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Tye KM, Miller EK, Taschbach FH, Benna MK, Rigotti M, Fusi S. Mixed selectivity: Cellular computations for complexity. Neuron 2024; 112:2289-2303. [PMID: 38729151 PMCID: PMC11257803 DOI: 10.1016/j.neuron.2024.04.017] [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: 12/11/2023] [Revised: 03/08/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024]
Abstract
The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.
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Affiliation(s)
- Kay M Tye
- Salk Institute for Biological Studies, La Jolla, CA, USA; Howard Hughes Medical Institute, La Jolla, CA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Kavli Institute for Brain and Mind, San Diego, CA, USA.
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Felix H Taschbach
- Salk Institute for Biological Studies, La Jolla, CA, USA; Biological Science Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Marcus K Benna
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | | | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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2
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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal Modeling of Cross-Sensory Visual Evoked Magnetoencephalography Responses in the Auditory Cortex. J Neurosci 2024; 44:e1119232024. [PMID: 38508715 PMCID: PMC11044114 DOI: 10.1523/jneurosci.1119-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/22/2024] Open
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by cross-sensory visual inputs. Intracortical laminar recordings in nonhuman primates have suggested a feedforward (FF) type profile for auditory evoked but feedback (FB) type for visual evoked activity in the auditory cortex. To test whether cross-sensory visual evoked activity in the auditory cortex is associated with FB inputs also in humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex regions of interest, auditory evoked response showed peaks at 37 and 90 ms and visual evoked response at 125 ms. The inputs to the auditory cortex were modeled through FF- and FB-type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which links cellular- and circuit-level mechanisms to MEG signals. HNN modeling suggested that the experimentally observed auditory response could be explained by an FF input followed by an FB input, whereas the cross-sensory visual response could be adequately explained by just an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
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3
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Herrera B, Schall JD, Riera JJ. Agranular frontal cortical microcircuit underlying cognitive control in macaques. Front Neural Circuits 2024; 18:1389110. [PMID: 38601266 PMCID: PMC11005916 DOI: 10.3389/fncir.2024.1389110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
The error-related negativity and an N2-component recorded over medial frontal cortex index core functions of cognitive control. While they are known to originate from agranular frontal areas, the underlying microcircuit mechanisms remain elusive. Most insights about microcircuit function have been derived from variations of the so-called canonical microcircuit model. These microcircuit architectures are based extensively on studies from granular sensory cortical areas in monkeys, cats, and rodents. However, evidence has shown striking cytoarchitectonic differences across species and differences in the functional relationships across cortical layers in agranular compared to granular sensory areas. In this minireview, we outline a tentative microcircuit model underlying cognitive control in the agranular frontal cortex of primates. The model incorporates the main GABAergic interneuron subclasses with specific laminar arrangements and target regions on pyramidal cells. We emphasize the role of layer 5 pyramidal cells in error and conflict detection. We offer several specific questions necessary for creating a specific intrinsic microcircuit model of the agranular frontal cortex.
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Affiliation(s)
- Beatriz Herrera
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jeffrey D. Schall
- Centre for Vision Research, Centre for Integrative & Applied Neuroscience, Department of Biology and Psychology, York University, Toronto, ON, Canada
| | - Jorge J. Riera
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
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4
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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.16.545371. [PMID: 37398025 PMCID: PMC10312796 DOI: 10.1101/2023.06.16.545371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Seppo P. Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
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5
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Tolley N, Rodrigues PLC, Gramfort A, Jones SR. Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. PLoS Comput Biol 2024; 20:e1011108. [PMID: 38408099 PMCID: PMC10919875 DOI: 10.1371/journal.pcbi.1011108] [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: 04/16/2023] [Revised: 03/07/2024] [Accepted: 02/10/2024] [Indexed: 02/28/2024] Open
Abstract
Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.
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Affiliation(s)
- Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
| | | | | | - Stephanie R. Jones
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
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6
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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.
