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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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2
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Aguado-López B, Palenciano AF, Peñalver JMG, Díaz-Gutiérrez P, López-García D, Avancini C, Ciria LF, Ruz M. Proactive selective attention across competition contexts. Cortex 2024; 176:113-128. [PMID: 38772050 DOI: 10.1016/j.cortex.2024.04.009] [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: 02/07/2024] [Revised: 04/03/2024] [Accepted: 04/16/2024] [Indexed: 05/23/2024]
Abstract
Selective attention is a cognitive function that helps filter out unwanted information. Theories such as the biased competition model (Desimone & Duncan, 1995) explain how attentional templates bias processing towards targets in contexts where multiple stimuli compete for resources. However, it is unclear how the anticipation of different levels of competition influences the nature of attentional templates, in a proactive fashion. In this study, we used electroencephalography (EEG) to investigate how the anticipated demands of attentional selection (either high or low stimuli competition contexts) modulate target-specific preparatory brain activity and its relationship with task performance. To do so, participants performed a sex/gender judgment task in a cue-target paradigm where, depending on the block, target and distractor stimuli appeared simultaneously (high competition) or sequentially (low competition). Multivariate Pattern Analysis (MVPA) showed that, in both competition contexts, there was a preactivation of the target category to select, with a ramping-up profile at the end of the preparatory interval. However, cross-classification showed no generalization across competition conditions, suggesting different preparatory formats. Notably, time-frequency analyses showed differences between anticipated competition demands, with higher theta band power for high than low competition, which mediated the impact of subsequent stimuli competition on behavioral performance. Overall, our results show that, whereas preactivation of the internal templates associated with the category to select are engaged in advance in high and low competition contexts, their underlying neural patterns differ. In addition, these codes could not be associated with theta power, suggesting that they reflect different preparatory processes. The implications of these findings are crucial to increase our understanding of the nature of top-down processes across different contexts.
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Affiliation(s)
- Blanca Aguado-López
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - Ana F Palenciano
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - José M G Peñalver
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - Paloma Díaz-Gutiérrez
- Department of Management, Faculty of Business and Economics, University of Antwerp, 2000, Belgium
| | - David López-García
- Data Science & Computational Intelligence Institute, University of Granada, CP 18071, Spain
| | - Chiara Avancini
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - Luis F Ciria
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - María Ruz
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain.
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3
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Morales-Torres R, Wing EA, Deng L, Davis SW, Cabeza R. Visual Recognition Memory of Scenes Is Driven by Categorical, Not Sensory, Visual Representations. J Neurosci 2024; 44:e1479232024. [PMID: 38569925 PMCID: PMC11112637 DOI: 10.1523/jneurosci.1479-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 04/05/2024] Open
Abstract
When we perceive a scene, our brain processes various types of visual information simultaneously, ranging from sensory features, such as line orientations and colors, to categorical features, such as objects and their arrangements. Whereas the role of sensory and categorical visual representations in predicting subsequent memory has been studied using isolated objects, their impact on memory for complex scenes remains largely unknown. To address this gap, we conducted an fMRI study in which female and male participants encoded pictures of familiar scenes (e.g., an airport picture) and later recalled them, while rating the vividness of their visual recall. Outside the scanner, participants had to distinguish each seen scene from three similar lures (e.g., three airport pictures). We modeled the sensory and categorical visual features of multiple scenes using both early and late layers of a deep convolutional neural network. Then, we applied representational similarity analysis to determine which brain regions represented stimuli in accordance with the sensory and categorical models. We found that categorical, but not sensory, representations predicted subsequent memory. In line with the previous result, only for the categorical model, the average recognition performance of each scene exhibited a positive correlation with the average visual dissimilarity between the item in question and its respective lures. These results strongly suggest that even in memory tests that ostensibly rely solely on visual cues (such as forced-choice visual recognition with similar distractors), memory decisions for scenes may be primarily influenced by categorical rather than sensory representations.
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Affiliation(s)
| | - Erik A Wing
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario M6A 2E1, Canada
| | - Lifu Deng
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina 27708
| | - Simon W Davis
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina 27708
- Department of Neurology, Duke University School of Medicine, Durham, North Carolina 27708
| | - Roberto Cabeza
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina 27708
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4
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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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5
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Storm JF, Klink PC, Aru J, Senn W, Goebel R, Pigorini A, Avanzini P, Vanduffel W, Roelfsema PR, Massimini M, Larkum ME, Pennartz CMA. An integrative, multiscale view on neural theories of consciousness. Neuron 2024; 112:1531-1552. [PMID: 38447578 DOI: 10.1016/j.neuron.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 03/08/2024]
Abstract
How is conscious experience related to material brain processes? A variety of theories aiming to answer this age-old question have emerged from the recent surge in consciousness research, and some are now hotly debated. Although most researchers have so far focused on the development and validation of their preferred theory in relative isolation, this article, written by a group of scientists representing different theories, takes an alternative approach. Noting that various theories often try to explain different aspects or mechanistic levels of consciousness, we argue that the theories do not necessarily contradict each other. Instead, several of them may converge on fundamental neuronal mechanisms and be partly compatible and complementary, so that multiple theories can simultaneously contribute to our understanding. Here, we consider unifying, integration-oriented approaches that have so far been largely neglected, seeking to combine valuable elements from various theories.
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Affiliation(s)
- Johan F Storm
- The Brain Signaling Group, Division of Physiology, IMB, Faculty of Medicine, University of Oslo, Domus Medica, Sognsvannsveien 9, Blindern, 0317 Oslo, Norway.
| | - P Christiaan Klink
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, 3584 CS Utrecht, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris 75012, France
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Walter Senn
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan 20122, Italy
| | - Pietro Avanzini
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, 43125 Parma, Italy
| | - Wim Vanduffel
- Department of Neurosciences, Laboratory of Neuro and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
| | - Pieter R Roelfsema
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris 75012, France; Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands; Department of Neurosurgery, Academisch Medisch Centrum, Postbus 22660, 1100 DD Amsterdam, the Netherlands
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan 20157, Italy; Istituto di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan 20122, Italy; Azrieli Program in Brain, Mind and Consciousness, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5G 1M1, Canada
| | - Matthew E Larkum
- Institute of Biology, Humboldt University Berlin, Berlin, Germany; Neurocure Center for Excellence, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cyriel M A Pennartz
- Swammerdam Institute for Life Sciences, Center for Neuroscience, Faculty of Science, University of Amsterdam, Sciencepark 904, Amsterdam 1098 XH, the Netherlands; Research Priority Program Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
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6
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Dijkstra N. Uncovering the Role of the Early Visual Cortex in Visual Mental Imagery. Vision (Basel) 2024; 8:29. [PMID: 38804350 PMCID: PMC11130976 DOI: 10.3390/vision8020029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/29/2024] Open
Abstract
The question of whether the early visual cortex (EVC) is involved in visual mental imagery remains a topic of debate. In this paper, I propose that the inconsistency in findings can be explained by the unique challenges associated with investigating EVC activity during imagery. During perception, the EVC processes low-level features, which means that activity is highly sensitive to variation in visual details. If the EVC has the same role during visual mental imagery, any change in the visual details of the mental image would lead to corresponding changes in EVC activity. Within this context, the question should not be whether the EVC is 'active' during imagery but how its activity relates to specific imagery properties. Studies using methods that are sensitive to variation in low-level features reveal that imagery can recruit the EVC in similar ways as perception. However, not all mental images contain a high level of visual details. Therefore, I end by considering a more nuanced view, which states that imagery can recruit the EVC, but that does not mean that it always does so.
