1
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Hepburn I, Lallouette J, Chen W, Gallimore AR, Nagasawa-Soeda SY, De Schutter E. Vesicle and reaction-diffusion hybrid modeling with STEPS. Commun Biol 2024; 7:573. [PMID: 38750123 PMCID: PMC11096338 DOI: 10.1038/s42003-024-06276-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 05/01/2024] [Indexed: 05/18/2024] Open
Abstract
Vesicles carry out many essential functions within cells through the processes of endocytosis, exocytosis, and passive and active transport. This includes transporting and delivering molecules between different parts of the cell, and storing and releasing neurotransmitters in neurons. To date, computational simulation of these key biological players has been rather limited and has not advanced at the same pace as other aspects of cell modeling, restricting the realism of computational models. We describe a general vesicle modeling tool that has been designed for wide application to a variety of cell models, implemented within our software STochastic Engine for Pathway Simulation (STEPS), a stochastic reaction-diffusion simulator that supports realistic reconstructions of cell tissue in tetrahedral meshes. The implementation is validated in an extensive test suite, parallel performance is demonstrated in a realistic synaptic bouton model, and example models are visualized in a Blender extension module.
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Affiliation(s)
- Iain Hepburn
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Okinawa, Japan
| | - Jules Lallouette
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Okinawa, Japan
| | - Weiliang Chen
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Okinawa, Japan
| | - Andrew R Gallimore
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Okinawa, Japan
| | - Sarah Y Nagasawa-Soeda
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Okinawa, Japan
| | - Erik De Schutter
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Okinawa, Japan.
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2
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Vignoud G, Venance L, Touboul JD. Anti-Hebbian plasticity drives sequence learning in striatum. Commun Biol 2024; 7:555. [PMID: 38724614 PMCID: PMC11082161 DOI: 10.1038/s42003-024-06203-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/17/2024] [Indexed: 05/12/2024] Open
Abstract
Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes.
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Affiliation(s)
- Gaëtan Vignoud
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, Paris, France
| | - Laurent Venance
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, Paris, France.
| | - Jonathan D Touboul
- Department of Mathematics and Volen National Center for Complex Systems, Brandeis University, Waltham, MA, USA.
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3
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Nicola W, Newton TR, Clopath C. The impact of spike timing precision and spike emission reliability on decoding accuracy. Sci Rep 2024; 14:10536. [PMID: 38719897 PMCID: PMC11078995 DOI: 10.1038/s41598-024-58524-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 04/01/2024] [Indexed: 05/12/2024] Open
Abstract
Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.
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Affiliation(s)
- Wilten Nicola
- University of Calgary, Calgary, Canada.
- Department of Cell Biology and Anatomy, Calgary, Canada.
- Hotchkiss Brain Institute, Calgary, Canada.
| | | | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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4
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Aitken K, Campagnola L, Garrett ME, Olsen SR, Mihalas S. Simple synaptic modulations implement diverse novelty computations. Cell Rep 2024; 43:114188. [PMID: 38713584 DOI: 10.1016/j.celrep.2024.114188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/09/2024] [Accepted: 04/17/2024] [Indexed: 05/09/2024] Open
Abstract
Detecting novelty is ethologically useful for an organism's survival. Recent experiments characterize how different types of novelty over timescales from seconds to weeks are reflected in the activity of excitatory and inhibitory neuron types. Here, we introduce a learning mechanism, familiarity-modulated synapses (FMSs), consisting of multiplicative modulations dependent on presynaptic or pre/postsynaptic neuron activity. With FMSs, network responses that encode novelty emerge under unsupervised continual learning and minimal connectivity constraints. Implementing FMSs within an experimentally constrained model of a visual cortical circuit, we demonstrate the generalizability of FMSs by simultaneously fitting absolute, contextual, and omission novelty effects. Our model also reproduces functional diversity within cell subpopulations, leading to experimentally testable predictions about connectivity and synaptic dynamics that can produce both population-level novelty responses and heterogeneous individual neuron signals. Altogether, our findings demonstrate how simple plasticity mechanisms within a cortical circuit structure can produce qualitatively distinct and complex novelty responses.
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Affiliation(s)
- Kyle Aitken
- Center for Data-Driven Discovery for Biology, Allen Institute, Seattle, WA 98109, USA.
| | | | | | - Shawn R Olsen
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Stefan Mihalas
- Center for Data-Driven Discovery for Biology, Allen Institute, Seattle, WA 98109, USA; Applied Mathematics, University of Washington, Seattle, WA 98195, USA
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5
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Lengyel M. Marr's three levels of analysis are useful as a framework for neuroscience. J Physiol 2024; 602:1911-1914. [PMID: 38628044 DOI: 10.1113/jp279549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/29/2023] [Indexed: 05/04/2024] Open
Affiliation(s)
- Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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6
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Pillow JW. Cross Talk opposing view: Marr's three levels of analysis are not useful as a framework for neuroscience. J Physiol 2024; 602:1915-1917. [PMID: 38628062 DOI: 10.1113/jp279550] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 02/14/2024] [Indexed: 05/04/2024] Open
Affiliation(s)
- Jonathan W Pillow
- Princeton Neuroscience Institute Princeton University, Princeton, New Jersey, USA
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7
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Schmidt M, Motta A, Sievers M, Helmstaedter M. RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nat Methods 2024; 21:908-913. [PMID: 38514779 PMCID: PMC11093750 DOI: 10.1038/s41592-024-02226-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.
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Affiliation(s)
- Martin Schmidt
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
| | - Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Meike Sievers
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
- Faculty of Science, Radboud University, Nijmegen, the Netherlands
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
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8
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Pillow JW. CrossTalk rebuttal. J Physiol 2024; 602:1921. [PMID: 38628075 DOI: 10.1113/jp286427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 03/19/2024] [Indexed: 05/04/2024] Open
Affiliation(s)
- Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA
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9
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Lengyel M. Crosstalk rebuttal: Marr's three levels of analysis are not useful as a framework for neuroscience. J Physiol 2024; 602:1919. [PMID: 38628068 DOI: 10.1113/jp286426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 02/19/2024] [Indexed: 05/04/2024] Open
Affiliation(s)
- Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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10
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Pachitariu M, Sridhar S, Pennington J, Stringer C. Spike sorting with Kilosort4. Nat Methods 2024; 21:914-921. [PMID: 38589517 PMCID: PMC11093732 DOI: 10.1038/s41592-024-02232-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/01/2024] [Indexed: 04/10/2024]
Abstract
Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.
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Affiliation(s)
| | - Shashwat Sridhar
- HHMI, Ashburn, VA, USA
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany
| | - Jacob Pennington
- HHMI, Ashburn, VA, USA
- Department of Mathematics, Washington State University, Vancouver, WA, USA
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11
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Hayashi S, Caron BA, Heinsfeld AS, Vinci-Booher S, McPherson B, Bullock DN, Bertò G, Niso G, Hanekamp S, Levitas D, Ray K, MacKenzie A, Avesani P, Kitchell L, Leong JK, Nascimento-Silva F, Koudoro S, Willis H, Jolly JK, Pisner D, Zuidema TR, Kurzawski JW, Mikellidou K, Bussalb A, Chaumon M, George N, Rorden C, Victory C, Bhatia D, Aydogan DB, Yeh FCF, Delogu F, Guaje J, Veraart J, Fischer J, Faskowitz J, Fabrega R, Hunt D, McKee S, Brown ST, Heyman S, Iacovella V, Mejia AF, Marinazzo D, Craddock RC, Olivetti E, Hanson JL, Garyfallidis E, Stanzione D, Carson J, Henschel R, Hancock DY, Stewart CA, Schnyer D, Eke DO, Poldrack RA, Bollmann S, Stewart A, Bridge H, Sani I, Freiwald WA, Puce A, Port NL, Pestilli F. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nat Methods 2024; 21:809-813. [PMID: 38605111 PMCID: PMC11093740 DOI: 10.1038/s41592-024-02237-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/05/2024] [Indexed: 04/13/2024]
Abstract
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.