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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
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7
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Adams NE, Jafarian A, Perry A, Rouse MA, Shaw AD, Murley AG, Cope TE, Bevan-Jones WR, Passamonti L, Street D, Holland N, Nesbitt D, Hughes LE, Friston KJ, Rowe JB. Neurophysiological consequences of synapse loss in progressive supranuclear palsy. Brain 2023; 146:2584-2594. [PMID: 36514918 PMCID: PMC10232290 DOI: 10.1093/brain/awac471] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/31/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from preclinical to clinical models of pathology and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations such as EEG and magnetoencephalography. Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by 11C-UCB-J PET, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson's syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild cortical atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using parametric empirical Bayesian inversion of a conductance-based canonical microcircuit model of magnetoencephalography data, we show that the inclusion of regional synaptic density-as a subject-specific prior on laminar-specific neuronal populations-markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology.
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Affiliation(s)
- Natalie E Adams
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Amirhossein Jafarian
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Alistair Perry
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Matthew A Rouse
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Alexander D Shaw
- Washington Singer Laboratories, University of Exeter, Exeter EX4 4QG, UK
| | - Alexander G Murley
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Thomas E Cope
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - W Richard Bevan-Jones
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Duncan Street
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Negin Holland
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - David Nesbitt
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Laura E Hughes
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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8
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Pinotsis DA, Fridman G, Miller EK. Cytoelectric Coupling: Electric fields sculpt neural activity and "tune" the brain's infrastructure. Prog Neurobiol 2023; 226:102465. [PMID: 37210066 DOI: 10.1016/j.pneurobio.2023.102465] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023]
Abstract
We propose and present converging evidence for the Cytoelectric Coupling Hypothesis: Electric fields generated by neurons are causal down to the level of the cytoskeleton. This could be achieved via electrodiffusion and mechanotransduction and exchanges between electrical, potential and chemical energy. Ephaptic coupling organizes neural activity, forming neural ensembles at the macroscale level. This information propagates to the neuron level, affecting spiking, and down to molecular level to stabilize the cytoskeleton, "tuning" it to process information more efficiently.
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Affiliation(s)
- Dimitris A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Gene Fridman
- Departments of Otolaryngology, Biomedical Engineering, and Electrical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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9
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Sanchez-Todo R, Bastos AM, Lopez-Sola E, Mercadal B, Santarnecchi E, Miller EK, Deco G, Ruffini G. A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings. Neuroimage 2023; 270:119938. [PMID: 36775081 DOI: 10.1016/j.neuroimage.2023.119938] [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: 08/27/2022] [Revised: 01/13/2023] [Accepted: 02/09/2023] [Indexed: 02/12/2023] Open
Abstract
Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.
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Affiliation(s)
- Roser Sanchez-Todo
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain; Center of Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - André M Bastos
- Department of Psychology and Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, United States
| | - Edmundo Lopez-Sola
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain
| | - Borja Mercadal
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain
| | - Emiliano Santarnecchi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gustavo Deco
- Center of Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Instituci'o Catalana de la Recerca i Estudis Avan,ats (ICREA), Passeig Llu's Companys 23, Barcelona, 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia
| | - Giulio Ruffini
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain; Starlab Barcelona, Av. Tibidabo 47b, 08035 Barcelona, Spain; Haskins Laboratories, 300 George Street, New Haven, CT, 06511, USA.