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Affiliation(s)
- Nadine Dijkstra
- Department of Imaging Neuroscience, Institute of Neurology, University College London, London WC1E 6BT, 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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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal Modeling of Cross-Sensory Visual Evoked Magnetoencephalography Responses in the Auditory Cortex. J Neurosci 2024; 44:e1119232024. [PMID: 38508715 PMCID: PMC11044114 DOI: 10.1523/jneurosci.1119-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/22/2024] Open
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by cross-sensory visual inputs. Intracortical laminar recordings in nonhuman primates have suggested a feedforward (FF) type profile for auditory evoked but feedback (FB) type for visual evoked activity in the auditory cortex. To test whether cross-sensory visual evoked activity in the auditory cortex is associated with FB inputs also in humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex regions of interest, auditory evoked response showed peaks at 37 and 90 ms and visual evoked response at 125 ms. The inputs to the auditory cortex were modeled through FF- and FB-type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which links cellular- and circuit-level mechanisms to MEG signals. HNN modeling suggested that the experimentally observed auditory response could be explained by an FF input followed by an FB input, whereas the cross-sensory visual response could be adequately explained by just an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
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9
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Ehrlich I, Ortiz-Tudela J, Tan YY, Muckli L, Shing YL. Mnemonic But Not Contextual Feedback Signals Defy Dedifferentiation in the Aging Early Visual Cortex. J Neurosci 2024; 44:e0607232023. [PMID: 38395614 PMCID: PMC11026335 DOI: 10.1523/jneurosci.0607-23.2023] [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: 06/13/2023] [Revised: 11/18/2023] [Accepted: 12/18/2023] [Indexed: 02/25/2024] Open
Abstract
Perception is an intricate interplay between feedforward visual input and internally generated feedback signals that comprise concurrent contextual and time-distant mnemonic (episodic and semantic) information. Yet, an unresolved question is how the composition of feedback signals changes across the lifespan and to what extent feedback signals undergo age-related dedifferentiation, that is, a decline in neural specificity. Previous research on this topic has focused on feedforward perceptual representation and episodic memory reinstatement, suggesting reduced fidelity of neural representations at the item and category levels. In this fMRI study, we combined an occlusion paradigm that filters feedforward input to the visual cortex and multivariate analysis techniques to investigate the information content in cortical feedback, focusing on age-related differences in its composition. We further asked to what extent differentiation in feedback signals (in the occluded region) is correlated to differentiation in feedforward signals. Comparing younger (18-30 years) and older female and male adults (65-75 years), we found that contextual but not mnemonic feedback was prone to age-related dedifferentiation. Semantic feedback signals were even better differentiated in older adults, highlighting the growing importance of generalized knowledge across ages. We also found that differentiation in feedforward signals was correlated with differentiation in episodic but not semantic feedback signals. Our results provide evidence for age-related adjustments in the composition of feedback signals and underscore the importance of examining dedifferentiation in aging for both feedforward and feedback processing.
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Affiliation(s)
- Isabelle Ehrlich
- Department of Psychology, Goethe University Frankfurt, Frankfurt Am Main 60323, Germany
| | - Javier Ortiz-Tudela
- Department of Psychology, Goethe University Frankfurt, Frankfurt Am Main 60323, Germany
- Department of Experimental Psychology, Mind, Brain, and Behavior Research Center, University of Granada, Granada 18013, Spain
| | - Yi You Tan
- Department of Psychology, Goethe University Frankfurt, Frankfurt Am Main 60323, Germany
| | - Lars Muckli
- School of Psychology and of Neuroscience, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | - Yee Lee Shing
- Department of Psychology, Goethe University Frankfurt, Frankfurt Am Main 60323, Germany
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main 60323, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main 60528, Germany
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10
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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.16.545371. [PMID: 37398025 PMCID: PMC10312796 DOI: 10.1101/2023.06.16.545371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Seppo P. Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
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11
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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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12
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Hodson R, Mehta M, Smith R. The empirical status of predictive coding and active inference. Neurosci Biobehav Rev 2024; 157:105473. [PMID: 38030100 DOI: 10.1016/j.neubiorev.2023.105473] [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: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.
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Affiliation(s)
| | | | - Ryan Smith
- Laureate Institute for Brain Research, USA.
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13
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Czajko S, Vignaud A, Eger E. Human brain representations of internally generated outcomes of approximate calculation revealed by ultra-high-field brain imaging. Nat Commun 2024; 15:572. [PMID: 38233387 PMCID: PMC10794709 DOI: 10.1038/s41467-024-44810-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
Much of human culture's advanced technology owes its existence to the ability to mentally manipulate quantities. Neuroscience has described the brain regions overall recruited by numerical tasks and the neuronal codes representing individual quantities during perceptual tasks. Nevertheless, it remains unknown how quantity representations are combined or transformed during mental computations and how specific quantities are coded in the brain when generated as the result of internal computations rather than evoked by a stimulus. Here, we imaged the brains of adult human subjects at 7 Tesla during an approximate calculation task designed to disentangle in- and outputs of the computation from the operation itself. While physically presented sample numerosities were distinguished in activity patterns along the dorsal visual pathway and within frontal and occipito-temporal regions, a representation of the internally generated result was most prominently detected in higher order regions such as angular gyrus and lateral prefrontal cortex. Behavioral precision in the task was related to cross-decoding performance between sample and result representations in medial IPS regions. This suggests the transformation of sample into result may be carried out within dorsal stream sensory-motor integration regions, and resulting outputs maintained for task purposes in higher-level regions in a format possibly detached from sensory-evoked inputs.
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Affiliation(s)
- Sébastien Czajko
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France
- EDUWELL team, Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, Lyon, France
| | - Alexandre Vignaud
- UNIRS, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France
| | - Evelyn Eger
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.
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14
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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] [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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15
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Kotlarz P, Lankinen K, Hakonen M, Turpin T, Polimeni JR, Ahveninen J. Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573208. [PMID: 38187540 PMCID: PMC10769454 DOI: 10.1101/2023.12.23.573208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. A neuroscience example is the hierarchy of connections between different cortical depths or "lamina". This hierarchy is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We then compared networks where the inter-regional connections were limited to a single cortical depth only ("layer-by-layer matrices") to those considering all possible connections between regions and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes such as network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial aspects of the cortex dominated information transfer and deeper aspects clustering. These differences were largest in frontotemporal and limbic brain regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information. Multilayer connectomics could provide a methodological framework for studies on how information flows across this hierarchy.
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Affiliation(s)
- Parker Kotlarz
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Maria Hakonen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | | | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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16
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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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17
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Chen L, Cichy RM, Kaiser D. Alpha-frequency feedback to early visual cortex orchestrates coherent naturalistic vision. SCIENCE ADVANCES 2023; 9:eadi2321. [PMID: 37948520 PMCID: PMC10637741 DOI: 10.1126/sciadv.adi2321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023]
Abstract
During naturalistic vision, the brain generates coherent percepts by integrating sensory inputs scattered across the visual field. Here, we asked whether this integration process is mediated by rhythmic cortical feedback. In electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) experiments, we experimentally manipulated integrative processing by changing the spatiotemporal coherence of naturalistic videos presented across visual hemifields. Our EEG data revealed that information about incoherent videos is coded in feedforward-related gamma activity while information about coherent videos is coded in feedback-related alpha activity, indicating that integration is indeed mediated by rhythmic activity. Our fMRI data identified scene-selective cortex and human middle temporal complex (hMT) as likely sources of this feedback. Analytically combining our EEG and fMRI data further revealed that feedback-related representations in the alpha band shape the earliest stages of visual processing in cortex. Together, our findings indicate that the construction of coherent visual experiences relies on cortical feedback rhythms that fully traverse the visual hierarchy.
