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Luu P, Tucker DM, Friston K. From active affordance to active inference: vertical integration of cognition in the cerebral cortex through dual subcortical control systems. Cereb Cortex 2024; 34:bhad458. [PMID: 38044461 DOI: 10.1093/cercor/bhad458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
In previous papers, we proposed that the dorsal attention system's top-down control is regulated by the dorsal division of the limbic system, providing a feedforward or impulsive form of control generating expectancies during active inference. In contrast, we proposed that the ventral attention system is regulated by the ventral limbic division, regulating feedback constraints and error-correction for active inference within the neocortical hierarchy. Here, we propose that these forms of cognitive control reflect vertical integration of subcortical arousal control systems that evolved for specific forms of behavior control. The feedforward impetus to action is regulated by phasic arousal, mediated by lemnothalamic projections from the reticular activating system of the lower brainstem, and then elaborated by the hippocampus and dorsal limbic division. In contrast, feedback constraint-based on environmental requirements-is regulated by the tonic activation furnished by collothalamic projections from the midbrain arousal control centers, and then sustained and elaborated by the amygdala, basal ganglia, and ventral limbic division. In an evolutionary-developmental analysis, understanding these differing forms of active affordance-for arousal and motor control within the subcortical vertebrate neuraxis-may help explain the evolution of active inference regulating the cognition of expectancy and error-correction within the mammalian 6-layered neocortex.
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Affiliation(s)
- Phan Luu
- Brain Electrophysiology Laboratory Company, Riverfront Research Park, 1776 Millrace Dr., Eugene, OR 97403, United States
- Department of Psychology, University of Oregon, Eugene, OR 97403, United States
| | - Don M Tucker
- Brain Electrophysiology Laboratory Company, Riverfront Research Park, 1776 Millrace Dr., Eugene, OR 97403, United States
- Department of Psychology, University of Oregon, Eugene, OR 97403, United States
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, United Kingdom
- VERSES AI Research Lab, Los Angeles, CA 90016, USA
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2
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Sajid N, Gajardo-Vidal A, Ekert JO, Lorca-Puls DL, Hope TMH, Green DW, Friston KJ, Price CJ. Degeneracy in the neurological model of auditory speech repetition. Commun Biol 2023; 6:1161. [PMID: 37957231 PMCID: PMC10643365 DOI: 10.1038/s42003-023-05515-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Both classic and contemporary models of auditory word repetition involve at least four left hemisphere regions: primary auditory cortex for processing sounds; pSTS (within Wernicke's area) for processing auditory images of speech; pOp (within Broca's area) for processing motor images of speech; and primary motor cortex for overt speech articulation. Previous functional-MRI (fMRI) studies confirm that auditory repetition activates these regions, in addition to many others. Crucially, however, contemporary models do not specify how regions interact and drive each other during auditory repetition. Here, we used dynamic causal modelling, to test the functional interplay among the four core brain regions during single auditory word and pseudoword repetition. Our analysis is grounded in the principle of degeneracy-i.e., many-to-one structure-function relationships-where multiple neural pathways can execute the same function. Contrary to expectation, we found that, for both word and pseudoword repetition, (i) the effective connectivity between pSTS and pOp was predominantly bidirectional and inhibitory; (ii) activity in the motor cortex could be driven by either pSTS or pOp; and (iii) the latter varied both within and between individuals. These results suggest that different neural pathways can support auditory speech repetition. This degeneracy may explain resilience to functional loss after brain damage.
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Affiliation(s)
- Noor Sajid
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK.
| | - Andrea Gajardo-Vidal
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
- Centro de Investigación en Complejidad Social (CICS), Universidad del Desarrollo, Concepción, Chile
| | - Justyna O Ekert
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
| | - Diego L Lorca-Puls
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
- Sección de Neurología, Departamento de Especialidades, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Thomas M H Hope
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
| | - David W Green
- Experimental Psychology, University College London, London, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
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Pinotsis DA, Miller EK. In vivo ephaptic coupling allows memory network formation. Cereb Cortex 2023; 33:9877-9895. [PMID: 37420330 PMCID: PMC10472500 DOI: 10.1093/cercor/bhad251] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/20/2023] [Accepted: 06/23/2023] [Indexed: 07/09/2023] Open
Abstract
It is increasingly clear that memories are distributed across multiple brain areas. Such "engram complexes" are important features of memory formation and consolidation. Here, we test the hypothesis that engram complexes are formed in part by bioelectric fields that sculpt and guide the neural activity and tie together the areas that participate in engram complexes. Like the conductor of an orchestra, the fields influence each musician or neuron and orchestrate the output, the symphony. Our results use the theory of synergetics, machine learning, and data from a spatial delayed saccade task and provide evidence for in vivo ephaptic coupling in memory representations.
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Affiliation(s)
- Dimitris A Pinotsis
- Department of Psychology, Centre for Mathematical Neuroscience and Psychology, University of London, London EC1V 0HB, United Kingdom
- The Picower Institute for Learning & Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
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Pinotsis DA, Fridman G, Miller EK. Cytoelectric Coupling: Electric fields sculpt neural activity and "tune" the brain's infrastructure. Prog Neurobiol 2023; 226:102465. [PMID: 37210066 DOI: 10.1016/j.pneurobio.2023.102465] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023]
Abstract
We propose and present converging evidence for the Cytoelectric Coupling Hypothesis: Electric fields generated by neurons are causal down to the level of the cytoskeleton. This could be achieved via electrodiffusion and mechanotransduction and exchanges between electrical, potential and chemical energy. Ephaptic coupling organizes neural activity, forming neural ensembles at the macroscale level. This information propagates to the neuron level, affecting spiking, and down to molecular level to stabilize the cytoskeleton, "tuning" it to process information more efficiently.
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Affiliation(s)
- Dimitris A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Gene Fridman
- Departments of Otolaryngology, Biomedical Engineering, and Electrical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Friston K. Computational psychiatry: from synapses to sentience. Mol Psychiatry 2023; 28:256-268. [PMID: 36056173 PMCID: PMC7614021 DOI: 10.1038/s41380-022-01743-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 01/09/2023]
Abstract
This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, WC1N 3AR, UK.
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Van de Steen F, Pinotsis D, Devos W, Colenbier N, Bassez I, Friston K, Marinazzo D. Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex. PLoS Comput Biol 2022; 18:e1009988. [PMID: 36574458 PMCID: PMC9829170 DOI: 10.1371/journal.pcbi.1009988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 01/09/2023] [Accepted: 12/16/2022] [Indexed: 12/29/2022] Open
Abstract
During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity-within the visual cortex-in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations-within each occipital source-were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.
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Affiliation(s)
- Frederik Van de Steen
- Department of Data Analysis, Ghent University, Ghent, Belgium
- Vrije Universiteit Brussel, AIMS laboratory, Brussel, Belgium
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- * E-mail:
| | - Dimitris Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City—University of London, London, United Kingdom
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Wouter Devos
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | | | - Iege Bassez
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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Hong CCH, Fallon JH, Friston KJ. fMRI Evidence for Default Mode Network Deactivation Associated with Rapid Eye Movements in Sleep. Brain Sci 2021; 11:brainsci11111528. [PMID: 34827529 PMCID: PMC8615877 DOI: 10.3390/brainsci11111528] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 11/25/2022] Open
Abstract
System-specific brain responses—time-locked to rapid eye movements (REMs) in sleep—are characteristically widespread, with robust and clear activation in the primary visual cortex and other structures involved in multisensory integration. This pattern suggests that REMs underwrite hierarchical processing of visual information in a time-locked manner, where REMs index the generation and scanning of virtual-world models, through multisensory integration in dreaming—as in awake states. Default mode network (DMN) activity increases during rest and reduces during various tasks including visual perception. The implicit anticorrelation between the DMN and task-positive network (TPN)—that persists in REM sleep—prompted us to focus on DMN responses to temporally-precise REM events. We timed REMs during sleep from the video recordings and quantified the neural correlates of REMs—using functional MRI (fMRI)—in 24 independent studies of 11 healthy participants. A reanalysis of these data revealed that the cortical areas exempt from widespread REM-locked brain activation were restricted to the DMN. Furthermore, our analysis revealed a modest temporally-precise REM-locked decrease—phasic deactivation—in key DMN nodes, in a subset of independent studies. These results are consistent with hierarchical predictive coding; namely, permissive deactivation of DMN at the top of the hierarchy (leading to the widespread cortical activation at lower levels; especially the primary visual cortex). Additional findings indicate REM-locked cerebral vasodilation and suggest putative mechanisms for dream forgetting.
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Affiliation(s)
- Charles Chong-Hwa Hong
- Patuxent Institution, Correctional Mental Health Center—Jessup, Jessup, MD 20794, USA
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University, Baltimore, MD 21205, USA
- Correspondence: ; Tel.: +1-410-596-1956
| | - James H. Fallon
- Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA;
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92697, USA
| | - Karl J. Friston
- The Well Come Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK;
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Nara S, Lizarazu M, Richter CG, Dima DC, Cichy RM, Bourguignon M, Molinaro N. Temporal uncertainty enhances suppression of neural responses to predictable visual stimuli. Neuroimage 2021; 239:118314. [PMID: 34175428 PMCID: PMC8363941 DOI: 10.1016/j.neuroimage.2021.118314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/20/2021] [Accepted: 06/24/2021] [Indexed: 11/27/2022] Open
Abstract
Does stimulus timing impact the processing of predicted visual features?. We evaluated if expectation suppression effects are modulated by temporal predictability. Expectation suppression was robust in both visual ERFs and feature decoding accuracy. Visual responses to predictable stimuli are greater for predictable vs. unpredictable timing. Sensory evidence is given less weight when timing is uncertain.
