1
|
Yu H, Cao W, Fang T, Jin J, Pei G. EEG β oscillations in aberrant data perception under cognitive load modulation. Sci Rep 2024; 14:22995. [PMID: 39362975 PMCID: PMC11450174 DOI: 10.1038/s41598-024-74381-w] [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: 05/13/2024] [Accepted: 09/25/2024] [Indexed: 10/05/2024] Open
Abstract
Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.
Collapse
Affiliation(s)
- Haihong Yu
- Maritime School, Ningbo University, Ningbo, China
- School of Economics and Management, Ningbo University of Technology, Ningbo, China
| | - Wei Cao
- Maritime School, Ningbo University, Ningbo, China
| | - Tie Fang
- Maritime School, Ningbo University, Ningbo, China
| | - Jia Jin
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, 550# Dalian West Road, Shanghai, 200083, China.
| | - Guanxiong Pei
- Zhejiang Laboratory of Philosophy and Social Sciences - Laboratory of Intelligent Society and Governance, Zhejiang Lab, 1818# Wenyixi Road, Hangzhou, 311121, China.
- Development Strategy and Cooperation Center, Zhejiang Lab, Hangzhou, China.
| |
Collapse
|
2
|
Kim J, Gim S, Yoo SBM, Woo CW. A computational mechanism of cue-stimulus integration for pain in the brain. SCIENCE ADVANCES 2024; 10:eado8230. [PMID: 39259795 PMCID: PMC11389792 DOI: 10.1126/sciadv.ado8230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 08/02/2024] [Indexed: 09/13/2024]
Abstract
The brain integrates information from pain-predictive cues and noxious inputs to construct the pain experience. Although previous studies have identified neural encodings of individual pain components, how they are integrated remains elusive. Here, using a cue-induced pain task, we examined temporal functional magnetic resonance imaging activities within the state space, where axes represent individual voxel activities. By analyzing the features of these activities at the large-scale network level, we demonstrated that overall brain networks preserve both cue and stimulus information in their respective subspaces within the state space. However, only higher-order brain networks, including limbic and default mode networks, could reconstruct the pattern of participants' reported pain by linear summation of subspace activities, providing evidence for the integration of cue and stimulus information. These results suggest a hierarchical organization of the brain for processing pain components and elucidate the mechanism for their integration underlying our pain perception.
Collapse
Affiliation(s)
- Jungwoo Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Suhwan Gim
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Seng Bum Michael Yoo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
- Department of Neurosurgery and McNair Scholar Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
- Life-inspired Neural Network for Prediction and Optimization Research Group, Suwon, South Korea
| |
Collapse
|
3
|
Sahoo B, Snyder AC. Neural Dynamics Underlying False Alarms in Extrastriate Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.06.611738. [PMID: 39314344 PMCID: PMC11418951 DOI: 10.1101/2024.09.06.611738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
The unfolding of neural population activity can be approximated as a dynamical system. Stability in the latent dynamics that characterize neural population activity has been linked with consistency in animal behavior, such as motor control or value-based decision-making. However, whether similar dynamics characterize perceptual activity and decision-making in the visual cortex is not well understood. To test this, we recorded V4 populations in monkeys engaged in a non-match-to-sample visual change-detection task that required sustained engagement. We measured how the stability in the latent dynamics in V4 might affect monkeys' perceptual behavior. Specifically, we reasoned that unstable sensory neural activity around dynamic attractor boundaries may make animals susceptible to taking incorrect actions when withholding action would have been correct ("false alarms"). We made three key discoveries: 1) greater stability was associated with longer trial sequences; 2) false alarm rate decreased (and reaction times slowed) when neural dynamics were more stable; and, 3) low stability predicted false alarms on a single-trial level, and this relationship depended on the elapsed time during the trial, consistent with the latent neural state approaching an attractor boundary. Our results suggest the same outward false alarm behavior can be attributed to two different potential strategies that can be disambiguated by examining neural stability: 1) premeditated false alarms that might lead to greater stability in population dynamics and faster reaction time and 2) false alarms due to unstable sensory activity consistent with misperception.
Collapse
Affiliation(s)
- Bikash Sahoo
- Brain & Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
| | - Adam C. Snyder
- Brain & Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
| |
Collapse
|
4
|
Wu S, Huang C, Snyder AC, Smith MA, Doiron B, Yu BM. Automated customization of large-scale spiking network models to neuronal population activity. NATURE COMPUTATIONAL SCIENCE 2024; 4:690-705. [PMID: 39285002 DOI: 10.1038/s43588-024-00688-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 08/08/2024] [Indexed: 09/22/2024]
Abstract
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity's dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function.
