <50 m s and was not predicted by linear superposition of individual inhibitory responses. For IPIs>50 m s, the duration of inhibition evoked by the test pulse was comparable to those evoked by a single pulse. Short ICMS trains evoked repetitive excitatory responses against a background of inhibition. However, the strength of the repetitive excitatory response declined during ICMS at higher frequencies. Further, the duration of inhibition at the cessation of ICMS at higher frequencies was prolonged compared to the duration following a single pulse. Biomimetic pulse trains evoked comparable neural response between the onset and offset phases despite the presence of stimulation induced inhibition. CONCLUSIONS The cortical column model replicated the short latency excitation and long-lasting inhibitory components of the stereotyped neural response documented in experimental studies of ICMS. Both cellular and synaptic mechanisms influenced the response components generated by ICMS. The non-linear interactions between response components resulted in dynamic ICMS-evoked neural activity and may play an important role in mediating the ICMS-induced precepts.
Collapse
Affiliation(s)
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA.
| |
Collapse
|
18
|
Ecker A, Egas Santander D, Bolaños-Puchet S, Isbister JB, Reimann MW. Cortical cell assemblies and their underlying connectivity: An in silico study. PLoS Comput Biol 2024; 20:e1011891. [PMID: 38466752 PMCID: PMC10927091 DOI: 10.1371/journal.pcbi.1011891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 02/05/2024] [Indexed: 03/13/2024] Open
Abstract
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.
Collapse
Affiliation(s)
- András Ecker
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Daniela Egas Santander
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Sirio Bolaños-Puchet
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - James B. Isbister
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Michael W. Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| |
Collapse
|
19
|
Takács V, Bardóczi Z, Orosz Á, Major A, Tar L, Berki P, Papp P, Mayer MI, Sebők H, Zsolt L, Sos KE, Káli S, Freund TF, Nyiri G. Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons. PLoS Biol 2024; 22:e3002539. [PMID: 38470935 PMCID: PMC10959371 DOI: 10.1371/journal.pbio.3002539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 03/22/2024] [Accepted: 02/06/2024] [Indexed: 03/14/2024] Open
Abstract
GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning, and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo, and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed, and make available several key data sets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The data set includes detailed scanning electron microscopy (SEM)-based 3D reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core data set with other anatomical data, patch-clamp electrophysiology, and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For example, we estimated that an OLM cell receives about 8,400, whereas an HS cell about 15,600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.
Collapse
Affiliation(s)
- Virág Takács
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Zsuzsanna Bardóczi
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Áron Orosz
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Abel Major
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Luca Tar
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- Roska Tamás Doctoral School of Sciences and Technology, Pázmány Péter Catholic University, Budapest, Hungary
| | - Péter Berki
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Péter Papp
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Márton I. Mayer
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Hunor Sebők
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Luca Zsolt
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Katalin E. Sos
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Szabolcs Káli
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Tamás F. Freund
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Gábor Nyiri
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| |
Collapse
|
20
|
Verardo C, Mele LJ, Selmi L, Palestri P. Finite-element modeling of neuromodulation via controlled delivery of potassium ions using conductive polymer-coated microelectrodes. J Neural Eng 2024; 21:026002. [PMID: 38306702 DOI: 10.1088/1741-2552/ad2581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. The controlled delivery of potassium is an interesting neuromodulation modality, being potassium ions involved in shaping neuron excitability, synaptic transmission, network synchronization, and playing a key role in pathological conditions like epilepsy and spreading depression. Despite many successful examples of pre-clinical devices able to influence the extracellular potassium concentration, computational frameworks capturing the corresponding impact on neuronal activity are still missing.Approach. We present a finite-element model describing a PEDOT:PSS-coated microelectrode (herein, simplyionic actuator) able to release potassium and thus modulate the activity of a cortical neuron in anin-vitro-like setting. The dynamics of ions in the ionic actuator, the neural membrane, and the cellular fluids are solved self-consistently.Main results. We showcase the capability of the model to describe on a physical basis the modulation of the intrinsic excitability of the cell and of the synaptic transmission following the electro-ionic stimulation produced by the actuator. We consider three case studies for the ionic actuator with different levels of selectivity to potassium: ideal selectivity, no selectivity, and selectivity achieved by embedding ionophores in the polymer.Significance. This work is the first step toward a comprehensive computational framework aimed to investigate novel neuromodulation devices targeting specific ionic species, as well as to optimize their design and performance, in terms of the induced modulation of neural activity.
Collapse
Affiliation(s)
- Claudio Verardo
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Leandro Julian Mele
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States of America
| | - Luca Selmi
- Department of Engineering "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Modena, Italy
| | - Pierpaolo Palestri
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- Department of Engineering "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Modena, Italy
| |
Collapse
|
21
|
Papo D, Buldú JM. Does the brain behave like a (complex) network? I. Dynamics. Phys Life Rev 2024; 48:47-98. [PMID: 38145591 DOI: 10.1016/j.plrev.2023.12.006] [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: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/27/2023]
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
Collapse
Affiliation(s)
- D Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy; Center for Translational Neurophysiology, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy.
| | - J M Buldú
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Madrid, Spain
| |
Collapse
|
22
|
Huang X, Wei X, Wang J, Yi G. Frequency-dependent membrane polarization across neocortical cell types and subcellular elements by transcranial alternating current stimulation. J Neural Eng 2024; 21:016034. [PMID: 38382101 DOI: 10.1088/1741-2552/ad2b8a] [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: 10/17/2023] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Objective.Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that directly interacts with ongoing brain oscillations in a frequency-dependent manner. However, it remains largely unclear how the cellular effects of tACS vary between cell types and subcellular elements.Approach.In this study, we use a set of morphologically realistic models of neocortical neurons to simulate the cellular response to uniform oscillating electric fields (EFs). We systematically characterize the membrane polarization in the soma, axons, and dendrites with varying field directions, intensities, and frequencies.Main results.Pyramidal cells are more sensitive to axial EF that is roughly parallel to the cortical column, while interneurons are sensitive to axial EF and transverse EF that is tangent to the cortical surface. Membrane polarization in each subcellular element increases linearly with EF intensity, and its slope, i.e. polarization length, highly depends on the stimulation frequency. At each frequency, pyramidal cells are more polarized than interneurons. Axons usually experience the highest polarization, followed by the dendrites and soma. Moreover, a visible frequency resonance presents in the apical dendrites of pyramidal cells, while the other subcellular elements primarily exhibit low-pass filtering properties. In contrast, each subcellular element of interneurons exhibits complex frequency-dependent polarization. Polarization phase in each subcellular element of cortical neurons lags that of field and exhibits high-pass filtering properties. These results demonstrate that the membrane polarization is not only frequency-dependent, but also cell type- and subcellular element-specific. Through relating effective length and ion mechanism with polarization, we emphasize the crucial role of cell morphology and biophysics in determining the frequency-dependent membrane polarization.Significance.Our findings highlight the diverse polarization patterns across cell types as well as subcellular elements, which provide some insights into the tACS cellular effects and should be considered when understanding the neural spiking activity by tACS.
