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Lundqvist M, Miller EK, Nordmark J, Liljefors J, Herman P. Beta: bursts of cognition. Trends Cogn Sci 2024; 28:662-676. [PMID: 38658218 DOI: 10.1016/j.tics.2024.03.010] [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: 09/11/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.
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Affiliation(s)
- Mikael Lundqvist
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonatan Nordmark
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Johan Liljefors
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Pawel Herman
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden
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2
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Datta D, Yang S, Joyce MKP, Woo E, McCarroll SA, Gonzalez-Burgos G, Perone I, Uchendu S, Ling E, Goldman M, Berretta S, Murray J, Morozov Y, Arellano J, Duque A, Rakic P, O’Dell R, van Dyck CH, Lewis DA, Wang M, Krienen FM, Arnsten AFT. Key Roles of CACNA1C/Cav1.2 and CALB1/Calbindin in Prefrontal Neurons Altered in Cognitive Disorders. JAMA Psychiatry 2024:2818736. [PMID: 38776078 PMCID: PMC11112502 DOI: 10.1001/jamapsychiatry.2024.1112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 03/14/2024] [Indexed: 05/25/2024]
Abstract
Importance The risk of mental disorders is consistently associated with variants in CACNA1C (L-type calcium channel Cav1.2) but it is not known why these channels are critical to cognition, and whether they affect the layer III pyramidal cells in the dorsolateral prefrontal cortex that are especially vulnerable in cognitive disorders. Objective To examine the molecular mechanisms expressed in layer III pyramidal cells in primate dorsolateral prefrontal cortices. Design, Setting, and Participants The design included transcriptomic analyses from human and macaque dorsolateral prefrontal cortex, and connectivity, protein expression, physiology, and cognitive behavior in macaques. The research was performed in academic laboratories at Yale, Harvard, Princeton, and the University of Pittsburgh. As dorsolateral prefrontal cortex only exists in primates, the work evaluated humans and macaques. Main Outcomes and Measures Outcome measures included transcriptomic signatures of human and macaque pyramidal cells, protein expression and interactions in layer III macaque pyramidal cells using light and electron microscopy, changes in neuronal firing during spatial working memory, and working memory performance following pharmacological treatments. Results Layer III pyramidal cells in dorsolateral prefrontal cortex coexpress a constellation of calcium-related proteins, delineated by CALB1 (calbindin), and high levels of CACNA1C (Cav1.2), GRIN2B (NMDA receptor GluN2B), and KCNN3 (SK3 potassium channel), concentrated in dendritic spines near the calcium-storing smooth endoplasmic reticulum. L-type calcium channels influenced neuronal firing needed for working memory, where either blockade or increased drive by β1-adrenoceptors, reduced neuronal firing by a mean (SD) 37.3% (5.5%) or 40% (6.3%), respectively, the latter via SK potassium channel opening. An L-type calcium channel blocker or β1-adrenoceptor antagonist protected working memory from stress. Conclusions and Relevance The layer III pyramidal cells in the dorsolateral prefrontal cortex especially vulnerable in cognitive disorders differentially express calbindin and a constellation of calcium-related proteins including L-type calcium channels Cav1.2 (CACNA1C), GluN2B-NMDA receptors (GRIN2B), and SK3 potassium channels (KCNN3), which influence memory-related neuronal firing. The finding that either inadequate or excessive L-type calcium channel activation reduced neuronal firing explains why either loss- or gain-of-function variants in CACNA1C were associated with increased risk of cognitive disorders. The selective expression of calbindin in these pyramidal cells highlights the importance of regulatory mechanisms in neurons with high calcium signaling, consistent with Alzheimer tau pathology emerging when calbindin is lost with age and/or inflammation.