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Affiliation(s)
| | - Bradley A Caron
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | - Sophia Vinci-Booher
- Indiana University, Bloomington, IN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Brent McPherson
- Indiana University, Bloomington, IN, USA
- McGill University, Montréal, Quebec, Canada
| | | | | | - Guiomar Niso
- Indiana University, Bloomington, IN, USA
- Cajal Institute, CSIC, Madrid, Spain
| | | | - Daniel Levitas
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | | | | | - Lindsey Kitchell
- Indiana University, Bloomington, IN, USA
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Josiah K Leong
- Indiana University, Bloomington, IN, USA
- University of Arkansas, Fayetteville, AR, USA
| | | | | | | | | | | | | | | | - Kyriaki Mikellidou
- University of Limassol, Nicosia, Cyprus
- University of Cyprus, Nicosia, Cyprus
| | - Aurore Bussalb
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | - Nathalie George
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | | | | | - Dogu Baran Aydogan
- University of Eastern Finland, Kuopio, Finland
- Aalto University School of Science, Espoo, Finland
| | | | - Franco Delogu
- Lawrence Technological University, Southfield, MI, USA
| | | | | | | | | | | | - David Hunt
- Indiana University, Bloomington, IN, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ashley Stewart
- University of Queensland, St Lucia, Queensland, Australia
| | | | - Ilaria Sani
- The Rockefeller University, New York, NY, USA
- University of Geneva, Geneva, Switzerland
| | | | - Aina Puce
- Indiana University, Bloomington, IN, USA
| | | | - Franco Pestilli
- Indiana University, Bloomington, IN, USA.
- The University of Texas, Austin, TX, USA.
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12
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Bégel V, Demos AP, Palmer C. Duet synchronization interventions affect social interactions. Sci Rep 2024; 14:9930. [PMID: 38688922 PMCID: PMC11061167 DOI: 10.1038/s41598-024-60485-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/23/2024] [Indexed: 05/02/2024] Open
Abstract
Humans' complex behavior, such as speech, music, or dance, requires us to coordinate our actions with external sounds as well as with social partners. The presence of a partner can influence individuals' synchronization, and, in turn, social connection with the partner may depend on the degree of synchronization. We manipulated the synchronization quality in intervention conditions to address the causal relationship between observed temporal synchrony and perceived social interaction. Pairs of musician and nonmusician participants first performed a turn-taking task consisting of alternating which partner tapped their melody in synchrony with a metronome (each tap generated the next tone in the melody). In two intervention conditions, participants attempted to synchronize their melodies simultaneously with their partner, either with normal auditory feedback (normal feedback) or randomly placed delayed feedback on 25% of melodic tones (delayed feedback). After each intervention, the turn-taking condition was repeated, and participants completed a questionnaire about connectedness, relationship, and feeling of synchronization with their partner. Results showed that partners' mean asynchronies were more negative following the delayed feedback intervention. In addition, nonmusician partners' tapping variability was larger following the delayed feedback intervention when they had the delayed feedback intervention first. Ratings of connectedness, relationship, and feeling of synchronization with their partner were reduced for all participants after the delayed feedback Intervention. We modeled participants' synchronization performance in the post-intervention turn-taking conditions using delay-coupling oscillator models. Reductions in synchronization performance after delayed feedback intervention were reflected in reduced coupling strength. These findings suggest that turn-taking synchronization performance and social connectedness are altered following short interventions that disrupt synchronization with a partner.
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Affiliation(s)
- Valentin Bégel
- Department of Psychology, McGill University, Montreal, Canada.
- Institut des Sciences du Sport-Santé de Paris (I3SP), Paris Cité University, 1 Rue Lacretelle, 75015, Paris, France.
| | - Alexander P Demos
- Department of Psychology, University of Illinois at Chicago, Chicago, USA
| | - Caroline Palmer
- Department of Psychology, McGill University, Montreal, Canada
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13
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Terada Y, Toyoizumi T. Chaotic neural dynamics facilitate probabilistic computations through sampling. Proc Natl Acad Sci U S A 2024; 121:e2312992121. [PMID: 38648479 PMCID: PMC11067032 DOI: 10.1073/pnas.2312992121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/13/2024] [Indexed: 04/25/2024] Open
Abstract
Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.
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Affiliation(s)
- Yu Terada
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
- The Institute for Physics of Intelligence, The University of Tokyo, Tokyo113-0033, Japan
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo113-8656, Japan
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14
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O’Reilly JA, Sunthornwiriya-Amon H, Aparprasith N, Kittichalao P, Chairojwong P, Klai-on T, Lannon EW. Blind source separation of event-related potentials using a recurrent neural network. bioRxiv 2024:2024.04.23.590794. [PMID: 38712076 PMCID: PMC11071372 DOI: 10.1101/2024.04.23.590794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Event-related potentials (ERPs) are a superposition of electric potential differences generated by neurophysiological activity associated with psychophysical events. Spatiotemporal dissociation of these signal sources can supplement conventional ERP analysis and improve source localization. However, results from established source separation methods applied to ERPs can be challenging to interpret. Hence, we have developed a recurrent neural network (RNN) method for blind source separation. The RNN transforms input step pulse signals representing events into corresponding ERP difference waveforms. Source waveforms are obtained from penultimate layer units and scalp maps are obtained from feed-forward output layer weights that project these source waveforms onto EEG electrode amplitudes. An interpretable, sparse source representation is achieved by incorporating L1 regularization of signals obtained from the penultimate layer of the network during training. This RNN method was applied to four ERP difference waveforms (MMN, N170, N400, P3) from the open-access ERP CORE database, and independent component analysis (ICA) was applied to the same data for comparison. The RNN decomposed these ERPs into eleven spatially and temporally separate sources that were less noisy, tended to be more ERP-specific, and were less similar to each other than ICA-derived sources. The RNN sources also had less ambiguity between source waveform amplitude, scalp potential polarity, and equivalent current dipole orientation than ICA sources. In conclusion, the proposed RNN blind source separation method can be effectively applied to grand-average ERP difference waves and holds promise for further development as a computational model of event-related neural signals.
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Affiliation(s)
- Jamie A. O’Reilly
- School of International & Interdisciplinary Engineering Programs, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Hassapong Sunthornwiriya-Amon
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Naradith Aparprasith
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Pannapa Kittichalao
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Pornnaphas Chairojwong
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Thanabodee Klai-on
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Edward W. Lannon
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 500 Pasteur Drive, Stanford, CA, United States of America
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15
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Hong R, Zheng T, Marra V, Yang D, Liu JK. Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration. J Neural Eng 2024; 21:021002. [PMID: 38621378 DOI: 10.1088/1741-2552/ad3eb4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.
