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Huang YN, Liang WK, Juan CH. Spatial prediction modulates the rhythm of attentional sampling. Cereb Cortex 2024; 34:bhae392. [PMID: 39329361 DOI: 10.1093/cercor/bhae392] [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: 11/21/2023] [Revised: 09/06/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
Recent studies demonstrate that behavioral performance during visual spatial attention fluctuates at theta (4 to 8 Hz) and alpha (8 to 16 Hz) frequencies, linked to phase-amplitude coupling of neural oscillations within the visual and attentional system depending on task demands. To investigate the influence of prior spatial prediction, we employed an adaptive discrimination task with variable cue-target onset asynchronies (300 to 1,300 ms) and different cue validity (100% & 50%). We recorded electroencephalography concurrently and adopted adaptive electroencephalography data analytical methods, namely, Holo-Holo-Hilbert spectral analysis and Holo-Hilbert cross-frequency phase clustering. Our findings indicate that response precision for near-threshold Landolt rings fluctuates at the theta band (4 Hz) under certain predictions and at alpha & beta bands (15 & 19 Hz) with uncertain predictions. Furthermore, spatial prediction strengthens theta-alpha modulations at parietal-occipital areas, frontal theta/parietal-occipital alpha phase-amplitude coupling, and within frontal theta-alpha phase-amplitude coupling. Notably, during the pretarget period, beta-modulated gamma oscillations in parietal-occipital areas predict response precision under uncertain prediction, while frontal theta/parietal-occipital alpha phase-amplitude coupling predicts response precision in spatially certain conditions. In conclusion, our study highlights the critical role of spatial prediction in attentional sampling rhythms with both behavioral and electroencephalography evidence.
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Affiliation(s)
- Yih-Ning Huang
- Institute of Cognitive Neuroscience, National Central University, No. 300, Jhongda Rd, Jhongli District, Taoyuan City 320, Taiwan
| | - Wei-Kuang Liang
- Institute of Cognitive Neuroscience, National Central University, No. 300, Jhongda Rd, Jhongli District, Taoyuan City 320, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, No. 300, Jhongda Rd, Jhongli District, Taoyuan City 320, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, National Central University, No. 300, Jhongda Rd, Jhongli District, Taoyuan City 320, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, No. 300, Jhongda Rd, Jhongli District, Taoyuan City 320, Taiwan
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2
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Ten Oever S, Titone L, te Rietmolen N, Martin AE. Phase-dependent word perception emerges from region-specific sensitivity to the statistics of language. Proc Natl Acad Sci U S A 2024; 121:e2320489121. [PMID: 38805278 PMCID: PMC11161766 DOI: 10.1073/pnas.2320489121] [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: 11/29/2023] [Accepted: 04/22/2024] [Indexed: 05/30/2024] Open
Abstract
Neural oscillations reflect fluctuations in excitability, which biases the percept of ambiguous sensory input. Why this bias occurs is still not fully understood. We hypothesized that neural populations representing likely events are more sensitive, and thereby become active on earlier oscillatory phases, when the ensemble itself is less excitable. Perception of ambiguous input presented during less-excitable phases should therefore be biased toward frequent or predictable stimuli that have lower activation thresholds. Here, we show such a frequency bias in spoken word recognition using psychophysics, magnetoencephalography (MEG), and computational modelling. With MEG, we found a double dissociation, where the phase of oscillations in the superior temporal gyrus and medial temporal gyrus biased word-identification behavior based on phoneme and lexical frequencies, respectively. This finding was reproduced in a computational model. These results demonstrate that oscillations provide a temporal ordering of neural activity based on the sensitivity of separable neural populations.