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Affiliation(s)
- Dibyadeep Datta
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Shengtao Yang
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Mary Kate P. Joyce
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Elizabeth Woo
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Steven A. McCarroll
- Department of Genetics, Harvard Medical School, Boston, Massachusetts
- Stanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | | | - Isabella Perone
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Stacy Uchendu
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Sabina Berretta
- Basic Neuroscience Division, McLean Hospital, Belmont, Massachusetts
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - John Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Yury Morozov
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Jon Arellano
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Alvaro Duque
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Pasko Rakic
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Ryan O’Dell
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Christopher H. van Dyck
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - David A. Lewis
- Departments of Psychiatry and Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Min Wang
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Fenna M. Krienen
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey
| | - Amy F. T. Arnsten
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
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3
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Abstract
Working memory enables us to bridge past sensory information to upcoming future behaviour. Accordingly, by its very nature, working memory is concerned with two components: the past and the future. Yet, in conventional laboratory tasks, these two components are often conflated, such as when sensory information in working memory is encoded and tested at the same location. We developed a task in which we dissociated the past (encoded location) and future (to-be-tested location) attributes of visual contents in working memory. This enabled us to independently track the utilisation of past and future memory attributes through gaze, as observed during mnemonic selection. Our results reveal the joint consideration of past and future locations. This was prevalent even at the single-trial level of individual saccades that were jointly biased to the past and future. This uncovers the rich nature of working memory representations, whereby both past and future memory attributes are retained and can be accessed together when memory contents become relevant for behaviour.
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Affiliation(s)
- Baiwei Liu
- Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Zampeta-Sofia Alexopoulou
- Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Freek van Ede
- Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit AmsterdamAmsterdamNetherlands
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4
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Pagnotta MF, Santo-Angles A, Temudo A, Barbosa J, Compte A, D'Esposito M, Sreenivasan KK. Alpha phase-coding supports feature binding during working memory maintenance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.21.576561. [PMID: 38328154 PMCID: PMC10849498 DOI: 10.1101/2024.01.21.576561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
The ability to successfully retain and manipulate information in working memory (WM) requires that objects' individual features are bound into cohesive representations; yet, the mechanisms supporting feature binding remain unclear. Binding (or swap) errors, where memorized features are erroneously associated with the wrong object, can provide a window into the intrinsic limits in capacity of WM that represent a key bottleneck in our cognitive ability. We tested the hypothesis that binding in WM is accomplished via neural phase synchrony and that swap errors result from perturbations in this synchrony. Using magnetoencephalography data collected from human subjects in a task designed to induce swap errors, we showed that swaps are characterized by reduced phase-locked oscillatory activity during memory retention, as predicted by an attractor model of spiking neural networks. Further, we found that this reduction arises from increased phase-coding variability in the alpha-band over a distributed network of sensorimotor areas. Our findings demonstrate that feature binding in WM is accomplished through phase-coding dynamics that emerge from the competition between different memories.
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5
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Ocklenburg S, Guo ZV. Cross-hemispheric communication: Insights on lateralized brain functions. Neuron 2024; 112:1222-1234. [PMID: 38458199 DOI: 10.1016/j.neuron.2024.02.010] [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: 07/31/2023] [Revised: 12/13/2023] [Accepted: 02/12/2024] [Indexed: 03/10/2024]
Abstract
On the surface, the two hemispheres of vertebrate brains look almost perfectly symmetrical, but several motor, sensory, and cognitive systems show a deeply lateralized organization. Importantly, the two hemispheres are connected by various commissures, white matter tracts that cross the brain's midline and enable cross-hemispheric communication. Cross-hemispheric communication has been suggested to play an important role in the emergence of lateralized brain functions. Here, we review current advances in understanding cross-hemispheric communication that have been made using modern neuroscientific tools in rodents and other model species, such as genetic labeling, large-scale recordings of neuronal activity, spatiotemporally precise perturbation, and quantitative behavior analyses. These findings suggest that the emergence of lateralized brain functions cannot be fully explained by largely static factors such as genetic variation and differences in structural brain asymmetries. In addition, learning-dependent asymmetric interactions between the left and right hemispheres shape lateralized brain functions.
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Affiliation(s)
- Sebastian Ocklenburg
- Department of Psychology, MSH Medical School Hamburg, Hamburg, Germany; ICAN Institute for Cognitive and Affective Neuroscience, MSH Medical School Hamburg, Hamburg, Germany; Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany.
| | - Zengcai V Guo
- School of Medicine, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Joint Center for Life Sciences, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
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6
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Yiling Y, Klon-Lipok J, Shapcott K, Lazar A, Singer W. Dynamic fading memory and expectancy effects in the monkey primary visual cortex. Proc Natl Acad Sci U S A 2024; 121:e2314855121. [PMID: 38354261 PMCID: PMC10895277 DOI: 10.1073/pnas.2314855121] [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: 08/30/2023] [Accepted: 01/10/2024] [Indexed: 02/16/2024] Open
Abstract
In order to investigate the involvement of the primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multi-unit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of WM contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.