Collapse
Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Chengcheng Huang
- Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam C Snyder
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
| | - Matthew A Smith
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neural Basis of Cognition, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
| |
Collapse
|
5
|
Tian GJ, Zhu O, Shirhatti V, Greenspon CM, Downey JE, Freedman DJ, Doiron B. Neuronal firing rate diversity lowers the dimension of population covariability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.30.610535. [PMID: 39257801 PMCID: PMC11383671 DOI: 10.1101/2024.08.30.610535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Populations of neurons produce activity with two central features. First, neuronal responses are very diverse - specific stimuli or behaviors prompt some neurons to emit many action potentials, while other neurons remain relatively silent. Second, the trial-to-trial fluctuations of neuronal response occupy a low dimensional space, owing to significant correlations between the activity of neurons. These two features define the quality of neuronal representation. We link these two aspects of population response using a recurrent circuit model and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of population trial-to-trial covariability. This surprising prediction is tested and validated using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Using our relation we present a theory where a more diverse neuronal code leads to better fine discrimination performance from population activity. In line with this theory, we show that neuronal populations across the brain exhibit both more diverse mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neuronal population response that is widely observed across the nervous system and acts to synergistically improve population representation.
Collapse
|
6
|
Parto-Dezfouli M, Vanegas I, Zarei M, Nesse WH, Clark KL, Noudoost B. Prefrontal working memory signal primarily controls phase-coded information within extrastriate cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.28.610140. [PMID: 39257783 PMCID: PMC11383686 DOI: 10.1101/2024.08.28.610140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
In order to understand how prefrontal cortex provides the benefits of working memory (WM) for visual processing we examined the influence of WM on the representation of visual signals in V4 neurons in two macaque monkeys. We found that WM induces strong β oscillations in V4 and that the timing of action potentials relative to this oscillation reflects sensory information- i.e., a phase coding of visual information. Pharmacologically inactivating the Frontal Eye Field part of prefrontal cortex, we confirmed the necessity of prefrontal signals for the WM-driven boost in phase coding of visual information. Indeed, changes in the average firing rate of V4 neurons could be accounted for by WM-induced oscillatory changes. We present a network model to describe how WM signals can recruit sensory areas primarily by inducing oscillations within these areas and discuss the implications of these findings for a sensory recruitment theory of WM through coherence.
Collapse
Affiliation(s)
- Mohsen Parto-Dezfouli
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Isabel Vanegas
- Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, UT, United States
| | - Mohammad Zarei
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - William H Nesse
- Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, UT, United States
| | - Kelsey L Clark
- Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, UT, United States
- Lead
| |
Collapse
|
7
|
Ha LJ, Kim M, Yeo HG, Baek I, Kim K, Lee M, Lee Y, Choi HJ. Development of an assessment method for freely moving nonhuman primates' eating behavior using manual and deep learning analysis. Heliyon 2024; 10:e25561. [PMID: 38356587 PMCID: PMC10865331 DOI: 10.1016/j.heliyon.2024.e25561] [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: 07/11/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
Purpose Although eating is imperative for survival, few comprehensive methods have been developed to assess freely moving nonhuman primates' eating behavior. In the current study, we distinguished eating behavior into appetitive and consummatory phases and developed nine indices to study them using manual and deep learning-based (DeepLabCut) techniques. Method The indices were utilized to three rhesus macaques by different palatability and hunger levels to validate their utility. To execute the experiment, we designed the eating behavior cage and manufactured the artificial food. The total number of trials was 3, with 1 trial conducted using natural food and 2 trials using artificial food. Result As a result, the indices of highest utility for hunger effect were approach frequency and consummatory duration. Appetitive composite score and consummatory duration showed the highest utility for palatability effect. To elucidate the effects of hunger and palatability, we developed 2D visualization plots based on manual indices. These 2D visualization methods could intuitively depict the palatability perception and hunger internal state. Furthermore, the developed deep learning-based analysis proved accurate and comparable with manual analysis. When comparing the time required for analysis, deep learning-based analysis was 24-times faster than manual analysis. Moreover, temporal and spatial dynamics were visualized via manual and deep learning-based analysis. Based on temporal dynamics analysis, the patterns were classified into four categories: early decline, steady decline, mid-peak with early incline, and late decline. Heatmap of spatial dynamics and trajectory-related visualization could elucidate a consumption posture and a higher spatial occupancy of food zone in hunger and with palatable food. Discussion Collectively, this study describes a newly developed and validated multi-phase method for assessing freely moving nonhuman primate eating behavior using manual and deep learning-based analyses. These effective tools will prove valuable in food reward (palatability effect) and homeostasis (hunger effect) research.