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Affiliation(s)
- Rongqi Hong
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Tingting Zheng
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | | | - Dongping Yang
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Jian K Liu
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
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16
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Garlichs A, Blank H. Prediction error processing and sharpening of expected information across the face-processing hierarchy. Nat Commun 2024; 15:3407. [PMID: 38649694 PMCID: PMC11035707 DOI: 10.1038/s41467-024-47749-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
The perception and neural processing of sensory information are strongly influenced by prior expectations. The integration of prior and sensory information can manifest through distinct underlying mechanisms: focusing on unexpected input, denoted as prediction error (PE) processing, or amplifying anticipated information via sharpened representation. In this study, we employed computational modeling using deep neural networks combined with representational similarity analyses of fMRI data to investigate these two processes during face perception. Participants were cued to see face images, some generated by morphing two faces, leading to ambiguity in face identity. We show that expected faces were identified faster and perception of ambiguous faces was shifted towards priors. Multivariate analyses uncovered evidence for PE processing across and beyond the face-processing hierarchy from the occipital face area (OFA), via the fusiform face area, to the anterior temporal lobe, and suggest sharpened representations in the OFA. Our findings support the proposition that the brain represents faces grounded in prior expectations.
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Affiliation(s)
- Annika Garlichs
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany.
| | - Helen Blank
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany.
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17
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Kivelev J, Saarenpää I, Karlsson A, Crisafulli P, Musciotto F, Piilo J, Mantegna RN. Complex networks approach to study comorbidities in patients with unruptured intracranial aneurysms. Sci Rep 2024; 14:9175. [PMID: 38649696 PMCID: PMC11035559 DOI: 10.1038/s41598-024-59919-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
The role of complex network analysis in patients with diagnosis of unruptured intracranial aneurysm is unexplored. The objective of this study is to assess the applicability of this methodology in aneurysm patients. We retrospectively analyze comprehensive unbiased local digital data of a large number of patients treated for any reason between January 2004 and July 2019. We apply an age-cohort approach to a total of 628,831 patients and construct the diagnostic history of each patient-and include the information how old the patient was when diagnosed for the first time with each diagnosis coded according to International Classification of Diseases. For each cohort of age within a 10 year interval and for each gender, we construct a statistically validated comorbidity network and focused on crucial comorbidity links that the aneurysm code has to other disease codes within the whole network. For all cohorts of different age and gender, the analysis shows that 267 diagnose codes have nearest neighbour statistically validated links to unruptured aneurysm ICD code. Among the 267 comorbidities, 204 (76%) were found in patients aged from 40 to 69-years old. Patterns of connectivity with aneurysms were found for smoking, hypertension, chronic obstructive pulmonary disease, dyslipidemia, and mood disorders. A few uncommon connections are also detected in cohorts of female patients. Our study explored the applicability of network analysis and statistical validation in aneurysm observational study.
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Affiliation(s)
- Juri Kivelev
- Department of Neurosurgery, Turku University Hospital, Turku, Finland.
| | - Ilkka Saarenpää
- Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | | | - Paride Crisafulli
- Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, Italy
- Instituto de Fısica Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), 07122, Palma de Mallorca, Spain
| | - Federico Musciotto
- Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, Italy
| | - Jyrki Piilo
- Department of Physics and Astronomy, University of Turku, Turku, Finland
| | - Rosario N Mantegna
- Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, Italy
- Complexity Science Hub, Vienna, Austria
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18
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Fei Y, Wu Q, Zhao S, Song K, Han J, Liu C. Diverse and asymmetric patterns of single-neuron projectome in regulating interhemispheric connectivity. Nat Commun 2024; 15:3403. [PMID: 38649683 PMCID: PMC11035633 DOI: 10.1038/s41467-024-47762-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 04/11/2024] [Indexed: 04/25/2024] Open
Abstract
The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric "heterogeneity" to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication.
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Affiliation(s)
- Yao Fei
- School of Automation, Northwestern Polytechnical University, Xi'an, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qihang Wu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
- Research & Development, Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.
| | - Kun Song
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
- Research & Development, Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.
| | - Cirong Liu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, China.
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19
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She X, Nix KC, Cline CC, Qi W, Tugin S, He Z, Baumer FM. Stability of transcranial magnetic stimulation electroencephalogram evoked potentials in pediatric epilepsy. Sci Rep 2024; 14:9045. [PMID: 38641629 PMCID: PMC11031596 DOI: 10.1038/s41598-024-59468-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/11/2024] [Indexed: 04/21/2024] Open
Abstract
Transcranial magnetic stimulation paired with electroencephalography (TMS-EEG) can measure local excitability and functional connectivity. To address trial-to-trial variability, responses to multiple TMS pulses are recorded to obtain an average TMS evoked potential (TEP). Balancing adequate data acquisition to establish stable TEPs with feasible experimental duration is critical when applying TMS-EEG to clinical populations. Here we aim to investigate the minimum number of pulses (MNP) required to achieve stable TEPs in children with epilepsy. Eighteen children with Self-Limited Epilepsy with Centrotemporal Spikes, a common epilepsy arising from the motor cortices, underwent multiple 100-pulse blocks of TMS to both motor cortices over two days. TMS was applied at 120% of resting motor threshold (rMT) up to a maximum of 100% maximum stimulator output. The average of all 100 pulses was used as a "gold-standard" TEP to which we compared "candidate" TEPs obtained by averaging subsets of pulses. We defined TEP stability as the MNP needed to achieve a concordance correlation coefficient of 80% between the candidate and "gold-standard" TEP. We additionally assessed whether experimental or clinical factors affected TEP stability. Results show that stable TEPs can be derived from fewer than 100 pulses, a number typically used for designing TMS-EEG experiments. The early segment (15-80 ms) of the TEP was less stable than the later segment (80-350 ms). Global mean field amplitude derived from all channels was less stable than local TEP derived from channels overlying the stimulated site. TEP stability did not differ depending on stimulated hemisphere, block order, or antiseizure medication use, but was greater in older children. Stimulation administered with an intensity above the rMT yielded more stable local TEPs. Studies of TMS-EEG in pediatrics have been limited by the complexity of experimental set-up and time course. This study serves as a critical starting point, demonstrating the feasibility of designing efficient TMS-EEG studies that use a relatively small number of pulses to study pediatric epilepsy and potentially other pediatric groups.
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Affiliation(s)
- Xiwei She
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Kerry C Nix
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Christopher C Cline
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Wendy Qi
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Sergei Tugin
- Department of Neurology, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Zihuai He
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Fiona M Baumer
- Department of Neurology, Stanford University, Stanford, CA, USA.
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20
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Kuai C, Pu J, Wang D, Tan Z, Wang Y, Xue SW. The association between gray matter volume in the hippocampal subfield and antidepressant efficacy mediated by abnormal dynamic functional connectivity. Sci Rep 2024; 14:8940. [PMID: 38637536 PMCID: PMC11026377 DOI: 10.1038/s41598-024-56866-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/12/2024] [Indexed: 04/20/2024] Open
Abstract
An abnormality of structures and functions in the hippocampus may have a key role in the pathophysiology of major depressive disorder (MDD). However, it is unclear whether structure factors of the hippocampus effectively impact antidepressant responses by hippocampal functional activity in MDD patients. We collected longitudinal data from 36 MDD patients before and after a 3-month course of antidepressant pharmacotherapy. Additionally, we obtained baseline data from 43 healthy controls matched for sex and age. Using resting-state functional magnetic resonance imaging (rs-fMRI), we estimated the dynamic functional connectivity (dFC) of the hippocampal subregions using a sliding-window method. The gray matter volume was calculated using voxel-based morphometry (VBM). The results indicated that patients with MDD exhibited significantly lower dFC of the left rostral hippocampus (rHipp.L) with the right precentral gyrus, left superior temporal gyrus and left postcentral gyrus compared to healthy controls at baseline. In MDD patients, the dFC of the rHipp.L with right precentral gyrus at baseline was correlated with both the rHipp.L volume and HAMD remission rate, and also mediated the effects of the rHipp.L volume on antidepressant performance. Our findings suggested that the interaction between hippocampal structure and functional activity might affect antidepressant performance, which provided a novel insight into the hippocampus-related neurobiological mechanism of MDD.