Collapse
Affiliation(s)
- Xuelin Huang
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xile Wei
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Guosheng Yi
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| |
Collapse
|
23
|
Haynes VR, Zhou Y, Crook SM. Discovering optimal features for neuron-type identification from extracellular recordings. Front Neuroinform 2024; 18:1303993. [PMID: 38371496 PMCID: PMC10869512 DOI: 10.3389/fninf.2024.1303993] [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: 09/28/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.
Collapse
Affiliation(s)
- Vergil R. Haynes
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| | - Yi Zhou
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Sharon M. Crook
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| |
Collapse
|
24
|
Borst A. Connectivity Matrix Seriation via Relaxation. PLoS Comput Biol 2024; 20:e1011904. [PMID: 38377134 PMCID: PMC10906871 DOI: 10.1371/journal.pcbi.1011904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 03/01/2024] [Accepted: 02/09/2024] [Indexed: 02/22/2024] Open
Abstract
Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesn't change the connectivity, it can make special connectivity patterns recognizable. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. Different algorithms are used to renumber matrices such as to minimize a given cost function, but either their performance becomes unsatisfying at a given size of the circuit or the CPU time needed to compute them scales in an unfavorable way with increasing number of neurons. Based on previous ideas, I describe an algorithm which is effective in matrix reordering with respect to both its performance as well as to its scaling in computing time. Rather than trying to reorder the matrix in discrete steps, the algorithm transiently relaxes the integer program by assigning a real-valued parameter to each cell describing its location on a continuous axis ('smooth-index') and finds the parameter set that minimizes the cost. I find that the smooth-index algorithm outperforms all algorithms I compared it to, including those based on topological sorting.
Collapse
Affiliation(s)
- Alexander Borst
- Max-Planck-Institute for Biological Intelligence, Martinsried, Germany
| |
Collapse
|
25
|
Garcia S, Windolf C, Boussard J, Dichter B, Buccino AP, Yger P. A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings. eNeuro 2024; 11:ENEURO.0229-23.2023. [PMID: 38238082 PMCID: PMC10897502 DOI: 10.1523/eneuro.0229-23.2023] [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: 06/29/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 02/28/2024] Open
Abstract
High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.
Collapse
Affiliation(s)
- Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRS, Lyon 69675, France
| | | | | | | | - Alessio P Buccino
- CatalystNeuro, Benicia, California 94510
- Allen Institute for Neural Dynamics, Seattle, Washington 98109
| | - Pierre Yger
- Institut de la Vision, Sorbonne Université, INSERM, Paris 75012, France
- Lille Neurosciences & Cognition (lilNCog)-U1172 (INSERM, Lille), Univ Lille, Centre Hospitalier Universitaire de Lille, Lille 59800, France
| |
Collapse
|
26
|
Shirani F, Choi H. On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks. J Comput Neurosci 2024; 52:73-107. [PMID: 37837534 DOI: 10.1007/s10827-023-00863-x] [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: 04/13/2023] [Revised: 06/25/2023] [Accepted: 09/08/2023] [Indexed: 10/16/2023]
Abstract
Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a network's baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. Our study based on extensive bifurcation analyses thus reveal the functional optimality and criticality of structural and physiological parameters in establishing the baseline operating state of local cortical networks.
Collapse
Affiliation(s)
- Farshad Shirani
- School of Mathematics, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA.
| | - Hannah Choi
- School of Mathematics, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA
| |
Collapse
|
27
|
Patel D, Shetty S, Acha C, Pantoja IEM, Zhao A, George D, Gracias DH. Microinstrumentation for Brain Organoids. Adv Healthc Mater 2024:e2302456. [PMID: 38217546 DOI: 10.1002/adhm.202302456] [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: 07/30/2023] [Revised: 12/10/2023] [Indexed: 01/15/2024]
Abstract
Brain organoids are three-dimensional aggregates of self-organized differentiated stem cells that mimic the structure and function of human brain regions. Organoids bridge the gaps between conventional drug screening models such as planar mammalian cell culture, animal studies, and clinical trials. They can revolutionize the fields of developmental biology, neuroscience, toxicology, and computer engineering. Conventional microinstrumentation for conventional cellular engineering, such as planar microfluidic chips; microelectrode arrays (MEAs); and optical, magnetic, and acoustic techniques, has limitations when applied to three-dimensional (3D) organoids, primarily due to their limits with inherently two-dimensional geometry and interfacing. Hence, there is an urgent need to develop new instrumentation compatible with live cell culture techniques and with scalable 3D formats relevant to organoids. This review discusses conventional planar approaches and emerging 3D microinstrumentation necessary for advanced organoid-machine interfaces. Specifically, this article surveys recently developed microinstrumentation, including 3D printed and curved microfluidics, 3D and fast-scan optical techniques, buckling and self-folding MEAs, 3D interfaces for electrochemical measurements, and 3D spatially controllable magnetic and acoustic technologies relevant to two-way information transfer with brain organoids. This article highlights key challenges that must be addressed for robust organoid culture and reliable 3D spatiotemporal information transfer.
Collapse
Affiliation(s)
- Devan Patel
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Saniya Shetty
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Chris Acha
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Itzy E Morales Pantoja
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Alice Zhao
- Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Derosh George
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - David H Gracias
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Chemistry, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, 21218, USA
- Sidney Kimmel Comprehensive Cancer Center (SKCCC), Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for MicroPhysiological Systems (MPS), Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| |
Collapse
|
28
|
Bai X, Yu C, Zhai J. Topological data analysis of the firings of a network of stochastic spiking neurons. Front Neural Circuits 2024; 17:1308629. [PMID: 38239606 PMCID: PMC10794443 DOI: 10.3389/fncir.2023.1308629] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/06/2023] [Indexed: 01/22/2024] Open
Abstract
Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.