Collapse
Affiliation(s)
- Leslie Jaesun Ha
- Department of Biomedical Sciences, Wide River Institute of Immunology, Neuroscience Research Institute, Seoul National University College of Medicine, Republic of Korea
| | - Meelim Kim
- Department of Biomedical Sciences, Wide River Institute of Immunology, Neuroscience Research Institute, Seoul National University College of Medicine, Republic of Korea
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Center for Wireless and Population Health Systems (CWPHS), University of California, San Diego, La Jolla, CA, 92093, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, United States
| | - Hyeon-Gu Yeo
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Republic of Korea
- KRIBB School of Bioscience, Korea National University of Science and Technology, Republic of Korea
| | - Inhyeok Baek
- Department of Biomedical Sciences, Wide River Institute of Immunology, Neuroscience Research Institute, Seoul National University College of Medicine, Republic of Korea
| | - Keonwoo Kim
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Republic of Korea
- School of Life Sciences, BK21 Plus KNU Creative BioResearch Group, Kyungpook National University, Republic of Korea
| | - Miwoo Lee
- Department of Biomedical Sciences, Wide River Institute of Immunology, Neuroscience Research Institute, Seoul National University College of Medicine, Republic of Korea
| | - Youngjeon Lee
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Republic of Korea
- KRIBB School of Bioscience, Korea National University of Science and Technology, Republic of Korea
| | - Hyung Jin Choi
- Department of Biomedical Sciences, Wide River Institute of Immunology, Neuroscience Research Institute, Seoul National University College of Medicine, Republic of Korea
| |
Collapse
|
8
|
Zani A, Crotti N, Marzorati M, Senerchia A, Proverbio AM. Acute hypoxia alters visuospatial attention orienting: an electrical neuroimaging study. Sci Rep 2023; 13:22746. [PMID: 38123610 PMCID: PMC10733389 DOI: 10.1038/s41598-023-49431-4] [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: 07/22/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Our study investigated the effects of hypoxia on visuospatial attention processing during preparation for a single/double-choice motor response. ERPs were recorded in two sessions in which participants breathed either ambient-air or oxygen-impoverished air. During each session, participants performed four cue-target attention orienting and/or alerting tasks. Replicating the classic findings of valid visuospatial attentional orienting modulation, ERPs to pre-target cues elicited both an Anterior directing attention negativity (ADAN)/CNV and a posterior Late directing attention positivity (LDAP)/TP, which in ambient air were larger for attention orienting than for alerting. Hypoxia increased the amplitude of both these potentials in the spatial orienting conditions for the upper visual hemifield, while, for the lower hemifield, it increased ADAN/CNV, but decreased LDAP/TP for the same attention conditions. To these ERP changes corresponded compensatory enhanced activation of right anterior cingulate cortex, left superior parietal lobule and frontal gyrus, as well as detrimental effects of hypoxia on behavioral overt performance. Together, these findings reveal for the first time, to our knowledge, that (1) these reversed alterations of the activation patterns during the time between cue and target occur at a larger extent in hypoxia than in air, and (2) acute normobaric hypoxia alters visuospatial attention orienting shifting in space.
Collapse
Affiliation(s)
- A Zani
- School of Psychology, Vita-Salute San Raffaele University, Via Olgettina 58-60, 20132, Milan, MI, Italy.
| | - N Crotti
- Department of Psychology, University of Milan-Bicocca, Milan (MI), Italy
| | - M Marzorati
- Institute of Biomedical Technologies, National Research Council (CNR ITB), Segrate, MI, Italy
| | - A Senerchia
- Department of Psychology, University of Milan-Bicocca, Milan (MI), Italy
| | - A M Proverbio
- Department of Psychology, University of Milan-Bicocca, Milan (MI), Italy
| |
Collapse
|
9
|
Wu S, Huang C, Snyder A, Smith M, Doiron B, Yu B. Automated customization of large-scale spiking network models to neuronal population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558920. [PMID: 37790533 PMCID: PMC10542160 DOI: 10.1101/2023.09.21.558920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.
Collapse
Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Chengcheng Huang
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Snyder
- Department of Neuroscience, University of Rochester, Rochester, NY, USA
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
- Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Matthew Smith
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Byron Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| |
Collapse
|
10
|
Campos LJ, Arokiaraj CM, Chuapoco MR, Chen X, Goeden N, Gradinaru V, Fox AS. Advances in AAV technology for delivering genetically encoded cargo to the nonhuman primate nervous system. CURRENT RESEARCH IN NEUROBIOLOGY 2023; 4:100086. [PMID: 37397806 PMCID: PMC10313870 DOI: 10.1016/j.crneur.2023.100086] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/05/2023] [Accepted: 03/17/2023] [Indexed: 07/04/2023] Open
Abstract
Modern neuroscience approaches including optogenetics, calcium imaging, and other genetic manipulations have facilitated our ability to dissect specific circuits in rodent models to study their role in neurological disease. These approaches regularly use viral vectors to deliver genetic cargo (e.g., opsins) to specific tissues and genetically-engineered rodents to achieve cell-type specificity. However, the translatability of these rodent models, cross-species validation of identified targets, and translational efficacy of potential therapeutics in larger animal models like nonhuman primates remains difficult due to the lack of efficient primate viral vectors. A refined understanding of the nonhuman primate nervous system promises to deliver insights that can guide the development of treatments for neurological and neurodegenerative diseases. Here, we outline recent advances in the development of adeno-associated viral vectors for optimized use in nonhuman primates. These tools promise to help open new avenues for study in translational neuroscience and further our understanding of the primate brain.
