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Phillips WA, Bachmann T, Spratling MW, Muckli L, Petro LS, Zolnik T. Cellular psychology: relating cognition to context-sensitive pyramidal cells. Trends Cogn Sci 2024:S1364-6613(24)00224-9. [PMID: 39353837 DOI: 10.1016/j.tics.2024.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 10/04/2024]
Abstract
'Cellular psychology' is a new field of inquiry that studies dendritic mechanisms for adapting mental events to the current context, thus increasing their coherence, flexibility, effectiveness, and comprehensibility. Apical dendrites of neocortical pyramidal cells have a crucial role in cognition - those dendrites receive input from diverse sources, including feedback, and can amplify the cell's feedforward transmission if relevant in that context. Specialized subsets of inhibitory interneurons regulate this cooperative context-sensitive processing by increasing or decreasing amplification. Apical input has different effects on cellular output depending on whether we are awake, deeply asleep, or dreaming. Furthermore, wakeful thought and imagery may depend on apical input. High-resolution neuroimaging in humans supports and complements evidence on these cellular mechanisms from other mammals.
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Affiliation(s)
- William A Phillips
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK.
| | - Talis Bachmann
- Institute of Psychology, University of Tartu, Tartu, Estonia.
| | - Michael W Spratling
- Department of Behavioral and Cognitive Sciences, University of Luxembourg, L-4366 Esch-Belval, Luxembourg
| | - Lars Muckli
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QB, UK; Imaging Centre of Excellence, College of Medical, Veterinary and Life Sciences, University of Glasgow and Queen Elizabeth University Hospital, Glasgow, UK
| | - Lucy S Petro
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QB, UK; Imaging Centre of Excellence, College of Medical, Veterinary and Life Sciences, University of Glasgow and Queen Elizabeth University Hospital, Glasgow, UK
| | - Timothy Zolnik
- Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin 10117, Germany; Department of Biology, Humboldt Universität zu Berlin, Berlin 10117, Germany
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2
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Degutis JK, Chaimow D, Haenelt D, Assem M, Duncan J, Haynes JD, Weiskopf N, Lorenz R. Dynamic layer-specific processing in the prefrontal cortex during working memory. Commun Biol 2024; 7:1140. [PMID: 39277694 PMCID: PMC11401931 DOI: 10.1038/s42003-024-06780-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 08/26/2024] [Indexed: 09/17/2024] Open
Abstract
The dorsolateral prefrontal cortex (dlPFC) is reliably engaged in working memory (WM) and comprises different cytoarchitectonic layers, yet their functional role in human WM is unclear. Here, participants completed a delayed-match-to-sample task while undergoing functional magnetic resonance imaging (fMRI) at ultra-high resolution. We examine layer-specific activity to manipulations in WM load and motor response. Superficial layers exhibit a preferential response to WM load during the delay and retrieval periods of a WM task, indicating a lamina-specific activation of the frontoparietal network. Multivariate patterns encoding WM load in the superficial layer dynamically change across the three periods of the task. Last, superficial and deep layers are non-differentially involved in the motor response, challenging earlier findings of a preferential deep layer activation. Taken together, our results provide new insights into the functional laminar circuitry of the dlPFC during WM and support a dynamic account of dlPFC coding.
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Affiliation(s)
- Jonas Karolis Degutis
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, corporate member of the Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
- Max Planck School of Cognition, Leipzig, Germany.
| | - Denis Chaimow
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - John-Dylan Haynes
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, corporate member of the Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Max Planck School of Cognition, Leipzig, Germany
- Research Training Group "Extrospection" and Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Research Cluster of Excellence "Science of Intelligence", Technische Universität Berlin, Berlin, Germany
- Collaborative Research Center "Volition and Cognitive Control", Technische Universität Dresden, Dresden, Germany
| | - Nikolaus Weiskopf
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Romy Lorenz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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3
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Knight EJ, Altschuler TS, Molholm S, Murphy JW, Freedman EG, Foxe JJ. It's all in the timing: delayed feedback in autism may weaken predictive mechanisms during contour integration. J Neurophysiol 2024; 132:628-642. [PMID: 38958283 PMCID: PMC11427042 DOI: 10.1152/jn.00058.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/31/2024] [Accepted: 06/16/2024] [Indexed: 07/04/2024] Open
Abstract
Humans rely on predictive and integrative mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. Although initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through automatic conveyance of statistical probabilities based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP)-the IC-effect-that occurs over lateral occipital scalp during the timeframe of the visual N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6- to 17-yr-old children with ASD (n = 32) or NT development (n = 53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21 ms later in ASD, even though initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared with NT children. This delay in the feedback-dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by visual feedback.NEW & NOTEWORTHY Children with autism often present with an atypical visual perceptual style that emphasizes parts or details over the whole. Using electroencephalography (EEG), this study identifies delays in the visual feedback from higher-order sensory brain areas to primary sensory regions. Because this type of visual feedback is thought to carry information about prior sensory experiences, individuals with autism may have difficulty efficiently using prior experience or putting together parts into a whole to help make sense of incoming new visual information. This provides empirical neural evidence to support theories of disrupted sensory perception mechanisms in autism.
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Affiliation(s)
- Emily J Knight
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, Department of Neuroscience, The Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States
- Development and Behavioral Pediatrics, Golisano Children's Hospital, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States
| | - Ted S Altschuler
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics and Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States
- Program in Cognitive Neuroscience, Departments of Psychology & Biology, City College of the City University of New York, New York, United States
| | - Sophie Molholm
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, Department of Neuroscience, The Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics and Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States
- Program in Cognitive Neuroscience, Departments of Psychology & Biology, City College of the City University of New York, New York, United States
| | - Jeremy W Murphy
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics and Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States
- Program in Cognitive Neuroscience, Departments of Psychology & Biology, City College of the City University of New York, New York, United States
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States
| | - Edward G Freedman
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, Department of Neuroscience, The Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States
| | - John J Foxe
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, Department of Neuroscience, The Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics and Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States
- Program in Cognitive Neuroscience, Departments of Psychology & Biology, City College of the City University of New York, New York, United States
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4
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Greco A, D'Alessandro M, Gallitto G, Rastelli C, Braun C, Caria A. Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses. BIOLOGY 2024; 13:576. [PMID: 39194514 DOI: 10.3390/biology13080576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/24/2024] [Accepted: 07/29/2024] [Indexed: 08/29/2024]
Abstract
Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.