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Affiliation(s)
- Sanne Ten Oever
- Language and Computation in Neural Systems group, Max Planck Institute for Psycholinguistics, NijmegenXD 6525, The Netherlands
- Language and Computation in Neural Systems group, Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, NijmegenEN 6525, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, EV 6229, The Netherlands
| | - Lorenzo Titone
- Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, LeipzigD-04303, Germany
| | - Noémie te Rietmolen
- Language and Computation in Neural Systems group, Max Planck Institute for Psycholinguistics, NijmegenXD 6525, The Netherlands
- Language and Computation in Neural Systems group, Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, NijmegenEN 6525, The Netherlands
| | - Andrea E. Martin
- Language and Computation in Neural Systems group, Max Planck Institute for Psycholinguistics, NijmegenXD 6525, The Netherlands
- Language and Computation in Neural Systems group, Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, NijmegenEN 6525, The Netherlands
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3
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van der Werf OJ, Schuhmann T, de Graaf T, Ten Oever S, Sack AT. Investigating the role of task relevance during rhythmic sampling of spatial locations. Sci Rep 2023; 13:12707. [PMID: 37543646 PMCID: PMC10404272 DOI: 10.1038/s41598-023-38968-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 07/18/2023] [Indexed: 08/07/2023] Open
Abstract
Recently it has been discovered that visuospatial attention operates rhythmically, rather than being stably employed over time. A low-frequency 7-8 Hz rhythmic mechanism coordinates periodic windows to sample relevant locations and to shift towards other, less relevant locations in a visual scene. Rhythmic sampling theories would predict that when two locations are relevant 8 Hz sampling mechanisms split into two, effectively resulting in a 4 Hz sampling frequency at each location. Therefore, it is expected that rhythmic sampling is influenced by the relative importance of locations for the task at hand. To test this, we employed an orienting task with an arrow cue, where participants were asked to respond to a target presented in one visual field. The cue-to-target interval was systematically varied, allowing us to assess whether performance follows a rhythmic pattern across cue-to-target delays. We manipulated a location's task relevance by altering the validity of the cue, thereby predicting the correct location in 60%, 80% or 100% of trials. Results revealed significant 4 Hz performance fluctuations at cued right visual field targets with low cue validity (60%), suggesting regular sampling of both locations. With high cue validity (80%), we observed a peak at 8 Hz towards non-cued targets, although not significant. These results were in line with our hypothesis suggesting a goal-directed balancing of attentional sampling (cued location) and shifting (non-cued location) depending on the relevance of locations in a visual scene. However, considering the hemifield specificity of the effect together with the absence of expected effects for cued trials in the high valid conditions we further discuss the interpretation of the data.
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Affiliation(s)
- Olof J van der Werf
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands.
- Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands.
| | - Teresa Schuhmann
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands
| | - Tom de Graaf
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands
| | - Sanne Ten Oever
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands
- Language and Computation in Neural Systems Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Alexander T Sack
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Brain and Nerve Centre, Maastricht University Medical Centre+ (MUMC+), Maastricht, The Netherlands
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4
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Mentzelopoulos G, Driscoll N, Shankar S, Kim B, Rich R, Fernandez-Nunez G, Stoll H, Erickson B, Medaglia JD, Vitale F. Alerting attention is sufficient to induce a phase-dependent behavior that can be predicted by frontal EEG. Front Behav Neurosci 2023; 17:1176865. [PMID: 37292166 PMCID: PMC10246752 DOI: 10.3389/fnbeh.2023.1176865] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/27/2023] [Indexed: 06/10/2023] Open
Abstract
Recent studies suggest that attention is rhythmic. Whether that rhythmicity can be explained by the phase of ongoing neural oscillations, however, is still debated. We contemplate that a step toward untangling the relationship between attention and phase stems from employing simple behavioral tasks that isolate attention from other cognitive functions (perception/decision-making) and by localized monitoring of neural activity with high spatiotemporal resolution over the brain regions associated with the attentional network. In this study, we investigated whether the phase of electroencephalography (EEG) oscillations predicts alerting attention. We isolated the alerting mechanism of attention using the Psychomotor Vigilance Task, which does not involve a perceptual component, and collected high resolution EEG using novel high-density dry EEG arrays at the frontal region of the scalp. We identified that alerting attention alone is sufficient to induce a phase-dependent modulation of behavior at EEG frequencies of 3, 6, and 8 Hz throughout the frontal region, and we quantified the phase that predicts the high and low attention states in our cohort. Our findings disambiguate the relationship between EEG phase and alerting attention.