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Affiliation(s)
- Yang Yiling
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main60528, Germany
| | - Johanna Klon-Lipok
- Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
| | - Katharine Shapcott
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main60528, Germany
| | - Andreea Lazar
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main60528, Germany
| | - Wolf Singer
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main60528, Germany
- Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt am Main60438, Germany
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7
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Kamarajan C, Pandey AK, Chorlian DB, Meyers JL, Kinreich S, Pandey G, Subbie-Saenz de Viteri S, Zhang J, Kuang W, Barr PB, Aliev F, Anokhin AP, Plawecki MH, Kuperman S, Almasy L, Merikangas A, Brislin SJ, Bauer L, Hesselbrock V, Chan G, Kramer J, Lai D, Hartz S, Bierut LJ, McCutcheon VV, Bucholz KK, Dick DM, Schuckit MA, Edenberg HJ, Porjesz B. Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. Behav Sci (Basel) 2023; 13:bs13050427. [PMID: 37232664 DOI: 10.3390/bs13050427] [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: 03/12/2023] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Ashwini K Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Subbie-Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Peter B Barr
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Fazil Aliev
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Andrey P Anokhin
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | | | - Samuel Kuperman
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Laura Almasy
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alison Merikangas
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah J Brislin
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Grace Chan
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - John Kramer
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Dongbing Lai
- Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sarah Hartz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Laura J Bierut
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Vivia V McCutcheon
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego, CA 92103, USA
| | | | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
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8
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Murphy E. ROSE: A Neurocomputational Architecture for Syntax. ARXIV 2023:arXiv:2303.08877v1. [PMID: 36994166 PMCID: PMC10055479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
A comprehensive model of natural language processing in the brain must accommodate four components: representations, operations, structures and encoding. It further requires a principled account of how these different components mechanistically, and causally, relate to each another. While previous models have isolated regions of interest for structure-building and lexical access, and have utilized specific neural recording measures to expose possible signatures of syntax, many gaps remain with respect to bridging distinct scales of analysis that map onto these four components. By expanding existing accounts of how neural oscillations can index various linguistic processes, this article proposes a neurocomputational architecture for syntax, termed the ROSE model (Representation, Operation, Structure, Encoding). Under ROSE, the basic data structures of syntax are atomic features, types of mental representations (R), and are coded at the single-unit and ensemble level. Elementary computations (O) that transform these units into manipulable objects accessible to subsequent structure-building levels are coded via high frequency broadband γ activity. Low frequency synchronization and cross-frequency coupling code for recursive categorial inferences (S). Distinct forms of low frequency coupling and phase-amplitude coupling (δ-θ coupling via pSTS-IFG; θ-γ coupling via IFG to conceptual hubs in lateral and ventral temporal cortex) then encode these structures onto distinct workspaces (E). Causally connecting R to O is spike-phase/LFP coupling; connecting O to S is phase-amplitude coupling; connecting S to E is a system of frontotemporal traveling oscillations; connecting E back to lower levels is low-frequency phase resetting of spike-LFP coupling. This compositional neural code has important implications for algorithmic accounts, since it makes concrete predictions for the appropriate level of study for psycholinguistic parsing models. ROSE is reliant on neurophysiologically plausible mechanisms, is supported at all four levels by a range of recent empirical research, and provides an anatomically precise and falsifiable grounding for the basic property of natural language syntax: hierarchical, recursive structure-building.