Collapse
Affiliation(s)
- Lillian J. Campos
- Department of Psychology and the California National Primate Research Center, University of California, Davis, CA, 05616, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Cynthia M. Arokiaraj
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Miguel R. Chuapoco
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Xinhong Chen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Nick Goeden
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Capsida Biotherapeutics, Thousand Oaks, CA, 91320, USA
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Andrew S. Fox
- Department of Psychology and the California National Primate Research Center, University of California, Davis, CA, 05616, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| |
Collapse
|
11
|
Sachse EM, Snyder AC. Dynamic attention signalling in V4: Relation to fast-spiking/non-fast-spiking cell class and population coupling. Eur J Neurosci 2023; 57:918-939. [PMID: 36732934 DOI: 10.1111/ejn.15928] [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: 08/03/2022] [Revised: 01/09/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023]
Abstract
The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time-varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time-varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so-called 'fast-spiking' [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast-spiking (more likely to be non-fast-spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.
Collapse
Affiliation(s)
- Elizabeth M Sachse
- Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA
- Neuroscience, University of Minnesota, Minneapolis, Minnesota, USA
| | - Adam C Snyder
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA
- Neuroscience, University of Rochester, Rochester, New York, USA
- Center for Visual Sciences, University of Rochester, Rochester, New York, USA
| |
Collapse
|
12
|
Dynamic and stable population coding of attentional instructions coexist in the prefrontal cortex. Proc Natl Acad Sci U S A 2022; 119:e2202564119. [PMID: 36161937 DOI: 10.1073/pnas.2202564119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A large body of recent work suggests that neural representations in prefrontal cortex (PFC) are changing over time to adapt to task demands. However, it remains unclear whether and how such dynamic coding schemes depend on the encoded variable and are influenced by anatomical constraints. Using a cued attention task and multivariate classification methods, we show that neuronal ensembles in PFC encode and retain in working memory spatial and color attentional instructions in an anatomically specific manner. Spatial instructions could be decoded both from the frontal eye field (FEF) and the ventrolateral PFC (vlPFC) population, albeit more robustly from FEF, whereas color instructions were decoded more robustly from vlPFC. Decoding spatial and color information from vlPFC activity in the high-dimensional state space indicated stronger dynamics for color, across the cue presentation and memory periods. The change in the color code was largely due to rapid changes in the network state during the transition to the delay period. However, we found that dynamic vlPFC activity contained time-invariant color information within a low-dimensional subspace of neural activity that allowed for stable decoding of color across time. Furthermore, spatial attention influenced decoding of stimuli features profoundly in vlPFC, but less so in visual area V4. Overall, our results suggest that dynamic population coding of attentional instructions within PFC is shaped by anatomical constraints and can coexist with stable subspace coding that allows time-invariant decoding of information about the future target.
Collapse
|
13
|
Mancuso L, Cavuoti-Cabanillas S, Liloia D, Manuello J, Buzi G, Cauda F, Costa T. Tasks activating the default mode network map multiple functional systems. Brain Struct Funct 2022; 227:1711-1734. [PMID: 35179638 PMCID: PMC9098625 DOI: 10.1007/s00429-022-02467-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/31/2022] [Indexed: 12/30/2022]
Abstract
Recent developments in network neuroscience suggest reconsidering what we thought we knew about the default mode network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation likelihood estimation maps were then computed. An additional map of task-induced deactivations was also created. A multidimensional scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to that of lateral cortices, involved in more external tasks. Further multivariate investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the various activation maps were often different from the canonical representation of the resting-state DMN, sometimes overlapping with it only in some peripheral nodes, and including external regions such as the insula. Altogether, our findings suggest that the intrinsic-extrinsic opposition may be better understood in the form of a continuous scale, rather than a dichotomy.
Collapse
Affiliation(s)
- Lorenzo Mancuso
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | | | - Donato Liloia
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Giulia Buzi
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | - Franco Cauda
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy.
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
| |
Collapse
|