Contextual information triggers predictions about the content (“what”) of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability (“when”) may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information (“what”) is affected by temporal predictability (“when”). Participants (N = 16) were presented with four consecutive Gabor patches (entrainers) with constant spatial frequency but with variable orientation and temporal onset. A fifth target Gabor was presented after a longer delay and with higher or lower spatial frequency that participants had to judge. We compared the neural responses to entrainers where the Gabor orientation could, or could not be temporally predicted along the entrainer sequence, and with inter-entrainer timing that was constant (predictable), or variable (unpredictable). We observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.
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Affiliation(s)
- Sanjeev Nara
- Basque Center for Cognition, Brain and Language (BCBL), University of the Basque Country UPV/EHU, 69, 20009 Donostia, San Sebastian, Spain.
| | - Mikel Lizarazu
- Basque Center for Cognition, Brain and Language (BCBL), University of the Basque Country UPV/EHU, 69, 20009 Donostia, San Sebastian, Spain
| | - Craig G Richter
- Basque Center for Cognition, Brain and Language (BCBL), University of the Basque Country UPV/EHU, 69, 20009 Donostia, San Sebastian, Spain
| | - Diana C Dima
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States
| | - Radoslaw M Cichy
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Mathieu Bourguignon
- Basque Center for Cognition, Brain and Language (BCBL), University of the Basque Country UPV/EHU, 69, 20009 Donostia, San Sebastian, Spain; Laboratoire de Cartographie fonctionelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Laboratory of neurophysiology and movement biomechanics, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicola Molinaro
- Basque Center for Cognition, Brain and Language (BCBL), University of the Basque Country UPV/EHU, 69, 20009 Donostia, San Sebastian, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
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Schöbi D, Homberg F, Frässle S, Endepols H, Moran RJ, Friston KJ, Tittgemeyer M, Heinzle J, Stephan KE. Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses. Neuroimage 2021; 237:118096. [PMID: 33940149 DOI: 10.1016/j.neuroimage.2021.118096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/12/2021] [Accepted: 04/19/2021] [Indexed: 01/09/2023] Open
Abstract
Drugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic causal model - embodying a small network of coupled cortical microcircuits - we estimated synaptic parameters and their change across pharmacological conditions. The ensuing parameter estimates associated with (the neuromodulation of) synaptic efficacy showed both graded muscarinic effects and predictive validity between agonistic and antagonistic pharmacological conditions. This finding illustrates the potential utility of generative models of electrophysiological data as computational assays of muscarinic function. In application to EEG data of patients from heterogeneous spectrum diseases, e.g. schizophrenia, such models might help identify subgroups of patients that respond differentially to cholinergic treatments. SIGNIFICANCE STATEMENT: In psychiatry, the vast majority of pharmacological treatments affect actions of neuromodulatory transmitters, e.g. dopamine or acetylcholine. As treatment is largely trial-and-error based, one of the goals for computational psychiatry is to construct mathematical models that can serve as "computational assays" and infer the status of specific neuromodulatory systems in individual patients. This translational neuromodeling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG.
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Affiliation(s)
- Dario Schöbi
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland
| | - Fabienne Homberg
- Boston Scientific Medizintechnik GmbH, Daniel-Goldbach-Strasse 17-27, 40880 Ratingen, Germany
| | - Stefan Frässle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland
| | - Heike Endepols
- Preclinical Imaging Group, Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50923 Cologne, Germany
| | - Rosalyn J Moran
- Department of Neuroimaging, Institute for Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London Se5 8AF, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N, 3AR, UK
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Gleueler Strasse 50, 50931 Cologne, Germany; Cluster of Excellence in Cellular Stress and Aging associated Disease (CECAD), 50931 Cologne, Germany
| | - Jakob Heinzle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland.
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland; Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N, 3AR, UK; Max Planck Institute for Metabolism Research, Gleueler Strasse 50, 50931 Cologne, Germany
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10
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Abstract
An established theoretical model, predictive coding, states that the brain is constantly building models (signifying changing predictions) of the environment. The brain does this by forming predictions and signaling sensory inputs which deviate from predictions (“prediction errors”). Various hypotheses exist about how predictive coding could be implemented in the brain. We recorded neural spiking and oscillations with laminar resolution in a network of cortical areas as monkeys performed a working memory task with changing stimulus predictability. Predictability modulated the patterns of feedforward/feedback flow, cortical layers, and oscillations used to process a visual stimulus. These data support the theory of predictive coding but suggest an alternate model for its neural implementation: predictive routing. In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted “errors.” Different models have suggested distinct cortical layers, and rhythms implement predictive coding. We recorded spikes and local field potentials from laminar electrodes in five cortical areas (visual area 4 [V4], lateral intraparietal [LIP], posterior parietal area 7A, frontal eye field [FEF], and prefrontal cortex [PFC]) while monkeys performed a task that modulated visual stimulus predictability. During predictable blocks, there was enhanced alpha (8 to 14 Hz) or beta (15 to 30 Hz) power in all areas during stimulus processing and prestimulus beta (15 to 30 Hz) functional connectivity in deep layers of PFC to the other areas. Unpredictable stimuli were associated with increases in spiking and in gamma-band (40 to 90 Hz) power/connectivity that fed forward up the cortical hierarchy via superficial-layer cortex. Power and spiking modulation by predictability was stimulus specific. Alpha/beta power in LIP, FEF, and PFC inhibited spiking in deep layers of V4. Area 7A uniquely showed increases in high-beta (∼22 to 28 Hz) power/connectivity to unpredictable stimuli. These results motivate a conceptual model, predictive routing. It suggests that predictive coding may be implemented via lower-frequency alpha/beta rhythms that “prepare” pathways processing-predicted inputs by inhibiting feedforward gamma rhythms and associated spiking.
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11
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Limanowski J, Litvak V, Friston K. Cortical beta oscillations reflect the contextual gating of visual action feedback. Neuroimage 2020; 222:117267. [PMID: 32818621 PMCID: PMC7779369 DOI: 10.1016/j.neuroimage.2020.117267] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 08/08/2020] [Accepted: 08/12/2020] [Indexed: 11/26/2022] Open
Abstract
We decouple seen and felt hand postures during action via virtual reality. Vision of the hand is either task-relevant or a distractor. Task-relevance of vision is reflected by in- or decreases of occipital beta power. DCM suggests underlying changes in cortical (visual) excitability. Occipital beta may indicate the contextual gating of visual action feedback.
In sensorimotor integration, the brain needs to decide how its predictions should accommodate novel evidence by ‘gating’ sensory data depending on the current context. Here, we examined the oscillatory correlates of this process by recording magnetoencephalography (MEG) data during a new task requiring action under intersensory conflict. We used virtual reality to decouple visual (virtual) and proprioceptive (real) hand postures during a task in which the phase of grasping movements tracked a target (in either modality). Thus, we rendered visual information either task-relevant or a (to-be-ignored) distractor. Under visuo-proprioceptive incongruence, occipital beta power decreased (relative to congruence) when vision was task-relevant but increased when it had to be ignored. Dynamic causal modeling (DCM) revealed that this interaction was best explained by diametrical, task-dependent changes in visual gain. These results suggest a crucial role for beta oscillations in the contextual gating (i.e., gain or precision control) of visual vs proprioceptive action feedback, depending on current behavioral demands.
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Affiliation(s)
- Jakub Limanowski
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom; Centre for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden, Germany.
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom
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12
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Min BK, Kim HS, Pinotsis DA, Pantazis D. Thalamocortical inhibitory dynamics support conscious perception. Neuroimage 2020; 220:117066. [PMID: 32565278 DOI: 10.1016/j.neuroimage.2020.117066] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/25/2020] [Accepted: 06/14/2020] [Indexed: 11/28/2022] Open
Abstract
Whether thalamocortical interactions play a decisive role in conscious perception remains an open question. We presented rapid red/green color flickering stimuli, which induced the mental perception of either an illusory orange color or non-fused red and green colors. Using magnetoencephalography, we observed 6-Hz thalamic activity associated with thalamocortical inhibitory coupling only during the conscious perception of the illusory orange color. This sustained thalamic disinhibition was temporally coupled with higher visual cortical activation during the conscious perception of the orange color, providing neurophysiological evidence of the role of thalamocortical synchronization in conscious awareness of mental representation. Bayesian model comparison consistently supported the thalamocortical model in conscious perception. Taken together, experimental and theoretical evidence established the thalamocortical inhibitory network as a gateway to conscious mental representations.