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Affiliation(s)
- Antonino Greco
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany
- Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany
- MEG Center, University of Tübingen, 72076 Tübingen, Germany
| | - Marco D'Alessandro
- Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy
| | - Giuseppe Gallitto
- Department of Neurology, University Hospital Essen, 45147 Essen, Germany
| | - Clara Rastelli
- MEG Center, University of Tübingen, 72076 Tübingen, Germany
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Christoph Braun
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany
- Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany
- MEG Center, University of Tübingen, 72076 Tübingen, Germany
| | - Andrea Caria
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
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5
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Carricarte T, Iamshchinina P, Trampel R, Chaimow D, Weiskopf N, Cichy RM. Laminar dissociation of feedforward and feedback in high-level ventral visual cortex during imagery and perception. iScience 2024; 27:110229. [PMID: 39006482 PMCID: PMC11246059 DOI: 10.1016/j.isci.2024.110229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/26/2024] [Accepted: 06/06/2024] [Indexed: 07/16/2024] Open
Abstract
Visual imagery and perception share neural machinery but rely on different information flow. While perception is driven by the integration of sensory feedforward and internally generated feedback information, imagery relies on feedback only. This suggests that although imagery and perception may activate overlapping brain regions, they do so in informationally distinctive ways. Using lamina-resolved MRI at 7 T, we measured the neural activity during imagery and perception of faces and scenes in high-level ventral visual cortex at the mesoscale of laminar organization that distinguishes feedforward from feedback signals. We found distinctive laminar profiles for imagery and perception of scenes and faces in the parahippocampal place area and the fusiform face area, respectively. Our findings provide insight into the neural basis of the phenomenology of visual imagery versus perception and shed new light into the mesoscale organization of feedforward and feedback information flow in high-level ventral visual cortex.
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Affiliation(s)
- Tony Carricarte
- Department of Education and Psychology, Freie Universität Berlin, 14195 Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Polina Iamshchinina
- Princeton Neuroscience Institute, Princeton University, New Jersey 08544, USA
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Denis Chaimow
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Universität Leipzig, 04103 Leipzig, Germany
| | - Radoslaw M. Cichy
- Department of Education and Psychology, Freie Universität Berlin, 14195 Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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6
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Thomas ER, Haarsma J, Nicholson J, Yon D, Kok P, Press C. Predictions and errors are distinctly represented across V1 layers. Curr Biol 2024; 34:2265-2271.e4. [PMID: 38697110 DOI: 10.1016/j.cub.2024.04.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/09/2024] [Accepted: 04/13/2024] [Indexed: 05/04/2024]
Abstract
Popular accounts of mind and brain propose that the brain continuously forms predictions about future sensory inputs and combines predictions with inputs to determine what we perceive.1,2,3,4,5,6 Under "predictive processing" schemes, such integration is supported by the hierarchical organization of the cortex, whereby feedback connections communicate predictions from higher-level deep layers to agranular (superficial and deep) lower-level layers.7,8,9,10 Predictions are compared with input to compute the "prediction error," which is transmitted up the hierarchy from superficial layers of lower cortical regions to the middle layers of higher areas, to update higher-level predictions until errors are reconciled.11,12,13,14,15 In the primary visual cortex (V1), predictions have thereby been proposed to influence representations in deep layers while error signals may be computed in superficial layers. Despite the framework's popularity, there is little evidence for these functional distinctions because, to our knowledge, unexpected sensory events have not previously been presented in human laminar paradigms to contrast against expected events. To this end, this 7T fMRI study contrasted V1 responses to expected (75% likely) and unexpected (25%) Gabor orientations. Multivariate decoding analyses revealed an interaction between expectation and layer, such that expected events could be decoded with comparable accuracy across layers, while unexpected events could only be decoded in superficial laminae. Although these results are in line with these accounts that have been popular for decades, such distinctions have not previously been demonstrated in humans. We discuss how both prediction and error processes may operate together to shape our unitary perceptual experiences.
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Affiliation(s)
- Emily R Thomas
- Neuroscience Institute, New York University Medical Center, 435 East 30(th) Street, New York 10016, USA; Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK.
| | - Joost Haarsma
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Jessica Nicholson
- Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
| | - Daniel Yon
- Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
| | - Peter Kok
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Clare Press
- Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK; Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK; Department of Experimental Psychology, University College London, 26 Bedford Way, London WC1H 0AP, UK.
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7
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Ishida T, Nittono H. Visual omitted stimulus potentials are not retinotopic. Neurosci Lett 2024; 830:137777. [PMID: 38621505 DOI: 10.1016/j.neulet.2024.137777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 04/03/2024] [Accepted: 04/12/2024] [Indexed: 04/17/2024]
Abstract
Omitted stimulus potentials (OSPs) are elicited in response to the omission of expected stimuli and are thought to reflect prediction errors. If prediction errors are signaled in the sensory cortex, OSPs are expected to be generated in the sensory cortex. The present study investigated the involvement of the early visual cortex in the generation of OSPs by testing whether omitted visual stimuli elicit brain responses in a spatially specific manner. Checkerboard pattern stimuli were presented alternately in the upper and lower visual fields, and the stimuli were omitted in 10 % of the trials. Event-related potentials were recorded from 33 participants. While a retinotopic C1 component was evoked by real visual stimuli, omitted stimuli did not produce any response reflecting retinotopy but did elicit a visual mismatch negativity, which was larger for omitted stimuli expected in the lower visual field than for those in the upper visual field. These results suggest that omitted visual stimuli are processed in a different pathway than actual stimuli.