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Affiliation(s)
- Changxiao Kuai
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China
| | - Jiayong Pu
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China
| | - Donglin Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China.
| | - Zhonglin Tan
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People's Republic of China
| | - Yan Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
| | - Shao-Wei Xue
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China.
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China.
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21
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Tustison NJ, Yassa MA, Rizvi B, Cook PA, Holbrook AJ, Sathishkumar MT, Tustison MG, Gee JC, Stone JR, Avants BB. ANTsX neuroimaging-derived structural phenotypes of UK Biobank. Sci Rep 2024; 14:8848. [PMID: 38632390 PMCID: PMC11024129 DOI: 10.1038/s41598-024-59440-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
UK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Schmahmann anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization.
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Affiliation(s)
- Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA.
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA.
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Batool Rizvi
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew J Holbrook
- Department of Biostatistics, University of California, Los Angeles, CA, USA
| | | | | | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - James R Stone
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Brian B Avants
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
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22
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Costa F, Schaft EV, Huiskamp G, Aarnoutse EJ, Van't Klooster MA, Krayenbühl N, Ramantani G, Zijlmans M, Indiveri G, Sarnthein J. Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework. Nat Commun 2024; 15:3255. [PMID: 38627406 PMCID: PMC11021517 DOI: 10.1038/s41467-024-47495-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman's ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.
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Affiliation(s)
- Filippo Costa
- Klinik für Neurochirurgie, Universitätsspital Zürich und Universität Zürich, Zürich, Switzerland.
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Eline V Schaft
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Geertjan Huiskamp
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maryse A Van't Klooster
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Niklaus Krayenbühl
- Division of Pediatric Neurosurgery, University Children's Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- Division of Pediatric Neurosurgery, University Children's Hospital Zurich and University of Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital Zürich und Universität Zürich, Zürich, Switzerland.
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland.
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23
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Subramoney A, Bellec G, Scherr F, Legenstein R, Maass W. Fast learning without synaptic plasticity in spiking neural networks. Sci Rep 2024; 14:8557. [PMID: 38609429 PMCID: PMC11015027 DOI: 10.1038/s41598-024-55769-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/27/2024] [Indexed: 04/14/2024] Open
Abstract
Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to reproduce fast learning capabilities of the brain in spiking neural networks. Biological data suggest that a synergy of synaptic plasticity on a slow time scale with network dynamics on a faster time scale is responsible for fast learning capabilities of the brain. We show here that a suitable orchestration of this synergy between synaptic plasticity and network dynamics does in fact reproduce fast learning capabilities of generic recurrent networks of spiking neurons. This points to the important role of recurrent connections in spiking networks, since these are necessary for enabling salient network dynamics. We show more specifically that the proposed synergy enables synaptic weights to encode more general information such as priors and task structures, since moment-to-moment processing of new information can be delegated to the network dynamics.
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Affiliation(s)
- Anand Subramoney
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
- Department of Computer Science, Royal Holloway University of London, Egham, UK
| | - Guillaume Bellec
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
- Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Franz Scherr
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Robert Legenstein
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Wolfgang Maass
- Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.
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24
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Basti A, Nolte G, Guidotti R, Ilmoniemi RJ, Romani GL, Pizzella V, Marzetti L. A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data. Sci Rep 2024; 14:8461. [PMID: 38605061 PMCID: PMC11009359 DOI: 10.1038/s41598-024-57014-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods.
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Affiliation(s)
- Alessio Basti
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy.
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, 02150, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, 00029, Helsinki, Finland
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
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25
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Myrov V, Siebenhühner F, Juvonen JJ, Arnulfo G, Palva S, Palva JM. Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture. Commun Biol 2024; 7:405. [PMID: 38570628 PMCID: PMC10991572 DOI: 10.1038/s42003-024-06083-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.
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Affiliation(s)
- Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki, Finland
| | - Joonas J Juvonen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, Genoa, Italy
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
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26
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Greene P, Bastian AJ, Schieber MH, Sarma SV. Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system. Proc Natl Acad Sci U S A 2024; 121:e2319313121. [PMID: 38551834 PMCID: PMC10998569 DOI: 10.1073/pnas.2319313121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/13/2024] [Indexed: 04/02/2024] Open
Abstract
Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error. We further show that complex reaches around obstacles can be achieved by augmenting our model with a path-planner based on via points. The path-planner revealed "avoidance" neurons that encode directions to reach around obstacles and "placement" neurons that make fine-tuned adjustments to via point placement. Our results demonstrate the surprising capability of computationally constrained systems and highlight interesting characteristics of the sensorimotor system.
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Affiliation(s)
- Patrick Greene
- Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD21218
| | - Amy J. Bastian
- Kennedy Krieger Institute, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - Marc H. Schieber
- Department of Neurology, University of Rochester, Rochester, NY14642
| | - Sridevi V. Sarma
- Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD21218
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine & Whiting School of Engineering, Baltimore, MD21218
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27
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Shahidi N, Franch M, Parajuli A, Schrater P, Wright A, Pitkow X, Dragoi V. Population coding of strategic variables during foraging in freely moving macaques. Nat Neurosci 2024; 27:772-781. [PMID: 38443701 PMCID: PMC11001579 DOI: 10.1038/s41593-024-01575-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/09/2024] [Indexed: 03/07/2024]
Abstract
Until now, it has been difficult to examine the neural bases of foraging in naturalistic environments because previous approaches have relied on restrained animals performing trial-based foraging tasks. Here we allowed unrestrained monkeys to freely interact with concurrent reward options while we wirelessly recorded population activity in the dorsolateral prefrontal cortex. The animals decided when and where to forage based on whether their prediction of reward was fulfilled or violated. This prediction was not solely based on a history of reward delivery, but also on the understanding that waiting longer improves the chance of reward. The task variables were continuously represented in a subspace of the high-dimensional population activity, and this compressed representation predicted the animal's subsequent choices better than the true task variables and as well as the raw neural activity. Our results indicate that monkeys' foraging strategies are based on a cortical model of reward dynamics as animals freely explore their environment.
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Affiliation(s)
- Neda Shahidi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
- Georg-Elias-Müller-Institute for Psychology, Georg August-Universität, Göttingen, Germany
- Cognitive Neuroscience Laboratory, German Primate Center, Göttingen, Germany
| | - Melissa Franch
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Arun Parajuli
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Paul Schrater
- Department of Computer Science, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Anthony Wright
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Neuroengineering Initiative, Rice University, Houston, TX, USA.