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10
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Todd J, Howard Z, Auksztulewicz R, Salisbury D. Computational Modeling of Oddball Sequence Processing Exposes Common and Differential Auditory Network Changes in First-Episode Schizophrenia-Spectrum Disorders and Schizophrenia. Schizophr Bull 2023; 49:407-416. [PMID: 36318221 PMCID: PMC10016421 DOI: 10.1093/schbul/sbac153] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND HYPOTHESIS Differences in sound relevance filtering in schizophrenia are proposed to represent a key index of biological changes in brain function in the illness. This study featured a computational modeling approach to test the hypothesis that processing differences might already be evident in first-episode, becoming more pronounced in the established illness. STUDY DESIGN Auditory event-related potentials to a typical oddball sequence (rare pitch deviations amongst regular sounds) were recorded from 90 persons with schizophrenia-spectrum disorders (40 first-episode schizophrenia-spectrum, 50 established illness) and age-matched healthy controls. The data were analyzed using dynamic causal modeling to identify the changes in effective connectivity that best explained group differences. STUDY RESULTS Group differences were linked to intrinsic (within brain region) connectivity changes. In activity-dependent measures these were restricted to the left auditory cortex in first-episode schizophrenia-spectrum but were more widespread in the established illness. Modeling suggested that both established illness and first-episode schizophrenia-spectrum groups expressed significantly lower inhibition of inhibitory interneuron activity and altered gain on superficial pyramidal cells with the data indicative of differences in both putative N-methyl-d-aspartate glutamate receptor activity-dependent plasticity and classic neuromodulation. CONCLUSIONS The study provides further support for the notion that examining the ability to alter responsiveness to structured sound sequences in schizophrenia and first-episode schizophrenia-spectrum could be informative to uncovering the nature and progression of changes in brain function during the illness. Furthermore, modeling suggested that limited differences present at first-episode schizophrenia-spectrum may become more expansive with illness progression.
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Affiliation(s)
- Juanita Todd
- School of Psychological Sciences, University of Newcastle, Australia.,Hunter Medical Research Foundation, Newcastle, Australia
| | - Zachary Howard
- School of Psychological Science, University of Western, Australia
| | - Ryszard Auksztulewicz
- European Neuroscience Institute, A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Göttingen, Germany
| | - Dean Salisbury
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA
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11
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Van de Steen F, Pinotsis D, Devos W, Colenbier N, Bassez I, Friston K, Marinazzo D. Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex. PLoS Comput Biol 2022; 18:e1009988. [PMID: 36574458 PMCID: PMC9829170 DOI: 10.1371/journal.pcbi.1009988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 01/09/2023] [Accepted: 12/16/2022] [Indexed: 12/29/2022] Open
Abstract
During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity-within the visual cortex-in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations-within each occipital source-were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.
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Affiliation(s)
- Frederik Van de Steen
- Department of Data Analysis, Ghent University, Ghent, Belgium
- Vrije Universiteit Brussel, AIMS laboratory, Brussel, Belgium
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- * E-mail:
| | - Dimitris Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City—University of London, London, United Kingdom
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Wouter Devos
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | | | - Iege Bassez
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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12
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Crofts JJ, Forrester M, Coombes S, O'Dea RD. Structure-function clustering in weighted brain networks. Sci Rep 2022; 12:16793. [PMID: 36202837 PMCID: PMC9537289 DOI: 10.1038/s41598-022-19994-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/07/2022] [Indexed: 11/09/2022] Open
Abstract
Functional networks, which typically describe patterns of activity taking place across the cerebral cortex, are widely studied in neuroscience. The dynamical features of these networks, and in particular their deviation from the relatively static structural network, are thought to be key to higher brain function. The interactions between such structural networks and emergent function, and the multimodal neuroimaging approaches and common analysis according to frequency band motivate a multilayer network approach. However, many such investigations rely on arbitrary threshold choices that convert dense, weighted networks to sparse, binary structures. Here, we generalise a measure of multiplex clustering to describe weighted multiplexes with arbitrarily-many layers. Moreover, we extend a recently-developed measure of structure-function clustering (that describes the disparity between anatomical connectivity and functional networks) to the weighted case. To demonstrate its utility we combine human connectome data with simulated neural activity and bifurcation analysis. Our results indicate that this new measure can extract neurologically relevant features not readily apparent in analogous single-layer analyses. In particular, we are able to deduce dynamical regimes under which multistable patterns of neural activity emerge. Importantly, these findings suggest a role for brain operation just beyond criticality to promote cognitive flexibility.