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Affiliation(s)
- Lixiang Chen
- Department of Education and Psychology, Freie Universität Berlin, Berlin 14195, Germany
| | - Radoslaw M. Cichy
- Department of Education and Psychology, Freie Universität Berlin, Berlin 14195, Germany
| | - Daniel Kaiser
- Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus-Liebig-Universität Gießen, Gießen 35392, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus-Liebig-Universität Gießen, Marburg 35032, Germany
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18
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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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19
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Papale P, Wang F, Morgan AT, Chen X, Gilhuis A, Petro LS, Muckli L, Roelfsema PR, Self MW. The representation of occluded image regions in area V1 of monkeys and humans. Curr Biol 2023; 33:3865-3871.e3. [PMID: 37643620 DOI: 10.1016/j.cub.2023.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/04/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023]
Abstract
Neuronal activity in the primary visual cortex (V1) is driven by feedforward input from within the neurons' receptive fields (RFs) and modulated by contextual information in regions surrounding the RF. The effect of contextual information on spiking activity occurs rapidly and is therefore challenging to dissociate from feedforward input. To address this challenge, we recorded the spiking activity of V1 neurons in monkeys viewing either natural scenes or scenes where the information in the RF was occluded, effectively removing the feedforward input. We found that V1 neurons responded rapidly and selectively to occluded scenes. V1 responses elicited by occluded stimuli could be used to decode individual scenes and could be predicted from those elicited by non-occluded images, indicating that there is an overlap between visually driven and contextual responses. We used representational similarity analysis to show that the structure of V1 representations of occluded scenes measured with electrophysiology in monkeys correlates strongly with the representations of the same scenes in humans measured with functional magnetic resonance imaging (fMRI). Our results reveal that contextual influences rapidly alter V1 spiking activity in monkeys over distances of several degrees in the visual field, carry information about individual scenes, and resemble those in human V1. VIDEO ABSTRACT.
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Affiliation(s)
- Paolo Papale
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), 1105 BA Amsterdam, the Netherlands.
| | - Feng Wang
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), 1105 BA Amsterdam, the Netherlands
| | - A Tyler Morgan
- Centre for Cognitive NeuroImaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, UK; Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Xing Chen
- Department of Ophthalmology, University of Pittsburgh School of Medicine, 203 Lothrop St, Pittsburgh, PA 15213, USA
| | - Amparo Gilhuis
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), 1105 BA Amsterdam, the Netherlands
| | - Lucy S Petro
- Centre for Cognitive NeuroImaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, UK; Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Lars Muckli
- Centre for Cognitive NeuroImaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, UK; Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Pieter R Roelfsema
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), 1105 BA Amsterdam, the Netherlands; Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands; Department of Neurosurgery, Academic Medical Centre, Postbus 22660, 1100 DD Amsterdam, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.
| | - Matthew W Self
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), 1105 BA Amsterdam, the Netherlands
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20
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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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21
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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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22
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Seydell-Greenwald A, Wang X, Newport EL, Bi Y, Striem-Amit E. Spoken language processing activates the primary visual cortex. PLoS One 2023; 18:e0289671. [PMID: 37566582 PMCID: PMC10420367 DOI: 10.1371/journal.pone.0289671] [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: 02/27/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Primary visual cortex (V1) is generally thought of as a low-level sensory area that primarily processes basic visual features. Although there is evidence for multisensory effects on its activity, these are typically found for the processing of simple sounds and their properties, for example spatially or temporally-congruent simple sounds. However, in congenitally blind individuals, V1 is involved in language processing, with no evidence of major changes in anatomical connectivity that could explain this seemingly drastic functional change. This is at odds with current accounts of neural plasticity, which emphasize the role of connectivity and conserved function in determining a neural tissue's role even after atypical early experiences. To reconcile what appears to be unprecedented functional reorganization with known accounts of plasticity limitations, we tested whether V1's multisensory roles include responses to spoken language in sighted individuals. Using fMRI, we found that V1 in normally sighted individuals was indeed activated by comprehensible spoken sentences as compared to an incomprehensible reversed speech control condition, and more strongly so in the left compared to the right hemisphere. Activation in V1 for language was also significant and comparable for abstract and concrete words, suggesting it was not driven by visual imagery. Last, this activation did not stem from increased attention to the auditory onset of words, nor was it correlated with attentional arousal ratings, making general attention accounts an unlikely explanation. Together these findings suggest that V1 responds to spoken language even in sighted individuals, reflecting the binding of multisensory high-level signals, potentially to predict visual input. This capability might be the basis for the strong V1 language activation observed in people born blind, re-affirming the notion that plasticity is guided by pre-existing connectivity and abilities in the typically developed brain.
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Affiliation(s)
- Anna Seydell-Greenwald
- Center for Brain Plasticity and Recovery, Georgetown University Medical Center, Washington, DC, United States of America
| | - Xiaoying Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Elissa L. Newport
- Center for Brain Plasticity and Recovery, Georgetown University Medical Center, Washington, DC, United States of America
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ella Striem-Amit
- Center for Brain Plasticity and Recovery, Georgetown University Medical Center, Washington, DC, United States of America
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States of America
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23
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Petro LS, Smith FW, Abbatecola C, Muckli L. The Spatial Precision of Contextual Feedback Signals in Human V1. BIOLOGY 2023; 12:1022. [PMID: 37508451 PMCID: PMC10376409 DOI: 10.3390/biology12071022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/23/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
Neurons in the primary visual cortex (V1) receive sensory inputs that describe small, local regions of the visual scene and cortical feedback inputs from higher visual areas processing the global scene context. Investigating the spatial precision of this visual contextual modulation will contribute to our understanding of the functional role of cortical feedback inputs in perceptual computations. We used human functional magnetic resonance imaging (fMRI) to test the spatial precision of contextual feedback inputs to V1 during natural scene processing. We measured brain activity patterns in the stimulated regions of V1 and in regions that we blocked from direct feedforward input, receiving information only from non-feedforward (i.e., feedback and lateral) inputs. We measured the spatial precision of contextual feedback signals by generalising brain activity patterns across parametrically spatially displaced versions of identical images using an MVPA cross-classification approach. We found that fMRI activity patterns in cortical feedback signals predicted our scene-specific features in V1 with a precision of approximately 4 degrees. The stimulated regions of V1 carried more precise scene information than non-stimulated regions; however, these regions also contained information patterns that generalised up to 4 degrees. This result shows that contextual signals relating to the global scene are similarly fed back to V1 when feedforward inputs are either present or absent. Our results are in line with contextual feedback signals from extrastriate areas to V1, describing global scene information and contributing to perceptual computations such as the hierarchical representation of feature boundaries within natural scenes.