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Affiliation(s)
- Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
| | - Nicolette Driscoll
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
| | - Sneha Shankar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
| | - Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | - Ryan Rich
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | | | - Harrison Stoll
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | - Brian Erickson
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | - John Dominic Medaglia
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Drexel University, Philadelphia, PA, United States
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States
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5
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Tosato T, Rohenkohl G, Dowdall JR, Fries P. Quantifying rhythmicity in perceptual reports. Neuroimage 2022; 262:119561. [PMID: 35973565 DOI: 10.1016/j.neuroimage.2022.119561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 10/31/2022] Open
Abstract
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the discrete Fourier transform (DFT) and the least square spectrum (LSS). DFT and LSS can be applied both on the average accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effects) or on the population (random-effects) of simulated participants. Multiple comparisons across frequencies were corrected using False Discovery Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated sensitivity, specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the average accuracy time course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effects approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
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Affiliation(s)
- Tommaso Tosato
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany.
| | - Gustavo Rohenkohl
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany; Department of Physiology, Institute of Biosciences, University of São Paulo, São Paulo 05508-000, Brazil
| | - Jarrod Robert Dowdall
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany; Robarts Research Institute, Western University, London, Ontario, Canada
| | - Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EN Nijmegen, the Netherlands.
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6
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Keitel C, Ruzzoli M, Dugué L, Busch NA, Benwell CSY. Rhythms in cognition: The evidence revisited. Eur J Neurosci 2022; 55:2991-3009. [PMID: 35696729 PMCID: PMC9544967 DOI: 10.1111/ejn.15740] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/27/2022] [Accepted: 05/30/2022] [Indexed: 12/27/2022]
Affiliation(s)
| | - Manuela Ruzzoli
- Basque Center on Cognition, Brain and Language (BCBL), Donostia/San Sebastian, Spain.,Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Laura Dugué
- Université Paris Cité, INCC UMR 8002, CNRS, Paris, France.,Institut Universitaire de France (IUF), Paris, France
| | - Niko A Busch
- Institute for Psychology, University of Münster, Münster, Germany
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7
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Merholz G, Grabot L, VanRullen R, Dugué L. Periodic attention operates faster during more complex visual search. Sci Rep 2022; 12:6688. [PMID: 35461325 PMCID: PMC9035177 DOI: 10.1038/s41598-022-10647-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/24/2022] [Indexed: 11/16/2022] Open
Abstract
Attention has been found to sample visual information periodically, in a wide range of frequencies below 20 Hz. This periodicity may be supported by brain oscillations at corresponding frequencies. We propose that part of the discrepancy in periodic frequencies observed in the literature is due to differences in attentional demands, resulting from heterogeneity in tasks performed. To test this hypothesis, we used visual search and manipulated task complexity, i.e., target discriminability (high, medium, low) and number of distractors (set size), while electro-encephalography was simultaneously recorded. We replicated previous results showing that the phase of pre-stimulus low-frequency oscillations predicts search performance. Crucially, such effects were observed at increasing frequencies within the theta-alpha range (6-18 Hz) for decreasing target discriminability. In medium and low discriminability conditions, correct responses were further associated with higher post-stimulus phase-locking than incorrect ones, in increasing frequency and latency. Finally, the larger the set size, the later the post-stimulus effect peaked. Together, these results suggest that increased complexity (lower discriminability or larger set size) requires more attentional cycles to perform the task, partially explaining discrepancies between reports of attentional sampling. Low-frequency oscillations structure the temporal dynamics of neural activity and aid top-down, attentional control for efficient visual processing.
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Affiliation(s)
- Garance Merholz
- Université Paris Cité, INCC UMR 8002, CNRS, 75006, Paris, France.
| | - Laetitia Grabot
- Université Paris Cité, INCC UMR 8002, CNRS, 75006, Paris, France
| | - Rufin VanRullen
- Centre National de la Recherche Scientifique, CerCo Unité Mixte de Recherche 5549, Université de Toulouse, 31052, Toulouse, France
| | - Laura Dugué
- Université Paris Cité, INCC UMR 8002, CNRS, 75006, Paris, France
- Institut Universitaire de France (IUF), Paris, France
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8
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Ten Oever S, van der Werf OJ, Schuhmann T, Sack AT. Absence of behavioral rhythms: noise or unexplained neuronal mechanisms? (response to Fiebelkorn, 2021). Eur J Neurosci 2022; 55:3121-3124. [PMID: 35193154 PMCID: PMC9545739 DOI: 10.1111/ejn.15628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 02/17/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Sanne Ten Oever
- Language and Computation in Neural Systems group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.,Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Olof J van der Werf
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands
| | - Teresa Schuhmann
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands
| | - Alexander T Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Brain and Nerve Centre, Maastricht University Medical Centre+ (MUMC+), Maastricht, The Netherlands
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