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Affiliation(s)
- Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Hyun Seok Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Dimitris A Pinotsis
- Center for Mathematical Neuroscience and Psychology, Department of Psychology, City-University of London, London, EC1V 0HB, UK; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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13
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Pinotsis DA. Statistical decision theory and multiscale analyses of human brain data. J Neurosci Methods 2020; 346:108912. [PMID: 32835705 DOI: 10.1016/j.jneumeth.2020.108912] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only. NEW METHOD We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data. RESULTS We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6. COMPARISON WITH EXISTING METHODS This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed. CONCLUSIONS We discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.
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Affiliation(s)
- D A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Kumar VG, Dutta S, Talwar S, Roy D, Banerjee A. Biophysical mechanisms governing large-scale brain network dynamics underlying individual-specific variability of perception. Eur J Neurosci 2020; 52:3746-3762. [PMID: 32304122 DOI: 10.1111/ejn.14747] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/07/2020] [Accepted: 04/08/2020] [Indexed: 11/30/2022]
Abstract
Perception necessitates interaction among neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter-individual variability observed in cross-modal speech perception. From raw EEG signals recorded from human participants when they were presented with speech vocalizations of McGurk-incongruent and congruent audio-visual (AV) stimuli, we computed the global coherence metric to capture the neural variability of large-scale networks. We identified that participants' McGurk susceptibility was negatively correlated to their alpha band global coherence. The proposed biophysical model conceptualized the global coherence dynamics emerge from coupling between the interacting neural masses-representing the sensory-specific auditory/visual areas and modality nonspecific associative/integrative regions. Subsequently, we could predict that an extremely weak direct AV coupling results in a decrease in alpha band global coherence-mimicking the cortical dynamics of participants with higher McGurk susceptibility. Source connectivity analysis also showed decreased connectivity between sensory-specific regions in participants more susceptible to McGurk effect, thus establishing an empirical validation to the prediction. Overall, our study provides an outline to link variability in structural and functional connectivity metrics to variability of performance that can be useful for several perception and action task paradigms.
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Affiliation(s)
- Vinodh G Kumar
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Gurgaon, India
| | - Shrey Dutta
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Gurgaon, India
| | - Siddharth Talwar
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Gurgaon, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Gurgaon, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Gurgaon, India
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Hamburg S, Rosch R, Startin CM, Friston KJ, Strydom A. Dynamic Causal Modeling of the Relationship between Cognition and Theta-alpha Oscillations in Adults with Down Syndrome. Cereb Cortex 2020; 29:2279-2290. [PMID: 30877793 PMCID: PMC6458903 DOI: 10.1093/cercor/bhz043] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 02/09/2019] [Indexed: 01/17/2023] Open
Abstract
Individuals with Down syndrome (DS) show high inter-subject variability in cognitive ability and have an ultra-high risk of developing dementia (90% lifetime prevalence). Elucidating factors underlying variability in cognitive function can inform us about intellectual disability (ID) and may improve our understanding of factors associated with later cognitive decline. Increased neuronal inhibition has been posited to contribute to ID in DS. Combining electroencephalography (EEG) with dynamic causal modeling (DCM) provides a non-invasive method for investigating excitatory/inhibitory mechanisms. Resting-state EEG recordings were obtained from 36 adults with DS with no evidence of cognitive decline. Theta–alpha activity (4–13 Hz) was characterized in relation to general cognitive ability (raw Kaufmann’s Brief Intelligence Test second Edition (KBIT-2) score). Higher KBIT-2 was associated with higher frontal alpha peak amplitude and higher theta–alpha band power across distributed regions. Modeling this association with DCM revealed intrinsic self-inhibition was the key network parameter underlying observed differences in 4–13 Hz power in relation to KBIT-2 and age. In particular, intrinsic self-inhibition in right V1 was negatively correlated with KBIT-2. Results suggest intrinsic self-inhibition within the alpha network is associated with individual differences in cognitive ability in adults with DS, and may provide a potential therapeutic target for cognitive enhancement.
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Affiliation(s)
- Sarah Hamburg
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
| | - Richard Rosch
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
| | - Carla Marie Startin
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
| | - Karl John Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
| | - André Strydom
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
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16
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Pinotsis DA, Buschman TJ, Miller EK. Working Memory Load Modulates Neuronal Coupling. Cereb Cortex 2020; 29:1670-1681. [PMID: 29608671 DOI: 10.1093/cercor/bhy065] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 02/22/2018] [Accepted: 02/28/2018] [Indexed: 12/27/2022] Open
Abstract
There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1-3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC-FEF-LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.
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Affiliation(s)
- Dimitris A Pinotsis
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.,The Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Timothy J Buschman
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.,Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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17
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Wei H, Jafarian A, Zeidman P, Litvak V, Razi A, Hu D, Friston KJ. Bayesian fusion and multimodal DCM for EEG and fMRI. Neuroimage 2020; 211:116595. [DOI: 10.1016/j.neuroimage.2020.116595] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 01/07/2020] [Accepted: 01/29/2020] [Indexed: 12/26/2022] Open
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18
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Demekas D, Parr T, Friston KJ. An Investigation of the Free Energy Principle for Emotion Recognition. Front Comput Neurosci 2020; 14:30. [PMID: 32390817 PMCID: PMC7189749 DOI: 10.3389/fncom.2020.00030] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 03/23/2020] [Indexed: 01/23/2023] Open
Abstract
This paper offers a prospectus of what might be achievable in the development of emotional recognition devices. It provides a conceptual overview of the free energy principle; including Markov blankets, active inference, and-in particular-a discussion of selfhood and theory of mind, followed by a brief explanation of how these concepts can explain both neural and cultural models of emotional inference. The underlying hypothesis is that emotion recognition and inference devices will evolve from state-of-the-art deep learning models into active inference schemes that go beyond marketing applications and become adjunct to psychiatric practice. Specifically, this paper proposes that a second wave of emotion recognition devices will be equipped with an emotional lexicon (or the ability to epistemically search for one), allowing the device to resolve uncertainty about emotional states by actively eliciting responses from the user and learning from these responses. Following this, a third wave of emotional devices will converge upon the user's generative model, resulting in the machine and human engaging in a reciprocal, prosocial emotional interaction, i.e., sharing a generative model of emotional states.
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Affiliation(s)
- Daphne Demekas
- Department of Mathematics, University College London, London, United Kingdom
| | - Thomas Parr
- Department of Mathematics, University College London, London, United Kingdom
| | - Karl J. Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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19
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Sato W, Kochiyama T, Uono S, Sawada R, Kubota Y, Yoshimura S, Toichi M. Resting-state neural activity and connectivity associated with subjective happiness. Sci Rep 2019; 9:12098. [PMID: 31431639 PMCID: PMC6702218 DOI: 10.1038/s41598-019-48510-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 08/07/2019] [Indexed: 11/23/2022] Open
Abstract
The majority of people throughout the world rate subjective happiness as the top of the important thing in life. A recent structural neuroimaging study exploring neurocognitive mechanisms underlying subjective happiness has suggested that the gray matter volume of the right precuneus is associated with Subjective Happiness Scale (SHS) scores. However, how the neural activity in this region, as well as the neural functional coupling between this and other regions, could be related to SHS scores remains unclear. To investigate these issues, we performed resting-state functional magnetic resonance imaging and analyzed the fractional amplitude of low-frequency fluctuation (fALFF) in participants, whose subjective happiness was evaluated using the SHS. Lower fALFF values in the right precuneus were associated with higher SHS scores. Furthermore, functional connectivity and spectral dynamic causal modeling analyses showed that both functional and effective connectivity of the right precuneus with the right amygdala were positively associated with SHS scores. These findings, together with other evidence on the information-processing functions of these brain regions, suggest the possibility that subjective happiness is associated with a reduction in self-referential mental processes, which are well integrated with emotional processing.
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Affiliation(s)
- Wataru Sato
- Kokoro Research Center, Kyoto University, Kyoto University, 46 Shimoadachi, Sakyo, Kyoto, 606-8501, Japan.
| | - Takanori Kochiyama
- Brain Activity Imaging Center, ATR-Promotions, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
| | - Shota Uono
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo, Kyoto, 606-8507, Japan
| | - Reiko Sawada
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo, Kyoto, 606-8507, Japan
| | - Yasutaka Kubota
- Health and Medical Services Center, Shiga University, 1-1-1, Baba, Hikone, Shiga, 522-8522, Japan
| | - Sayaka Yoshimura
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo, Kyoto, 606-8507, Japan
| | - Motomi Toichi
- Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan.,The Organization for Promoting Neurodevelopmental Disorder Research, 40 Shogoin-Sannocho, Sakyo, Kyoto, 606-8392, Japan
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20
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Pinotsis DA, Loonis R, Bastos AM, Miller EK, Friston KJ. Bayesian Modelling of Induced Responses and Neuronal Rhythms. Brain Topogr 2019; 32:569-582. [PMID: 27718099 PMCID: PMC6592965 DOI: 10.1007/s10548-016-0526-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 09/23/2016] [Indexed: 12/18/2022]
Abstract
Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-posed problem of reconstructing-and explaining-oscillatory responses. We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses-and the identification of important sources of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. We hope to show that electrophysiological measurements contain much more spatial information than is often thought: on the one hand, the dynamic causal modelling of non-invasive (low spatial resolution) electrophysiology can afford sub-millimetre (hyper-acute) resolution that is limited only by the (spatial) complexity of the underlying (dynamic causal) forward model. On the other hand, invasive microelectrode recordings (that penetrate different cortical layers) can reveal laminar-specific responses and elucidate hierarchical message passing and information processing within and between cortical regions at a macroscopic scale. In short, the careful and biophysically grounded modelling of sparse data enables one to characterise the neuronal architectures generating oscillations in a remarkable detail.