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Affiliation(s)
- Jonathan J Crofts
- Department of Physics and Mathematics, Nottingham Trent University, Nottingham, NG11 8NS, UK
| | - Michael Forrester
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.
| | - Stephen Coombes
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Reuben D O'Dea
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
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13
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Herrera B, Westerberg JA, Schall MS, Maier A, Woodman GF, Schall JD, Riera JJ. Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates. Neuroimage 2022; 263:119593. [PMID: 36031184 PMCID: PMC9968827 DOI: 10.1016/j.neuroimage.2022.119593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 08/16/2022] [Accepted: 08/24/2022] [Indexed: 10/31/2022] Open
Abstract
Event-related potentials (ERP) are among the most widely measured indices for studying human cognition. While their timing and magnitude provide valuable insights, their usefulness is limited by our understanding of their neural generators at the circuit level. Inverse source localization offers insights into such generators, but their solutions are not unique. To address this problem, scientists have assumed the source space generating such signals comprises a set of discrete equivalent current dipoles, representing the activity of small cortical regions. Based on this notion, theoretical studies have employed forward modeling of scalp potentials to understand how changes in circuit-level dynamics translate into macroscopic ERPs. However, experimental validation is lacking because it requires in vivo measurements of intracranial brain sources. Laminar local field potentials (LFP) offer a mechanism for estimating intracranial current sources. Yet, a theoretical link between LFPs and intracranial brain sources is missing. Here, we present a forward modeling approach for estimating mesoscopic intracranial brain sources from LFPs and predict their contribution to macroscopic ERPs. We evaluate the accuracy of this LFP-based representation of brain sources utilizing synthetic laminar neurophysiological measurements and then demonstrate the power of the approach in vivo to clarify the source of a representative cognitive ERP component. To that end, LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. We show that area V4 generates dipoles through layer-specific transsynaptic currents that biophysically recapitulate the ERP component through the detailed forward modeling. The constraints imposed on EEG production by this method also revealed an important dissociation between computational and biophysical contributors. As such, this approach represents an important bridge between laminar microcircuitry, through the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp.
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Affiliation(s)
- Beatriz Herrera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
| | - Jacob A. Westerberg
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States,Corresponding author. (J.A. Westerberg)
| | - Michelle S. Schall
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Alexander Maier
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Geoffrey F. Woodman
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Jeffrey D. Schall
- Centre for Vision Research, Departments of Biology and Psychology, Vision: Science to Applications Program, York University, Toronto, ON M3J 1P3, Canada
| | - Jorge J. Riera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
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14
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Pinotsis DA, Miller EK. Beyond dimension reduction: Stable electric fields emerge from and allow representational drift. Neuroimage 2022; 253:119058. [PMID: 35272022 DOI: 10.1016/j.neuroimage.2022.119058] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/03/2022] [Accepted: 03/03/2022] [Indexed: 01/18/2023] Open
Abstract
It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. We show that electric fields carry information about working memory content. The electric fields, in turn, can act as "guard rails" that funnel higher dimensional variable neural activity along stable lower dimensional routes. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.
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Affiliation(s)
- Dimitris A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City-University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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15
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Xie X, Cai C, Damasceno PF, Nagarajan SS, Raj A. Emergence of canonical functional networks from the structural connectome. Neuroimage 2021; 237:118190. [PMID: 34022382 PMCID: PMC8451304 DOI: 10.1016/j.neuroimage.2021.118190] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/05/2021] [Accepted: 05/18/2021] [Indexed: 01/21/2023] Open
Abstract
How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.
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Affiliation(s)
- Xihe Xie
- Department of Neuroscience, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10028, United States.
| | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, United States
| | - Pablo F Damasceno
- Center for Intelligent Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA, United States
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, United States.
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, United States.