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Affiliation(s)
- Elliot Murphy
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, UTHealth, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, UTHealth, Houston, TX, USA
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9
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Hahn LA, Balakhonov D, Lundqvist M, Nieder A, Rose J. Oscillations without cortex: Working memory modulates brainwaves in the endbrain of crows. Prog Neurobiol 2022; 219:102372. [DOI: 10.1016/j.pneurobio.2022.102372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/21/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
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10
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Adaikkan C, Wang J, Abdelaal K, Middleton SJ, Bozzelli PL, Wickersham IR, McHugh TJ, Tsai LH. Alterations in a cross-hemispheric circuit associates with novelty discrimination deficits in mouse models of neurodegeneration. Neuron 2022; 110:3091-3105.e9. [PMID: 35987206 PMCID: PMC9547933 DOI: 10.1016/j.neuron.2022.07.023] [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: 07/08/2021] [Revised: 02/23/2022] [Accepted: 07/22/2022] [Indexed: 10/15/2022]
Abstract
A major pathological hallmark of neurodegenerative diseases, including Alzheimer's, is a significant reduction in the white matter connecting the two cerebral hemispheres, as well as in the correlated activity between anatomically corresponding bilateral brain areas. However, the underlying circuit mechanisms and the cognitive relevance of cross-hemispheric (CH) communication remain poorly understood. Here, we show that novelty discrimination behavior activates CH neurons and enhances homotopic synchronized neural oscillations in the visual cortex. CH neurons provide excitatory drive required for synchronous neural oscillations between hemispheres, and unilateral inhibition of the CH circuit is sufficient to impair synchronous oscillations and novelty discrimination behavior. In the 5XFAD and Tau P301S mouse models, CH communication is altered, and novelty discrimination is impaired. These data reveal a hitherto uncharacterized CH circuit in the visual cortex, establishing a causal link between this circuit and novelty discrimination behavior and highlighting its impairment in mouse models of neurodegeneration.
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Affiliation(s)
- Chinnakkaruppan Adaikkan
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Jun Wang
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Karim Abdelaal
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Steven J Middleton
- Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wakoshi, Saitama 351-0198, Japan
| | - P Lorenzo Bozzelli
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ian R Wickersham
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thomas J McHugh
- Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wakoshi, Saitama 351-0198, Japan; Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Lawless WF. Interdependent Autonomous Human-Machine Systems: The Complementarity of Fitness, Vulnerability and Evolution. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1308. [PMID: 36141193 PMCID: PMC9497611 DOI: 10.3390/e24091308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/25/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
For the science of autonomous human-machine systems, traditional causal-time interpretations of reality in known contexts are sufficient for rational decisions and actions to be taken, but not for uncertain or dynamic contexts, nor for building the best teams. First, unlike game theory where the contexts are constructed for players, or machine learning where contexts must be stable, when facing uncertainty or conflict, a rational process is insufficient for decisions or actions to be taken; second, as supported by the literature, rational explanations cannot disaggregate human-machine teams. In the first case, interdependent humans facing uncertainty spontaneously engage in debate over complementary tradeoffs in a search for the best path forward, characterized by maximum entropy production (MEP); however, in the second case, signified by a reduction in structural entropy production (SEP), interdependent team structures make it rationally impossible to discern what creates better teams. In our review of evidence for SEP-MEP complementarity for teams, we found that structural redundancy for top global oil producers, replicated for top global militaries, impedes interdependence and promotes corruption. Next, using UN data for Middle Eastern North African nations plus Israel, we found that a nation's structure of education is significantly associated with MEP by the number of patents it produces; this conflicts with our earlier finding that a U.S. Air Force education in air combat maneuvering was not associated with the best performance in air combat, but air combat flight training was. These last two results exemplify that SEP-MEP interactions by the team's best members are made by orthogonal contributions. We extend our theory to find that competition between teams hinges on vulnerability, a complementary excess of SEP and reduced MEP, which generalizes to autonomous human-machine systems.
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Affiliation(s)
- William F Lawless
- Departments of Mathematics and Psychology, Paine College, Augusta, GA 30901, USA
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12
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Yin X, Wang Y, Li J, Guo ZV. Lateralization of short-term memory in the frontal cortex. Cell Rep 2022; 40:111190. [PMID: 35977520 DOI: 10.1016/j.celrep.2022.111190] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 06/04/2022] [Accepted: 07/20/2022] [Indexed: 11/03/2022] Open
Abstract
Despite essentially symmetric structures in mammalian brains, the left and right hemispheres do not contribute equally to certain cognitive functions. How both hemispheres interact to cause this asymmetry remains unclear. Here, we study this question in the anterior lateral motor cortex (ALM) of mice performing five versions of a tactile-based decision-making task with a short-term memory (STM) component. Unilateral inhibition of ALM produces variable behavioral deficits across tasks, with the left, right, or both ALMs playing critical roles in STM. Neural activity and its encoding capability are similar across hemispheres, despite that only one hemisphere dominates in behavior. Inhibition of the dominant ALM disrupts encoding capability in the non-dominant ALM, but not vice versa. Variable behavioral deficits are predicted by the influence on contralateral activity across sessions, mice, and tasks. Together, these results reveal that the left and right ALM interact asymmetrically, leading to their differential contributions to STM.