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Affiliation(s)
- 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 for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Fraser W Smith
- School of Psychology, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Clement Abbatecola
- 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 for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - 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 for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
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Pais-Roldán P, Yun SD, Palomero-Gallagher N, Shah NJ. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front Neurosci 2023; 17:1151544. [PMID: 37274214 PMCID: PMC10232833 DOI: 10.3389/fnins.2023.1151544] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/26/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Recent laminar-fMRI studies have substantially improved understanding of the evoked cortical responses in multiple sub-systems; in contrast, the laminar component of resting-state networks spread over the whole brain has been less studied due to technical limitations. Animal research strongly suggests that the supragranular layers of the cortex play a critical role in maintaining communication within the default mode network (DMN); however, whether this is true in this and other human cortical networks remains unclear. Methods Here, we used EPIK, which offers unprecedented coverage at sub-millimeter resolution, to investigate cortical broad resting-state dynamics with depth specificity in healthy volunteers. Results Our results suggest that human DMN connectivity is primarily supported by intermediate and superficial layers of the cortex, and furthermore, the preferred cortical depth used for communication can vary from one network to another. In addition, the laminar connectivity profile of some networks showed a tendency to change upon engagement in a motor task. In line with these connectivity changes, we observed that the amplitude of the low-frequency-fluctuations (ALFF), as well as the regional homogeneity (ReHo), exhibited a different laminar slope when subjects were either performing a task or were in a resting state (less variation among laminae, i.e., lower slope, during task performance compared to rest). Discussion The identification of varied laminar profiles concerning network connectivity, ALFF, and ReHo, observed across two brain states (task vs. rest) has major implications for the characterization of network-related diseases and suggests the potential diagnostic value of laminar fMRI in psychiatric disorders, e.g., to differentiate the cortical dynamics associated with disease stages linked, or not linked, to behavioral changes. The evaluation of laminar-fMRI across the brain encompasses computational challenges; nonetheless, it enables the investigation of a new dimension of the human neocortex, which may be key to understanding neurological disorders from a novel perspective.
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Affiliation(s)
- Patricia Pais-Roldán
- Institute of Neuroscience and Medicine 4, Medical Imaging Physics, Forschungszentrum Jülich, Jülich, Germany
| | - Seong Dae Yun
- Institute of Neuroscience and Medicine 4, Medical Imaging Physics, Forschungszentrum Jülich, Jülich, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine 1, Structural and Functional Organisation of the Brain, Forschungszentrum Jülich, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, Heinrich-Heine-University, Düsseldorf, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, Medical Imaging Physics, Forschungszentrum Jülich, Jülich, Germany
- Institute of Neuroscience and Medicine 11, Molecular Neuroscience and Neuroimaging, JARA, Forschungszentrum Jülich, Jülich, Germany
- JARA–BRAIN–Translational Medicine, Aachen, Germany
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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25
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Katsumi Y, Zhang J, Chen D, Kamona N, Bunce JG, Hutchinson JB, Yarossi M, Tunik E, Dickerson BC, Quigley KS, Barrett LF. Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus. Commun Biol 2023; 6:401. [PMID: 37046050 PMCID: PMC10097701 DOI: 10.1038/s42003-023-04796-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function.
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Affiliation(s)
- Yuta Katsumi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
| | - Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Danlei Chen
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Nada Kamona
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Jamie G Bunce
- Department of Biology, Northeastern University, Boston, MA, 02115, USA
| | | | - Mathew Yarossi
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
- Department of Physical Therapy, Movement, and Rehabilitation Science, Northeastern University, Boston, MA, 02115, USA
| | - Eugene Tunik
- Department of Physical Therapy, Movement, and Rehabilitation Science, Northeastern University, Boston, MA, 02115, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Karen S Quigley
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
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26
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Olman CA. What multiplexing means for the interpretation of functional MRI data. Front Hum Neurosci 2023; 17:1134811. [PMID: 37091812 PMCID: PMC10117671 DOI: 10.3389/fnhum.2023.1134811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
Despite technology advances that have enabled routine acquisition of functional MRI data with sub-millimeter resolution, the inferences that cognitive neuroscientists must make to link fMRI data to behavior are complicated. Thus, a single dataset subjected to different analyses can be interpreted in different ways. This article presents two optical analogies that can be useful for framing fMRI analyses in a way that allows for multiple interpretations of fMRI data to be valid simultaneously without undermining each other. The first is reflection: when an object is reflected in a mirrored surface, it appears as if the reflected object is sharing space with the mirrored object, but of course it is not. This analogy can be a good guide for interpreting the fMRI signal, since even at sub-millimeter resolutions the signal is determined by a mixture of local and long-range neural computations. The second is refraction. If we view an object through a multi-faceted prism or gemstone, our view will change-sometimes dramatically-depending on our viewing angle. In the same way, interpretation of fMRI data (inference of underlying neuronal activity) can and should be different depending on the analysis approach. Rather than representing a weakness of the methodology, or the superiority of one approach over the other (for example, simple regression analysis versus multi-voxel pattern analysis), this is an expected consequence of how information is multiplexed in the neural networks of the brain: multiple streams of information are simultaneously present in each location. The fact that any one analysis typically shows only one view of the data also puts some parentheses around fMRI practitioners' constant search for ground truth against which to compare their data. By holding our interpretations lightly and understanding that many interpretations of the data can all be true at the same time, we do a better job of preparing ourselves to appreciate, and eventually understand, the complexity of the brain and the behavior it produces.
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Affiliation(s)
- Cheryl A. Olman
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
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27
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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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Top-down specific preparatory activations for selective attention and perceptual expectations. Neuroimage 2023; 271:119960. [PMID: 36854351 DOI: 10.1016/j.neuroimage.2023.119960] [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: 12/16/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 03/01/2023] Open
Abstract
Proactive cognition brain models are mainstream nowadays. Within these, preparation is understood as an endogenous, top-down function that takes place prior to the actual perception of a stimulus and improves subsequent behavior. Neuroimaging has shown the existence of such preparatory activity separately in different cognitive domains, however no research to date has sought to uncover their potential similarities and differences. Two of these, often confounded in the literature, are Selective Attention (information relevance) and Perceptual Expectation (information probability). We used EEG to characterize the mechanisms that pre-activate specific contents in Attention and Expectation. In different blocks, participants were cued to the relevance or to the probability of target categories, faces vs. names, in a gender discrimination task. Multivariate Pattern (MVPA) and Representational Similarity Analyses (RSA) during the preparation window showed that both manipulations led to a significant, ramping-up prediction of the relevant or expected target category. However, classifiers trained with data from one condition did not generalize to the other, indicating the existence of unique anticipatory neural patterns. In addition, a Canonical Template Tracking procedure showed that there was stronger anticipatory perceptual reinstatement for relevance than for expectation blocks. Overall, the results indicate that preparation during attention and expectation acts through distinguishable neural mechanisms. These findings have important implications for current models of brain functioning, as they are a first step towards characterizing and dissociating the neural mechanisms involved in top-down anticipatory processing.
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Scheeringa R, Bonnefond M, van Mourik T, Jensen O, Norris DG, Koopmans PJ. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cereb Cortex 2023; 33:1537-1549. [PMID: 35512361 PMCID: PMC9977363 DOI: 10.1093/cercor/bhac154] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Laminar functional magnetic resonance imaging (fMRI) holds the potential to study connectivity at the laminar level in humans. Here we analyze simultaneously recorded electroencephalography (EEG) and high-resolution fMRI data to investigate how EEG power modulations, induced by a task with an attentional component, relate to changes in fMRI laminar connectivity between and within brain regions in visual cortex. Our results indicate that our task-induced decrease in beta power relates to an increase in deep-to-deep layer coupling between regions and to an increase in deep/middle-to-superficial layer connectivity within brain regions. The attention-related alpha power decrease predominantly relates to reduced connectivity between deep and superficial layers within brain regions, since, unlike beta power, alpha power was found to be positively correlated to connectivity. We observed no strong relation between laminar connectivity and gamma band oscillations. These results indicate that especially beta band, and to a lesser extent, alpha band oscillations relate to laminar-specific fMRI connectivity. The differential effects for alpha and beta bands indicate that they relate to different feedback-related neural processes that are differentially expressed in intra-region laminar fMRI-based connectivity.