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Affiliation(s)
- Dimitris A Pinotsis
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- The Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK.
| | - Roman Loonis
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Andre M Bastos
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK
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21
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Abadi AK, Yahya K, Amini M, Friston K, Heinke D. Excitatory versus inhibitory feedback in Bayesian formulations of scene construction. J R Soc Interface 2019; 16:20180344. [PMID: 31039693 PMCID: PMC6544897 DOI: 10.1098/rsif.2018.0344] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.
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Affiliation(s)
- Alireza Khatoon Abadi
- 1 Department of Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University , Tehran 14115-134 , Iran
| | - Keyvan Yahya
- 2 Faculty of Informatics, Chemnitz University of Technology , Straße der Nationen 62, R. B216, 09111 Chemnitz , Germany
| | - Massoud Amini
- 1 Department of Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University , Tehran 14115-134 , Iran
| | - Karl Friston
- 3 Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London , 12 Queen Square, London WC1N 3BG , UK
| | - Dietmar Heinke
- 4 Centre for Computational Neuroscience and Cognitive Robotics, School of Psychology, University of Birmingham , Edgbaston, Birmingham B15 2TT , UK
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22
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Rohenkohl G, Bosman CA, Fries P. Gamma Synchronization between V1 and V4 Improves Behavioral Performance. Neuron 2018; 100:953-963.e3. [PMID: 30318415 DOI: 10.1016/j.neuron.2018.09.019] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 08/09/2018] [Accepted: 09/11/2018] [Indexed: 10/28/2022]
Abstract
Behavior is often driven by visual stimuli, relying on feedforward communication from lower to higher visual areas. Effective communication depends on enhanced interareal coherence, but it remains unclear whether this coherence occurs at an optimal phase relation that actually improves stimulus transmission to behavioral report. We recorded local field potentials from V1 and V4 of macaques performing an attention task during which they reported changes in the attended stimulus. V1-V4 gamma synchronization immediately preceding the stimulus change partly predicted subsequent reaction times (RTs). RTs slowed systematically as trial-by-trial interareal gamma phase relations deviated from the phase relation at which V1 and V4 synchronized on average. V1-V4 gamma phase relations accounted for RT differences of 13-31 ms. Effects were specific to the attended stimulus and not explained by local power or phase. Thus, interareal gamma synchronization occurs at the optimal phase relation for transmission of sensory inputs to motor responses.
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Affiliation(s)
- Gustavo Rohenkohl
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Conrado Arturo Bosman
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, the Netherlands; Swammerdam Institute for Life Sciences, Center for Neuroscience, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
| | - Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, the Netherlands.
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23
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Generic dynamic causal modelling: An illustrative application to Parkinson's disease. Neuroimage 2018; 181:818-830. [PMID: 30130648 PMCID: PMC7343527 DOI: 10.1016/j.neuroimage.2018.08.039] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 08/15/2018] [Accepted: 08/16/2018] [Indexed: 12/26/2022] Open
Abstract
We present a technical development in the dynamic causal modelling of
electrophysiological responses that combines qualitatively different neural mass
models within a single network. This affords the option to couple various
cortical and subcortical nodes that differ in their form and dynamics. Moreover,
it enables users to implement new neural mass models in a straightforward and
standardized way. This generic framework hence supports flexibility and
facilitates the exploration of increasingly plausible models. We illustrate this
by coupling a basal ganglia-thalamus model to a (previously validated) cortical
model developed specifically for motor cortex. The ensuing DCM is used to infer
pathways that contribute to the suppression of beta oscillations induced by
dopaminergic medication in patients with Parkinson's disease.
Experimental recordings were obtained from deep brain stimulation electrodes
(implanted in the subthalamic nucleus) and simultaneous magnetoencephalography.
In line with previous studies, our results indicate a reduction of synaptic
efficacy within the circuit between the subthalamic nucleus and external
pallidum, as well as reduced efficacy in connections of the hyperdirect and
indirect pathway leading to this circuit. This work forms the foundation for a
range of modelling studies of the synaptic mechanisms (and pathophysiology)
underlying event-related potentials and cross-spectral densities.
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Abstract
Theta rhythms, ≈3–8 Hz, have been found in many different parts of the brain. They are predominant in the rodent hippocampus, yet have also been described in the neocortex, primarily in frontal and parietal areas in relation to executive functions. Here, we show a ≈4-Hz theta rhythm in awake macaque monkey area V4 and primary visual cortex. This theta rhythm was spatially coextensive with visually induced gamma-band activity, and gamma power was modulated by theta phase. The strength of theta and of theta-rhythmic gamma modulation was markedly reduced by selective attention. Theta rhythmicity has been observed in microsaccade sequences, and microsaccades influence early visual activity. Yet, removing (the effects of) microsaccades did not influence the results. Theta rhythms govern rodent sniffing and whisking, and human language processing. Human psychophysics suggests a role for theta also in visual attention. However, little is known about theta in visual areas and its attentional modulation. We used electrocorticography (ECoG) to record local field potentials (LFPs) simultaneously from areas V1, V2, V4, and TEO of two macaque monkeys performing a selective visual attention task. We found a ≈4-Hz theta rhythm within both the V1–V2 and the V4–TEO region, and theta synchronization between them, with a predominantly feedforward directed influence. ECoG coverage of large parts of these regions revealed a surprising spatial correspondence between theta and visually induced gamma. Furthermore, gamma power was modulated with theta phase. Selective attention to the respective visual stimulus strongly reduced these theta-rhythmic processes, leading to an unusually strong attention effect for V1. Microsaccades (MSs) were partly locked to theta. However, neuronal theta rhythms tended to be even more pronounced for epochs devoid of MSs. Thus, we find an MS-independent theta rhythm specific to visually driven parts of V1–V2, which rhythmically modulates local gamma and entrains V4–TEO, and which is strongly reduced by attention. We propose that the less theta-rhythmic and thereby more continuous processing of the attended stimulus serves the exploitation of this behaviorally most relevant information. The theta-rhythmic and thereby intermittent processing of the unattended stimulus likely reflects the ecologically important exploration of less relevant sources of information.
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Friston KJ, Parr T, de Vries B. The graphical brain: Belief propagation and active inference. Netw Neurosci 2017; 1:381-414. [PMID: 29417960 PMCID: PMC5798592 DOI: 10.1162/netn_a_00018] [Citation(s) in RCA: 190] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 05/10/2017] [Indexed: 12/19/2022] Open
Abstract
This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. AUTHOR SUMMARY This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain.
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Affiliation(s)
- Karl J. Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom
| | - Bert de Vries
- Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, The Netherlands
- GN Hearing, Eindhoven, The Netherlands
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26
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Scheeringa R, Fries P. Cortical layers, rhythms and BOLD signals. Neuroimage 2017; 197:689-698. [PMID: 29108940 PMCID: PMC6666418 DOI: 10.1016/j.neuroimage.2017.11.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 10/16/2017] [Accepted: 11/01/2017] [Indexed: 12/22/2022] Open
Abstract
This review investigates how laminar fMRI can complement insights into brain function derived from the study of rhythmic neuronal synchronization. Neuronal synchronization in various frequency bands plays an important role in neuronal communication between brain areas, and it does so on the backbone of layer-specific interareal anatomical projections. Feedforward projections originate predominantly in supragranular cortical layers and terminate in layer 4, and this pattern is reflected in inter-laminar and interareal directed gamma-band influences. Thus, gamma-band synchronization likely subserves feedforward signaling. By contrast, anatomical feedback projections originate predominantly in infragranular layers and terminate outside layer 4, and this pattern is reflected in inter-laminar and interareal directed alpha- and/or beta-band influences. Thus, alpha-beta band synchronization likely subserves feedback signaling. Furthermore, these rhythms explain part of the BOLD signal, with independent contributions of alpha-beta and gamma. These findings suggest that laminar fMRI can provide us with a potentially useful method to test some of the predictions derived from the study of neuronal synchronization. We review central findings regarding the role of layer-specific neuronal synchronization for brain function, and regarding the link between neuronal synchronization and the BOLD signal. We discuss the role that laminar fMRI could play by comparing it to invasive and non-invasive electrophysiological recordings. Compared to direct electrophysiological recordings, this method provides a metric of neuronal activity that is slow and indirect, but that is uniquely non-invasive and layer-specific with potentially whole brain coverage.
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Affiliation(s)
- René Scheeringa
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Institut National De La Santé Et De La Recherche Médicale U1028, Centre National De La Recherche Scientifique UMR S5292, Centre De Recherche En Neurosciences De Lyon, Bron, France
| | - Pascal Fries
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany.