Collapse
Affiliation(s)
| | - Chaojun Yu
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | | |
Collapse
|
29
|
Lehtimaki M, Paunonen L, Linne ML. Accelerating Neural ODEs Using Model Order Reduction. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:519-531. [PMID: 35617183 DOI: 10.1109/tnnls.2022.3175757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Embedding nonlinear dynamical systems into artificial neural networks is a powerful new formalism for machine learning. By parameterizing ordinary differential equations (ODEs) as neural network layers, these Neural ODEs are memory-efficient to train, process time series naturally, and incorporate knowledge of physical systems into deep learning (DL) models. However, the practical applications of Neural ODEs are limited due to long inference times because the outputs of the embedded ODE layers are computed numerically with differential equation solvers that can be computationally demanding. Here, we show that mathematical model order reduction (MOR) methods can be used for compressing and accelerating Neural ODEs by accurately simulating the continuous nonlinear dynamics in low-dimensional subspaces. We implement our novel compression method by developing Neural ODEs that integrate the necessary subspace-projection and interpolation operations as layers of the neural network. We validate our approach by comparing it to neuron pruning and singular value decomposition (SVD)-based weight truncation methods from the literature in image and time-series classification tasks. The methods are evaluated by acceleration versus accuracy when adjusting the level of compression. On this spectrum, we achieve a favorable balance over existing methods by using MOR when compressing a convolutional Neural ODE. In compressing a recurrent Neural ODE, SVD-based weight truncation yields good performance. Based on our results, our integration of MOR with Neural ODEs can facilitate efficient, dynamical system-driven DL in resource-constrained applications.
Collapse
|
30
|
Georgiev DD. Evolution of Consciousness. Life (Basel) 2023; 14:48. [PMID: 38255663 PMCID: PMC10817314 DOI: 10.3390/life14010048] [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: 09/06/2023] [Revised: 12/01/2023] [Accepted: 12/25/2023] [Indexed: 01/24/2024] Open
Abstract
The natural evolution of consciousness in different animal species mandates that conscious experiences are causally potent in order to confer any advantage in the struggle for survival. Any endeavor to construct a physical theory of consciousness based on emergence within the framework of classical physics, however, leads to causally impotent conscious experiences in direct contradiction to evolutionary theory since epiphenomenal consciousness cannot evolve through natural selection. Here, we review recent theoretical advances in describing sentience and free will as fundamental aspects of reality granted by quantum physical laws. Modern quantum information theory considers quantum states as a physical resource that endows quantum systems with the capacity to perform physical tasks that are classically impossible. Reductive identification of conscious experiences with the quantum information comprised in quantum brain states allows for causally potent consciousness that is capable of performing genuine choices for future courses of physical action. The consequent evolution of brain cortical networks contributes to increased computational power, memory capacity, and cognitive intelligence of the living organisms.
Collapse
Affiliation(s)
- Danko D Georgiev
- Institute for Advanced Study, 30 Vasilaki Papadopulu Str., 9010 Varna, Bulgaria
| |
Collapse
|
31
|
Dura-Bernal S, Griffith EY, Barczak A, O'Connell MN, McGinnis T, Moreira JVS, Schroeder CE, Lytton WW, Lakatos P, Neymotin SA. Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics. Cell Rep 2023; 42:113378. [PMID: 37925640 PMCID: PMC10727489 DOI: 10.1016/j.celrep.2023.113378] [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: 09/07/2022] [Revised: 09/05/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023] Open
Abstract
We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.
Collapse
Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Erica Y Griffith
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Annamaria Barczak
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Monica N O'Connell
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Tammy McGinnis
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University Medical Center, New York, NY, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Kings County Hospital Center, Brooklyn, NY, USA
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
| |
Collapse
|
32
|
Herrera B, Sajad A, Errington SP, Schall JD, Riera JJ. Cortical origin of theta error signals. Cereb Cortex 2023; 33:11300-11319. [PMID: 37804250 PMCID: PMC10690871 DOI: 10.1093/cercor/bhad367] [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: 06/23/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 10/09/2023] Open
Abstract
A multi-scale approach elucidated the origin of the error-related-negativity (ERN), with its associated theta-rhythm, and the post-error-positivity (Pe) in macaque supplementary eye field (SEF). Using biophysical modeling, synaptic inputs to a subpopulation of layer-3 (L3) and layer-5 (L5) pyramidal cells (PCs) were optimized to reproduce error-related spiking modulation and inter-spike intervals. The intrinsic dynamics of dendrites in L5 but not L3 error PCs generate theta rhythmicity with random phases. Saccades synchronized the phases of the theta-rhythm, which was magnified on errors. Contributions from error PCs to the laminar current source density (CSD) observed in SEF were negligible and could not explain the observed association between error-related spiking modulation in L3 PCs and scalp-EEG. CSD from recorded laminar field potentials in SEF was comprised of multipolar components, with monopoles indicating strong electro-diffusion, dendritic/axonal electrotonic current leakage outside SEF, or violations of the model assumptions. Our results also demonstrate the involvement of secondary cortical regions, in addition to SEF, particularly for the later Pe component. The dipolar component from the observed CSD paralleled the ERN dynamics, while the quadrupolar component paralleled the Pe. These results provide the most advanced explanation to date of the cellular mechanisms generating the ERN.
Collapse
Affiliation(s)
- Beatriz Herrera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
| | - Amirsaman Sajad
- Department of Psychology, Vanderbilt Vision Research Center, Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37203, United States
| | - Steven P Errington
- Department of Psychology, Vanderbilt Vision Research Center, Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN 37203, United States
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Jeffrey D Schall
- Centre for Vision Research, Vision: Science to Applications Program, Departments of Biology and Psychology, York University, Toronto, ON M3J 1P3, Canada
| | - Jorge J Riera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
| |
Collapse
|
33
|
Sorensen SA, Gouwens NW, Wang Y, Mallory M, Budzillo A, Dalley R, Lee B, Gliko O, Kuo HC, Kuang X, Mann R, Ahmadinia L, Alfiler L, Baftizadeh F, Baker K, Bannick S, Bertagnolli D, Bickley K, Bohn P, Brown D, Bomben J, Brouner K, Chen C, Chen K, Chvilicek M, Collman F, Daigle T, Dawes T, de Frates R, Dee N, DePartee M, Egdorf T, El-Hifnawi L, Enstrom R, Esposito L, Farrell C, Gala R, Glomb A, Gamlin C, Gary A, Goldy J, Gu H, Hadley K, Hawrylycz M, Henry A, Hill D, Hirokawa KE, Huang Z, Johnson K, Juneau Z, Kebede S, Kim L, Lee C, Lesnar P, Li A, Glomb A, Li Y, Liang E, Link K, Maxwell M, McGraw M, McMillen DA, Mukora A, Ng L, Ochoa T, Oldre A, Park D, Pom CA, Popovich Z, Potekhina L, Rajanbabu R, Ransford S, Reding M, Ruiz A, Sandman D, Siverts L, Smith KA, Stoecklin M, Sulc J, Tieu M, Ting J, Trinh J, Vargas S, Vumbaco D, Walker M, Wang M, Wanner A, Waters J, Williams G, Wilson J, Xiong W, Lein E, Berg J, Kalmbach B, Yao S, Gong H, Luo Q, Ng L, Sümbül U, Jarsky T, Yao Z, Tasic B, Zeng H. Connecting single-cell transcriptomes to projectomes in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.25.568393. [PMID: 38168270 PMCID: PMC10760188 DOI: 10.1101/2023.11.25.568393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.
