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Sookprao P, Benjasupawan K, Phangwiwat T, Chatnuntawech I, Lertladaluck K, Gutchess A, Chunharas C, Itthipuripat S. Conflicting Sensory Information Sharpens the Neural Representations of Early Selective Visuospatial Attention. J Neurosci 2024; 44:e2012232024. [PMID: 38955488 PMCID: PMC11326869 DOI: 10.1523/jneurosci.2012-23.2024] [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: 10/18/2023] [Revised: 06/19/2024] [Accepted: 06/25/2024] [Indexed: 07/04/2024] Open
Abstract
Adaptive behaviors require the ability to resolve conflicting information caused by the processing of incompatible sensory inputs. Prominent theories of attention have posited that early selective attention helps mitigate cognitive interference caused by conflicting sensory information by facilitating the processing of task-relevant sensory inputs and filtering out behaviorally irrelevant information. Surprisingly, many recent studies that investigated the role of early selective attention on conflict mitigation have failed to provide positive evidence. Here, we examined changes in the selectivity of early visuospatial attention in male and female human subjects performing an attention-cueing Eriksen flanker task, where they discriminated the shape of a visual target surrounded by congruent or incongruent distractors. We used the inverted encoding model to reconstruct spatial representations of visual selective attention from the topographical patterns of amplitude modulations in alpha band oscillations in scalp EEG (∼8-12 Hz). We found that the fidelity of the alpha-based spatial reconstruction was significantly higher in the incongruent compared with the congruent condition. Importantly, these conflict-related modulations in the reconstruction fidelity occurred at a much earlier time window than those of the lateralized posterior event-related potentials associated with target selection and distractor suppression processes, as well as conflict-related modulations in the frontocentral negative-going wave and midline-frontal theta oscillations (∼3-7 Hz), thought to track executive control functions. Taken together, our data suggest that conflict resolution is supported by the cascade of neural processes underlying early selective visuospatial attention and frontal executive functions that unfold over time.
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Affiliation(s)
- Panchalee Sookprao
- Neuroscience Center for Research and Innovation (NX), Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok 10330, Thailand
- Cognitive Clinical and Computational Neuroscience Center of Excellence, Department of Internal Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
- SCG Digital Office, Bangkok 10800, Thailand
| | - Kanyarat Benjasupawan
- Neuroscience Center for Research and Innovation (NX), Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok 10330, Thailand
- Cognitive Clinical and Computational Neuroscience Center of Excellence, Department of Internal Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Tanagrit Phangwiwat
- Neuroscience Center for Research and Innovation (NX), Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Big Data Experience Center (BX), Department of Computer Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10600, Thailand
- Computer Engineering Department, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
| | - Itthi Chatnuntawech
- National Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani 12120, Thailand
| | - Kanda Lertladaluck
- Neuroscience Center for Research and Innovation (NX), Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
| | - Angela Gutchess
- Department of Psychology, Neuroscience Program, Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02453
| | - Chaipat Chunharas
- Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok 10330, Thailand
- Cognitive Clinical and Computational Neuroscience Center of Excellence, Department of Internal Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Sirawaj Itthipuripat
- Neuroscience Center for Research and Innovation (NX), Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Big Data Experience Center (BX), Department of Computer Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10600, Thailand
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2
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Tünçok E, Carrasco M, Winawer J. Spatial attention alters visual cortical representation during target anticipation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.02.583127. [PMID: 38496524 PMCID: PMC10942396 DOI: 10.1101/2024.03.02.583127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Attention enables us to efficiently and flexibly interact with the environment by prioritizing some image features in preparation for responding to a stimulus. Using a concurrent psychophysics- fMRI experiment, we investigated how covert spatial attention affects responses in human visual cortex prior to target onset, and how it affects subsequent behavioral performance. Performance improved at cued locations and worsened at uncued locations, relative to distributed attention, demonstrating a selective tradeoff in processing. Pre-target BOLD responses in cortical visual field maps changed in two ways: First, there was a stimulus-independent baseline shift, positive in map locations near the cued location and negative elsewhere, paralleling the behavioral results. Second, population receptive field centers shifted toward the attended location. Both effects increased in higher visual areas. Together, the results show that spatial attention has large effects on visual cortex prior to target appearance, altering neural response properties throughout and across multiple visual field maps.