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Zeidman P, Silson EH, Schwarzkopf DS, Baker CI, Penny W. Bayesian population receptive field modelling. Neuroimage 2017; 180:173-187. [PMID: 28890416 PMCID: PMC7417811 DOI: 10.1016/j.neuroimage.2017.09.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 06/28/2017] [Accepted: 09/05/2017] [Indexed: 12/25/2022] Open
Abstract
We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intended to work with stimuli of any dimension and is demonstrated and validated in the context of 2D retinotopic mapping. The framework enables the experimenter to specify generative (encoding) models of fMRI timeseries, in which experimental stimuli enter a pRF model of neural activity, which in turns drives a nonlinear model of neurovascular coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal and haemodynamic parameters are estimated together on a voxel-by-voxel or region-of-interest basis using a Bayesian estimation algorithm (variational Laplace). This offers several novel contributions to receptive field modelling. The variance/covariance of parameters are estimated, enabling receptive fields to be plotted while properly representing uncertainty about pRF size and location. Variability in the haemodynamic response across the brain is accounted for. Furthermore, the framework introduces formal hypothesis testing to pRF analysis, enabling competing models to be evaluated based on their log model evidence (approximated by the variational free energy), which represents the optimal tradeoff between accuracy and complexity. Using simulations and empirical data, we found that parameters typically used to represent pRF size and neuronal scaling are strongly correlated, which is taken into account by the Bayesian methods we describe when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7 T functional MRI data and we found a circular Difference of Gaussians (DoG) model to be the best explanation for our data overall. We hope this framework will prove useful for mapping stimulus spaces with any number of dimensions onto the anatomy of the brain. We introduce a Bayesian toolbox for population receptive field (pRF) mapping. Neuronal and haemodynamic parameters are estimated per voxel or per region. Hypotheses can be tested by comparing pRF models based on their evidence. The uncertainty over parameters (such as pRF size) is estimated and visualised. We establish face validity using simulations and test-rest reliability with 7 T fMRI.
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Affiliation(s)
- Peter Zeidman
- The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London, WC1N 3BG, UK.
| | - Edward Harry Silson
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, 20892-1366, USA
| | - Dietrich Samuel Schwarzkopf
- Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK; UCL Institute of Cognitive Neuroscience, 17-19 Queen Square, London, WC1N 3AR, UK
| | - Chris Ian Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, 20892-1366, USA
| | - Will Penny
- The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London, WC1N 3BG, UK
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Shaw AD, Moran RJ, Muthukumaraswamy SD, Brealy J, Linden DE, Friston KJ, Singh KD. Neurophysiologically-informed markers of individual variability and pharmacological manipulation of human cortical gamma. Neuroimage 2017; 161:19-31. [PMID: 28807873 PMCID: PMC5692925 DOI: 10.1016/j.neuroimage.2017.08.034] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 08/08/2017] [Accepted: 08/10/2017] [Indexed: 12/13/2022] Open
Abstract
The ability to quantify synaptic function at the level of cortical microcircuits from non-invasive data would be enormously useful in the study of neuronal processing in humans and the pathophysiology that attends many neuropsychiatric disorders. Here, we provide proof of principle that one can estimate inter-and intra-laminar interactions among specific neuronal populations using induced gamma responses in the visual cortex of human subjects - using dynamic causal modelling based upon the canonical microcircuit (CMC; a simplistic model of a cortical column). Using variability in induced (spectral) responses over a large cohort of normal subjects, we find that the predominant determinants of gamma responses rest on recurrent and intrinsic connections between superficial pyramidal cells and inhibitory interneurons. Furthermore, variations in beta responses were mediated by inter-subject differences in the intrinsic connections between deep pyramidal cells and inhibitory interneurons. Interestingly, we also show that increasing the self-inhibition of superficial pyramidal cells suppresses the amplitude of gamma activity, while increasing its peak frequency. This systematic and nonlinear relationship was only disclosed by modelling the causes of induced responses. Crucially, we were able to validate this form of neurophysiological phenotyping by showing a selective effect of the GABA re-uptake inhibitor tiagabine on the rate constants of inhibitory interneurons. Remarkably, we were able to recover the pharmacodynamics of this effect over the course of several hours on a per subject basis. These findings speak to the possibility of measuring population specific synaptic function - and its response to pharmacological intervention - to provide subject-specific biomarkers of mesoscopic neuronal processes using non-invasive data. Finally, our results demonstrate that, using the CMC as a proxy, the synaptic mechanisms that underlie the gain control of neuronal message passing within and between different levels of cortical hierarchies may now be amenable to quantitative study using non-invasive (MEG) procedures.
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Affiliation(s)
- A D Shaw
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK
| | - R J Moran
- Department of Engineering Mathematics, Merchant Venturers School of Engineering, University of Bristol, UK
| | - S D Muthukumaraswamy
- School of Pharmacy, The University of Auckland, Auckland, New Zealand; School of Psychology, The University of Auckland, Auckland, New Zealand
| | - J Brealy
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK
| | - D E Linden
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - K J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, UK
| | - K D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK.
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The Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream. J Neurosci 2017; 37:6751-6760. [PMID: 28607165 PMCID: PMC5508257 DOI: 10.1523/jneurosci.0291-17.2017] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 05/02/2017] [Accepted: 05/04/2017] [Indexed: 01/02/2023] Open
Abstract
Stimulus predictability can lead to substantial modulations of brain activity, such as shifts in sustained magnetic field amplitude, measured with magnetoencephalography (MEG). Here, we provide a mechanistic explanation of these effects using MEG data acquired from healthy human volunteers (N = 13, 7 female). In a source-level analysis of induced responses, we established the effects of orthogonal predictability manipulations of rapid tone-pip sequences (namely, sequence regularity and alphabet size) along the auditory processing stream. In auditory cortex, regular sequences with smaller alphabets induced greater gamma activity. Furthermore, sequence regularity shifted induced activity in frontal regions toward higher frequencies. To model these effects in terms of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and estimated slow fluctuations in neural (postsynaptic) gain. Using the model-based parameters, we accurately explain the sensor-level sustained field amplitude, demonstrating that slow changes in synaptic efficacy, combined with sustained sensory input, can result in profound and sustained effects on neural responses to predictable sensory streams. SIGNIFICANCE STATEMENT Brain activity can be strongly modulated by the predictability of stimuli it is currently processing. An example of such a modulation is a shift in sustained magnetic field amplitude, measured with magnetoencephalography. Here, we provide a mechanistic explanation of these effects. First, we establish the oscillatory neural correlates of independent predictability manipulations in hierarchically distinct areas of the auditory processing stream. Next, we use a biophysically realistic computational model to explain these effects in terms of the underlying neurophysiology. Finally, using the model-based parameters describing neural gain modulation, we can explain the previously unexplained effects observed at the sensor level. This demonstrates that slow modulations of synaptic gain can result in profound and sustained effects on neural activity.
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30
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On memories, neural ensembles and mental flexibility. Neuroimage 2017; 157:297-313. [PMID: 28602817 DOI: 10.1016/j.neuroimage.2017.05.068] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Revised: 05/30/2017] [Accepted: 05/31/2017] [Indexed: 12/18/2022] Open
Abstract
Memories are assumed to be represented by groups of co-activated neurons, called neural ensembles. Describing ensembles is a challenge: complexity of the underlying micro-circuitry is immense. Current approaches use a piecemeal fashion, focusing on single neurons and employing local measures like pairwise correlations. We introduce an alternative approach that identifies ensembles and describes the effective connectivity between them in a holistic fashion. It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed. Using unsupervised learning, biophysical modeling and graph theory, we analyze multi-electrode LFPs from frontal cortex during a spatial delayed response task. We find distinct ensembles for different cues and more parsimonious connectivity for cues on the horizontal axis, which may explain the oblique effect in psychophysics. Our approach paves the way for biophysical models with learned parameters that can guide future Brain Computer Interface development.
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Abstract
Several recent studies have demonstrated that the bottom-up signaling of a visual stimulus is subserved by interareal gamma-band synchronization, whereas top-down influences are mediated by alpha-beta band synchronization. These processes may implement top-down control of stimulus processing if top-down and bottom-up mediating rhythms are coupled via cross-frequency interaction. To test this possibility, we investigated Granger-causal influences among awake macaque primary visual area V1, higher visual area V4, and parietal control area 7a during attentional task performance. Top-down 7a-to-V1 beta-band influences enhanced visually driven V1-to-V4 gamma-band influences. This enhancement was spatially specific and largest when beta-band activity preceded gamma-band activity by ∼0.1 s, suggesting a causal effect of top-down processes on bottom-up processes. We propose that this cross-frequency interaction mechanistically subserves the attentional control of stimulus selection.SIGNIFICANCE STATEMENT Contemporary research indicates that the alpha-beta frequency band underlies top-down control, whereas the gamma-band mediates bottom-up stimulus processing. This arrangement inspires an attractive hypothesis, which posits that top-down beta-band influences directly modulate bottom-up gamma band influences via cross-frequency interaction. We evaluate this hypothesis determining that beta-band top-down influences from parietal area 7a to visual area V1 are correlated with bottom-up gamma frequency influences from V1 to area V4, in a spatially specific manner, and that this correlation is maximal when top-down activity precedes bottom-up activity. These results show that for top-down processes such as spatial attention, elevated top-down beta-band influences directly enhance feedforward stimulus-induced gamma-band processing, leading to enhancement of the selected stimulus.