Collapse
Affiliation(s)
| | | | - Yun Wang
- Allen Institute for Brain Science
| | | | | | | | | | | | | | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | | | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Kai Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science
| | | | | | | | | | | | | | | | | | | | | | | | - Hong Gu
- Allen Institute for Brain Science
| | | | | | | | | | | | - Zili Huang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | - Lisa Kim
- Allen Institute for Brain Science
| | | | | | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | | | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | - Zoran Popovich
- University of Washington, Dept. of Computer Science and Engineering
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Ed Lein
- Allen Institute for Brain Science
| | - Jim Berg
- Allen Institute for Brain Science
| | | | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Qingming Luo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Lydia Ng
- Allen Institute for Brain Science
| | | | | | | | | | | |
Collapse
|
34
|
El Hayek L, DeVries D, Gogate A, Aiken A, Kaur K, Chahrour MH. Disruption of the autism gene and chromatin regulator KDM5A alters hippocampal cell identity. SCIENCE ADVANCES 2023; 9:eadi0074. [PMID: 37992166 PMCID: PMC10664992 DOI: 10.1126/sciadv.adi0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Chromatin regulation plays a pivotal role in establishing and maintaining cellular identity and is one of the top pathways disrupted in autism spectrum disorder (ASD). The hippocampus, composed of distinct cell types, is often affected in patients with ASD. However, the specific hippocampal cell types and their transcriptional programs that are dysregulated in ASD are unknown. Using single-nucleus RNA sequencing, we show that the ASD gene, lysine demethylase 5A (KDM5A), regulates the development of specific subtypes of excitatory and inhibitory neurons. We found that KDM5A is essential for establishing hippocampal cell identity by controlling a differentiation switch early in development. Our findings define a role for the chromatin regulator KDM5A in establishing hippocampal cell identity and contribute to the emerging convergent mechanisms across ASD.
Collapse
Affiliation(s)
- Lauretta El Hayek
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Darlene DeVries
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ashlesha Gogate
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ariel Aiken
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Kiran Kaur
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Maria H. Chahrour
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| |
Collapse
|
35
|
Arnaudon A, Reva M, Zbili M, Markram H, Van Geit W, Kanari L. Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. iScience 2023; 26:108222. [PMID: 37953946 PMCID: PMC10638024 DOI: 10.1016/j.isci.2023.108222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/14/2023] Open
Abstract
Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.
Collapse
Affiliation(s)
- Alexis Arnaudon
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Maria Reva
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Mickael Zbili
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Lida Kanari
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| |
Collapse
|
36
|
Gonda S, Riedel C, Reiner A, Köhler I, Wahle P. Axons of cortical basket cells originating from dendrites develop higher local complexity than axons emerging from basket cell somata. Development 2023; 150:dev202305. [PMID: 37902086 PMCID: PMC10690106 DOI: 10.1242/dev.202305] [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: 08/28/2023] [Accepted: 10/24/2023] [Indexed: 10/31/2023]
Abstract
Neuronal differentiation is regulated by neuronal activity. Here, we analyzed dendritic and axonal growth of Basket cells (BCs) and non-Basket cells (non-BCs) using sparse transfection of channelrhodopsin-YFP and repetitive optogenetic stimulation in slice cultures of rat visual cortex. Neocortical interneurons often display axon-carrying dendrites (AcDs). We found that the AcDs of BCs and non-BCs were, on average, the most complex dendrites. Further, the AcD configuration had an influence on BC axonal development. Axons originating from an AcD formed denser arborizations with more terminal endings within the dendritic field of the parent cell. Intriguingly, this occurred already in unstimulated BCs, and complexity was not increased further by optogenetic stimulation. However, optogenetic stimulation exerted a growth-promoting effect on axons emerging from BC somata. The axons of non-BCs neither responded to the AcD configuration nor to the optogenetic stimulation. The results suggest that the formation of locally dense BC plexuses is regulated by spontaneous activity. Moreover, in the AcD configuration, the AcD and the axon it carries mutually support each other's growth.
Collapse
Affiliation(s)
- Steffen Gonda
- Developmental Neurobiology, Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| | - Christian Riedel
- Developmental Neurobiology, Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| | - Andreas Reiner
- Cellular Neurobiology, Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| | - Ina Köhler
- Developmental Neurobiology, Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| | - Petra Wahle
- Developmental Neurobiology, Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| |
Collapse
|
37
|
Reva M, Rössert C, Arnaudon A, Damart T, Mandge D, Tuncel A, Ramaswamy S, Markram H, Van Geit W. A universal workflow for creation, validation, and generalization of detailed neuronal models. PATTERNS (NEW YORK, N.Y.) 2023; 4:100855. [PMID: 38035193 PMCID: PMC10682753 DOI: 10.1016/j.patter.2023.100855] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/24/2023] [Accepted: 09/12/2023] [Indexed: 12/02/2023]
Abstract
Detailed single-neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools focus on a limited set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex. Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms. We use an evolutionary algorithm to optimize neuronal parameters to match the electrophysiological features extracted from experimental data. Then we validate the optimized models against additional stimuli and assess their generalizability on a population of similar morphologies. Compared to the state-of-the-art canonical models, our models show 5-fold improved generalizability. This versatile approach can be used to build robust models of any neuronal type.
Collapse
Affiliation(s)
- Maria Reva
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Christian Rössert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Alexis Arnaudon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Tanguy Damart
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Darshan Mandge
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Anıl Tuncel
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, School of Life Sciences, École polytechnique fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| |
Collapse
|
38
|
Chien VSC, Wang P, Maess B, Fishman Y, Knösche TR. Laminar neural dynamics of auditory evoked responses: Computational modeling of local field potentials in auditory cortex of non-human primates. Neuroimage 2023; 281:120364. [PMID: 37683810 DOI: 10.1016/j.neuroimage.2023.120364] [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: 01/09/2023] [Revised: 08/15/2023] [Accepted: 09/04/2023] [Indexed: 09/10/2023] Open
Abstract
Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms. In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition. In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.
