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Crombie D, Spacek MA, Leibold C, Busse L. Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales. PLoS Biol 2024; 22:e3002614. [PMID: 38743775 PMCID: PMC11093384 DOI: 10.1371/journal.pbio.3002614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 04/05/2024] [Indexed: 05/16/2024] Open
Abstract
The processing of sensory information, even at early stages, is influenced by the internal state of the animal. Internal states, such as arousal, are often characterized by relating neural activity to a single "level" of arousal, defined by a behavioral indicator such as pupil size. In this study, we expand the understanding of arousal-related modulations in sensory systems by uncovering multiple timescales of pupil dynamics and their relationship to neural activity. Specifically, we observed a robust coupling between spiking activity in the mouse dorsolateral geniculate nucleus (dLGN) of the thalamus and pupil dynamics across timescales spanning a few seconds to several minutes. Throughout all these timescales, 2 distinct spiking modes-individual tonic spikes and tightly clustered bursts of spikes-preferred opposite phases of pupil dynamics. This multi-scale coupling reveals modulations distinct from those captured by pupil size per se, locomotion, and eye movements. Furthermore, coupling persisted even during viewing of a naturalistic movie, where it contributed to differences in the encoding of visual information. We conclude that dLGN spiking activity is under the simultaneous influence of multiple arousal-related processes associated with pupil dynamics occurring over a broad range of timescales.
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Affiliation(s)
- Davide Crombie
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU Munich, Munich, Germany
| | - Martin A. Spacek
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
| | - Christian Leibold
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
- Fakultät für Biologie & Bernstein Center Freiburg, Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau, Germany
| | - Laura Busse
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany
- Bernstein Center for Computational Neuroscience, Munich, Germany
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2
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Wolman A, Çatal Y, Klar P, Steffener J, Northoff G. Repertoire of timescales in uni - and transmodal regions mediate working memory capacity. Neuroimage 2024; 291:120602. [PMID: 38579900 DOI: 10.1016/j.neuroimage.2024.120602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024] Open
Abstract
Working memory (WM) describes the dynamic process of maintenance and manipulation of information over a certain time delay. Neuronally, WM recruits a distributed network of cortical regions like the visual and dorsolateral prefrontal cortex as well as the subcortical hippocampus. How the input dynamics and subsequent neural dynamics impact WM remains unclear though. To answer this question, we combined the analysis of behavioral WM capacity with measuring neural dynamics through task-related power spectrum changes, e.g., median frequency (MF) in functional magnetic resonance imaging (fMRI). We show that the processing of the input dynamics, e.g., the task structure's specific timescale, leads to changes in the unimodal visual cortex's corresponding timescale which also relates to working memory capacity. While the more transmodal hippocampus relates to working memory capacity through its balance across multiple timescales or frequencies. In conclusion, we here show the relevance of both input dynamics and different neural timescales for WM capacity in uni - and transmodal regions like visual cortex and hippocampus for the subject's WM performance.
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Affiliation(s)
- Angelika Wolman
- School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa, ON K1N 6N5, Canada; Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada.
| | - Yasir Çatal
- Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada
| | - Philipp Klar
- Faculty of Mathematics and Natural Sciences, Institute of Experimental Psychology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Jason Steffener
- Interdisciplinary School of Health Science, University of Ottawa, 200 Lees Ave, Ottawa, ON K1N 6N5, Canada
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada
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3
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Fontenele AJ, Sooter JS, Norman VK, Gautam SH, Shew WL. Low-dimensional criticality embedded in high-dimensional awake brain dynamics. SCIENCE ADVANCES 2024; 10:eadj9303. [PMID: 38669340 PMCID: PMC11051676 DOI: 10.1126/sciadv.adj9303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 03/26/2024] [Indexed: 04/28/2024]
Abstract
Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. We offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality-a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescales agrees with established theory of critical dynamics. Our results suggest that the cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces.
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4
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Manea AMG, Maisson DJN, Voloh B, Zilverstand A, Hayden B, Zimmermann J. Neural timescales reflect behavioral demands in freely moving rhesus macaques. Nat Commun 2024; 15:2151. [PMID: 38461167 PMCID: PMC10925022 DOI: 10.1038/s41467-024-46488-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 02/28/2024] [Indexed: 03/11/2024] Open
Abstract
Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas' relative position in the hierarchy was stable. Next, we demonstrated that the change in neural timescales is dynamic and contains functionally-relevant information, differentiating between similar events in terms of motor demands and associated reward. Finally, we demonstrated that brain areas are differentially affected by these behavioral demands. These results demonstrate that while the space of neural timescales is anatomically constrained, the observed hierarchical organization and magnitude is dependent on behavioral demands.