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Affiliation(s)
- Tomomi Ishida
- Graduate School of Human Sciences, Osaka University, Osaka, Japan.
| | - Hiroshi Nittono
- Graduate School of Human Sciences, Osaka University, Osaka, Japan.
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8
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Shaw S, Kilpatrick ZP. Representing stimulus motion with waves in adaptive neural fields. J Comput Neurosci 2024; 52:145-164. [PMID: 38607466 DOI: 10.1007/s10827-024-00869-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 04/13/2024]
Abstract
Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.
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Affiliation(s)
- Sage Shaw
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary P Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA.
- Institute for Cognitive Sciences, University of Colorado Boulder, Boulder, CO, USA.
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9
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Bergmann J, Petro LS, Abbatecola C, Li MS, Morgan AT, Muckli L. Cortical depth profiles in primary visual cortex for illusory and imaginary experiences. Nat Commun 2024; 15:1002. [PMID: 38307834 PMCID: PMC10837448 DOI: 10.1038/s41467-024-45065-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/12/2024] [Indexed: 02/04/2024] Open
Abstract
Visual illusions and mental imagery are non-physical sensory experiences that involve cortical feedback processing in the primary visual cortex. Using laminar functional magnetic resonance imaging (fMRI) in two studies, we investigate if information about these internal experiences is visible in the activation patterns of different layers of primary visual cortex (V1). We find that imagery content is decodable mainly from deep layers of V1, whereas seemingly 'real' illusory content is decodable mainly from superficial layers. Furthermore, illusory content shares information with perceptual content, whilst imagery content does not generalise to illusory or perceptual information. Together, our results suggest that illusions and imagery, which differ immensely in their subjective experiences, also involve partially distinct early visual microcircuits. However, overlapping microcircuit recruitment might emerge based on the nuanced nature of subjective conscious experience.
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Affiliation(s)
- Johanna Bergmann
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK.
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Lucy S Petro
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Clement Abbatecola
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Min S Li
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Centre for Computational Neuroscience and Cognitive Robotics, School of Psychology, University of Birmingham, Birmingham, UK
| | - A Tyler Morgan
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Functional MRI Core Facility, National Institute of Mental Health, NIH, Bethesda, MD, 20817, USA
| | - Lars Muckli
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK.
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
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10
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Shipp S. Computational components of visual predictive coding circuitry. Front Neural Circuits 2024; 17:1254009. [PMID: 38259953 PMCID: PMC10800426 DOI: 10.3389/fncir.2023.1254009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.
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Affiliation(s)
- Stewart Shipp
- Institute of Ophthalmology, University College London, London, United Kingdom
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11
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Ward EK, Press C. Sixty years of predictive perception. Cortex 2024; 170:57-63. [PMID: 38104029 DOI: 10.1016/j.cortex.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/14/2023] [Accepted: 11/14/2023] [Indexed: 12/19/2023]
Affiliation(s)
- Emma K Ward
- School of Psychological Sciences, Birkbeck, University of London, UK.
| | - Clare Press
- School of Psychological Sciences, Birkbeck, University of London, UK; Wellcome Centre for Human Neuroimaging, UCL, UK
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12
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Peelen MV, Berlot E, de Lange FP. Predictive processing of scenes and objects. NATURE REVIEWS PSYCHOLOGY 2024; 3:13-26. [PMID: 38989004 PMCID: PMC7616164 DOI: 10.1038/s44159-023-00254-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 07/12/2024]
Abstract
Real-world visual input consists of rich scenes that are meaningfully composed of multiple objects which interact in complex, but predictable, ways. Despite this complexity, we recognize scenes, and objects within these scenes, from a brief glance at an image. In this review, we synthesize recent behavioral and neural findings that elucidate the mechanisms underlying this impressive ability. First, we review evidence that visual object and scene processing is partly implemented in parallel, allowing for a rapid initial gist of both objects and scenes concurrently. Next, we discuss recent evidence for bidirectional interactions between object and scene processing, with scene information modulating the visual processing of objects, and object information modulating the visual processing of scenes. Finally, we review evidence that objects also combine with each other to form object constellations, modulating the processing of individual objects within the object pathway. Altogether, these findings can be understood by conceptualizing object and scene perception as the outcome of a joint probabilistic inference, in which "best guesses" about objects act as priors for scene perception and vice versa, in order to concurrently optimize visual inference of objects and scenes.
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Affiliation(s)
- Marius V Peelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Eva Berlot
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Floris P de Lange
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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13
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Shaw S, Kilpatrick ZP. Representing stimulus motion with waves in adaptive neural fields. ARXIV 2023:arXiv:2312.06100v1. [PMID: 38168459 PMCID: PMC10760209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.
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Affiliation(s)
- Sage Shaw
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary P Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
- Institute for Cognitive Sciences, University of Colorado Boulder, Boulder, CO, USA
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14
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Niraula S, Hauser WL, Rouse AG, Subramanian J. Repeated passive visual experience modulates spontaneous and non-familiar stimuli-evoked neural activity. Sci Rep 2023; 13:20907. [PMID: 38017135 PMCID: PMC10684504 DOI: 10.1038/s41598-023-47957-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023] Open
Abstract
Familiarity creates subjective memory of repeated innocuous experiences, reduces neural and behavioral responsiveness to those experiences, and enhances novelty detection. The neural correlates of the internal model of familiarity and the cellular mechanisms of enhanced novelty detection following multi-day repeated passive experience remain elusive. Using the mouse visual cortex as a model system, we test how the repeated passive experience of a 45° orientation-grating stimulus for multiple days alters spontaneous and non-familiar stimuli evoked neural activity in neurons tuned to familiar or non-familiar stimuli. We found that familiarity elicits stimulus competition such that stimulus selectivity reduces in neurons tuned to the familiar 45° stimulus; it increases in those tuned to the 90° stimulus but does not affect neurons tuned to the orthogonal 135° stimulus. Furthermore, neurons tuned to orientations 45° apart from the familiar stimulus dominate local functional connectivity. Interestingly, responsiveness to natural images, which consists of familiar and non-familiar orientations, increases subtly in neurons that exhibit stimulus competition. We also show the similarity between familiar grating stimulus-evoked and spontaneous activity increases, indicative of an internal model of altered experience.