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32
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Díez Á, Ranlund S, Pinotsis D, Calafato S, Shaikh M, Hall MH, Walshe M, Nevado Á, Friston KJ, Adams RA, Bramon E. Abnormal frontoparietal synaptic gain mediating the P300 in patients with psychotic disorder and their unaffected relatives. Hum Brain Mapp 2017; 38:3262-3276. [PMID: 28345275 DOI: 10.1002/hbm.23588] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 03/14/2017] [Accepted: 03/15/2017] [Indexed: 01/29/2023] Open
Abstract
The "dysconnection hypothesis" of psychosis suggests that a disruption of functional integration underlies cognitive deficits and clinical symptoms. Impairments in the P300 potential are well documented in psychosis. Intrinsic (self-)connectivity in a frontoparietal cortical hierarchy during a P300 experiment was investigated. Dynamic Causal Modeling was used to estimate how evoked activity results from the dynamics of coupled neural populations and how neural coupling changes with the experimental factors. Twenty-four patients with psychotic disorder, twenty-four unaffected relatives, and twenty-five controls underwent EEG recordings during an auditory oddball paradigm. Sixteen frontoparietal network models (including primary auditory, superior parietal, and superior frontal sources) were analyzed and an optimal model of neural coupling, explaining diagnosis and genetic risk effects, as well as their interactions with task condition were identified. The winning model included changes in connectivity at all three hierarchical levels. Patients showed decreased self-inhibition-that is, increased cortical excitability-in left superior frontal gyrus across task conditions, compared with unaffected participants. Relatives had similar increases in excitability in left superior frontal and right superior parietal sources, and a reversal of the normal synaptic gain changes in response to targets relative to standard tones. It was confirmed that both subjects with psychotic disorder and their relatives show a context-independent loss of synaptic gain control at the highest hierarchy levels. The relatives also showed abnormal gain modulation responses to task-relevant stimuli. These may be caused by NMDA-receptor and/or GABAergic pathologies that change the excitability of superficial pyramidal cells and may be a potential biological marker for psychosis. Hum Brain Mapp 38:3262-3276, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Álvaro Díez
- Division of Psychiatry, University College London, London, United Kingdom.,Department of Basic Psychology II - Cognitive processes, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain.,Laboratory of Cognitive and Computational Neuroscience - Centre for Biomedical Technology (CTB), Complutense University and Technical University of Madrid, Madrid, Spain
| | - Siri Ranlund
- Division of Psychiatry, University College London, London, United Kingdom.,Psychology & Neuroscience - King's College London, Institute of Psychiatry, London, United Kingdom
| | - Dimitris Pinotsis
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Stella Calafato
- Division of Psychiatry, University College London, London, United Kingdom
| | - Madiha Shaikh
- North East London NHS Foundation Trust, London, United Kingdom.,Psychology & Neuroscience - King's College London, Institute of Psychiatry, London, United Kingdom
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, McLean Hospital, Harvard Medical School, Belmont, Massachusetts
| | - Muriel Walshe
- Division of Psychiatry, University College London, London, United Kingdom.,Psychology & Neuroscience - King's College London, Institute of Psychiatry, London, United Kingdom
| | - Ángel Nevado
- Department of Basic Psychology II - Cognitive processes, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain.,Laboratory of Cognitive and Computational Neuroscience - Centre for Biomedical Technology (CTB), Complutense University and Technical University of Madrid, Madrid, Spain
| | - Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Rick A Adams
- Division of Psychiatry, University College London, London, United Kingdom.,Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Elvira Bramon
- Division of Psychiatry, University College London, London, United Kingdom.,Psychology & Neuroscience - King's College London, Institute of Psychiatry, London, United Kingdom.,Institute of Cognitive Neuroscience, University College London, London, United Kingdom
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33
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Fardo F, Auksztulewicz R, Allen M, Dietz MJ, Roepstorff A, Friston KJ. Expectation violation and attention to pain jointly modulate neural gain in somatosensory cortex. Neuroimage 2017; 153:109-121. [PMID: 28341164 PMCID: PMC5460976 DOI: 10.1016/j.neuroimage.2017.03.041] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 01/08/2017] [Accepted: 03/20/2017] [Indexed: 10/27/2022] Open
Abstract
The neural processing and experience of pain are influenced by both expectations and attention. For example, the amplitude of event-related pain responses is enhanced by both novel and unexpected pain, and by moving the focus of attention towards a painful stimulus. Under predictive coding, this congruence can be explained by appeal to a precision-weighting mechanism, which mediates bottom-up and top-down attentional processes by modulating the influence of feedforward and feedback signals throughout the cortical hierarchy. The influence of expectation and attention on pain processing can be mapped onto changes in effective connectivity between or within specific neuronal populations, using a canonical microcircuit (CMC) model of hierarchical processing. We thus implemented a CMC within dynamic causal modelling for magnetoencephalography in human subjects, to investigate how expectation violation and attention to pain modulate intrinsic (within-source) and extrinsic (between-source) connectivity in the somatosensory hierarchy. This enabled us to establish whether both expectancy and attentional processes are mediated by a similar precision-encoding mechanism within a network of somatosensory, frontal and parietal sources. We found that both unexpected and attended pain modulated the gain of superficial pyramidal cells in primary and secondary somatosensory cortex. This modulation occurred in the context of increased lateralized recurrent connectivity between somatosensory and fronto-parietal sources, driven by unexpected painful occurrences. Finally, the strength of effective connectivity parameters in S1, S2 and IFG predicted individual differences in subjective pain modulation ratings. Our findings suggest that neuromodulatory gain control in the somatosensory hierarchy underlies the influence of both expectation violation and attention on cortical processing and pain perception.
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Affiliation(s)
- Francesca Fardo
- Danish Pain Centre, Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark; Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark; Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom.
| | - Ryszard Auksztulewicz
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford OX3 7JX, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
| | - Micah Allen
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
| | - Martin J Dietz
- Center for Functionally Integrative Neuroscience, Aarhus University, 8000 Aarhus, Denmark
| | - Andreas Roepstorff
- Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark; Center for Functionally Integrative Neuroscience, Aarhus University, 8000 Aarhus, Denmark
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
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Pinotsis DA, Geerts JP, Pinto L, FitzGerald THB, Litvak V, Auksztulewicz R, Friston KJ. Linking canonical microcircuits and neuronal activity: Dynamic causal modelling of laminar recordings. Neuroimage 2017; 146:355-366. [PMID: 27871922 PMCID: PMC5312791 DOI: 10.1016/j.neuroimage.2016.11.041] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 11/10/2016] [Accepted: 11/16/2016] [Indexed: 12/20/2022] Open
Abstract
Neural models describe brain activity at different scales, ranging from single cells to whole brain networks. Here, we attempt to reconcile models operating at the microscopic (compartmental) and mesoscopic (neural mass) scales to analyse data from microelectrode recordings of intralaminar neural activity. Although these two classes of models operate at different scales, it is relatively straightforward to create neural mass models of ensemble activity that are equipped with priors obtained after fitting data generated by detailed microscopic models. This provides generative (forward) models of measured neuronal responses that retain construct validity in relation to compartmental models. We illustrate our approach using cross spectral responses obtained from V1 during a visual perception paradigm that involved optogenetic manipulation of the basal forebrain. We find that the resulting neural mass model can distinguish between activity in distinct cortical layers - both with and without optogenetic activation - and that cholinergic input appears to enhance (disinhibit) superficial layer activity relative to deep layers. This is particularly interesting from the perspective of predictive coding, where neuromodulators are thought to boost prediction errors that ascend the cortical hierarchy.
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Affiliation(s)
- D A Pinotsis
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
| | - J P Geerts
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - L Pinto
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States
| | - T H B FitzGerald
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK; MPS - UCL Centre for Computational Psychiatry and Ageing Research, Russell Square House, London, WC1B 5EH, UK
| | - V Litvak
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - R Auksztulewicz
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK; Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - K J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
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35
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Friston KJ, Parr T, de Vries B. The graphical brain: Belief propagation and active inference. Netw Neurosci 2017. [PMID: 29417960 DOI: 10.1162/netn˙a˙00018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
UNLABELLED This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. AUTHOR SUMMARY This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain.
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Affiliation(s)
- Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom
| | - Bert de Vries
- Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, The Netherlands
- GN Hearing, Eindhoven, The Netherlands
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Mejias JF, Murray JD, Kennedy H, Wang XJ. Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. SCIENCE ADVANCES 2016; 2:e1601335. [PMID: 28138530 PMCID: PMC5262462 DOI: 10.1126/sciadv.1601335] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 10/20/2016] [Indexed: 05/25/2023]
Abstract
Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.