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Abstract
The increased democratization of the creation, implementation, and attendance of academic conferences has been a serendipitous benefit of the movement toward virtual meetings. The Coronavirus Disease 2019 (COVID-19) pandemic has accelerated the transition to online conferences and, in parallel, their democratization, by necessity. This manifests not just in the mitigation of barriers to attending traditional physical conferences but also in the presentation of new, and more importantly attainable, opportunities for young scientists to carve out a niche in the landscape of academic meetings. Here, we describe an early “proof of principle” of this democratizing power via our experience organizing the Canadian Computational Neuroscience Spotlight (CCNS; crowdcast.io/e/CCNS), a free 2-day virtual meeting that was built entirely amid the pandemic using only virtual tools. While our experience was unique considering the obstacles faced in creating a conference during a pandemic, this was not the only factor differentiating both our experience and the resulting meeting from other contemporary online conferences. Specifically, CCNS was crafted entirely by early career researchers (ECRs) without any sponsors or partners, advertised primarily using social media and “word of mouth,” and designed specifically to highlight and engage trainees. From this experience, we have distilled “10 simple rules” as a blueprint for the design of new virtual academic meetings, especially in the absence of institutional support or partnerships, in this unprecedented environment. By highlighting the lessons learned in implementing our meeting under these arduous circumstances, we hope to encourage other young scientists to embrace this challenge, which would serve as a critical next step in further democratizing academic meetings.
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Pinotsis DA, Miller EK. Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity. Commun Biol 2020; 3:707. [PMID: 33239652 PMCID: PMC7688644 DOI: 10.1038/s42003-020-01438-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 10/22/2020] [Indexed: 11/09/2022] Open
Abstract
Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject's hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.
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Affiliation(s)
- Dimitris A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London, EC1V 0HB, UK.
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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18
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Pinotsis DA. Statistical decision theory and multiscale analyses of human brain data. J Neurosci Methods 2020; 346:108912. [PMID: 32835705 DOI: 10.1016/j.jneumeth.2020.108912] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only. NEW METHOD We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data. RESULTS We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6. COMPARISON WITH EXISTING METHODS This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed. CONCLUSIONS We discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.
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Affiliation(s)
- D A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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19
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Raj A, Cai C, Xie X, Palacios E, Owen J, Mukherjee P, Nagarajan S. Spectral graph theory of brain oscillations. Hum Brain Mapp 2020; 41:2980-2998. [PMID: 32202027 PMCID: PMC7336150 DOI: 10.1002/hbm.24991] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/13/2020] [Accepted: 02/16/2020] [Indexed: 11/10/2022] Open
Abstract
The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Chang Cai
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School of Medical SciencesWeill Cornell MedicineNew YorkNew YorkUSA
| | - Eva Palacios
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Julia Owen
- Department of RadiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Wei H, Jafarian A, Zeidman P, Litvak V, Razi A, Hu D, Friston KJ. Bayesian fusion and multimodal DCM for EEG and fMRI. Neuroimage 2020; 211:116595. [DOI: 10.1016/j.neuroimage.2020.116595] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 01/07/2020] [Accepted: 01/29/2020] [Indexed: 12/26/2022] Open
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Neymotin SA, Daniels DS, Caldwell B, McDougal RA, Carnevale NT, Jas M, Moore CI, Hines ML, Hämäläinen M, Jones SR. Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data. eLife 2020; 9:e51214. [PMID: 31967544 PMCID: PMC7018509 DOI: 10.7554/elife.51214] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/22/2020] [Indexed: 12/26/2022] Open
Abstract
Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.
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Affiliation(s)
- Samuel A Neymotin
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
- Center for Biomedical Imaging and NeuromodulationNathan S. Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Dylan S Daniels
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Blake Caldwell
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Robert A McDougal
- Department NeuroscienceYale UniversityNew HavenUnited States
- Department of BiostatisticsYale UniversityNew HavenUnited States
| | | | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUnited States
- Harvard Medical SchoolBostonUnited States
| | - Christopher I Moore
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Michael L Hines
- Department NeuroscienceYale UniversityNew HavenUnited States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUnited States
- Harvard Medical SchoolBostonUnited States
| | - Stephanie R Jones
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
- Center for Neurorestoration and NeurotechnologyProvidence VAMCProvidenceUnited States
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Bielczyk NZ, Llera A, Buitelaar JK, Glennon JC, Beckmann CF. Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions. Netw Neurosci 2019; 3:1009-1037. [PMID: 31637336 PMCID: PMC6779268 DOI: 10.1162/netn_a_00099] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 06/03/2019] [Indexed: 12/20/2022] Open
Abstract
Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.