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Affiliation(s)
- Suraj Niraula
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, 66045, USA
| | - William L Hauser
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, 66045, USA
| | - Adam G Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, 66103, USA
| | - Jaichandar Subramanian
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, 66045, USA.
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15
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Haarsma J, Deveci N, Corbin N, Callaghan MF, Kok P. Expectation Cues and False Percepts Generate Stimulus-Specific Activity in Distinct Layers of the Early Visual Cortex. J Neurosci 2023; 43:7946-7957. [PMID: 37739797 PMCID: PMC10669763 DOI: 10.1523/jneurosci.0998-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/10/2023] [Accepted: 09/13/2023] [Indexed: 09/24/2023] Open
Abstract
Perception has been proposed to result from the integration of feedforward sensory signals with internally generated feedback signals. Feedback signals are believed to play an important role in driving false percepts, that is, seeing things that are not actually there. Feedforward and feedback influences on perception can be studied using layer-specific fMRI, which we used here to interrogate neural activity underlying high-confidence false percepts while healthy human participants (N = 25, male and female) performed a perceptual orientation discrimination task. Auditory cues implicitly signaled the most likely upcoming orientation (referred to here as expectations). These expectations induced orientation-specific templates in the deep and superficial layers of V2, without affecting perception. In contrast, the orientation of falsely perceived stimuli with high confidence was reflected in the middle input layers of V2, suggesting a feedforward signal contributing to false percepts. The prevalence of high-confidence false percepts was related to everyday hallucination severity in a separate online sample (N = 100), suggesting a possible link with abnormal perceptual experiences. These results reveal a potential feedforward mechanism underlying false percepts, reflected by spontaneous stimulus-like activity in the input layers of the visual cortex, independent of top-down signals reflecting cued orientations.SIGNIFICANCE STATEMENT False percepts have been suggested to arise through excessive feedback signals. However, feedforward contributions to false percepts have remained largely understudied. Laminar fMRI has been shown to be useful in distinguishing feedforward from feedback activity as it allows the imaging of different cortical layers. In the present study we demonstrate that although cued orientations are encoded in the feedback layers of the visual cortex, the content of the false percepts are encoded in the feedforward layers and did not rely on these cued orientations. This shows that false percepts can in principle emerge from random feedforward signals in the visual cortex, with possible implications for disorders hallmarked by hallucinations like schizophrenia and Parkinson's disease.
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Affiliation(s)
- Joost Haarsma
- Wellcome Centre for Human Neuroimaging, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - Narin Deveci
- Wellcome Centre for Human Neuroimaging, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - Nadege Corbin
- Wellcome Centre for Human Neuroimaging, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
- Centre de Résonance Magnétique des Systèmes Biologiques, Unité Mixte de Recherche 5536, Centre National de la Recherche Scientifique, Université de Bordeaux, 33076 Bordeaux, France
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - Peter Kok
- Wellcome Centre for Human Neuroimaging, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
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16
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Cerracchio E, Miletić S, Forstmann BU. Modelling decision-making biases. Front Comput Neurosci 2023; 17:1222924. [PMID: 37927545 PMCID: PMC10622807 DOI: 10.3389/fncom.2023.1222924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate.
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Affiliation(s)
- Ettore Cerracchio
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
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17
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Malekian V, Graedel NN, Hickling A, Aghaeifar A, Dymerska B, Corbin N, Josephs O, Maguire EA, Callaghan MF. Mitigating susceptibility-induced distortions in high-resolution 3DEPI fMRI at 7T. Neuroimage 2023; 279:120294. [PMID: 37517572 PMCID: PMC10951962 DOI: 10.1016/j.neuroimage.2023.120294] [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: 05/12/2023] [Revised: 07/08/2023] [Accepted: 07/22/2023] [Indexed: 08/01/2023] Open
Abstract
Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI.
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Affiliation(s)
- Vahid Malekian
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK.
| | - Nadine N Graedel
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Alice Hickling
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Ali Aghaeifar
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK; MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Barbara Dymerska
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Nadège Corbin
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK; Centre de Résonance Magnétique des Systèmes Biologiques, CNRS-University Bordeaux, Bordeaux, France
| | - Oliver Josephs
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
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18
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Stange L, Ossandón JP, Röder B. Crossmodal visual predictions elicit spatially specific early visual cortex activity but later than real visual stimuli. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220339. [PMID: 37545314 PMCID: PMC10404923 DOI: 10.1098/rstb.2022.0339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/30/2023] [Indexed: 08/08/2023] Open
Abstract
Previous studies have indicated that crossmodal visual predictions are instrumental in controlling early visual cortex activity. The exact time course and spatial precision of such crossmodal top-down influences on the visual cortex have been unknown. In the present study, participants were exposed to audiovisual combinations comprising one of two sounds and a Gabor patch either in the top left or in the bottom right visual field. Event-related potentials (ERPs) were recorded to these frequent crossmodal combinations (standards) as well as to trials in which the visual stimulus was omitted (omissions) or the visual and auditory stimuli were recombined (deviants). Standards and deviants elicited an ERP between 50 and 100 ms of opposite polarity known as the C1 effect commonly associated with retinotopic processing in early visual cortex. By contrast, a C1 effect was not observed in omission trials. Spatially specific omission and mismatch effects (deviants minus standards) started only later with a latency of 230 ms and 170 ms, respectively. These results suggest that crossmodal visual predictions control visual cortex activity in a spatially specific manner. However, visual predictions do not modulate visual cortex activity with the same timing as visual stimulation activates these areas but rather seem to involve distinct neural mechanisms. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
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Affiliation(s)
- Liesa Stange
- Biological Psychology and Neuropsychology, Hamburg University, Von-Melle-Park 11, Hamburg 20148, Germany
| | - José P. Ossandón
- Biological Psychology and Neuropsychology, Hamburg University, Von-Melle-Park 11, Hamburg 20148, Germany
| | - Brigitte Röder
- Biological Psychology and Neuropsychology, Hamburg University, Von-Melle-Park 11, Hamburg 20148, Germany
- LV Prasad Eye Institute, Hyderabad 500 034, India
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19
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Jia K, Goebel R, Kourtzi Z. Ultra-High Field Imaging of Human Visual Cognition. Annu Rev Vis Sci 2023; 9:479-500. [PMID: 37137282 DOI: 10.1146/annurev-vision-111022-123830] [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] [Indexed: 05/05/2023]
Abstract
Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition.