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Affiliation(s)
- Jorge F. Mejias
- Center for Neural Science, New York University (NYU), New York, NY 10003, USA
| | - John D. Murray
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Henry Kennedy
- Stem Cell and Brain Research Institute, INSERM U846, Bron, France
- Université de Lyon, Université Lyon I, Lyon, France
| | - Xiao-Jing Wang
- Center for Neural Science, New York University (NYU), New York, NY 10003, USA
- NYU–East China Normal University Institute for Brain and Cognitive Science, NYU Shanghai, Shanghai, China
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37
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Pinotsis DA, Perry G, Litvak V, Singh KD, Friston KJ. Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields. Hum Brain Mapp 2016; 37:4597-4614. [PMID: 27593199 PMCID: PMC5111616 DOI: 10.1002/hbm.23331] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 07/21/2016] [Accepted: 07/22/2016] [Indexed: 12/11/2022] Open
Abstract
This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation‐inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject‐specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal‐interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models—and optimize the corresponding connectivity estimates at the within and between‐subject level. We also consider Bayesian cross‐validation to obtain predictive estimates for gamma‐response phenotypes, using a leave‐one‐out procedure. Hum Brain Mapp 37:4597–4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Dimitris A Pinotsis
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts.,The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG
| | - Gavin Perry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Park Place, Cardiff, Wales, CF10 3AT, United Kingdom
| | - Vladimir Litvak
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Park Place, Cardiff, Wales, CF10 3AT, United Kingdom
| | - Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG
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38
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de Bruin L, Strijbos D. Direct social perception, mindreading and Bayesian predictive coding. Conscious Cogn 2015; 36:565-70. [DOI: 10.1016/j.concog.2015.04.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 04/23/2015] [Accepted: 04/24/2015] [Indexed: 10/23/2022]
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39
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Ranlund S, Adams RA, Díez Á, Constante M, Dutt A, Hall MH, Maestro Carbayo A, McDonald C, Petrella S, Schulze K, Shaikh M, Walshe M, Friston K, Pinotsis D, Bramon E. Impaired prefrontal synaptic gain in people with psychosis and their relatives during the mismatch negativity. Hum Brain Mapp 2015; 37:351-65. [PMID: 26503033 PMCID: PMC4843949 DOI: 10.1002/hbm.23035] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 09/30/2015] [Accepted: 10/13/2015] [Indexed: 12/11/2022] Open
Abstract
The mismatch negativity (MMN) evoked potential, a preattentive brain response to a discriminable change in auditory stimulation, is significantly reduced in psychosis. Glutamatergic theories of psychosis propose that hypofunction of NMDA receptors (on pyramidal cells and inhibitory interneurons) causes a loss of synaptic gain control. We measured changes in neuronal effective connectivity underlying the MMN using dynamic causal modeling (DCM), where the gain (excitability) of superficial pyramidal cells is explicitly parameterised. EEG data were obtained during a MMN task—for 24 patients with psychosis, 25 of their first‐degree unaffected relatives, and 35 controls—and DCM was used to estimate the excitability (modeled as self‐inhibition) of (source‐specific) superficial pyramidal populations. The MMN sources, based on previous research, included primary and secondary auditory cortices, and the right inferior frontal gyrus. Both patients with psychosis and unaffected relatives (to a lesser degree) showed increased excitability in right inferior frontal gyrus across task conditions, compared to controls. Furthermore, in the same region, both patients and their relatives showed a reversal of the normal response to deviant stimuli; that is, a decrease in excitability in comparison to standard conditions. Our results suggest that psychosis and genetic risk for the illness are associated with both context‐dependent (condition‐specific) and context‐independent abnormalities of the excitability of superficial pyramidal cell populations in the MMN paradigm. These abnormalities could relate to NMDA receptor hypofunction on both pyramidal cells and inhibitory interneurons, and appear to be linked to the genetic aetiology of the illness, thereby constituting potential endophenotypes for psychosis. Hum Brain Mapp 37:351–365, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Siri Ranlund
- Division of Psychiatry, University College London, London, United Kingdom
| | - Rick A Adams
- Division of Psychiatry, University College London, London, United Kingdom.,Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Álvaro Díez
- Division of Psychiatry, University College London, London, United Kingdom
| | - Miguel Constante
- Department of Psychiatry, Hospital Beatriz Angelo, Lisbon, Portugal
| | - Anirban Dutt
- The South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Mei-Hua Hall
- Psychology Research Laboratory, Harvard Medical School, McLean Hospital, Belmont, Massachusetts, USA
| | - Amparo Maestro Carbayo
- The South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Colm McDonald
- Department of Psychiatry, Clinical Science Institute, National University of Ireland, Galway, Ireland
| | - Sabrina Petrella
- The South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.,Department of Psychiatry, Clinical and Experimental Science Institute, University of Foggia, Italy
| | - Katja Schulze
- The South London and Maudsley NHS Foundation Trust, University Hospital Lewisham, London, United Kingdom
| | - Madiha Shaikh
- The South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.,Neuroepidemiology and Ageing Research Unit, Imperial College, London, United Kingdom
| | - Muriel Walshe
- Division of Psychiatry, University College London, London, United Kingdom.,The South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Dimitris Pinotsis
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Elvira Bramon
- Division of Psychiatry, University College London, London, United Kingdom.,Institute of Cognitive Neuroscience, University College London, London, United Kingdom.,The South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
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40
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Bosman CA, Aboitiz F. Functional constraints in the evolution of brain circuits. Front Neurosci 2015; 9:303. [PMID: 26388716 PMCID: PMC4555059 DOI: 10.3389/fnins.2015.00303] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 08/10/2015] [Indexed: 12/12/2022] Open
Abstract
Regardless of major anatomical and neurodevelopmental differences, the vertebrate isocortex shows a remarkably well-conserved organization. In the isocortex, reciprocal connections between excitatory and inhibitory neurons are distributed across multiple layers, encompassing modular, dynamical and recurrent functional networks during information processing. These dynamical brain networks are often organized in neuronal assemblies interacting through rhythmic phase relationships. Accordingly, these oscillatory interactions are observed across multiple brain scale levels, and they are associated with several sensory, motor, and cognitive processes. Most notably, oscillatory interactions are also found in the complete spectrum of vertebrates. Yet, it is unknown why this functional organization is so well conserved in evolution. In this perspective, we propose some ideas about how functional requirements of the isocortex can account for the evolutionary stability observed in microcircuits across vertebrates. We argue that isocortex architectures represent canonical microcircuits resulting from: (i) the early selection of neuronal architectures based on the oscillatory excitatory-inhibitory balance, which lead to the implementation of compartmentalized oscillations and (ii) the subsequent emergence of inferential coding strategies (predictive coding), which are able to expand computational capacities. We also argue that these functional constraints may be the result of several advantages that oscillatory activity contributes to brain network processes, such as information transmission and code reliability. In this manner, similarities in mesoscale brain circuitry and input-output organization between different vertebrate groups may reflect evolutionary constraints imposed by these functional requirements, which may or may not be traceable to a common ancestor.
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Affiliation(s)
- Conrado A Bosman
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, Faculty of Science, University of Amsterdam Amsterdam, Netherlands ; Facultad de Ciencias de la Salud, Universidad Autónoma de Chile Santiago, Chile
| | - Francisco Aboitiz
- Departamento de Psiquiatría, Centro Interdisciplinario de Neurociencia, Escuela de Medicina, Pontificia Universidad Católica de Chile Santiago, Chile
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41
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Cooray GK, Sengupta B, Douglas P, Englund M, Wickstrom R, Friston K. Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling. Neuroimage 2015; 118:508-19. [PMID: 26032883 PMCID: PMC4558461 DOI: 10.1016/j.neuroimage.2015.05.064] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Revised: 04/16/2015] [Accepted: 05/24/2015] [Indexed: 01/27/2023] Open
Abstract
We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory–inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis. We characterised seizures in patient with anti-NMDA-R encephalitis using EEG. Dynamic causal modelling was used to estimate causes of seizure activity. Characteristic variation of excitatory–inhibitory balance during seizure activity. This variation was seen for seizures within and between patients.
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Affiliation(s)
- Gerald K Cooray
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Biswa Sengupta
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Pamela Douglas
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Marita Englund
- Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
| | - Ronny Wickstrom
- Neuropediatric Unit, Department of Women's and Children's Health, Karolinska Institutet, Sweden
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
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42
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Perry G, Randle JM, Koelewijn L, Routley BC, Singh KD. Linear tuning of gamma amplitude and frequency to luminance contrast: evidence from a continuous mapping paradigm. PLoS One 2015; 10:e0124798. [PMID: 25906070 PMCID: PMC4408014 DOI: 10.1371/journal.pone.0124798] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Accepted: 03/18/2015] [Indexed: 02/02/2023] Open
Abstract
Individual differences in the visual gamma (30–100Hz) response and their potential as trait markers of underlying physiology (particularly related to GABAergic inhibition) have become a matter of increasing interest in recent years. There is growing evidence, however, that properties of the gamma response (e.g., its amplitude and frequency) are highly stimulus dependent, and that individual differences in the gamma response may reflect individual differences in the stimulus tuning functions of gamma oscillations. Here, we measured the tuning functions of gamma amplitude and frequency to luminance contrast in eighteen participants using MEG. We used a grating stimulus in which stimulus contrast was modulated continuously over time. We found that both gamma amplitude and frequency were linearly modulated by stimulus contrast, but that the gain of this modulation (as reflected in the linear gradient) varied across individuals. We additionally observed a stimulus-induced response in the beta frequency range (10–25Hz), but neither the amplitude nor the frequency of this response was consistently modulated by the stimulus over time. Importantly, we did not find a correlation between the gain of the gamma-band amplitude and frequency tuning functions across individuals, suggesting that these may be independent traits driven by distinct neurophysiological processes.