Collapse
Affiliation(s)
- Vincent S C Chien
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Institute of Computer Science of the Czech Academy of Sciences, Czech Republic
| | - Peng Wang
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Institute of Psychology, University of Greifswald, Germany; Institute of Psychology, University of Regensburg, Germany
| | - Burkhard Maess
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany
| | - Yonatan Fishman
- Departments of Neurology and Neuroscience, Albert Einstein College of Medicine, USA
| | - Thomas R Knösche
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany.
| |
Collapse
|
39
|
Aberra AS, Wang R, Grill WM, Peterchev AV. Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry. Brain Stimul 2023; 16:1776-1791. [PMID: 38056825 PMCID: PMC10842743 DOI: 10.1016/j.brs.2023.11.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/06/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation modality that can alter cortical excitability. However, it remains unclear how the subcellular elements of different neuron types are polarized by specific electric field (E-field) distributions. OBJECTIVE To quantify neuronal polarization generated by tDCS in a multi-scale computational model. METHODS We embedded layer-specific, morphologically-realistic cortical neuron models in a finite element model of the E-field in a human head and simulated steady-state polarization generated by conventional primary-motor-cortex-supraorbital (M1-SO) and 4 × 1 high-definition (HD) tDCS. We quantified somatic, axonal, and dendritic polarization of excitatory pyramidal cells in layers 2/3, 5, and 6, as well as inhibitory interneurons in layers 1 and 4 of the hand knob. RESULTS Axonal and dendritic terminals were polarized more than the soma in all neurons, with peak axonal and dendritic polarization of 0.92 mV and 0.21 mV, respectively, compared to peak somatic polarization of 0.07 mV for 1.8 mA M1-SO stimulation. Both montages generated regions of depolarization and hyperpolarization beneath the M1 anode; M1-SO produced slightly stronger, more diffuse polarization peaking in the central sulcus, while 4 × 1 HD produced higher peak polarization in the gyral crown. The E-field component normal to the cortical surface correlated strongly with pyramidal neuron somatic polarization (R2>0.9), but exhibited weaker correlations with peak pyramidal axonal and dendritic polarization (R2:0.5-0.9) and peak polarization in all subcellular regions of interneurons (R2:0.3-0.6). Simulating polarization by uniform local E-field extracted at the soma approximated the spatial distribution of tDCS polarization but produced large errors in some regions (median absolute percent error: 7.9 %). CONCLUSIONS Polarization of pre- and postsynaptic compartments of excitatory and inhibitory cortical neurons may play a significant role in tDCS neuromodulation. These effects cannot be predicted from the E-field distribution alone but rather require calculation of the neuronal response.
Collapse
Affiliation(s)
- Aman S Aberra
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA.
| | - Ruochen Wang
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, NC, USA.
| | - Warren M Grill
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Neurobiology, School of Medicine, Duke University, NC, USA; Dept. of Neurosurgery, School of Medicine, Duke University, NC, USA.
| | - Angel V Peterchev
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, NC, USA; Dept. of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Neurosurgery, School of Medicine, Duke University, NC, USA.
| |
Collapse
|
40
|
Stiefel KM, Coggan JS. The energy challenges of artificial superintelligence. Front Artif Intell 2023; 6:1240653. [PMID: 37941679 PMCID: PMC10629395 DOI: 10.3389/frai.2023.1240653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/05/2023] [Indexed: 11/10/2023] Open
Abstract
We argue here that contemporary semiconductor computing technology poses a significant if not insurmountable barrier to the emergence of any artificial general intelligence system, let alone one anticipated by many to be "superintelligent". This limit on artificial superintelligence (ASI) emerges from the energy requirements of a system that would be more intelligent but orders of magnitude less efficient in energy use than human brains. An ASI would have to supersede not only a single brain but a large population given the effects of collective behavior on the advancement of societies, further multiplying the energy requirement. A hypothetical ASI would likely consume orders of magnitude more energy than what is available in highly-industrialized nations. We estimate the energy use of ASI with an equation we term the "Erasi equation", for the Energy Requirement for Artificial SuperIntelligence. Additional efficiency consequences will emerge from the current unfocussed and scattered developmental trajectory of AI research. Taken together, these arguments suggest that the emergence of an ASI is highly unlikely in the foreseeable future based on current computer architectures, primarily due to energy constraints, with biomimicry or other new technologies being possible solutions.
Collapse
Affiliation(s)
| | - Jay S. Coggan
- NeuroLinx Research Institute, La Jolla, CA, United States
| |
Collapse
|
41
|
Chartrand T, Dalley R, Close J, Goriounova NA, Lee BR, Mann R, Miller JA, Molnar G, Mukora A, Alfiler L, Baker K, Bakken TE, Berg J, Bertagnolli D, Braun T, Brouner K, Casper T, Csajbok EA, Dee N, Egdorf T, Enstrom R, Galakhova AA, Gary A, Gelfand E, Goldy J, Hadley K, Heistek TS, Hill D, Jorstad N, Kim L, Kocsis AK, Kruse L, Kunst M, Leon G, Long B, Mallory M, McGraw M, McMillen D, Melief EJ, Mihut N, Ng L, Nyhus J, Oláh G, Ozsvár A, Omstead V, Peterfi Z, Pom A, Potekhina L, Rajanbabu R, Rozsa M, Ruiz A, Sandle J, Sunkin SM, Szots I, Tieu M, Toth M, Trinh J, Vargas S, Vumbaco D, Williams G, Wilson J, Yao Z, Barzo P, Cobbs C, Ellenbogen RG, Esposito L, Ferreira M, Gouwens NW, Grannan B, Gwinn RP, Hauptman JS, Jarsky T, Keene CD, Ko AL, Koch C, Ojemann JG, Patel A, Ruzevick J, Silbergeld DL, Smith K, Sorensen SA, Tasic B, Ting JT, Waters J, de Kock CPJ, Mansvelder HD, Tamas G, Zeng H, Kalmbach B, Lein ES. Morphoelectric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex. Science 2023; 382:eadf0805. [PMID: 37824667 DOI: 10.1126/science.adf0805] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 09/09/2023] [Indexed: 10/14/2023]
Abstract
Neocortical layer 1 (L1) is a site of convergence between pyramidal-neuron dendrites and feedback axons where local inhibitory signaling can profoundly shape cortical processing. Evolutionary expansion of human neocortex is marked by distinctive pyramidal neurons with extensive L1 branching, but whether L1 interneurons are similarly diverse is underexplored. Using Patch-seq recordings from human neurosurgical tissue, we identified four transcriptomic subclasses with mouse L1 homologs, along with distinct subtypes and types unmatched in mouse L1. Subclass and subtype comparisons showed stronger transcriptomic differences in human L1 and were correlated with strong morphoelectric variability along dimensions distinct from mouse L1 variability. Accompanied by greater layer thickness and other cytoarchitecture changes, these findings suggest that L1 has diverged in evolution, reflecting the demands of regulating the expanded human neocortical circuit.