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Affiliation(s)
- René Scheeringa
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, UNESCO-Weltkulturerbe Zollverein, University of Duisburg-Essen, Kokereiallee 7, 45141 Essen, Germany.,High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45147 Essen, Germany.,Lyon Neuroscience Research Center; CRNL, INSERM U1028, CNRS UMR5292, University of Lyon 1, Université de Lyon, Bâtiment 462 - Neurocampus, 95 Bd Pinel, 69500 Bron, France.,Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Trigon 204, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Mathilde Bonnefond
- Lyon Neuroscience Research Center; CRNL, INSERM U1028, CNRS UMR5292, University of Lyon 1, Université de Lyon, Bâtiment 462 - Neurocampus, 95 Bd Pinel, 69500 Bron, France
| | - Tim van Mourik
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Trigon 204, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Ole Jensen
- School of Psychology, Centre for Human Brain Health, University of Birmingham, Hills Building, Birmingham B15 2TT, United Kingdom
| | - David G Norris
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, UNESCO-Weltkulturerbe Zollverein, University of Duisburg-Essen, Kokereiallee 7, 45141 Essen, Germany.,Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Trigon 204, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Peter J Koopmans
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, UNESCO-Weltkulturerbe Zollverein, University of Duisburg-Essen, Kokereiallee 7, 45141 Essen, Germany.,High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45147 Essen, Germany.,Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Trigon 204, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.,Department of Radiation Oncology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
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30
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Lankinen K, Ahlfors SP, Mamashli F, Blazejewska AI, Raij T, Turpin T, Polimeni JR, Ahveninen J. Cortical depth profiles of auditory and visual 7 T functional MRI responses in human superior temporal areas. Hum Brain Mapp 2023; 44:362-372. [PMID: 35980015 PMCID: PMC9842898 DOI: 10.1002/hbm.26046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 07/06/2022] [Accepted: 07/16/2022] [Indexed: 02/02/2023] Open
Abstract
Invasive neurophysiological studies in nonhuman primates have shown different laminar activation profiles to auditory vs. visual stimuli in auditory cortices and adjacent polymodal areas. Means to examine the underlying feedforward vs. feedback type influences noninvasively have been limited in humans. Here, using 1-mm isotropic resolution 3D echo-planar imaging at 7 T, we studied the intracortical depth profiles of functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) signals to brief auditory (noise bursts) and visual (checkerboard) stimuli. BOLD percent-signal-changes were estimated at 11 equally spaced intracortical depths, within regions-of-interest encompassing auditory (Heschl's gyrus, Heschl's sulcus, planum temporale, and posterior superior temporal gyrus) and polymodal (middle and posterior superior temporal sulcus) areas. Effects of differing BOLD signal strengths for auditory and visual stimuli were controlled via normalization and statistical modeling. The BOLD depth profile shapes, modeled with quadratic regression, were significantly different for auditory vs. visual stimuli in auditory cortices, but not in polymodal areas. The different depth profiles could reflect sensory-specific feedforward versus cross-sensory feedback influences, previously shown in laminar recordings in nonhuman primates. The results suggest that intracortical BOLD profiles can help distinguish between feedforward and feedback type influences in the human brain. Further experimental studies are still needed to clarify how underlying signal strength influences BOLD depth profiles under different stimulus conditions.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Seppo P. Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Anna I. Blazejewska
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Tori Turpin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Division of Health Sciences and TechnologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
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Concurrent contextual and time-distant mnemonic information co-exist as feedback in the human visual cortex. Neuroimage 2023; 265:119778. [PMID: 36462731 PMCID: PMC9878579 DOI: 10.1016/j.neuroimage.2022.119778] [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: 09/30/2022] [Revised: 11/14/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022] Open
Abstract
Efficient processing of the visual environment necessitates the integration of incoming sensory evidence with concurrent contextual inputs and mnemonic content from our past experiences. To examine how this integration takes place in the brain, we isolated different types of feedback signals from the neural patterns of non-stimulated areas of the early visual cortex in humans (i.e., V1 and V2). Using multivariate pattern analysis, we showed that both contextual and time-distant information, coexist in V1 and V2 as feedback signals. In addition, we found that the extent to which mnemonic information is reinstated in V1 and V2 depends on whether the information is retrieved episodically or semantically. Critically, this reinstatement was independent on the retrieval route in the object-selective cortex. These results demonstrate that our early visual processing contains not just direct and indirect information from the visual surrounding, but also memory-based predictions.
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Liu TT, Fu JZ, Chai Y, Japee S, Chen G, Ungerleider LG, Merriam EP. Layer-specific, retinotopically-diffuse modulation in human visual cortex in response to viewing emotionally expressive faces. Nat Commun 2022; 13:6302. [PMID: 36273204 PMCID: PMC9588045 DOI: 10.1038/s41467-022-33580-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/22/2022] [Indexed: 12/25/2022] Open
Abstract
Viewing faces that are perceived as emotionally expressive evokes enhanced neural responses in multiple brain regions, a phenomenon thought to depend critically on the amygdala. This emotion-related modulation is evident even in primary visual cortex (V1), providing a potential neural substrate by which emotionally salient stimuli can affect perception. How does emotional valence information, computed in the amygdala, reach V1? Here we use high-resolution functional MRI to investigate the layer profile and retinotopic distribution of neural activity specific to emotional facial expressions. Across three experiments, human participants viewed centrally presented face stimuli varying in emotional expression and performed a gender judgment task. We found that facial valence sensitivity was evident only in superficial cortical layers and was not restricted to the retinotopic location of the stimuli, consistent with diffuse feedback-like projections from the amygdala. Together, our results provide a feedback mechanism by which the amygdala directly modulates activity at the earliest stage of visual processing.
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Affiliation(s)
- Tina T. Liu
- grid.416868.50000 0004 0464 0574Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892 MD USA
| | - Jason Z Fu
- grid.416868.50000 0004 0464 0574Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892 MD USA
| | - Yuhui Chai
- grid.416868.50000 0004 0464 0574Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892 MD USA
| | - Shruti Japee
- grid.416868.50000 0004 0464 0574Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892 MD USA
| | - Gang Chen
- grid.416868.50000 0004 0464 0574Scientific and Statistical Computing Core, National Institute of Mental Health, NIH, Bethesda, 20892 MD USA
| | - Leslie G. Ungerleider
- grid.416868.50000 0004 0464 0574Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892 MD USA
| | - Elisha P. Merriam
- grid.416868.50000 0004 0464 0574Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892 MD USA
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33
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Demirayak P, Deshpande G, Visscher K. Laminar functional magnetic resonance imaging in vision research. Front Neurosci 2022; 16:910443. [PMID: 36267240 PMCID: PMC9577024 DOI: 10.3389/fnins.2022.910443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) scanners at ultra-high magnetic fields have become available to use in humans, thus enabling researchers to investigate the human brain in detail. By increasing the spatial resolution, ultra-high field MR allows both structural and functional characterization of cortical layers. Techniques that can differentiate cortical layers, such as histological studies and electrode-based measurements have made critical contributions to the understanding of brain function, but these techniques are invasive and thus mainly available in animal models. There are likely to be differences in the organization of circuits between humans and even our closest evolutionary neighbors. Thus research on the human brain is essential. Ultra-high field MRI can observe differences between cortical layers, but is non-invasive and can be used in humans. Extensive previous literature has shown that neuronal connections between brain areas that transmit feedback and feedforward information terminate in different layers of the cortex. Layer-specific functional MRI (fMRI) allows the identification of layer-specific hemodynamic responses, distinguishing feedback and feedforward pathways. This capability has been particularly important for understanding visual processing, as it has allowed researchers to test hypotheses concerning feedback and feedforward information in visual cortical areas. In this review, we provide a general overview of successful ultra-high field MRI applications in vision research as examples of future research.