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Affiliation(s)
- Ke Jia
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom;
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom;
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20
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Niraula S, Hauser WL, Rouse AG, Subramanian J. Repeated passive visual experience modulates spontaneous and non-familiar stimulievoked neural activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529278. [PMID: 36865208 PMCID: PMC9980096 DOI: 10.1101/2023.02.21.529278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Familiarity creates subjective memory of repeated innocuous experiences, reduces neural and behavioral responsiveness to those experiences, and enhances novelty detection. The neural correlates of the internal model of familiarity and the cellular mechanisms of enhanced novelty detection following multi-day repeated passive experience remain elusive. Using the mouse visual cortex as a model system, we test how the repeated passive experience of a 45° orientation-grating stimulus for multiple days alters spontaneous and non-familiar stimuli evoked neural activity in neurons tuned to familiar or non-familiar stimuli. We found that familiarity elicits stimulus competition such that stimulus selectivity reduces in neurons tuned to the familiar 45° stimulus; it increases in those tuned to the 90° stimulus but does not affect neurons tuned to the orthogonal 135° stimulus. Furthermore, neurons tuned to orientations 45° apart from the familiar stimulus dominate local functional connectivity. Interestingly, responsiveness to natural images, which consists of familiar and non-familiar orientations, increases subtly in neurons that exhibit stimulus competition. We also show the similarity between familiar grating stimulus-evoked and spontaneous activity increases, indicative of an internal model of altered experience.
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Affiliation(s)
- Suraj Niraula
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS 66045, USA
| | - William L. Hauser
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS 66045, USA
| | - Adam G. Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Jaichandar Subramanian
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS 66045, USA
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21
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Riley SN, Davies J. Vividness as the similarity between generated imagery and an internal model. Brain Cogn 2023; 169:105988. [PMID: 37150045 DOI: 10.1016/j.bandc.2023.105988] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/09/2023]
Abstract
Vividness in visual mental imagery has been relatively under-explored compared to imagery's representational format and neural mechanisms. In this paper, we take a deeper look at vividness and suggest that in re-framing it, we can potentially reconcile disparate findings regarding visual cortex activation during imagery. Unlike traditional views of vividness that define the concept in terms of perception, we frame vividness in terms of imagery's relation to an internal model; the closer the generated imagery is to this model, the more vivid it is. This view is considered alongside existing neuroscientific, psychological, and philosophical research, as well as directions for future research.
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Affiliation(s)
- Sean N Riley
- Department of Cognitive Science, Carleton University, Canada
| | - Jim Davies
- Department of Cognitive Science, Carleton University, Canada.
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22
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Son S, Moon J, Kim YJ, Kang MS, Lee J. Frontal-to-visual information flow explains predictive motion tracking. Neuroimage 2023; 269:119914. [PMID: 36736637 DOI: 10.1016/j.neuroimage.2023.119914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Predictive tracking demonstrates our ability to maintain a line of vision on moving objects even when they temporarily disappear. Models of smooth pursuit eye movements posit that our brain achieves this ability by directly streamlining motor programming from continuously updated sensory motion information. To test this hypothesis, we obtained sensory motion representation from multivariate electroencephalogram activity while human participants covertly tracked a temporarily occluded moving stimulus with their eyes remaining stationary at the fixation point. The sensory motion representation of the occluded target evolves to its maximum strength at the expected timing of reappearance, suggesting a timely modulation of the internal model of the visual target. We further characterize the spatiotemporal dynamics of the task-relevant motion information by computing the phase gradients of slow oscillations. We discovered a predominant posterior-to-anterior phase gradient immediately after stimulus occlusion; however, at the expected timing of reappearance, the axis reverses the gradient, becoming anterior-to-posterior. The behavioral bias of smooth pursuit eye movements, which is a signature of the predictive process of the pursuit, was correlated with the posterior division of the gradient. These results suggest that the sensory motion area modulated by the prediction signal is involved in updating motor programming.
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Affiliation(s)
- Sangkyu Son
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea
| | - Joonsik Moon
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea
| | - Yee-Joon Kim
- Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon 34141, South Korea
| | - Min-Suk Kang
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea; Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea.
| | - Joonyeol Lee
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, South Korea.
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23
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Knudsen L, Bailey CJ, Blicher JU, Yang Y, Zhang P, Lund TE. Improved sensitivity and microvascular weighting of 3T laminar fMRI with GE-BOLD using NORDIC and phase regression. Neuroimage 2023; 271:120011. [PMID: 36914107 DOI: 10.1016/j.neuroimage.2023.120011] [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: 10/09/2022] [Revised: 03/03/2023] [Accepted: 03/09/2023] [Indexed: 03/15/2023] Open
Abstract
INTRODUCTION Functional MRI with spatial resolution in the submillimeter domain enables measurements of activation across cortical layers in humans. This is valuable as different types of cortical computations, e.g., feedforward versus feedback related activity, take place in different cortical layers. Laminar fMRI studies have almost exclusively employed 7T scanners to overcome the reduced signal stability associated with small voxels. However, such systems are relatively rare and only a subset of those are clinically approved. In the present study, we examined if the feasibility of laminar fMRI at 3T could be improved by use of NORDIC denoising and phase regression. METHODS 5 healthy subjects were scanned on a Siemens MAGNETOM Prisma 3T scanner. To assess across-session reliability, each subject was scanned in 3-8 sessions on 3-4 consecutive days. A 3D gradient echo EPI (GE-EPI) sequence was used for BOLD acquisitions (voxel size 0.82 mm isotopic, TR = 2.2 s) using a block design finger tapping paradigm. NORDIC denoising was applied to the magnitude and phase time series to overcome limitations in temporal signal-to-noise ratio (tSNR) and the denoised phase time series were subsequently used to correct for large vein contamination through phase regression. RESULTS AND CONCLUSION NORDIC denoising resulted in tSNR values comparable to or higher than commonly observed at 7T. Layer-dependent activation profiles could thus be extracted robustly, within and across sessions, from regions of interest located in the hand knob of the primary motor cortex (M1). Phase regression led to substantially reduced superficial bias in obtained layer profiles, although residual macrovascular contribution remained. We believe the present results support an improved feasibility of laminar fMRI at 3T.