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Affiliation(s)
- Ana M G Manea
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
| | - David J-N Maisson
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin Voloh
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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5
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Chen Z. Editorial for "MRI Assessment of Intrinsic Neural Timescale and Grey Matter Volume in Parkinson's Disease". J Magn Reson Imaging 2024; 59:996-997. [PMID: 37326136 DOI: 10.1002/jmri.28866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 06/17/2023] Open
Abstract
Level of Evidence5Technical Efficacy Stage3
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
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6
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Karimi-Rouzbahani H. Evidence for Multiscale Multiplexed Representation of Visual Features in EEG. Neural Comput 2024; 36:412-436. [PMID: 38363657 DOI: 10.1162/neco_a_01649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 12/01/2023] [Indexed: 02/18/2024]
Abstract
Distinct neural processes such as sensory and memory processes are often encoded over distinct timescales of neural activations. Animal studies have shown that this multiscale coding strategy is also implemented for individual components of a single process, such as individual features of a multifeature stimulus in sensory coding. However, the generalizability of this encoding strategy to the human brain has remained unclear. We asked if individual features of visual stimuli were encoded over distinct timescales. We applied a multiscale time-resolved decoding method to electroencephalography (EEG) collected from human subjects presented with grating visual stimuli to estimate the timescale of individual stimulus features. We observed that the orientation and color of the stimuli were encoded in shorter timescales, whereas spatial frequency and the contrast of the same stimuli were encoded in longer timescales. The stimulus features appeared in temporally overlapping windows along the trial supporting a multiplexed coding strategy. These results provide evidence for a multiplexed, multiscale coding strategy in the human visual system.
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Affiliation(s)
- Hamid Karimi-Rouzbahani
- Neurosciences Centre, Mater Hospital, Brisbane 4101, Australia
- Queensland Brain Institute, University of Queensland, Brisbane 4067, Australia
- Mater Research Institute, University of Queensland, Brisbane 4101, Australia
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7
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Trepka E, Spitmaan M, Qi XL, Constantinidis C, Soltani A. Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory. J Neurosci 2024; 44:e2442212023. [PMID: 37973375 PMCID: PMC10866190 DOI: 10.1523/jneurosci.2442-21.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, for example, from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales are inherent properties of neurons and/or depend on training in a specific task and if the latter, how their modulations contribute to task performance. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.
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Affiliation(s)
- Ethan Trepka
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover 03755, New Hampshire
- Neurosciences Program, Stanford University, Stanford 94305, California
| | - Mehran Spitmaan
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover 03755, New Hampshire
| | - Xue-Lian Qi
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem 27157, North Carolina
| | | | - Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover 03755, New Hampshire
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8
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Rowland JM, van der Plas TL, Loidolt M, Lees RM, Keeling J, Dehning J, Akam T, Priesemann V, Packer AM. Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Nat Neurosci 2023; 26:1584-1594. [PMID: 37640911 PMCID: PMC10471496 DOI: 10.1038/s41593-023-01413-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions-the primary and secondary somatosensory cortex (S1 and S2)-in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.
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Affiliation(s)
- James M Rowland
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Thijs L van der Plas
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Matthias Loidolt
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Laboratory for Molecular Cell Biology, University College London, London, UK
| | - Robert M Lees
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Science and Technology Facilities Council, Octopus Imaging Facility, Research Complex at Harwell, Harwell Campus, Oxfordshire, UK
| | - Joshua Keeling
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Jonas Dehning
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Thomas Akam
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Adam M Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK.
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9
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Trepka E, Spitmaan M, Qi XL, Constantinidis C, Soltani A. Training-dependent gradients of timescales of neural dynamics in the primate prefrontal cortex and their contributions to working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.01.555857. [PMID: 37693584 PMCID: PMC10491183 DOI: 10.1101/2023.09.01.555857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, e.g., from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales depend on training in a specific task and/or are an inherent property of neurons, and whether more fine-grained hierarchies of timescales exist within specific cortical regions. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition. Significance statement Recent studies have demonstrated that neural responses exhibit dynamics with different timescales that follow a certain order or hierarchy across cortical areas. While the hierarchy of timescales is consistent across different tasks, it is unknown if these timescales emerge only after training or if they represent inherent properties of neurons. To answer this question, we estimated multiple timescales in neural response across five subregions of the monkeys' lateral prefrontal cortex before and after training on a working-memory task. Our results provide evidence for fine-grained gradients related to certain neural dynamics. Moreover, we show that these timescales depend on and can be modulated by training in a cognitive task, and contribute to encoding of task-relevant information at single-cell and population levels.