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Itthipuripat S, Phangwiwat T, Wiwatphonthana P, Sawetsuttipan P, Chang KY, Störmer VS, Woodman GF, Serences JT. Dissociable Neural Mechanisms Underlie the Effects of Attention on Visual Appearance and Response Bias. J Neurosci 2023; 43:6628-6652. [PMID: 37620156 PMCID: PMC10538590 DOI: 10.1523/jneurosci.2192-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 07/10/2023] [Accepted: 08/13/2023] [Indexed: 08/26/2023] Open
Abstract
A prominent theoretical framework spanning philosophy, psychology, and neuroscience holds that selective attention penetrates early stages of perceptual processing to alter the subjective visual experience of behaviorally relevant stimuli. For example, searching for a red apple at the grocery store might make the relevant color appear brighter and more saturated compared with seeing the exact same red apple while searching for a yellow banana. In contrast, recent proposals argue that data supporting attention-related changes in appearance reflect decision- and motor-level response biases without concurrent changes in perceptual experience. Here, we tested these accounts by evaluating attentional modulations of EEG responses recorded from male and female human subjects while they compared the perceived contrast of attended and unattended visual stimuli rendered at different levels of physical contrast. We found that attention enhanced the amplitude of the P1 component, an early evoked potential measured over visual cortex. A linking model based on signal detection theory suggests that response gain modulations of the P1 component track attention-induced changes in perceived contrast as measured with behavior. In contrast, attentional cues induced changes in the baseline amplitude of posterior alpha band oscillations (∼9-12 Hz), an effect that best accounts for cue-induced response biases, particularly when no stimuli are presented or when competing stimuli are similar and decisional uncertainty is high. The observation of dissociable neural markers that are linked to changes in subjective appearance and response bias supports a more unified theoretical account and demonstrates an approach to isolate subjective aspects of selective information processing.SIGNIFICANCE STATEMENT Does attention alter visual appearance, or does it simply induce response bias? In the present study, we examined these competing accounts using EEG and linking models based on signal detection theory. We found that response gain modulations of the visually evoked P1 component best accounted for attention-induced changes in visual appearance. In contrast, cue-induced baseline shifts in alpha band activity better explained response biases. Together, these results suggest that attention concurrently impacts visual appearance and response bias, and that these processes can be experimentally isolated.
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Affiliation(s)
- Sirawaj Itthipuripat
- Neuroscience Center for Research and Innovation, Learning Institute, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand
- Big Data Experience Center, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand
| | - Tanagrit Phangwiwat
- Neuroscience Center for Research and Innovation, Learning Institute, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand
- Big Data Experience Center, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand
- Computer Engineering Department, Faculty of Engineering, King Mongkut’s University of Technology Thonburi Bangkok, 10140, Thailand
| | - Praewpiraya Wiwatphonthana
- Neuroscience Center for Research and Innovation, Learning Institute, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand
- SECCLO Consortium, Department of Computer Science, Aalto University School of Science, Espoo, 02150, Finland
| | - Prapasiri Sawetsuttipan
- Neuroscience Center for Research and Innovation, Learning Institute, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand
- Big Data Experience Center, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand
- Computer Engineering Department, Faculty of Engineering, King Mongkut’s University of Technology Thonburi Bangkok, 10140, Thailand
| | - Kai-Yu Chang
- Department of Cognitive Science, University of California–San Diego, La Jolla, California 92093-1090
| | - Viola S. Störmer
- Department of Psychological and Brain Science, Dartmouth College, Hanover, New Hampshire 03755
| | - Geoffrey F. Woodman
- Department of Psychology, Center for Integrative and Cognitive Neuroscience, and Interdisciplinary Program in Neuroscience, Vanderbilt University, Nashville, Tennessee 37235
| | - John T. Serences
- Neurosciences Graduate Program, Department of Psychology, University of California–San Diego, La Jolla, California 92093-1090
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4
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Kim B, Erickson BA, Fernandez-Nunez G, Rich R, Mentzelopoulos G, Vitale F, Medaglia JD. EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio. eNeuro 2023; 10:ENEURO.0050-23.2023. [PMID: 37558464 PMCID: PMC10481640 DOI: 10.1523/eneuro.0050-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/25/2023] [Accepted: 06/15/2023] [Indexed: 08/11/2023] Open
Abstract
EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.