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28
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Kauf C, Tuckute G, Levy R, Andreas J, Fedorenko E. Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-Brain Similarity of fMRI Responses in the Language Network. Neurobiol Lang (Camb) 2024; 5:7-42. [PMID: 38645614 PMCID: PMC11025651 DOI: 10.1162/nol_a_00116] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/11/2023] [Indexed: 04/23/2024]
Abstract
Representations from artificial neural network (ANN) language models have been shown to predict human brain activity in the language network. To understand what aspects of linguistic stimuli contribute to ANN-to-brain similarity, we used an fMRI data set of responses to n = 627 naturalistic English sentences (Pereira et al., 2018) and systematically manipulated the stimuli for which ANN representations were extracted. In particular, we (i) perturbed sentences' word order, (ii) removed different subsets of words, or (iii) replaced sentences with other sentences of varying semantic similarity. We found that the lexical-semantic content of the sentence (largely carried by content words) rather than the sentence's syntactic form (conveyed via word order or function words) is primarily responsible for the ANN-to-brain similarity. In follow-up analyses, we found that perturbation manipulations that adversely affect brain predictivity also lead to more divergent representations in the ANN's embedding space and decrease the ANN's ability to predict upcoming tokens in those stimuli. Further, results are robust as to whether the mapping model is trained on intact or perturbed stimuli and whether the ANN sentence representations are conditioned on the same linguistic context that humans saw. The critical result-that lexical-semantic content is the main contributor to the similarity between ANN representations and neural ones-aligns with the idea that the goal of the human language system is to extract meaning from linguistic strings. Finally, this work highlights the strength of systematic experimental manipulations for evaluating how close we are to accurate and generalizable models of the human language network.
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Affiliation(s)
- Carina Kauf
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Greta Tuckute
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Roger Levy
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jacob Andreas
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
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29
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Jain S, Vo VA, Wehbe L, Huth AG. Computational Language Modeling and the Promise of In Silico Experimentation. Neurobiol Lang (Camb) 2024; 5:80-106. [PMID: 38645624 PMCID: PMC11025654 DOI: 10.1162/nol_a_00101] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 01/18/2023] [Indexed: 04/23/2024]
Abstract
Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus experiments. These approaches have complementary advantages which allow them to address distinct aspects of the neurobiology of language, but each approach also comes with drawbacks. Here we discuss a third paradigm-in silico experimentation using deep learning-based encoding models-that has been enabled by recent advances in cognitive computational neuroscience. This paradigm promises to combine the interpretability of controlled experiments with the generalizability and broad scope of natural stimulus experiments. We show four examples of simulating language neuroscience experiments in silico and then discuss both the advantages and caveats of this approach.
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Affiliation(s)
- Shailee Jain
- Department of Computer Science, University of Texas at Austin, Austin, TX, USA
| | - Vy A. Vo
- Brain-Inspired Computing Lab, Intel Labs, Hillsboro, OR, USA
| | - Leila Wehbe
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alexander G. Huth
- Department of Computer Science, University of Texas at Austin, Austin, TX, USA
- Department of Neuroscience, University of Texas at Austin, Austin, TX, USA
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30
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Jarne C. Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights. Front Syst Neurosci 2024; 18:1269190. [PMID: 38600907 PMCID: PMC11004305 DOI: 10.3389/fnsys.2024.1269190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
Training neural networks to perform different tasks is relevant across various disciplines. In particular, Recurrent Neural Networks (RNNs) are of great interest in Computational Neuroscience. Open-source frameworks dedicated to Machine Learning, such as Tensorflow and Keras have produced significant changes in the development of technologies that we currently use. This work contributes by comprehensively investigating and describing the application of RNNs for temporal processing through a study of a 3-bit Flip Flop memory implementation. We delve into the entire modeling process, encompassing equations, task parametrization, and software development. The obtained networks are meticulously analyzed to elucidate dynamics, aided by an array of visualization and analysis tools. Moreover, the provided code is versatile enough to facilitate the modeling of diverse tasks and systems. Furthermore, we present how memory states can be efficiently stored in the vertices of a cube in the dimensionally reduced space, supplementing previous results with a distinct approach.
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Affiliation(s)
- Cecilia Jarne
- Departamento de Ciencia y Tecnologia de la Universidad Nacional de Quilmes, Bernal, Quilmes, Buenos Aires, Argentina
- CONICET, Buenos Aires, Argentina
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
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31
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Qian P, Manubens-Gil L, Jiang S, Peng H. Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks. Cell Rep 2024; 43:113871. [PMID: 38451816 DOI: 10.1016/j.celrep.2024.113871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 01/08/2024] [Accepted: 02/09/2024] [Indexed: 03/09/2024] Open
Abstract
We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.
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Affiliation(s)
- Penghao Qian
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Linus Manubens-Gil
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
| | - Shengdian Jiang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Hanchuan Peng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
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32
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Singer B, Meling D, Hirsch-Hoffmann M, Michels L, Kometer M, Smigielski L, Dornbierer D, Seifritz E, Vollenweider FX, Scheidegger M. Psilocybin enhances insightfulness in meditation: a perspective on the global topology of brain imaging during meditation. Sci Rep 2024; 14:7211. [PMID: 38531905 PMCID: PMC10966054 DOI: 10.1038/s41598-024-55726-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/27/2024] [Indexed: 03/28/2024] Open
Abstract
In this study, for the first time, we explored a dataset of functional magnetic resonance images collected during focused attention and open monitoring meditation before and after a five-day psilocybin-assisted meditation retreat using a recently established approach, based on the Mapper algorithm from topological data analysis. After generating subject-specific maps for two groups (psilocybin vs. placebo, 18 subjects/group) of experienced meditators, organizational principles were uncovered using graph topological tools, including the optimal transport (OT) distance, a geometrically rich measure of similarity between brain activity patterns. This revealed characteristics of the topology (i.e. shape) in space (i.e. abstract space of voxels) and time dimension of whole-brain activity patterns during different styles of meditation and psilocybin-induced alterations. Most interestingly, we found that (psilocybin-induced) positive derealization, which fosters insightfulness specifically when accompanied by enhanced open-monitoring meditation, was linked to the OT distance between open-monitoring and resting state. Our findings suggest that enhanced meta-awareness through meditation practice in experienced meditators combined with potential psilocybin-induced positive alterations in perception mediate insightfulness. Together, these findings provide a novel perspective on meditation and psychedelics that may reveal potential novel brain markers for positive synergistic effects between mindfulness practices and psilocybin.
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Affiliation(s)
- Berit Singer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
| | - Daniel Meling
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Department of Psychosomatic Medicine and Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - Matthias Hirsch-Hoffmann
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, University Hospital Zurich, Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Michael Kometer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Lukasz Smigielski
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Dario Dornbierer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Franz X Vollenweider
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
| | - Milan Scheidegger
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Department of Neuroradiology, University Hospital Zurich, Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
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33
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Palacios ER, Chadderton P, Friston K, Houghton C. Cerebellar state estimation enables resilient coupling across behavioural domains. Sci Rep 2024; 14:6641. [PMID: 38503802 PMCID: PMC10951354 DOI: 10.1038/s41598-024-56811-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Cerebellar computations are necessary for fine behavioural control and may rely on internal models for estimation of behaviourally relevant states. Here, we propose that the central cerebellar function is to estimate how states interact with each other, and to use these estimates to coordinates extra-cerebellar neuronal dynamics underpinning a range of interconnected behaviours. To support this claim, we describe a cerebellar model for state estimation that includes state interactions, and link this model with the neuronal architecture and dynamics observed empirically. This is formalised using the free energy principle, which provides a dual perspective on a system in terms of both the dynamics of its physical-in this case neuronal-states, and the inferential process they entail. As a demonstration of this proposal, we simulate cerebellar-dependent synchronisation of whisking and respiration, which are known to be tightly coupled in rodents, as well as limb and tail coordination during locomotion. In summary, we propose that the ubiquitous involvement of the cerebellum in behaviour arises from its central role in precisely coupling behavioural domains.