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Affiliation(s)
- Pinar Demirayak
- Civitan International Research Center, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Pinar Demirayak,
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
| | - Kristina Visscher
- Civitan International Research Center, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
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Katsumi Y, Theriault JE, Quigley KS, Barrett LF. Allostasis as a core feature of hierarchical gradients in the human brain. Netw Neurosci 2022; 6:1010-1031. [PMID: 38800458 PMCID: PMC11117115 DOI: 10.1162/netn_a_00240] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/11/2022] [Indexed: 05/29/2024] Open
Abstract
This paper integrates emerging evidence from two broad streams of scientific literature into one common framework: (a) hierarchical gradients of functional connectivity that reflect the brain's large-scale structural architecture (e.g., a lamination gradient in the cerebral cortex); and (b) approaches to predictive processing and one of its specific instantiations called allostasis (i.e., the predictive regulation of energetic resources in the service of coordinating the body's internal systems). This synthesis begins to sketch a coherent, neurobiologically inspired framework suggesting that predictive energy regulation is at the core of human brain function, and by extension, psychological and behavioral phenomena, providing a shared vocabulary for theory building and knowledge accumulation.
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Affiliation(s)
- Yuta Katsumi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Karen S. Quigley
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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35
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van Dyck LE, Denzler SJ, Gruber WR. Guiding visual attention in deep convolutional neural networks based on human eye movements. Front Neurosci 2022; 16:975639. [PMID: 36177359 PMCID: PMC9514055 DOI: 10.3389/fnins.2022.975639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroimaging and neural time series data. As recent advances in deep learning seem to decrease this similarity, computational neuroscience is challenged to reverse-engineer the biological plausibility to obtain useful models. While previous studies have shown that biologically inspired architectures are able to amplify the human-likeness of the models, in this study, we investigate a purely data-driven approach. We use human eye tracking data to directly modify training examples and thereby guide the models’ visual attention during object recognition in natural images either toward or away from the focus of human fixations. We compare and validate different manipulation types (i.e., standard, human-like, and non-human-like attention) through GradCAM saliency maps against human participant eye tracking data. Our results demonstrate that the proposed guided focus manipulation works as intended in the negative direction and non-human-like models focus on significantly dissimilar image parts compared to humans. The observed effects were highly category-specific, enhanced by animacy and face presence, developed only after feedforward processing was completed, and indicated a strong influence on face detection. With this approach, however, no significantly increased human-likeness was found. Possible applications of overt visual attention in DCNNs and further implications for theories of face detection are discussed.
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Affiliation(s)
- Leonard Elia van Dyck
- Department of Psychology, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- *Correspondence: Leonard Elia van Dyck,
| | | | - Walter Roland Gruber
- Department of Psychology, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
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36
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Bailey KM, Giordano BL, Kaas AL, Smith FW. Decoding sounds depicting hand-object interactions in primary somatosensory cortex. Cereb Cortex 2022; 33:3621-3635. [PMID: 36045002 DOI: 10.1093/cercor/bhac296] [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: 02/10/2022] [Revised: 05/24/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
Neurons, even in the earliest sensory regions of cortex, are subject to a great deal of contextual influences from both within and across modality connections. Recent work has shown that primary sensory areas can respond to and, in some cases, discriminate stimuli that are not of their target modality: for example, primary somatosensory cortex (SI) discriminates visual images of graspable objects. In the present work, we investigated whether SI would discriminate sounds depicting hand-object interactions (e.g. bouncing a ball). In a rapid event-related functional magnetic resonance imaging experiment, participants listened attentively to sounds from 3 categories: hand-object interactions, and control categories of pure tones and animal vocalizations, while performing a one-back repetition detection task. Multivoxel pattern analysis revealed significant decoding of hand-object interaction sounds within SI, but not for either control category. Crucially, in the hand-sensitive voxels defined from an independent tactile localizer, decoding accuracies were significantly higher for hand-object interactions compared to pure tones in left SI. Our findings indicate that simply hearing sounds depicting familiar hand-object interactions elicit different patterns of activity in SI, despite the complete absence of tactile stimulation. These results highlight the rich contextual information that can be transmitted across sensory modalities even to primary sensory areas.
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Affiliation(s)
- Kerri M Bailey
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
| | - Bruno L Giordano
- Institut des Neurosciences de La Timone, CNRS UMR 7289, Université Aix-Marseille, Marseille CNRS UMR 7289, France
| | - Amanda L Kaas
- Department of Cognitive Neuroscience, Maastricht University, Maastricht 6229 EV, The Netherlands
| | - Fraser W Smith
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
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Himmelberg MM, Gardner JL, Winawer J. What has vision science taught us about functional MRI? Neuroimage 2022; 261:119536. [PMID: 35931310 DOI: 10.1016/j.neuroimage.2022.119536] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/21/2022] [Accepted: 08/02/2022] [Indexed: 10/31/2022] Open
Abstract
In the domain of human neuroimaging, much attention has been paid to the question of whether and how the development of functional magnetic resonance imaging (fMRI) has advanced our scientific knowledge of the human brain. However, the opposite question is also important; how has our knowledge of the visual system advanced our understanding of fMRI? Here, we discuss how and why scientific knowledge about the human and animal visual system has been used to answer fundamental questions about fMRI as a brain measurement tool and how these answers have contributed to scientific discoveries beyond vision science.
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Affiliation(s)
- Marc M Himmelberg
- Department of Psychology, New York University, NY, USA; Center for Neural Science, New York University, NY, USA.
| | | | - Jonathan Winawer
- Department of Psychology, New York University, NY, USA; Center for Neural Science, New York University, NY, USA
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38
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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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Tran D, DiGiacomo P, Born DE, Georgiadis M, Zeineh M. Iron and Alzheimer's Disease: From Pathology to Imaging. Front Hum Neurosci 2022; 16:838692. [PMID: 35911597 PMCID: PMC9327617 DOI: 10.3389/fnhum.2022.838692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 05/09/2022] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a debilitating brain disorder that afflicts millions worldwide with no effective treatment. Currently, AD progression has primarily been characterized by abnormal accumulations of β-amyloid within plaques and phosphorylated tau within neurofibrillary tangles, giving rise to neurodegeneration due to synaptic and neuronal loss. While β-amyloid and tau deposition are required for clinical diagnosis of AD, presence of such abnormalities does not tell the complete story, and the actual mechanisms behind neurodegeneration in AD progression are still not well understood. Support for abnormal iron accumulation playing a role in AD pathogenesis includes its presence in the early stages of the disease, its interactions with β-amyloid and tau, and the important role it plays in AD related inflammation. In this review, we present the existing evidence of pathological iron accumulation in the human AD brain, as well as discuss the imaging tools and peripheral measures available to characterize iron accumulation and dysregulation in AD, which may help in developing iron-based biomarkers or therapeutic targets for the disease.
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Affiliation(s)
- Dean Tran
- Department of Radiology, Stanford School of Medicine, Stanford, CA, United States
| | - Phillip DiGiacomo
- Department of Radiology, Stanford School of Medicine, Stanford, CA, United States
| | - Donald E. Born
- Department of Pathology, Stanford School of Medicine, Stanford, CA, United States
| | - Marios Georgiadis
- Department of Radiology, Stanford School of Medicine, Stanford, CA, United States
| | - Michael Zeineh
- Department of Radiology, Stanford School of Medicine, Stanford, CA, United States
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40
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Haarsma J, Kok P, Browning M. The promise of layer-specific neuroimaging for testing predictive coding theories of psychosis. Schizophr Res 2022; 245:68-76. [PMID: 33199171 PMCID: PMC9241988 DOI: 10.1016/j.schres.2020.10.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/03/2020] [Accepted: 10/28/2020] [Indexed: 12/24/2022]
Abstract
Predictive coding potentially provides an explanatory model for understanding the neurocognitive mechanisms of psychosis. It proposes that cognitive processes, such as perception and inference, are implemented by a hierarchical system, with the influence of each level being a function of the estimated precision of beliefs at that level. However, predictive coding models of psychosis are insufficiently constrained-any phenomenon can be explained in multiple ways by postulating different changes to precision at different levels of processing. One reason for the lack of constraint in these models is that the core processes are thought to be implemented by the function of specific cortical layers, and the technology to measure layer specific neural activity in humans has until recently been lacking. As a result, our ability to constrain the models with empirical data has been limited. In this review we provide a brief overview of predictive processing models of psychosis and then describe the potential for newly developed, layer specific neuroimaging techniques to test and thus constrain these models. We conclude by discussing the most promising avenues for this research as well as the technical and conceptual challenges which may limit its application.