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Affiliation(s)
- Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Brian A Erickson
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | | | - Ryan Rich
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, Pennsylvania 19146
| | - John D Medaglia
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Drexel University, Philadelphia, Pennsylvania 19104
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5
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Chen J, Golomb JD. Dynamic neural reconstructions of attended object location and features using EEG. J Neurophysiol 2023; 130:139-154. [PMID: 37283457 PMCID: PMC10393364 DOI: 10.1152/jn.00180.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/10/2023] [Accepted: 06/02/2023] [Indexed: 06/08/2023] Open
Abstract
Attention allows us to select relevant and ignore irrelevant information from our complex environments. What happens when attention shifts from one item to another? To answer this question, it is critical to have tools that accurately recover neural representations of both feature and location information with high temporal resolution. In the present study, we used human electroencephalography (EEG) and machine learning to explore how neural representations of object features and locations update across dynamic shifts of attention. We demonstrate that EEG can be used to create simultaneous time courses of neural representations of attended features (time point-by-time point inverted encoding model reconstructions) and attended location (time point-by-time point decoding) during both stable periods and across dynamic shifts of attention. Each trial presented two oriented gratings that flickered at the same frequency but had different orientations; participants were cued to attend one of them and on half of trials received a shift cue midtrial. We trained models on a stable period from Hold attention trials and then reconstructed/decoded the attended orientation/location at each time point on Shift attention trials. Our results showed that both feature reconstruction and location decoding dynamically track the shift of attention and that there may be time points during the shifting of attention when 1) feature and location representations become uncoupled and 2) both the previously attended and currently attended orientations are represented with roughly equal strength. The results offer insight into our understanding of attentional shifts, and the noninvasive techniques developed in the present study lend themselves well to a wide variety of future applications.NEW & NOTEWORTHY We used human EEG and machine learning to reconstruct neural response profiles during dynamic shifts of attention. Specifically, we demonstrated that we could simultaneously read out both location and feature information from an attended item in a multistimulus display. Moreover, we examined how that readout evolves over time during the dynamic process of attentional shifts. These results provide insight into our understanding of attention, and this technique carries substantial potential for versatile extensions and applications.
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Affiliation(s)
- Jiageng Chen
- Department of Psychology, The Ohio State University, Columbus, Ohio, United States
| | - Julie D Golomb
- Department of Psychology, The Ohio State University, Columbus, Ohio, United States
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6
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Sawetsuttipan P, Phunchongharn P, Ounjai K, Salazar A, Pongsuwan S, Intrachooto S, Serences JT, Itthipuripat S. Perceptual Difficulty Regulates Attentional Gain Modulations in Human Visual Cortex. J Neurosci 2023; 43:3312-3330. [PMID: 36963848 PMCID: PMC10162463 DOI: 10.1523/jneurosci.0519-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 02/18/2023] [Accepted: 03/13/2023] [Indexed: 03/26/2023] Open
Abstract
Perceptual difficulty is sometimes used to manipulate selective attention. However, these two factors are logically distinct. Selective attention is defined by priority given to specific stimuli based on their behavioral relevance, whereas perceptual difficulty is often determined by perceptual demands required to discriminate relevant stimuli. That said, both perceptual difficulty and selective attention are thought to modulate the gain of neural responses in early sensory areas. Previous studies found that selectively attending to a stimulus or increasing perceptual difficulty enhanced the gain of neurons in visual cortex. However, some other studies suggest that perceptual difficulty can have either a null or even reversed effect on gain modulations in visual cortex. According to Yerkes-Dodson's Law, it is possible that this discrepancy arises because of an interaction between perceptual difficulty and attentional gain modulations yielding a nonlinear inverted-U function. Here, we used EEG to measure modulations in the visual cortex of male and female human participants performing an attention-cueing task where we systematically manipulated perceptual difficulty across blocks of trials. The behavioral and neural data implicate a nonlinear inverted-U relationship between selective attention and perceptual difficulty: a focused-attention cue led to larger response gain in both neural and behavioral data at intermediate difficulty levels compared with when the task was more or less difficult. Moreover, difficulty-related changes in attentional gain positively correlated with those predicted by quantitative modeling of the behavioral data. These findings suggest that perceptual difficulty mediates attention-related changes in perceptual performance via selective neural modulations in human visual cortex.SIGNIFICANCE STATEMENT Both perceptual difficulty and selective attention are thought to influence perceptual performance by modulating response gain in early sensory areas. That said, less is known about how selective attention interacts with perceptual difficulty. Here, we measured neural gain modulations in the visual cortex of human participants performing an attention-cueing task where perceptual difficulty was systematically manipulated. Consistent with Yerkes-Dodson's Law, our behavioral and neural data implicate a nonlinear inverted-U relationship between selective attention and perceptual difficulty. These results suggest that perceptual difficulty mediates attention-related changes in perceptual performance via selective neural modulations in visual cortex, extending our understanding of the attentional operation under different levels of perceptual demands.