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Affiliation(s)
- Ensor Rafael Palacios
- University of Bristol, School of Physiology Pharmacology and Neuroscience, Bristol, BS8 1TD, UK.
| | - Paul Chadderton
- University of Bristol, School of Physiology Pharmacology and Neuroscience, Bristol, BS8 1TD, UK
| | - Karl Friston
- UCL, Wellcome Centre for Human Neuroimaging, London, WC1N 3AR, UK
| | - Conor Houghton
- University of Bristol, Department of Computer Science, Bristol, BS8 1UB, UK
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34
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Zemliak V, Mayer J, Nieters P, Pipa G. Spike synchrony as a measure of Gestalt structure. Sci Rep 2024; 14:5910. [PMID: 38467630 PMCID: PMC10928224 DOI: 10.1038/s41598-024-54755-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 02/16/2024] [Indexed: 03/13/2024] Open
Abstract
The function of spike synchrony is debatable: some researchers view it as a mechanism for binding perceptual features, others - as a byproduct of brain activity. We argue for an alternative computational role: synchrony can estimate the prior probability of incoming stimuli. In V1, this can be achieved by comparing input with previously acquired visual experience, which is encoded in plastic horizontal intracortical connections. V1 connectivity structure can encode the acquired visual experience in the form of its aggregate statistics. Since the aggregate statistics of natural images tend to follow the Gestalt principles, we can assume that V1 is more often exposed to Gestalt-like stimuli, and this is manifested in its connectivity structure. At the same time, the connectivity structure has an impact on spike synchrony in V1. We used a spiking model with V1-like connectivity to demonstrate that spike synchrony reflects the Gestalt structure of the stimulus. We conducted simulation experiments with three Gestalt laws: proximity, similarity, and continuity, and found substantial differences in firing synchrony for stimuli with varying degrees of Gestalt-likeness. This allows us to conclude that spike synchrony indeed reflects the Gestalt structure of the stimulus, which can be interpreted as a mechanism for prior probability estimation.
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Affiliation(s)
- Viktoria Zemliak
- Institute of Cognitive Science, University of Osnabrück, 49074, Osnabrück, Germany.
| | - Julius Mayer
- Institute of Cognitive Science, University of Osnabrück, 49074, Osnabrück, Germany
| | - Pascal Nieters
- Institute of Cognitive Science, University of Osnabrück, 49074, Osnabrück, Germany
| | - Gordon Pipa
- Institute of Cognitive Science, University of Osnabrück, 49074, Osnabrück, Germany
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35
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Vazquez-Guerrero P, Tuladhar R, Psychalinos C, Elwakil A, Chacron MJ, Santamaria F. Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function. Sci Rep 2024; 14:5817. [PMID: 38461365 PMCID: PMC10925066 DOI: 10.1038/s41598-024-55784-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/27/2024] [Indexed: 03/11/2024] Open
Abstract
There is an increasing need to implement neuromorphic systems that are both energetically and computationally efficient. There is also great interest in using electric elements with memory, memelements, that can implement complex neuronal functions intrinsically. A feature not widely incorporated in neuromorphic systems is history-dependent action potential time adaptation which is widely seen in real cells. Previous theoretical work shows that power-law history dependent spike time adaptation, seen in several brain areas and species, can be modeled with fractional order differential equations. Here, we show that fractional order spiking neurons can be implemented using super-capacitors. The super-capacitors have fractional order derivative and memcapacitive properties. We implemented two circuits, a leaky integrate and fire and a Hodgkin-Huxley. Both circuits show power-law spiking time adaptation and optimal coding properties. The spiking dynamics reproduced previously published computer simulations. However, the fractional order Hodgkin-Huxley circuit showed novel dynamics consistent with criticality. We compared the responses of this circuit to recordings from neurons in the weakly-electric fish that have previously been shown to perform fractional order differentiation of their sensory input. The criticality seen in the circuit was confirmed in spontaneous recordings in the live fish. Furthermore, the circuit also predicted long-lasting stimulation that was also corroborated experimentally. Our work shows that fractional order memcapacitors provide intrinsic memory dependence that could allow implementation of computationally efficient neuromorphic devices. Memcapacitors are static elements that consume less energy than the most widely studied memristors, thus allowing the realization of energetically efficient neuromorphic devices.
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Affiliation(s)
- Patricia Vazquez-Guerrero
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, 78349, USA
| | - Rohisha Tuladhar
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, 78349, USA
| | | | - Ahmed Elwakil
- Department of Electrical and Computer Engineering, University of Sharjah, PO Box 27272, Sharjah, UAE
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, T2N 1N4, Canada
| | - Maurice J Chacron
- Department of Physiology, McGill University, Quebec, H3G 1Y6, Canada
| | - Fidel Santamaria
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, 78349, USA.
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36
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Chinichian N, Lindner M, Yanchuk S, Schwalger T, Schöll E, Berner R. Modeling brain network flexibility in networks of coupled oscillators: a feasibility study. Sci Rep 2024; 14:5713. [PMID: 38459077 PMCID: PMC10923875 DOI: 10.1038/s41598-024-55753-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.
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Affiliation(s)
- Narges Chinichian
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.
- Psychiatry Department, Charité-Universitätsmedizin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Michael Lindner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Mathematics, Humboldt Universität zu Berlin, Berlin, Germany
- School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Tilo Schwalger
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
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37
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Gonzalez JE, Nieto N, Brusco P, Gravano A, Kamienkowski JE. Speech-induced suppression during natural dialogues. Commun Biol 2024; 7:291. [PMID: 38459110 PMCID: PMC10923813 DOI: 10.1038/s42003-024-05945-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024] Open
Abstract
When engaged in a conversation, one receives auditory information from the other's speech but also from their own speech. However, this information is processed differently by an effect called Speech-Induced Suppression. Here, we studied brain representation of acoustic properties of speech in natural unscripted dialogues, using electroencephalography (EEG) and high-quality speech recordings from both participants. Using encoding techniques, we were able to reproduce a broad range of previous findings on listening to another's speech, and achieving even better performances when predicting EEG signal in this complex scenario. Furthermore, we found no response when listening to oneself, using different acoustic features (spectrogram, envelope, etc.) and frequency bands, evidencing a strong effect of SIS. The present work shows that this mechanism is present, and even stronger, during natural dialogues. Moreover, the methodology presented here opens the possibility of a deeper understanding of the related mechanisms in a wider range of contexts.