Collapse
Affiliation(s)
| | | | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Natalia A Goriounova
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Gabor Molnar
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Eva Adrienn Csajbok
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Anna A Galakhova
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Tim S Heistek
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nik Jorstad
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Agnes Katalin Kocsis
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Erica J Melief
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Norbert Mihut
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Gáspár Oláh
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Attila Ozsvár
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Zoltan Peterfi
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Alice Pom
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Marton Rozsa
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Joanna Sandle
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Ildiko Szots
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Martin Toth
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Sara Vargas
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Julia Wilson
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Pal Barzo
- Department of Neurosurgery, University of Szeged, Szeged, Hungary
| | | | | | | | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Benjamin Grannan
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Jason S Hauptman
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Anoop Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Jacob Ruzevick
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | | | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Christiaan P J de Kock
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Huib D Mansvelder
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Gabor Tamas
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| |
Collapse
|
42
|
Lee BR, Dalley R, Miller JA, Chartrand T, Close J, Mann R, Mukora A, Ng L, Alfiler L, Baker K, Bertagnolli D, Brouner K, Casper T, Csajbok E, Donadio N, Driessens SLW, Egdorf T, Enstrom R, Galakhova AA, Gary A, Gelfand E, Goldy J, Hadley K, Heistek TS, Hill D, Hou WH, Johansen N, Jorstad N, Kim L, Kocsis AK, Kruse L, Kunst M, León G, Long B, Mallory M, Maxwell M, McGraw M, McMillen D, Melief EJ, Molnar G, Mortrud MT, Newman D, Nyhus J, Opitz-Araya X, Ozsvár A, Pham T, Pom A, Potekhina L, Rajanbabu R, Ruiz A, Sunkin SM, Szöts I, Taskin N, Thyagarajan B, Tieu M, Trinh J, Vargas S, Vumbaco D, Waleboer F, Walling-Bell S, Weed N, Williams G, Wilson J, Yao S, Zhou T, Barzó P, Bakken T, Cobbs C, Dee N, Ellenbogen RG, Esposito L, Ferreira M, Gouwens NW, Grannan B, Gwinn RP, Hauptman JS, Hodge R, Jarsky T, Keene CD, Ko AL, Korshoej AR, Levi BP, Meier K, Ojemann JG, Patel A, Ruzevick J, Silbergeld DL, Smith K, Sørensen JC, Waters J, Zeng H, Berg J, Capogna M, Goriounova NA, Kalmbach B, de Kock CPJ, Mansvelder HD, Sorensen SA, Tamas G, Lein ES, Ting JT. Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex. Science 2023; 382:eadf6484. [PMID: 37824669 DOI: 10.1126/science.adf6484] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 09/08/2023] [Indexed: 10/14/2023]
Abstract
Human cortex transcriptomic studies have revealed a hierarchical organization of γ-aminobutyric acid-producing (GABAergic) neurons from subclasses to a high diversity of more granular types. Rapid GABAergic neuron viral genetic labeling plus Patch-seq (patch-clamp electrophysiology plus single-cell RNA sequencing) sampling in human brain slices was used to reliably target and analyze GABAergic neuron subclasses and individual transcriptomic types. This characterization elucidated transitions between PVALB and SST subclasses, revealed morphological heterogeneity within an abundant transcriptomic type, identified multiple spatially distinct types of the primate-specialized double bouquet cells (DBCs), and shed light on cellular differences between homologous mouse and human neocortical GABAergic neuron types. These results highlight the importance of multimodal phenotypic characterization for refinement of emerging transcriptomic cell type taxonomies and for understanding conserved and specialized cellular properties of human brain cell types.
Collapse
Affiliation(s)
- Brian R Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Thomas Chartrand
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lauren Alfiler
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Eva Csajbok
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | | | - Stan L W Driessens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Enstrom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anna A Galakhova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Emily Gelfand
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Dijon Hill
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wen-Hsien Hou
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | | | - Nik Jorstad
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Agnes Katalin Kocsis
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Kunst
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Gabriela León
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Erica J Melief
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Gabor Molnar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | | | - Dakota Newman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Attila Ozsvár
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | | | - Alice Pom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Ram Rajanbabu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Augustin Ruiz
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ildikó Szöts
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Naz Taskin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jessica Trinh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Sara Vargas
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Vumbaco
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Femke Waleboer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | | | - Natalie Weed
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julia Wilson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Zhou
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Pál Barzó
- Department of Neurosurgery, University of Szeged, 6725 Szeged, Hungary
| | - Trygve Bakken
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Charles Cobbs
- Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Richard G Ellenbogen
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Luke Esposito
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | | | - Benjamin Grannan
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Ryder P Gwinn
- Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Jason S Hauptman
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | | | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kaare Meier
- Department of Neurosurgery, Aarhus University Hospital, 8200 Aarhus, Denmark
- Department of Anesthesiology, Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Anoop Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Jacob Ruzevick
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jens Christian Sørensen
- Department of Neurosurgery, Aarhus University Hospital, 8200 Aarhus, Denmark
- Center for Experimental Neuroscience, Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Marco Capogna
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Huib D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | | | - Gabor Tamas
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| |
Collapse
|
43
|
Han X, Guo S, Ji N, Li T, Liu J, Ye X, Wang Y, Yun Z, Xiong F, Rong J, Liu D, Ma H, Wang Y, Huang Y, Zhang P, Wu W, Ding L, Hawrylycz M, Lein E, Ascoli GA, Xie W, Liu L, Zhang L, Peng H. Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy. SCIENCE ADVANCES 2023; 9:eadf3771. [PMID: 37824619 PMCID: PMC10569712 DOI: 10.1126/sciadv.adf3771] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 04/18/2023] [Indexed: 10/14/2023]
Abstract
Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.