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Affiliation(s)
- Lasse Knudsen
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Universitetsbyen 3, Aarhus C 8000, Denmark; Sino-Danish Center for Education and Research (SDC), University of Chinese Academy of Sciences, 380 Huaibeizhuang, Huairou District, Beijing 101400, PR China.
| | - Christopher J Bailey
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Universitetsbyen 3, Aarhus C 8000, Denmark; Sino-Danish Center for Education and Research (SDC), University of Chinese Academy of Sciences, 380 Huaibeizhuang, Huairou District, Beijing 101400, PR China
| | - Jakob U Blicher
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Universitetsbyen 3, Aarhus C 8000, Denmark; Department of Neurology, Aalborg University Hospital, Aalborg, Denmark
| | - Yan Yang
- Sino-Danish Center for Education and Research (SDC), University of Chinese Academy of Sciences, 380 Huaibeizhuang, Huairou District, Beijing 101400, PR China; Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China
| | - Peng Zhang
- Sino-Danish Center for Education and Research (SDC), University of Chinese Academy of Sciences, 380 Huaibeizhuang, Huairou District, Beijing 101400, PR China; Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China
| | - Torben E Lund
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Universitetsbyen 3, Aarhus C 8000, Denmark
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24
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Banerjee A, Wang BA, Teutsch J, Helmchen F, Pleger B. Analogous cognitive strategies for tactile learning in the rodent and human brain. Prog Neurobiol 2023; 222:102401. [PMID: 36608783 DOI: 10.1016/j.pneurobio.2023.102401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 12/21/2022] [Accepted: 01/02/2023] [Indexed: 01/05/2023]
Abstract
Evolution has molded individual species' sensory capacities and abilities. In rodents, who mostly inhabit dark tunnels and burrows, the whisker-based somatosensory system has developed as the dominant sensory modality, essential for environmental exploration and spatial navigation. In contrast, humans rely more on visual and auditory inputs when collecting information from their surrounding sensory space in everyday life. As a result of such species-specific differences in sensory dominance, cognitive relevance and capacities, the evidence for analogous sensory-cognitive mechanisms across species remains sparse. However, recent research in rodents and humans yielded surprisingly comparable processing rules for detecting tactile stimuli, integrating touch information into percepts, and goal-directed rule learning. Here, we review how the brain, across species, harnesses such processing rules to establish decision-making during tactile learning, following canonical circuits from the thalamus and the primary somatosensory cortex up to the frontal cortex. We discuss concordances between empirical and computational evidence from micro- and mesoscopic circuit studies in rodents to findings from macroscopic imaging in humans. Furthermore, we discuss the relevance and challenges for future cross-species research in addressing mutual context-dependent evaluation processes underpinning perceptual learning.
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Affiliation(s)
- Abhishek Banerjee
- Adaptive Decisions Lab, Biosciences Institute, Newcastle University, United Kingdom.
| | - Bin A Wang
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr University Bochum, Germany; Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr University Bochum, Germany.
| | - Jasper Teutsch
- Adaptive Decisions Lab, Biosciences Institute, Newcastle University, United Kingdom
| | - Fritjof Helmchen
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zürich, Switzerland
| | - Burkhard Pleger
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr University Bochum, Germany; Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr University Bochum, Germany
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25
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Akbari A, Bollmann S, Ali TS, Barth M. Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex. Hum Brain Mapp 2022; 44:710-726. [PMID: 36189837 PMCID: PMC9842911 DOI: 10.1002/hbm.26094] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/05/2022] [Accepted: 08/07/2022] [Indexed: 01/25/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) using a blood-oxygenation-level-dependent (BOLD) contrast is a common method for studying human brain function noninvasively. Gradient-echo (GRE) BOLD is highly sensitive to the blood oxygenation change in blood vessels; however, the spatial signal specificity can be degraded due to signal leakage from activated lower layers to superficial layers in depth-dependent (also called laminar or layer-specific) fMRI. Alternatively, physiological variables such as cerebral blood volume using the VAscular-Space-Occupancy (VASO) contrast have shown higher spatial specificity compared to BOLD. To better understand the physiological mechanisms such as blood volume and oxygenation changes and to interpret the measured depth-dependent responses, models are needed which reflect vascular properties at this scale. For this purpose, we extended and modified the "cortical vascular model" previously developed to predict layer-specific BOLD signal changes in human primary visual cortex to also predict a layer-specific VASO response. To evaluate the model, we compared the predictions with experimental results of simultaneous VASO and BOLD measurements in a group of healthy participants. Fitting the model to our experimental data provided an estimate of CBV change in different vascular compartments upon neural activity. We found that stimulus-evoked CBV change mainly occurs in small arterioles, capillaries, and intracortical arteries and that the contribution from venules and ICVs is smaller. Our results confirm that VASO is less susceptible to large vessel effects compared to BOLD, as blood volume changes in intracortical arteries did not substantially affect the resulting depth-dependent VASO profiles, whereas depth-dependent BOLD profiles showed a bias towards signal contributions from intracortical veins.