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Affiliation(s)
- J. Haarsma
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom,Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Corresponding author at: Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.
| | - P. Kok
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - M. Browning
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Oxford Health NHS Trust, Oxford, United Kingdom
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41
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Zarkali A, Weil RS. Using network approaches to unravel the mysteries of visual hallucinations in Lewy body dementia. Brain 2022; 145:1883-1885. [PMID: 35642563 DOI: 10.1093/brain/awac170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 04/26/2022] [Indexed: 11/14/2022] Open
Abstract
This scientific commentary refers to ‘Functional and structural brain network correlates of visual hallucinations in Lewy body dementia’ by Mehraram et al. (https://doi.org/10.1093/brain/awac094).
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Affiliation(s)
| | - Rimona S Weil
- Dementia Research Centre, UCL, London, UK.,Wellcome Centre for Human Neuroimaging, UCL, London, UK
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Pennartz CMA. What is neurorepresentationalism? From neural activity and predictive processing to multi-level representations and consciousness. Behav Brain Res 2022; 432:113969. [PMID: 35718232 DOI: 10.1016/j.bbr.2022.113969] [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: 10/29/2021] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 11/02/2022]
Abstract
This review provides an update on Neurorepresentationalism, a theoretical framework that defines conscious experience as multimodal, situational survey and explains its neural basis from brain systems constructing best-guess representations of sensations originating in our environment and body [1]. It posits that conscious experience is characterized by five essential hallmarks: (i) multimodal richness, (ii) situatedness and immersion, (iii) unity and integration, (iv) dynamics and stability, and (v) intentionality. Consciousness is furthermore proposed to have a biological function, framed by the contrast between reflexes and habits (not requiring consciousness) versus goal-directed, planned behavior (requiring multimodal, situational survey). Conscious experience is therefore understood as a sensorily rich, spatially encompassing representation of body and environment, while we nevertheless have the impression of experiencing external reality directly. Contributions to understanding neural mechanisms underlying consciousness are derived from models for predictive processing, which are trained in an unsupervised manner, do not necessarily require overt action, and have been extended to deep neural networks. Even with predictive processing in place, however, the question remains why this type of neural network activity would give rise to phenomenal experience. Here, I propose to tackle the Hard Problem with the concept of multi-level representations which emergently give rise to multimodal, spatially wide superinferences corresponding to phenomenal experiences. Finally, Neurorepresentationalism is compared to other neural theories of consciousness, and its implications for defining indicators of consciousness in animals, artificial intelligence devices and immobile or unresponsive patients with disorders of consciousness are discussed.
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Affiliation(s)
- Cyriel M A Pennartz
- Swammerdam Institute for Life Sciences, Center for Neuroscience, Faculty of Science, University of Amsterdam, the Netherlands; Research Priority Program Brain and Cognition, University of Amsterdam, the Netherlands.
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43
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Sihn D, Kim SP. Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System. Front Comput Neurosci 2022; 16:890447. [PMID: 35694611 PMCID: PMC9184804 DOI: 10.3389/fncom.2022.890447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/09/2022] [Indexed: 11/17/2022] Open
Abstract
Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures.
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Affiliation(s)
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
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44
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From representations in predictive processing to degrees of representational features. Minds Mach (Dordr) 2022. [DOI: 10.1007/s11023-022-09599-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
AbstractWhilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success.
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45
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Wang J, Nasr S, Roe AW, Polimeni JR. Critical factors in achieving fine-scale functional MRI: Removing sources of inadvertent spatial smoothing. Hum Brain Mapp 2022; 43:3311-3331. [PMID: 35417073 PMCID: PMC9248309 DOI: 10.1002/hbm.25867] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/04/2022] [Accepted: 03/30/2022] [Indexed: 11/09/2022] Open
Abstract
Ultra‐high Field (≥7T) functional magnetic resonance imaging (UHF‐fMRI) provides opportunities to resolve fine‐scale features of functional architecture such as cerebral cortical columns and layers, in vivo. While the nominal resolution of modern fMRI acquisitions may appear to be sufficient to resolve these features, several common data preprocessing steps can introduce unwanted spatial blurring, especially those that require interpolation of the data. These resolution losses can impede the detection of the fine‐scale features of interest. To examine quantitatively and systematically the sources of spatial resolution losses occurring during preprocessing, we used synthetic fMRI data and real fMRI data from the human visual cortex—the spatially interdigitated human V2 “thin” and “thick” stripes. The pattern of these cortical columns lies along the cortical surface and thus can be best appreciated using surface‐based fMRI analysis. We used this as a testbed for evaluating strategies that can reduce spatial blurring of fMRI data. Our results show that resolution losses can be mitigated at multiple points in preprocessing pathway. We show that unwanted blur is introduced at each step of volume transformation and surface projection, and can be ameliorated by replacing multi‐step transformations with equivalent single‐step transformations. Surprisingly, the simple approaches of volume upsampling and of cortical mesh refinement also helped to reduce resolution losses caused by interpolation. Volume upsampling also serves to improve motion estimation accuracy, which helps to reduce blur. Moreover, we demonstrate that the level of spatial blurring is nonuniform over the brain—knowledge which is critical for interpreting data in high‐resolution fMRI studies. Importantly, our study provides recommendations for reducing unwanted blurring during preprocessing as well as methods that enable quantitative comparisons between preprocessing strategies. These findings highlight several underappreciated sources of a spatial blur. Individually, the factors that contribute to spatial blur may appear to be minor, but in combination, the cumulative effects can hinder the interpretation of fine‐scale fMRI and the detectability of these fine‐scale features of functional architecture.
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Affiliation(s)
- Jianbao Wang
- Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Shahin Nasr
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Anna Wang Roe
- Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Latency shortening with enhanced sparseness and responsiveness in V1 during active visual sensing. Sci Rep 2022; 12:6021. [PMID: 35410997 PMCID: PMC9001710 DOI: 10.1038/s41598-022-09405-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/21/2022] [Indexed: 12/03/2022] Open
Abstract
In natural vision, neuronal responses to visual stimuli occur due to self-initiated eye movements. Here, we compare single-unit activity in the primary visual cortex (V1) of non-human primates to flashed natural scenes (passive vision condition) to when they freely explore the images by self-initiated eye movements (active vision condition). Active vision enhances the number of neurons responding, and the response latencies become shorter and less variable across neurons. The increased responsiveness and shortened latency during active vision were not explained by increased visual contrast. While the neuronal activities in all layers of V1 show enhanced responsiveness and shortened latency, a significant increase in lifetime sparseness during active vision is observed only in the supragranular layer. These findings demonstrate that the neuronal responses become more distinct in active vision than passive vision, interpreted as consequences of top-down predictive mechanisms.