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Affiliation(s)
- Gavin Perry
- Cardiff University Brain Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- * E-mail:
| | - James M. Randle
- Cardiff University Brain Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Loes Koelewijn
- Cardiff University Brain Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Bethany C. Routley
- Cardiff University Brain Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Krish D. Singh
- Cardiff University Brain Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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43
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Bastos AM, Litvak V, Moran R, Bosman CA, Fries P, Friston KJ. A DCM study of spectral asymmetries in feedforward and feedback connections between visual areas V1 and V4 in the monkey. Neuroimage 2015; 108:460-75. [PMID: 25585017 PMCID: PMC4334664 DOI: 10.1016/j.neuroimage.2014.12.081] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Revised: 12/08/2014] [Accepted: 12/30/2014] [Indexed: 11/22/2022] Open
Abstract
This paper reports a dynamic causal modeling study of electrocorticographic (ECoG) data that addresses functional asymmetries between forward and backward connections in the visual cortical hierarchy. Specifically, we ask whether forward connections employ gamma-band frequencies, while backward connections preferentially use lower (beta-band) frequencies. We addressed this question by modeling empirical cross spectra using a neural mass model equipped with superficial and deep pyramidal cell populations-that model the source of forward and backward connections, respectively. This enabled us to reconstruct the transfer functions and associated spectra of specific subpopulations within cortical sources. We first established that Bayesian model comparison was able to discriminate between forward and backward connections, defined in terms of their cells of origin. We then confirmed that model selection was able to identify extrastriate (V4) sources as being hierarchically higher than early visual (V1) sources. Finally, an examination of the auto spectra and transfer functions associated with superficial and deep pyramidal cells confirmed that forward connections employed predominantly higher (gamma) frequencies, while backward connections were mediated by lower (alpha/beta) frequencies. We discuss these findings in relation to current views about alpha, beta, and gamma oscillations and predictive coding in the brain.
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Affiliation(s)
- A M Bastos
- Ernst Strüngmann Institute (ESI) in Cooperation with Max Planck Society, Deutschordenstraße 46, Frankfurt 60528, Germany; Center for Neuroscience and Center for Mind and Brain, University of California, Davis, Davis, CA 95618, USA.
| | - V Litvak
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - R Moran
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - C A Bosman
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Kapittelweg 29, Nijmegen 6535 EN, Netherlands; Cognitive and Systems Neuroscience Group, Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam 1098 XH, Netherlands
| | - P Fries
- Ernst Strüngmann Institute (ESI) in Cooperation with Max Planck Society, Deutschordenstraße 46, Frankfurt 60528, Germany; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Kapittelweg 29, Nijmegen 6535 EN, Netherlands
| | - K J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
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Youssofzadeh V, Prasad G, Wong-Lin K. On self-feedback connectivity in neural mass models applied to event-related potentials. Neuroimage 2015; 108:364-76. [PMID: 25562823 DOI: 10.1016/j.neuroimage.2014.12.067] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2014] [Revised: 12/22/2014] [Accepted: 12/25/2014] [Indexed: 12/13/2022] Open
Abstract
Neural mass models (NMMs) applied to neuroimaging data often do not emphasise intrinsic self-feedback within a neural population. However, based on mean-field theory, any population of coupled neurons is intrinsically endowed with effective self-coupling. In this work, we examine the effectiveness of three cortical NMMs with different self-feedbacks using a dynamic causal modelling approach. Specifically, we compare the classic Jansen and Rit (1995) model (no self-feedback), a modified model by Moran et al. (2007) (only inhibitory self-feedback), and our proposed model with inhibitory and excitatory self-feedbacks. Using bifurcation analysis, we show that single-unit Jansen-Rit model is less robust in generating oscillatory behaviour than the other two models. Next, under Bayesian inversion, we simulate single-channel event-related potentials (ERPs) within a mismatch negativity auditory oddball paradigm. We found fully self-feedback model (FSM) to provide the best fit to single-channel data. By analysing the posterior covariances of model parameters, we show that self-feedback connections are less sensitive to the generated evoked responses than the other model parameters, and hence can be treated analogously to "higher-order" parameter corrections of the original Jansen-Rit model. This is further supported in the more realistic multi-area case where FSM can replicate data better than JRM and MoM in the majority of subjects by capturing the finer features of the ERP data more accurately. Our work informs how NMMs with full self-feedback connectivity are not only more consistent with the underlying neurophysiology, but can also account for more complex features in ERP data.
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Affiliation(s)
- Vahab Youssofzadeh
- Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, L'Derry BT48 7JL, UK
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, L'Derry BT48 7JL, UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, L'Derry BT48 7JL, UK.
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45
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Zarka D, Cevallos C, Petieau M, Hoellinger T, Dan B, Cheron G. Neural rhythmic symphony of human walking observation: Upside-down and Uncoordinated condition on cortical theta, alpha, beta and gamma oscillations. Front Syst Neurosci 2014; 8:169. [PMID: 25278847 PMCID: PMC4166901 DOI: 10.3389/fnsys.2014.00169] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 08/31/2014] [Indexed: 11/20/2022] Open
Abstract
Biological motion observation has been recognized to produce dynamic change in sensorimotor activation according to the observed kinematics. Physical plausibility of the spatial-kinematic relationship of human movement may play a major role in the top-down processing of human motion recognition. Here, we investigated the time course of scalp activation during observation of human gait in order to extract and use it on future integrated brain-computer interface using virtual reality (VR). We analyzed event related potentials (ERP), the event related spectral perturbation (ERSP) and the inter-trial coherence (ITC) from high-density EEG recording during video display onset (−200–600 ms) and the steady state visual evoked potentials (SSVEP) inside the video of human walking 3D-animation in three conditions: Normal; Upside-down (inverted images); and Uncoordinated (pseudo-randomly mixed images). We found that early visual evoked response P120 was decreased in Upside-down condition. The N170 and P300b amplitudes were decreased in Uncoordinated condition. In Upside-down and Uncoordinated conditions, we found decreased alpha power and theta phase-locking. As regards gamma oscillation, power was increased during the Upside-down animation and decreased during the Uncoordinated animation. An SSVEP-like response oscillating at about 10 Hz was also described showing that the oscillating pattern is enhanced 300 ms after the heel strike event only in the Normal but not in the Upside-down condition. Our results are consistent with most of previous point-light display studies, further supporting possible use of virtual reality for neurofeedback applications.
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Affiliation(s)
- David Zarka
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles Brussels, Belgium
| | - Carlos Cevallos
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles Brussels, Belgium
| | - Mathieu Petieau
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles Brussels, Belgium
| | - Thomas Hoellinger
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles Brussels, Belgium
| | - Bernard Dan
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles Brussels, Belgium ; Department of Neurology, Hopital Universitaire des Enfants reine Fabiola, Université Libre de Bruxelles Bruxelles, Belgium
| | - Guy Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles Brussels, Belgium ; Laboratory of Electrophysiology, Université de Mons-Hainaut Bruxelles, Belgium
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Friston KJ, Bastos AM, Pinotsis D, Litvak V. LFP and oscillations-what do they tell us? Curr Opin Neurobiol 2014; 31:1-6. [PMID: 25079053 PMCID: PMC4376394 DOI: 10.1016/j.conb.2014.05.004] [Citation(s) in RCA: 110] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2014] [Revised: 05/07/2014] [Accepted: 05/08/2014] [Indexed: 11/28/2022]
Abstract
A brief treatment of dynamic coordination in terms of predictive coding. Understanding synchronous message passing in terms of hierarchical predictive coding. Characterising cortical gain control with the dynamic causal modelling of neural fields. Characterising pathophysiological oscillations with dynamic causal modelling of neural masses.
This review surveys recent trends in the use of local field potentials—and their non-invasive counterparts—to address the principles of functional brain architectures. In particular, we treat oscillations as the (observable) signature of context-sensitive changes in synaptic efficacy that underlie coordinated dynamics and message-passing in the brain. This rich source of information is now being exploited by various procedures—like dynamic causal modelling—to test hypotheses about neuronal circuits in health and disease. Furthermore, the roles played by neuromodulatory mechanisms can be addressed directly through their effects on oscillatory phenomena. These neuromodulatory or gain control processes are central to many theories of normal brain function (e.g. attention) and the pathophysiology of several neuropsychiatric conditions (e.g. Parkinson's disease).
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Affiliation(s)
- Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
| | - André M Bastos
- Center for Neuroscience and Center for Mind and Brain, University of California-Davis, Davis, CA 95618, USA; Ernst Strüngmann Institute in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany
| | - Dimitris Pinotsis
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - Vladimir Litvak
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
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47
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Pinotsis DA. Extracting novel information from neuroimaging data using neural fields. BMC Neurosci 2014. [PMCID: PMC4124966 DOI: 10.1186/1471-2202-15-s1-o4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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