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Affiliation(s)
- Atena Akbari
- Centre for Advanced ImagingUniversity of QueenslandBrisbaneAustralia
| | - Saskia Bollmann
- Centre for Advanced ImagingUniversity of QueenslandBrisbaneAustralia
| | - Tonima S. Ali
- Centre for Advanced ImagingUniversity of QueenslandBrisbaneAustralia
| | - Markus Barth
- Centre for Advanced ImagingUniversity of QueenslandBrisbaneAustralia,ARC Training Centre for Innovation in Biomedical Imaging TechnologyThe University of QueenslandBrisbaneAustralia,School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneQueenslandAustralia
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26
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Dual counterstream architecture may support separation between vision and predictions. Conscious Cogn 2022; 103:103375. [DOI: 10.1016/j.concog.2022.103375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/03/2021] [Accepted: 06/28/2022] [Indexed: 11/24/2022]
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27
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Aitken F, Kok P. Hippocampal representations switch from errors to predictions during acquisition of predictive associations. Nat Commun 2022; 13:3294. [PMID: 35676285 PMCID: PMC9178037 DOI: 10.1038/s41467-022-31040-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 05/11/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractWe constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N = 24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we find that the hippocampus initially preferentially represents unexpected shapes (i.e., those that violate the cue regularities), but later switches to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus is involved both acquiring and exploiting predictive associations, and is dominated by either errors or predictions depending on whether learning is ongoing or complete.
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28
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Dijkstra N, Kok P, Fleming SM. Perceptual reality monitoring: Neural mechanisms dissociating imagination from reality. Neurosci Biobehav Rev 2022; 135:104557. [PMID: 35122782 DOI: 10.1016/j.neubiorev.2022.104557] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 01/12/2022] [Accepted: 01/30/2022] [Indexed: 01/21/2023]
Abstract
There is increasing evidence that imagination relies on similar neural mechanisms as externally triggered perception. This overlap presents a challenge for perceptual reality monitoring: deciding what is real and what is imagined. Here, we explore how perceptual reality monitoring might be implemented in the brain. We first describe sensory and cognitive factors that could dissociate imagery and perception and conclude that no single factor unambiguously signals whether an experience is internally or externally generated. We suggest that reality monitoring is implemented by higher-level cortical circuits that evaluate first-order sensory and cognitive factors to determine the source of sensory signals. According to this interpretation, perceptual reality monitoring shares core computations with metacognition. This multi-level architecture might explain several types of source confusion as well as dissociations between simply knowing whether something is real and actually experiencing it as real. We discuss avenues for future research to further our understanding of perceptual reality monitoring, an endeavour that has important implications for our understanding of clinical symptoms as well as general cognitive function.
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Affiliation(s)
- Nadine Dijkstra
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom.
| | - Peter Kok
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom; Max Planck UCL Centre for Computational Psychiatry and Aging Research, University College London, United Kingdom; Department of Experimental Psychology, University College London, United Kingdom
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29
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Braga A, Schönwiesner M. Neural Substrates and Models of Omission Responses and Predictive Processes. Front Neural Circuits 2022; 16:799581. [PMID: 35177967 PMCID: PMC8844463 DOI: 10.3389/fncir.2022.799581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/05/2022] [Indexed: 11/24/2022] Open
Abstract
Predictive coding theories argue that deviance detection phenomena, such as mismatch responses and omission responses, are generated by predictive processes with possibly overlapping neural substrates. Molecular imaging and electrophysiology studies of mismatch responses and corollary discharge in the rodent model allowed the development of mechanistic and computational models of these phenomena. These models enable translation between human and non-human animal research and help to uncover fundamental features of change-processing microcircuitry in the neocortex. This microcircuitry is characterized by stimulus-specific adaptation and feedforward inhibition of stimulus-selective populations of pyramidal neurons and interneurons, with specific contributions from different interneuron types. The overlap of the substrates of different types of responses to deviant stimuli remains to be understood. Omission responses, which are observed both in corollary discharge and mismatch response protocols in humans, are underutilized in animal research and may be pivotal in uncovering the substrates of predictive processes. Omission studies comprise a range of methods centered on the withholding of an expected stimulus. This review aims to provide an overview of omission protocols and showcase their potential to integrate and complement the different models and procedures employed to study prediction and deviance detection.This approach may reveal the biological foundations of core concepts of predictive coding, and allow an empirical test of the framework's promise to unify theoretical models of attention and perception.
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Affiliation(s)
- Alessandro Braga
- Institute of Biology, Faculty of Life Sciences, University of Leipzig, Leipzig, Germany
- International Max Plank Research School, Max Plank Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Marc Schönwiesner
- Institute of Biology, Faculty of Life Sciences, University of Leipzig, Leipzig, Germany
- International Laboratory for Research on Brain, Music, and Sound (BRAMS), Université de Montréal, Montreal, QC, Canada
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30
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Hogendoorn H. Perception in real-time: predicting the present, reconstructing the past. Trends Cogn Sci 2022; 26:128-141. [PMID: 34973925 DOI: 10.1016/j.tics.2021.11.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 01/06/2023]
Abstract
We feel that we perceive events in the environment as they unfold in real-time. However, this intuitive view of perception is impossible to implement in the nervous system due to biological constraints such as neural transmission delays. I propose a new way of thinking about real-time perception: at any given moment, instead of representing a single timepoint, perceptual mechanisms represent an entire timeline. On this timeline, predictive mechanisms predict ahead to compensate for delays in incoming sensory input, and reconstruction mechanisms retroactively revise perception when those predictions do not come true. This proposal integrates and extends previous work to address a crucial gap in our understanding of a fundamental aspect of our everyday life: the experience of perceiving the present.
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Affiliation(s)
- Hinze Hogendoorn
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia.