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47
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Leptourgos P, Bouttier V, Denève S, Jardri R. From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis. Neurosci Biobehav Rev 2022; 135:104593. [PMID: 35217108 DOI: 10.1016/j.neubiorev.2022.104593] [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: 05/14/2021] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 10/19/2022]
Abstract
Psychedelics distort perception and induce visual and multimodal hallucinations as well as synaesthesia. This is in contradiction with the high prevalence of distressing voices in schizophrenia. Here we introduce a unifying account of unimodal and multimodal erroneous percepts based on circular inference. We show that amplification of top-down predictions (descending loops) leads to an excessive reliance on priors and aberrant levels of integration of the sensory representations, resulting in crossmodal percepts and stronger illusions. By contrast, amplification of bottom-up information (ascending loops) results in overinterpretation of unreliable sensory inputs and high levels of segregation between sensory modalities, bringing about unimodal hallucinations and reduced vulnerability to illusions. We delineate a canonical microcircuit in which layer-specific inhibition controls the propagation of information across hierarchical levels: inhibitory interneurons in the deep layers exert control over priors, removing descending loops. Conversely, inhibition in the supragranular layers counterbalances the effects of the ascending loops. Overall, we put forward a multiscale and transnosographic account of erroneous percepts with important theoretical, conceptual and clinical implications.
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Affiliation(s)
- Pantelis Leptourgos
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT, USA; Laboratoire de Neurosciences Cognitives & Computationnelles (LNC²), ENS, INSERM U-960, PSL Research University, Paris, France.
| | - Vincent Bouttier
- Laboratoire de Neurosciences Cognitives & Computationnelles (LNC²), ENS, INSERM U-960, PSL Research University, Paris, France; Univ Lille, INSERM U-1172, Lille Neurosciences & Cognition Centre, Plasticity and Subjectivity Team, & CHU Lille, Fontan Hospital, CURE Platform, Lille, France
| | - Sophie Denève
- Laboratoire de Neurosciences Cognitives & Computationnelles (LNC²), ENS, INSERM U-960, PSL Research University, Paris, France
| | - Renaud Jardri
- Laboratoire de Neurosciences Cognitives & Computationnelles (LNC²), ENS, INSERM U-960, PSL Research University, Paris, France; Univ Lille, INSERM U-1172, Lille Neurosciences & Cognition Centre, Plasticity and Subjectivity Team, & CHU Lille, Fontan Hospital, CURE Platform, Lille, France.
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48
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Deshpande G, Wang Y, Robinson J. Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain. Brain Inform 2022; 9:2. [PMID: 35038072 PMCID: PMC8764001 DOI: 10.1186/s40708-021-00150-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/28/2021] [Indexed: 11/10/2022] Open
Abstract
Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I-III) was stronger compared to deep layers (layers V-VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.
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Affiliation(s)
- Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA. .,Department of Psychological Sciences, Auburn University, Auburn, AL, USA. .,Alabama Advanced Imaging Consortium, Birmingham, AL, USA. .,Center for Neuroscience, Auburn University, Auburn, AL, USA. .,Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China. .,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India. .,Centre for Brain Research, Indian Institute of Science, Bangalore, India.
| | - Yun Wang
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.,Department of Psychiatry, Columbia University, New York, NY, USA
| | - Jennifer Robinson
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.,Department of Psychological Sciences, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Birmingham, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA
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49
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van Kemenade BM, Wilbertz G, Müller A, Sterzer P. Non-stimulated regions in early visual cortex encode the contents of conscious visual perception. Hum Brain Mapp 2021; 43:1394-1402. [PMID: 34862702 PMCID: PMC8837582 DOI: 10.1002/hbm.25731] [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: 06/15/2021] [Revised: 11/07/2021] [Accepted: 11/08/2021] [Indexed: 11/11/2022] Open
Abstract
Predictions shape our perception. The theory of predictive processing poses that our brains make sense of incoming sensory input by generating predictions, which are sent back from higher to lower levels of the processing hierarchy. These predictions are based on our internal model of the world and enable inferences about the hidden causes of the sensory input data. It has been proposed that conscious perception corresponds to the currently most probable internal model of the world. Accordingly, predictions influencing conscious perception should be fed back from higher to lower levels of the processing hierarchy. Here, we used functional magnetic resonance imaging and multivoxel pattern analysis to show that non‐stimulated regions of early visual areas contain information about the conscious perception of an ambiguous visual stimulus. These results indicate that early sensory cortices in the human brain receive predictive feedback signals that reflect the current contents of conscious perception.
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Affiliation(s)
- Bianca M van Kemenade
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK.,Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany.,Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - Gregor Wilbertz
- Department of Psychology, Freie Universität Berlin, Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - Annalena Müller
- Department of Experimental and Biological Psychology, University of Potsdam, Potsdam, Germany.,Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - Philipp Sterzer
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
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50
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Yudintsev G, Asilador AR, Sons S, Vaithiyalingam Chandra Sekaran N, Coppinger M, Nair K, Prasad M, Xiao G, Ibrahim BA, Shinagawa Y, Llano DA. Evidence for Layer-Specific Connectional Heterogeneity in the Mouse Auditory Corticocollicular System. J Neurosci 2021; 41:9906-9918. [PMID: 34670851 PMCID: PMC8638684 DOI: 10.1523/jneurosci.2624-20.2021] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 10/07/2021] [Accepted: 10/07/2021] [Indexed: 11/21/2022] Open
Abstract
The auditory cortex (AC) sends long-range projections to virtually all subcortical auditory structures. One of the largest and most complex of these-the projection between AC and inferior colliculus (IC; the corticocollicular pathway)-originates from layer 5 and deep layer 6. Though previous work has shown that these two corticocollicular projection systems have different physiological properties and network connectivities, their functional organization is poorly understood. Here, using a combination of traditional and viral tracers combined with in vivo imaging in both sexes of the mouse, we observed that layer 5 and layer 6 corticocollicular neurons differ in their areas of origin and termination patterns. Layer 5 corticocollicular neurons are concentrated in primary AC, while layer 6 corticocollicular neurons emanate from broad auditory and limbic areas in the temporal cortex. In addition, layer 5 sends dense projections of both small and large (>1 µm2 area) terminals to all regions of nonlemniscal IC, while layer 6 sends small terminals to the most superficial 50-100 µm of the IC. These findings suggest that layer 5 and 6 corticocollicular projections are optimized to play distinct roles in corticofugal modulation. Layer 5 neurons provide strong, rapid, and unimodal feedback to the nonlemniscal IC, while layer 6 neurons provide heteromodal and limbic modulation diffusely to the nonlemniscal IC. Such organizational diversity in the corticocollicular pathway may help to explain the heterogeneous effects of corticocollicular manipulations and, given similar diversity in corticothalamic pathways, may be a general principle in top-down modulation.SIGNIFICANCE STATEMENT We demonstrate that a major descending system in the brain is actually two systems. That is, the auditory corticocollicular projection, which exerts considerable influence over the midbrain, comprises two projections: one from layer 5 and the other from layer 6. The layer 6 projection is diffusely organized, receives multisensory inputs, and ends in small terminals; while the layer 5 projection is derived from a circumscribed auditory cortical area and ends in large terminals. These data suggest that the varied effects of cortical manipulations on the midbrain may be related to effects on two disparate systems. These findings have broader implications because other descending systems derive from two layers. Therefore, a duplex organization may be a common motif in descending control.
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Affiliation(s)
- Georgiy Yudintsev
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Alexander R Asilador
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Stacy Sons
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Nathiya Vaithiyalingam Chandra Sekaran
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Macey Coppinger
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Kavya Nair
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Masumi Prasad
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Gang Xiao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Baher A Ibrahim
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Yoshitaka Shinagawa
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - Daniel A Llano
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
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