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Affiliation(s)
- Xinxin Yin
- School of Medicine, Tsinghua University, 100084 Beijing, China; IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China; School of Life Sciences, Tsinghua University, 100084 Beijing, China
| | - Yu Wang
- IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China; School of Life Sciences, Tsinghua University, 100084 Beijing, China
| | - Jiejue Li
- IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China; School of Life Sciences, Tsinghua University, 100084 Beijing, China
| | - Zengcai V Guo
- School of Medicine, Tsinghua University, 100084 Beijing, China; IDG/McGovern Institute for Brain Research, Tsinghua University, 100084 Beijing, China; Tsinghua-Peking Joint Center for Life Sciences, 100084 Beijing, China.
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Abstract
A biologically inspired cognitive architecture is described which uses navigation maps (i.e., spatial locations of objects) as its main data elements. The navigation maps are also used to represent higher-level concepts as well as to direct operations to perform on other navigation maps. Incoming sensory information is mapped to local sensory navigation maps which then are in turn matched with the closest multisensory maps, and then mapped onto a best-matched multisensory navigation map. Enhancements of the biologically inspired feedback pathways allow the intermediate results of operations performed on the best-matched multisensory navigation map to be fed back, temporarily stored, and re-processed in the next cognitive cycle. This allows the exploration and generation of cause-and-effect behavior. In the re-processing of these intermediate results, navigation maps can, by core analogical mechanisms, lead to other navigation maps which offer an improved solution to many routine problems the architecture is exposed to. Given that the architecture is brain-inspired, analogical processing may also form a key mechanism in the human brain, consistent with psychological evidence. Similarly, for conventional artificial intelligence systems, analogical processing as a core mechanism may possibly allow enhanced performance.
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14
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15
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Dijkstra N, Kok P, Fleming SM. Perceptual reality monitoring: Neural mechanisms dissociating imagination from reality. Neurosci Biobehav Rev 2022; 135:104557. [PMID: 35122782 DOI: 10.1016/j.neubiorev.2022.104557] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 01/12/2022] [Accepted: 01/30/2022] [Indexed: 01/21/2023]
Abstract
There is increasing evidence that imagination relies on similar neural mechanisms as externally triggered perception. This overlap presents a challenge for perceptual reality monitoring: deciding what is real and what is imagined. Here, we explore how perceptual reality monitoring might be implemented in the brain. We first describe sensory and cognitive factors that could dissociate imagery and perception and conclude that no single factor unambiguously signals whether an experience is internally or externally generated. We suggest that reality monitoring is implemented by higher-level cortical circuits that evaluate first-order sensory and cognitive factors to determine the source of sensory signals. According to this interpretation, perceptual reality monitoring shares core computations with metacognition. This multi-level architecture might explain several types of source confusion as well as dissociations between simply knowing whether something is real and actually experiencing it as real. We discuss avenues for future research to further our understanding of perceptual reality monitoring, an endeavour that has important implications for our understanding of clinical symptoms as well as general cognitive function.
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Affiliation(s)
- Nadine Dijkstra
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom.