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10
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Delaney TJ, O’Donnell C. Fast-local and slow-global neural ensembles in the mouse brain. Netw Neurosci 2023; 7:731-742. [PMID: 37397884 PMCID: PMC10312261 DOI: 10.1162/netn_a_00309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 02/08/2023] [Indexed: 08/03/2023] Open
Abstract
Ensembles of neurons are thought to be coactive when participating in brain computations. However, it is unclear what principles determine whether an ensemble remains localised within a single brain region, or spans multiple brain regions. To address this, we analysed electrophysiological neural population data from hundreds of neurons recorded simultaneously across nine brain regions in awake mice. At fast subsecond timescales, spike count correlations between pairs of neurons in the same brain region were stronger than for pairs of neurons spread across different brain regions. In contrast at slower timescales, within- and between-region spike count correlations were similar. Correlations between high-firing-rate neuron pairs showed a stronger dependence on timescale than low-firing-rate neuron pairs. We applied an ensemble detection algorithm to the neural correlation data and found that at fast timescales each ensemble was mostly contained within a single brain region, whereas at slower timescales ensembles spanned multiple brain regions. These results suggest that the mouse brain may perform fast-local and slow-global computations in parallel.
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Affiliation(s)
- Thomas J. Delaney
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol, UK
| | - Cian O’Donnell
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol, UK
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry, UK
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11
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Cusinato R, Alnes SL, van Maren E, Boccalaro I, Ledergerber D, Adamantidis A, Imbach LL, Schindler K, Baud MO, Tzovara A. Intrinsic Neural Timescales in the Temporal Lobe Support an Auditory Processing Hierarchy. J Neurosci 2023; 43:3696-3707. [PMID: 37045604 PMCID: PMC10198454 DOI: 10.1523/jneurosci.1941-22.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/21/2023] [Accepted: 03/02/2023] [Indexed: 04/14/2023] Open
Abstract
During rest, intrinsic neural dynamics manifest at multiple timescales, which progressively increase along visual and somatosensory hierarchies. Theoretically, intrinsic timescales are thought to facilitate processing of external stimuli at multiple stages. However, direct links between timescales at rest and sensory processing, as well as translation to the auditory system are lacking. Here, we measured intracranial EEG in 11 human patients with epilepsy (4 women), while listening to pure tones. We show that, in the auditory network, intrinsic neural timescales progressively increase, while the spectral exponent flattens, from temporal to entorhinal cortex, hippocampus, and amygdala. Within the neocortex, intrinsic timescales exhibit spatial gradients that follow the temporal lobe anatomy. Crucially, intrinsic timescales at baseline can explain the latency of auditory responses: as intrinsic timescales increase, so do the single-electrode response onset and peak latencies. Our results suggest that the human auditory network exhibits a repertoire of intrinsic neural dynamics, which manifest in cortical gradients with millimeter resolution and may provide a variety of temporal windows to support auditory processing.SIGNIFICANCE STATEMENT Endogenous neural dynamics are often characterized by their intrinsic timescales. These are thought to facilitate processing of external stimuli. However, a direct link between intrinsic timing at rest and sensory processing is missing. Here, with intracranial EEG, we show that intrinsic timescales progressively increase from temporal to entorhinal cortex, hippocampus, and amygdala. Intrinsic timescales at baseline can explain the variability in the timing of intracranial EEG responses to sounds: cortical electrodes with fast timescales also show fast- and short-lasting responses to auditory stimuli, which progressively increase in the hippocampus and amygdala. Our results suggest that a hierarchy of neural dynamics in the temporal lobe manifests across cortical and limbic structures and can explain the temporal richness of auditory responses.
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Affiliation(s)
- Riccardo Cusinato
- Institute of Computer Science, University of Bern, Bern 3012, Switzerland
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Sigurd L Alnes
- Institute of Computer Science, University of Bern, Bern 3012, Switzerland
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Ellen van Maren
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Ida Boccalaro
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | | | - Antoine Adamantidis
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Lukas L Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich 8008, Switzerland
| | - Kaspar Schindler
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Maxime O Baud
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern 3012, Switzerland
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Helen Wills Neuroscience Institute, University of California-Berkeley, Berkeley 94720, California
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