Collapse
Affiliation(s)
- Xiaofeng Han
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Tian Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jian Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Xiangqiao Ye
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yi Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhixi Yun
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Feng Xiong
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jing Rong
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Di Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Hui Ma
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yujin Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yue Huang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhao Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liya Ding
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Krasnow Institute for Advanced Studies and Bioengineering Department, College of Engineering and Computing, George Mason University, Fairfax, VA, USA
| | - Wei Xie
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
- The Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Lijuan Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| |
Collapse
|
44
|
Roland PE. How far neuroscience is from understanding brains. Front Syst Neurosci 2023; 17:1147896. [PMID: 37867627 PMCID: PMC10585277 DOI: 10.3389/fnsys.2023.1147896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/31/2023] [Indexed: 10/24/2023] Open
Abstract
The cellular biology of brains is relatively well-understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about the brain mechanisms, I attempt here to identify major obstacles to progress in neuroscientific understanding of brains and central nervous systems. Most of the obstacles to our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task-related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the independent variable are insufficient to account for central nervous system activities. Spatial dynamics may be a practical solution. The general hypothesis that measurements of changes in fundamental brain variables, action potentials, transmitter releases, post-synaptic transmembrane currents, etc., propagating in central nervous systems reveal how they work, carries no additional assumptions. Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Furthermore, the facts that pooling and averaging of data destroy their underlying dynamics imply that single-trial designs and statistics are necessary.
Collapse
Affiliation(s)
- Per E. Roland
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
45
|
Chameh HM, Falby M, Movahed M, Arbabi K, Rich S, Zhang L, Lefebvre J, Tripathy SJ, De Pittà M, Valiante TA. Distinctive biophysical features of human cell-types: insights from studies of neurosurgically resected brain tissue. Front Synaptic Neurosci 2023; 15:1250834. [PMID: 37860223 PMCID: PMC10584155 DOI: 10.3389/fnsyn.2023.1250834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/21/2023] [Indexed: 10/21/2023] Open
Abstract
Electrophysiological characterization of live human tissue from epilepsy patients has been performed for many decades. Although initially these studies sought to understand the biophysical and synaptic changes associated with human epilepsy, recently, it has become the mainstay for exploring the distinctive biophysical and synaptic features of human cell-types. Both epochs of these human cellular electrophysiological explorations have faced criticism. Early studies revealed that cortical pyramidal neurons obtained from individuals with epilepsy appeared to function "normally" in comparison to neurons from non-epilepsy controls or neurons from other species and thus there was little to gain from the study of human neurons from epilepsy patients. On the other hand, contemporary studies are often questioned for the "normalcy" of the recorded neurons since they are derived from epilepsy patients. In this review, we discuss our current understanding of the distinct biophysical features of human cortical neurons and glia obtained from tissue removed from patients with epilepsy and tumors. We then explore the concept of within cell-type diversity and its loss (i.e., "neural homogenization"). We introduce neural homogenization to help reconcile the epileptogenicity of seemingly "normal" human cortical cells and circuits. We propose that there should be continued efforts to study cortical tissue from epilepsy patients in the quest to understand what makes human cell-types "human".
Collapse
Affiliation(s)
- Homeira Moradi Chameh
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Madeleine Falby
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Mandana Movahed
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Keon Arbabi
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Scott Rich
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Liang Zhang
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Jérémie Lefebvre
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
- Department of Mathematics, University of Toronto, Toronto, ON, Canada
| | - Shreejoy J. Tripathy
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Maurizio De Pittà
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada
- Department of Physiology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Basque Center for Applied Mathematics, Bilbao, Spain
- Faculty of Medicine, University of the Basque Country, Leioa, Spain
| | - Taufik A. Valiante
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
46
|
Cudone E, Lower AM, McDougal RA. Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories. PLoS Comput Biol 2023; 19:e1011548. [PMID: 37824576 PMCID: PMC10597496 DOI: 10.1371/journal.pcbi.1011548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/24/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Biophysically detailed simulations of neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the initial cell states. Additional realism from incorporating more biological detail is achieved at the cost of increasingly more states, more computational resources, and more modeling assumptions. We show that for both a point and morphologically-detailed cell model, the presence and timing of future action potentials is probabilistically well-characterized by the relative timings of a moderate number of recent events alone. Knowledge of initial conditions or full synaptic input history is not required. While model time constants, etc. impact the specifics, we demonstrate that for both individual spikes and sustained cellular activity, the uncertainty in spike response decreases as the number of known input events increases, to the point of approximate determinism. Further, we show cellular model states are reconstructable from ongoing synaptic events, despite unknown initial conditions. We propose that a strictly event-based modeling framework is capable of representing the complexity of cellular dynamics of the differential-equations models with significantly less per-cell state variables, thus offering a pathway toward utilizing modern data-driven modeling to scale up to larger network models while preserving individual cellular biophysics.
Collapse
Affiliation(s)
- Evan Cudone
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Amelia M. Lower
- Yale College, Yale University, New Haven, Connecticut, United States of America
| | - Robert A. McDougal
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, United States of America
- Wu Tsai Institute, Yale University, New Haven, Connecticut, United States of America
| |
Collapse
|
47
|
Kelley C, Antic SD, Carnevale NT, Kubie JL, Lytton WW. Simulations predict differing phase responses to excitation vs. inhibition in theta-resonant pyramidal neurons. J Neurophysiol 2023; 130:910-924. [PMID: 37609720 PMCID: PMC10648938 DOI: 10.1152/jn.00160.2023] [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: 04/20/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 08/24/2023] Open
Abstract
Rhythmic activity is ubiquitous in neural systems, with theta-resonant pyramidal neurons integrating rhythmic inputs in many cortical structures. Impedance analysis has been widely used to examine frequency-dependent responses of neuronal membranes to rhythmic inputs, but it assumes that the neuronal membrane is a linear system, requiring the use of small signals to stay in a near-linear regime. However, postsynaptic potentials are often large and trigger nonlinear mechanisms (voltage-gated ion channels). The goals of this work were to 1) develop an analysis method to evaluate membrane responses in this nonlinear domain and 2) explore phase relationships between rhythmic stimuli and subthreshold and spiking membrane potential (Vmemb) responses in models of theta-resonant pyramidal neurons. Responses in these output regimes were asymmetrical, with different phase shifts during hyperpolarizing and depolarizing half-cycles. Suprathreshold theta-rhythmic stimuli produced nonstationary Vmemb responses. Sinusoidal inputs produced "phase retreat": action potentials occurred progressively later in cycles of the input stimulus, resulting from adaptation. Sinusoidal current with increasing amplitude over cycles produced "phase advance": action potentials occurred progressively earlier. Phase retreat, phase advance, and subthreshold phase shifts were modulated by multiple ion channel conductances. Our results suggest differential responses of cortical neurons depending on the frequency of oscillatory input, which will play a role in neuronal responses to shifts in network state. We hypothesize that intrinsic cellular properties complement network properties and contribute to in vivo phase-shift phenomena such as phase precession, seen in place and grid cells, and phase roll, also observed in hippocampal CA1 neurons.NEW & NOTEWORTHY We augmented electrical impedance analysis to characterize phase shifts between large-amplitude current stimuli and nonlinear, asymmetric membrane potential responses. We predict different frequency-dependent phase shifts in response excitation vs. inhibition, as well as shifts in spike timing over multiple input cycles, in theta-resonant pyramidal neurons. We hypothesize that these effects contribute to navigation-related phenomena such as phase precession and phase roll. Our neuron-level hypothesis complements, rather than falsifies, prior network-level explanations of these phenomena.