| | - Peter Kok
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom; Max Planck UCL Centre for Computational Psychiatry and Aging Research, University College London, United Kingdom; Department of Experimental Psychology, University College London, United Kingdom
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16
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Roussy M, Mendoza-Halliday D, Martinez-Trujillo JC. Neural Substrates of Visual Perception and Working Memory: Two Sides of the Same Coin or Two Different Coins? Front Neural Circuits 2021; 15:764177. [PMID: 34899197 PMCID: PMC8662382 DOI: 10.3389/fncir.2021.764177] [Citation(s) in RCA: 3] [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: 08/25/2021] [Accepted: 10/25/2021] [Indexed: 11/18/2022] Open
Abstract
Visual perception occurs when a set of physical signals emanating from the environment enter the visual system and the brain interprets such signals as a percept. Visual working memory occurs when the brain produces and maintains a mental representation of a percept while the physical signals corresponding to that percept are not available. Early studies in humans and non-human primates demonstrated that lesions of the prefrontal cortex impair performance during visual working memory tasks but not during perceptual tasks. These studies attributed a fundamental role in working memory and a lesser role in visual perception to the prefrontal cortex. Indeed, single cell recording studies have found that neurons in the lateral prefrontal cortex of macaques encode working memory representations via persistent firing, validating the results of lesion studies. However, other studies have reported that neurons in some areas of the parietal and temporal lobe-classically associated with visual perception-similarly encode working memory representations via persistent firing. This prompted a line of enquiry about the role of the prefrontal and other associative cortices in working memory and perception. Here, we review evidence from single neuron studies in macaque monkeys examining working memory representations across different areas of the visual hierarchy and link them to studies examining the role of the same areas in visual perception. We conclude that neurons in early visual areas of both ventral (V1-V2-V4) and dorsal (V1-V3-MT) visual pathways of macaques mainly encode perceptual signals. On the other hand, areas downstream from V4 and MT contain subpopulations of neurons that encode both perceptual and/or working memory signals. Differences in cortical architecture (neuronal types, layer composition, and synaptic density and distribution) may be linked to the differential encoding of perceptual and working memory signals between early visual areas and higher association areas.
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Affiliation(s)
- Megan Roussy
- Department of Physiology and Pharmacology, Schulich School of Medicine & Dentistry, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Diego Mendoza-Halliday
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Julio C. Martinez-Trujillo
- Department of Physiology and Pharmacology, Schulich School of Medicine & Dentistry, Robarts Research Institute, University of Western Ontario, London, ON, Canada
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Zhao YJ, Kay KN, Tian Y, Ku Y. Sensory Recruitment Revisited: Ipsilateral V1 Involved in Visual Working Memory. Cereb Cortex 2021; 32:1470-1479. [PMID: 34476462 DOI: 10.1093/cercor/bhab300] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022] Open
Abstract
The "sensory recruitment hypothesis" posits an essential role of sensory cortices in working memory, beyond the well-accepted frontoparietal areas. Yet, this hypothesis has recently been challenged. In the present study, participants performed a delayed orientation recall task while high-spatial-resolution 3 T functional magnetic resonance imaging (fMRI) signals were measured in posterior cortices. A multivariate inverted encoding model approach was used to decode remembered orientations based on blood oxygen level-dependent fMRI signals from visual cortices during the delay period. We found that not only did activity in the contralateral primary visual cortex (V1) retain high-fidelity representations of the visual stimuli, but activity in the ipsilateral V1 also contained such orientation tuning. Moreover, although the encoded tuning was faded in the contralateral V1 during the late delay period, tuning information in the ipsilateral V1 remained sustained. Furthermore, the ipsilateral representation was presented in secondary visual cortex (V2) as well, but not in other higher-level visual areas. These results thus supported the sensory recruitment hypothesis and extended it to the ipsilateral sensory areas, which indicated the distributed involvement of visual areas in visual working memory.
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Affiliation(s)
- Yi-Jie Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Center for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.,Peng Cheng Laboratory, Shenzhen 518055, China.,School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Kendrick N Kay
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yonghong Tian
- Peng Cheng Laboratory, Shenzhen 518055, China.,School of Electronic Engineering and Computer Science, Peking University, Beijing 100871, China
| | - Yixuan Ku
- Center for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.,Peng Cheng Laboratory, Shenzhen 518055, China
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18
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Fries P. Hemispheres in harmony. Neuron 2021; 109:916-917. [PMID: 33735614 DOI: 10.1016/j.neuron.2021.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this issue of Neuron, Brincat et al. (2021) track in real time the working memory representations in prefrontal cortex of both hemispheres, as a saccade moves the remembered location from one hemifield to the other. They reveal a soft interhemispheric handover subserved by local rhythms and their interhemispheric synchronization.
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Affiliation(s)
- Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, Netherlands.
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