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31
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Cerliani L, Bhandari R, De Angelis L, van der Zwaag W, Bazin PL, Gazzola V, Keysers C. Predictive coding during action observation - a depth-resolved intersubject functional correlation study at 7T. Cortex 2022; 148:121-138. [DOI: 10.1016/j.cortex.2021.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/23/2021] [Accepted: 12/22/2021] [Indexed: 11/03/2022]
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32
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Zhang B, Weidner R, Allenmark F, Bertleff S, Fink GR, Shi Z, Müller HJ. Statistical Learning of Frequent Distractor Locations in Visual Search Involves Regional Signal Suppression in Early Visual Cortex. Cereb Cortex 2021; 32:2729-2744. [PMID: 34727169 DOI: 10.1093/cercor/bhab377] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 09/16/2021] [Accepted: 09/17/2021] [Indexed: 11/13/2022] Open
Abstract
Observers can learn locations where salient distractors appear frequently to reduce potential interference-an effect attributed to better suppression of distractors at frequent locations. But how distractor suppression is implemented in the visual cortex and within the frontoparietal attention networks remains unclear. We used fMRI and a regional distractor-location learning paradigm with two types of distractors defined in either the same (orientation) or a different (color) dimension to the target to investigate this issue. fMRI results showed that BOLD signals in early visual cortex were significantly reduced for distractors (as well as targets) occurring at the frequent versus rare locations, mirroring behavioral patterns. This reduction was more robust with same-dimension distractors. Crucially, behavioral interference was correlated with distractor-evoked visual activity only for same- (but not different-) dimension distractors. Moreover, with different- (but not same-) dimension distractors, a color-processing area within the fusiform gyrus was activated more when a distractor was present in the rare region versus being absent and more with a distractor in the rare versus frequent locations. These results support statistical learning of frequent distractor locations involving regional suppression in early visual cortex and point to differential neural mechanisms of distractor handling with different- versus same-dimension distractors.
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Affiliation(s)
- Bei Zhang
- General and Experimental Psychology, Ludwig-Maximilians-Universität München, München 80802, Germany
| | - Ralph Weidner
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich 52428, Germany
| | - Fredrik Allenmark
- General and Experimental Psychology, Ludwig-Maximilians-Universität München, München 80802, Germany
| | - Sabine Bertleff
- Traffic Psychology and Acceptance, Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen 52074, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich 52428, Germany.,Department of Neurology, University Hospital Cologne, Cologne University, Cologne 50937, Germany
| | - Zhuanghua Shi
- General and Experimental Psychology, Ludwig-Maximilians-Universität München, München 80802, Germany
| | - Hermann J Müller
- General and Experimental Psychology, Ludwig-Maximilians-Universität München, München 80802, Germany
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33
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Iamshchinina P, Kaiser D, Yakupov R, Haenelt D, Sciarra A, Mattern H, Luesebrink F, Duezel E, Speck O, Weiskopf N, Cichy RM. Perceived and mentally rotated contents are differentially represented in cortical depth of V1. Commun Biol 2021; 4:1069. [PMID: 34521987 PMCID: PMC8440580 DOI: 10.1038/s42003-021-02582-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 08/20/2021] [Indexed: 11/12/2022] Open
Abstract
Primary visual cortex (V1) in humans is known to represent both veridically perceived external input and internally-generated contents underlying imagery and mental rotation. However, it is unknown how the brain keeps these contents separate thus avoiding a mixture of the perceived and the imagined which could lead to potentially detrimental consequences. Inspired by neuroanatomical studies showing that feedforward and feedback connections in V1 terminate in different cortical layers, we hypothesized that this anatomical compartmentalization underlies functional segregation of external and internally-generated visual contents, respectively. We used high-resolution layer-specific fMRI to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth bins (i.e. superficial and deep). At the same time perceived contents were represented stronger at the middle cortical bin. These results identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. These results indicate that feedforward and feedback manifest in distinct subdivisions of the early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region.
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Affiliation(s)
- Polina Iamshchinina
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Daniel Kaiser
- Department of Psychology, University of York, Heslington, York, UK
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alessandro Sciarra
- Department of Biomedical Magnetic Resonance, Institute for Physics, Otto-von-Guericke-University, Magdeburg, Germany
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Hendrik Mattern
- Department of Biomedical Magnetic Resonance, Institute for Physics, Otto-von-Guericke-University, Magdeburg, Germany
| | - Falk Luesebrink
- Department of Biomedical Magnetic Resonance, Institute for Physics, Otto-von-Guericke-University, Magdeburg, Germany
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Emrah Duezel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Oliver Speck
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Biomedical Magnetic Resonance, Institute for Physics, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Radoslaw Martin Cichy
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
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34
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Aue T, Dricu M, Singh L, Moser DA, Raviteja K. Enhanced Sensitivity to Optimistic Cues is Manifested in Brain Structure: A Voxel-Based Morphometry Study. Soc Cogn Affect Neurosci 2021; 16:1170-1181. [PMID: 34128051 PMCID: PMC8599192 DOI: 10.1093/scan/nsab075] [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: 01/28/2021] [Revised: 04/25/2021] [Accepted: 06/14/2021] [Indexed: 01/07/2023] Open
Abstract
Recent research shows that congruent outcomes are more rapidly (and incongruent less rapidly) detected when individuals receive optimistic rather than pessimistic cues, an effect that was termed optimism robustness. In the current voxel-based morphometry study, we examined whether optimism robustness has a counterpart in brain structure. The participants' task was to detect two different letters (symbolizing monetary gain or loss) in a visual search matrix. Prior to each onset of the search matrix, two different verbal cues informed our participants about a high probability to gain (optimistic expectancy) or lose (pessimistic expectancy) money. The target presented was either congruent or incongruent with these induced expectancies. Optimism robustness revealed in the participants' reaction times correlated positively with gray matter volume (GMV) in brain regions involved in selective attention (medial visual association area, intraparietal sulcus), emphasizing the strong intertwinement of optimistic expectancies and attention deployment. In addition, GMV in the primary visual cortex diminished with increasing optimism robustness, in line with the interpretation of optimism robustness arising from a global, context-oriented perception. Future studies should address the malleability of these structural correlates of optimism robustness. Our results may assist in the identification of treatment targets in depression.
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Affiliation(s)
- Tatjana Aue
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Mihai Dricu
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Laura Singh
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Dominik A Moser
- Institute of Psychology, University of Bern, Bern, Switzerland
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