Collapse
Affiliation(s)
- Craig Kelley
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York, United States
| | - Srdjan D Antic
- Institute of Systems Genomics, Neuroscience Department, University of Connecticut Health, Farmington, Connecticut, United States
| | - Nicholas T Carnevale
- Department of Neuroscience, Yale University, New Haven, Connecticut, United States
| | - John L Kubie
- The Robert F. Furchgott Center for Neural and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Cell Biology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
| | - William W Lytton
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York, United States
- The Robert F. Furchgott Center for Neural and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Neurology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Neurology, Kings County Hospital Center, Brooklyn, New York, United States
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland, United States
| |
Collapse
|
48
|
Zolboot N, Xiao Y, Du JX, Ghanem MM, Choi SY, Junn MJ, Zampa F, Huang Z, MacRae IJ, Lippi G. MicroRNAs are necessary for the emergence of Purkinje cell identity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.28.560023. [PMID: 37808721 PMCID: PMC10557743 DOI: 10.1101/2023.09.28.560023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Brain computations are dictated by the unique morphology and connectivity of neuronal subtypes, features established by closely timed developmental events. MicroRNAs (miRNAs) are critical for brain development, but current technologies lack the spatiotemporal resolution to determine how miRNAs instruct the steps leading to subtype identity. Here, we developed new tools to tackle this major gap. Fast and reversible miRNA loss-of-function revealed that miRNAs are necessary for cerebellar Purkinje cell (PC) differentiation, which previously appeared miRNA-independent, and resolved distinct miRNA critical windows in PC dendritogenesis and climbing fiber synaptogenesis, key determinants of PC identity. To identify underlying mechanisms, we generated a mouse model, which enables precise mapping of miRNAs and their targets in rare cell types. With PC-specific maps, we found that the PC-enriched miR-206 drives exuberant dendritogenesis and modulates synaptogenesis. Our results showcase vastly improved approaches for dissecting miRNA function and reveal that many critical miRNA mechanisms remain largely unexplored. Highlights Fast miRNA loss-of-function with T6B impairs postnatal Purkinje cell developmentReversible T6B reveals critical miRNA windows for dendritogenesis and synaptogenesisConditional Spy3-Ago2 mouse line enables miRNA-target network mapping in rare cellsPurkinje cell-enriched miR-206 regulates its unique dendritic and synaptic morphology.
Collapse
|
49
|
Varghese N, Moscoso B, Chavez A, Springer K, Ortiz E, Soh H, Santaniello S, Maheshwari A, Tzingounis AV. KCNQ2/3 Gain-of-Function Variants and Cell Excitability: Differential Effects in CA1 versus L2/3 Pyramidal Neurons. J Neurosci 2023; 43:6479-6494. [PMID: 37607817 PMCID: PMC10513074 DOI: 10.1523/jneurosci.0980-23.2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 08/24/2023] Open
Abstract
Gain-of-function (GOF) pathogenic variants in the potassium channels KCNQ2 and KCNQ3 lead to hyperexcitability disorders such as epilepsy and autism spectrum disorders. However, the underlying cellular mechanisms of how these variants impair forebrain function are unclear. Here, we show that the R201C variant in KCNQ2 has opposite effects on the excitability of two types of mouse pyramidal neurons of either sex, causing hyperexcitability in layer 2/3 (L2/3) pyramidal neurons and hypoexcitability in CA1 pyramidal neurons. Similarly, the homologous R231C variant in KCNQ3 leads to hyperexcitability in L2/3 pyramidal neurons and hypoexcitability in CA1 pyramidal neurons. However, the effects of KCNQ3 gain-of-function on excitability are specific to superficial CA1 pyramidal neurons. These findings reveal a new level of complexity in the function of KCNQ2 and KCNQ3 channels in the forebrain and provide a framework for understanding the effects of gain-of-function variants and potassium channels in the brain.SIGNIFICANCE STATEMENT KCNQ2/3 gain-of-function (GOF) variants lead to severe forms of neurodevelopmental disorders, but the mechanisms by which these channels affect neuronal activity are poorly understood. In this study, using a series of transgenic mice we demonstrate that the same KCNQ2/3 GOF variants can lead to either hyperexcitability or hypoexcitability in different types of pyramidal neurons [CA1 vs layer (L)2/3]. Additionally, we show that expression of the recurrent KCNQ2 GOF variant R201C in forebrain pyramidal neurons could lead to seizures and SUDEP. Our data suggest that the effects of KCNQ2/3 GOF variants depend on specific cell types and brain regions, possibly accounting for the diverse range of phenotypes observed in individuals with KCNQ2/3 GOF variants.
Collapse
Affiliation(s)
- Nissi Varghese
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
| | - Bruno Moscoso
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Ana Chavez
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Kristen Springer
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
| | - Erika Ortiz
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Heun Soh
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
| | - Sabato Santaniello
- Department of Biomedical Engineering and Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, Connecticut 06269
| | - Atul Maheshwari
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Anastasios V Tzingounis
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
| |
Collapse
|
50
|
Zhang Y, He G, Ma L, Liu X, Hjorth JJJ, Kozlov A, He Y, Zhang S, Kotaleski JH, Tian Y, Grillner S, Du K, Huang T. A GPU-based computational framework that bridges neuron simulation and artificial intelligence. Nat Commun 2023; 14:5798. [PMID: 37723170 PMCID: PMC10507119 DOI: 10.1038/s41467-023-41553-7] [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: 06/30/2022] [Accepted: 09/08/2023] [Indexed: 09/20/2023] Open
Abstract
Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.
Collapse
Affiliation(s)
- Yichen Zhang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Gan He
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Lei Ma
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, 100084, China
| | - Xiaofei Liu
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- School of Information Science and Engineering, Yunnan University, Kunming, 650500, China
| | - J J Johannes Hjorth
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
| | - Alexander Kozlov
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Yutao He
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Shenjian Zhang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Yonghong Tian
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- School of Electrical and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China
| | - Sten Grillner
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Kai Du
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| | - Tiejun Huang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, 100084, China
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China
| |
Collapse
|