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Affiliation(s)
- Joaquin E Gonzalez
- Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de la Computación (Universidad de Buenos Aires - Consejo Nacional de Investigaciones Cientificas y Tecnicas), Buenos Aires, Argentina.
| | - Nicolás Nieto
- Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i) (Universidad Nacional del Litoral - Consejo Nacional de Investigaciones Cientificas y Tecnicas), Santa Fe, Argentina
- Instituto de Matemática Aplicada del Litoral, IMAL-UNL/CONICET, Santa Fe, Argentina
| | - Pablo Brusco
- Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Agustín Gravano
- Laboratorio de Inteligencia Artificial, Universidad Torcuato Di Tella, Buenos Aires, Argentina
- Escuela de Negocios, Universidad Torcuato Di Tella, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
| | - Juan E Kamienkowski
- Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de la Computación (Universidad de Buenos Aires - Consejo Nacional de Investigaciones Cientificas y Tecnicas), Buenos Aires, Argentina
- Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
- Maestria de Explotación de Datos y Descubrimiento del Conocimiento, Facultad de Ciencias Exactas y Naturales - Facultad de Ingenieria, Universidad de Buenos Aires, Buenos Aires, Argentina
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38
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Liebenow B, Jiang A, DiMarco EK, Sands LP, Moya-Mendez M, Laxton AW, Siddiqui MS, Ul Haq I, Kishida KT. Subjective feelings associated with expectations and rewards during risky decision-making in impulse control disorder. Sci Rep 2024; 14:4627. [PMID: 38438386 PMCID: PMC10912783 DOI: 10.1038/s41598-024-53076-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 01/27/2024] [Indexed: 03/06/2024] Open
Abstract
Impulse Control Disorder (ICD) in Parkinson's disease is a behavioral addiction induced by dopaminergic therapies, but otherwise unclear etiology. The current study investigates the interaction of reward processing variables, dopaminergic therapy, and risky decision-making and subjective feelings in patients with versus without ICD. Patients with (n = 18) and without (n = 12) ICD performed a risky decision-making task both 'on' and 'off' standard-of-care dopaminergic therapies (the task was performed on 2 different days with the order of on and off visits randomized for each patient). During each trial of the task, participants choose between two options, a gamble or a certain reward, and reported how they felt about decision outcomes. Subjective feelings of 'pleasure' are differentially driven by expectations of possible outcomes in patients with, versus without ICD. While off medication, the influence of expectations about risky-decisions on subjective feelings is reduced in patients with ICD versus without ICD. While on medication, the influence of expected outcomes in patients with ICD versus without ICD becomes similar. Computational modeling of behavior supports the idea that latent decision-making factors drive subjective feelings in patients with Parkinson's disease and that ICD status is associated with a change in the relationship between factors associated with risky behavior and subjective feelings about the experienced outcomes. Our results also suggest that dopaminergic medications modulate the impact expectations have on the participants' subjective reports. Altogether our results suggest that expectations about risky decisions may be decoupled from subjective feelings in patients with ICD, and that dopaminergic medications may reengage these circuits and increase emotional reactivity in patients with ICD.
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Affiliation(s)
- Brittany Liebenow
- Neuroscience Graduate Program, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Translational Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Angela Jiang
- Department of Translational Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Emily K DiMarco
- Neuroscience Graduate Program, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Translational Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - L Paul Sands
- Neuroscience Graduate Program, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Translational Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, 24016, USA
| | | | - Adrian W Laxton
- Department of Neurosurgery, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Mustafa S Siddiqui
- Department of Neurology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Ihtsham Ul Haq
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kenneth T Kishida
- Neuroscience Graduate Program, Wake Forest School of Medicine, Winston-Salem, NC, USA.
- Department of Translational Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA.
- Department of Neurosurgery, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
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39
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Matheson AMM, Lanz AJ, Medina AM, Licata AM, Currier TA, Syed MH, Nagel KI. Addendum: A neural circuit for wind-guided olfactory navigation. Nat Commun 2024; 15:1903. [PMID: 38429279 PMCID: PMC10907363 DOI: 10.1038/s41467-024-46225-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024] Open
Affiliation(s)
- Andrew M M Matheson
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
- Department of Biological Sciences, Columbia University, 600 Sherman Fairchild Center, New York, NY, 10027, USA
| | - Aaron J Lanz
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
| | - Ashley M Medina
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
| | - Al M Licata
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
| | - Timothy A Currier
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
- Center for Neural Science, NYU, New York, NY, 4 Washington Place, New York, NY, 10003, USA
- Department of Neurobiology, Stanford University, 299W. Campus Drive, Stanford, CA, 94305, USA
| | - Mubarak H Syed
- Department of Biology, 219 Yale Blvd NE, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Katherine I Nagel
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA.
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40
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Spens E, Burgess N. A generative model of memory construction and consolidation. Nat Hum Behav 2024; 8:526-543. [PMID: 38242925 PMCID: PMC10963272 DOI: 10.1038/s41562-023-01799-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 12/05/2023] [Indexed: 01/21/2024]
Abstract
Episodic memories are (re)constructed, share neural substrates with imagination, combine unique features with schema-based predictions and show schema-based distortions that increase with consolidation. Here we present a computational model in which hippocampal replay (from an autoassociative network) trains generative models (variational autoencoders) to (re)create sensory experiences from latent variable representations in entorhinal, medial prefrontal and anterolateral temporal cortices via the hippocampal formation. Simulations show effects of memory age and hippocampal lesions in agreement with previous models, but also provide mechanisms for semantic memory, imagination, episodic future thinking, relational inference and schema-based distortions including boundary extension. The model explains how unique sensory and predictable conceptual elements of memories are stored and reconstructed by efficiently combining both hippocampal and neocortical systems, optimizing the use of limited hippocampal storage for new and unusual information. Overall, we believe hippocampal replay training generative models provides a comprehensive account of memory construction, imagination and consolidation.
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Affiliation(s)
- Eleanor Spens
- UCL Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Neil Burgess
- UCL Institute of Cognitive Neuroscience, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
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41
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Prescott TJ, Montes González FM, Gurney K, Humphries MD, Redgrave P. Simulated Dopamine Modulation of a Neurorobotic Model of the Basal Ganglia. Biomimetics (Basel) 2024; 9:139. [PMID: 38534824 DOI: 10.3390/biomimetics9030139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/28/2024] Open
Abstract
The vertebrate basal ganglia play an important role in action selection-the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement. These states were partially relieved by increased salience levels (stronger sensory/motivational input). Conversely, increased simulated dopamine caused distortion of the robot's motor acts through partially expressed motor activity relating to losing actions. This could also lead to an increased frequency of behaviour switching. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause a loss of behavioural integration, sometimes leaving the robot in a 'behavioral trap'. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that robotic models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction.
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Affiliation(s)
- Tony J Prescott
- Department of Computer Science, University of Sheffield, Sheffield S10 2TN, UK
| | | | - Kevin Gurney
- Department of Psychology, University of Sheffield, Sheffield S10 2TN, UK
| | - Mark D Humphries
- School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK
| | - Peter Redgrave
- Department of Psychology, University of Sheffield, Sheffield S10 2TN, UK
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42
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Irastorza-Valera L, Benítez JM, Montáns FJ, Saucedo-Mora L. An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates. Biomimetics (Basel) 2024; 9:101. [PMID: 38392147 PMCID: PMC10886514 DOI: 10.3390/biomimetics9020101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/16/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024] Open
Abstract
The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- PIMM Laboratory, Arts et Métiers Institute of Technology, 151 Bd de l'Hôpital, 75013 Paris, France
| | - José María Benítez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
| | - Francisco J Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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43
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Tanaka T. Evaluating the Bayesian causal inference model of intentional binding through computational modeling. Sci Rep 2024; 14:2979. [PMID: 38316822 PMCID: PMC10844324 DOI: 10.1038/s41598-024-53071-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 01/27/2024] [Indexed: 02/07/2024] Open
Abstract
Intentional binding refers to the subjective compression of the time interval between an action and its consequence. While intentional binding has been widely used as a proxy for the sense of agency, its underlying mechanism has been largely veiled. Bayesian causal inference (BCI) has gained attention as a potential explanation, but currently lacks sufficient empirical support. Thus, this study implemented various computational models to describe the possible mechanisms of intentional binding, fitted them to individual observed data, and quantitatively evaluated their performance. The BCI models successfully isolated the parameters that potentially contributed to intentional binding (i.e., causal belief and temporal prediction) and generally better explained an observer's time estimation than traditional models such as maximum likelihood estimation. The estimated parameter values suggested that the time compression resulted from an expectation that the actions would immediately cause sensory outcomes. Furthermore, I investigated the algorithm that realized this BCI and found probability-matching to be a plausible candidate; people might heuristically reconstruct event timing depending on causal uncertainty rather than optimally integrating causal and temporal posteriors. The evidence demonstrated the utility of computational modeling to investigate how humans infer the causal and temporal structures of events and individual differences in that process.