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Affiliation(s)
- Natalia Z. Bielczyk
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Jeffrey C. Glennon
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
- Radboud University Nijmegen, Nijmegen, the Netherlands
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
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Towards a Unified View on Pathways and Functions of Neural Recurrent Processing. Trends Neurosci 2019; 42:589-603. [PMID: 31399289 DOI: 10.1016/j.tins.2019.07.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 06/21/2019] [Accepted: 07/11/2019] [Indexed: 11/20/2022]
Abstract
There are three neural feedback pathways to the primary visual cortex (V1): corticocortical, pulvinocortical, and cholinergic. What are the respective functions of these three projections? Possible functions range from contextual modulation of stimulus processing and feedback of high-level information to predictive processing (PP). How are these functions subserved by different pathways and can they be integrated into an overarching theoretical framework? We propose that corticocortical and pulvinocortical connections are involved in all three functions, whereas the role of cholinergic projections is limited by their slow response to stimuli. PP provides a broad explanatory framework under which stimulus-context modulation and high-level processing are subsumed, involving multiple feedback pathways that provide mechanisms for inferring and interpreting what sensory inputs are about.
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A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks. Brain Topogr 2018; 32:28-65. [PMID: 30076488 DOI: 10.1007/s10548-018-0666-3] [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: 11/08/2017] [Accepted: 07/28/2018] [Indexed: 10/28/2022]
Abstract
Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).
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Madi MK, Karameh FN. Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling. J Neural Eng 2018; 15:046028. [PMID: 29749350 DOI: 10.1088/1741-2552/aac3f7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. APPROACH We herein introduce an adaptive framework in which optimal input design is integrated with square root cubature Kalman filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. MAIN RESULTS For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 ms in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. SIGNIFICANCE Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications.
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Affiliation(s)
- Mahmoud K Madi
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
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26
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Focal versus distributed temporal cortex activity for speech sound category assignment. Proc Natl Acad Sci U S A 2018; 115:E1299-E1308. [PMID: 29363598 PMCID: PMC5819402 DOI: 10.1073/pnas.1714279115] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
When listening to speech, phonemes are represented in a distributed fashion in our temporal and prefrontal cortices. How these representations are selected in a phonemic decision context, and in particular whether distributed or focal neural information is required for explicit phoneme recognition, is unclear. We hypothesized that focal and early neural encoding of acoustic signals is sufficiently informative to access speech sound representations and permit phoneme recognition. We tested this hypothesis by combining a simple speech-phoneme categorization task with univariate and multivariate analyses of fMRI, magnetoencephalography, intracortical, and clinical data. We show that neural information available focally in the temporal cortex prior to decision-related neural activity is specific enough to account for human phonemic identification. Percepts and words can be decoded from distributed neural activity measures. However, the existence of widespread representations might conflict with the more classical notions of hierarchical processing and efficient coding, which are especially relevant in speech processing. Using fMRI and magnetoencephalography during syllable identification, we show that sensory and decisional activity colocalize to a restricted part of the posterior superior temporal gyrus (pSTG). Next, using intracortical recordings, we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine decoding was possible from neuronal activity sampled across different regions of the temporal and frontal lobes, despite weak or absent sensory or decision-related responses. These findings show that speech-sound categorization relies on an efficient readout of focal pSTG neural activity, while more distributed activity patterns, although classifiable by machine learning, instead reflect collateral processes of sensory perception and decision.
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