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Affiliation(s)
- Takumi Tanaka
- Graduate School of Humanities and Sociology and Faculty of Letters, The University of Tokyo, Tokyo, Japan.
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44
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Naud R, Longtin A. Connecting levels of analysis in the computational era. J Physiol 2024; 602:417-420. [PMID: 38071740 DOI: 10.1113/jp286013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 02/02/2024] Open
Affiliation(s)
- Richard Naud
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
- Department of Physics, University of Ottawa, Ottawa, ON, Canada
- Center for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - André Longtin
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
- Department of Physics, University of Ottawa, Ottawa, ON, Canada
- Center for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
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45
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Cisler JM, Dunsmoor JE, Fonzo GA, Nemeroff CB. Latent-state and model-based learning in PTSD. Trends Neurosci 2024; 47:150-162. [PMID: 38212163 PMCID: PMC10923154 DOI: 10.1016/j.tins.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 01/13/2024]
Abstract
Post-traumatic stress disorder (PTSD) is characterized by altered emotional and behavioral responding following a traumatic event. In this article, we review the concepts of latent-state and model-based learning (i.e., learning and inferring abstract task representations) and discuss their relevance for clinical and neuroscience models of PTSD. Recent data demonstrate evidence for brain and behavioral biases in these learning processes in PTSD. These new data potentially recast excessive fear towards trauma cues as a problem in learning and updating abstract task representations, as opposed to traditional conceptualizations focused on stimulus-specific learning. Biases in latent-state and model-based learning may also be a common mechanism targeted in common therapies for PTSD. We highlight key knowledge gaps that need to be addressed to further elaborate how latent-state learning and its associated neurocircuitry mechanisms function in PTSD and how to optimize treatments to target these processes.
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Affiliation(s)
- Josh M Cisler
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA.
| | - Joseph E Dunsmoor
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA
| | - Gregory A Fonzo
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA
| | - Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA
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46
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Ponce-Alvarez A, Deco G. The Hopf whole-brain model and its linear approximation. Sci Rep 2024; 14:2615. [PMID: 38297071 PMCID: PMC10831083 DOI: 10.1038/s41598-024-53105-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/27/2024] [Indexed: 02/02/2024] Open
Abstract
Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation-that holds in the case of heterogeneous parameters and time-delays-allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.
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Affiliation(s)
- Adrián Ponce-Alvarez
- Departament de Matemàtiques, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), 08010, Barcelona, Spain
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47
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Zerr P, Gayet S, Van der Stigchel S. Memory reports are biased by all relevant contents of working memory. Sci Rep 2024; 14:2507. [PMID: 38291049 PMCID: PMC10827710 DOI: 10.1038/s41598-024-51595-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 01/07/2024] [Indexed: 02/01/2024] Open
Abstract
Sensory input is inherently noisy while the world is inherently predictable. When multiple observations of the same object are available, integration of the available information necessarily increases the reliability of a world estimate. Optimal integration of multiple instances of sensory evidence has already been demonstrated during multisensory perception but could benefit unimodal perception as well. In the present study 330 participants observed a sequence of four orientations and were cued to report one of them. Reports were biased by all simultaneously memorized items that were similar and relevant to the target item, weighted by their reliability (signal-to-noise ratio). Orientations presented before and presented after the target biased report, demonstrating that the bias emerges in memory and not (exclusively) during perception or encoding. Only attended, task-relevant items biased report. We suggest that these results reflect how the visual system integrates information that is sampled from the same object at consecutive timepoints to promote perceptual stability and behavioural effectiveness in a dynamic world. We suggest that similar response biases, such as serial dependence, might be instances of a more general mechanism of working memory averaging. Data is available at https://osf.io/embcf/ .
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Affiliation(s)
- Paul Zerr
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands.
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
| | - Surya Gayet
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
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48
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Ragone E, Tanner J, Jo Y, Zamani Esfahlani F, Faskowitz J, Pope M, Coletta L, Gozzi A, Betzel R. Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains. Commun Biol 2024; 7:126. [PMID: 38267534 PMCID: PMC10810083 DOI: 10.1038/s42003-024-05766-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.
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Affiliation(s)
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
| | - Youngheun Jo
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Farnaz Zamani Esfahlani
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Maria Pope
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Richard Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA.
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49
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Cone I, Clopath C. Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure. Nat Commun 2024; 15:687. [PMID: 38263408 PMCID: PMC10806076 DOI: 10.1038/s41467-024-44871-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the task's complex structure, which can depend on abstract combinations of sensory stimuli and internal logic. The hippocampus is known to develop representations of this complex structure, forming a so-called "cognitive map". However, the precise biophysical mechanisms driving the emergence of task-relevant maps at the population level remain unclear. We propose a model in which plateau-based learning at the single cell level, combined with reinforcement learning in an agent, leads to latent representational structures codependently evolving with behavior in a task-specific manner. In agreement with recent experimental data, we show that the model successfully develops latent structures essential for task-solving (cue-dependent "splitters") while excluding irrelevant ones. Finally, our model makes testable predictions concerning the co-dependent interactions between split representations and split behavioral policy during their evolution.
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Affiliation(s)
- Ian Cone
- Department of Bioengineering, Imperial College London, London, UK.
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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50
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Gorgan Mohammadi A, Ganjtabesh M. On computational models of theory of mind and the imitative reinforcement learning in spiking neural networks. Sci Rep 2024; 14:1945. [PMID: 38253595 PMCID: PMC10803361 DOI: 10.1038/s41598-024-52299-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Theory of Mind is referred to the ability of inferring other's mental states, and it plays a crucial role in social cognition and learning. Biological evidences indicate that complex circuits are involved in this ability, including the mirror neuron system. The mirror neuron system influences imitation abilities and action understanding, leading to learn through observing others. To simulate this imitative learning behavior, a Theory-of-Mind-based Imitative Reinforcement Learning (ToM-based ImRL) framework is proposed. Employing the bio-inspired spiking neural networks and the mechanisms of the mirror neuron system, ToM-based ImRL is a bio-inspired computational model which enables an agent to effectively learn how to act in an interactive environment through observing an expert, inferring its goals, and imitating its behaviors. The aim of this paper is to review some computational attempts in modeling ToM and to explain the proposed ToM-based ImRL framework which is tested in the environment of River Raid game from Atari 2600 series.
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Affiliation(s)
- Ashena Gorgan Mohammadi
- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Mohammad Ganjtabesh
- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran.
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