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Affiliation(s)
- Prapasiri Sawetsuttipan
- Neuroscience Center for Research and Innovation, Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Computer Engineering Department, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Big Data Experience Center, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
| | - Phond Phunchongharn
- Computer Engineering Department, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Big Data Experience Center, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
| | - Kajornvut Ounjai
- Neuroscience Center for Research and Innovation, Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
| | - Annalisa Salazar
- Department of Psychology, University of California, San Diego, La Jolla, California 92093-1090
| | - Sarigga Pongsuwan
- Happiness Science Hub, Research & Innovation for Sustainability Center (RISC), Bangkok 10260, Thailand
| | - Singh Intrachooto
- Happiness Science Hub, Research & Innovation for Sustainability Center (RISC), Bangkok 10260, Thailand
| | - John T Serences
- Department of Psychology, University of California, San Diego, La Jolla, California 92093-1090
- Neurosciences Graduate Program and Kavli Foundation for the Brain and Mind, University of California, San Diego, La Jolla, California 92093-1090
| | - Sirawaj Itthipuripat
- Neuroscience Center for Research and Innovation, Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
- Big Data Experience Center, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
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7
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Ye Z, Ding J, Tu Y, Zhang Q, Chen S, Yu H, Sun Q, Hua T. Suppression of top-down influence decreases both behavioral and V1 neuronal response sensitivity to stimulus orientations in cats. Front Behav Neurosci 2023; 17:1061980. [PMID: 36844652 PMCID: PMC9944033 DOI: 10.3389/fnbeh.2023.1061980] [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: 10/05/2022] [Accepted: 01/24/2023] [Indexed: 02/11/2023] Open
Abstract
How top-down influence affects behavioral detection of visual signals and neuronal response sensitivity in the primary visual cortex (V1) remains poorly understood. This study examined both behavioral performance in stimulus orientation identification and neuronal response sensitivity to stimulus orientations in the V1 of cat before and after top-down influence of area 7 (A7) was modulated by non-invasive transcranial direct current stimulation (tDCS). Our results showed that cathode (c) but not sham (s) tDCS in A7 significantly increased the behavioral threshold in identifying stimulus orientation difference, which effect recovered after the tDCS effect vanished. Consistently, c-tDCS but not s-tDCS in A7 significantly decreased the response selectivity bias of V1 neurons for stimulus orientations, which effect could recover after withdrawal of the tDCS effect. Further analysis showed that c-tDCS induced reduction of V1 neurons in response selectivity was not resulted from alterations of neuronal preferred orientation, nor of spontaneous activity. Instead, c-tDCS in A7 significantly lowered the visually-evoked response, especially the maximum response of V1 neurons, which caused a decrease in response selectivity and signal-to-noise ratio. By contrast, s-tDCS exerted no significant effect on the responses of V1 neurons. These results indicate that top-down influence of A7 may enhance behavioral identification of stimulus orientations by increasing neuronal visually-evoked response and response selectivity in the V1.
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Affiliation(s)
- Zheng Ye
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Jian Ding
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China,School of Basic Medical, Wannan Medical College, Wuhu, Anhui, China
| | - Yanni Tu
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Qiuyu Zhang
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Shunshun Chen
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Hao Yu
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Qingyan Sun
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Tianmiao Hua
- College of Life sciences, Anhui Normal University, Wuhu, Anhui, China,*Correspondence: Tianmiao Hua,
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8
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Foster JJ, Ling S. Feature-Based Attention Multiplicatively Scales the fMRI-BOLD Contrast-Response Function. J Neurosci 2022; 42:6894-6906. [PMID: 35868860 PMCID: PMC9464014 DOI: 10.1523/jneurosci.0513-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/05/2022] [Accepted: 07/15/2022] [Indexed: 11/21/2022] Open
Abstract
fMRI plays a key role in the study of attention. However, there remains a puzzling discrepancy between attention effects measured with fMRI and with electrophysiological methods. While electrophysiological studies find that attention increases sensory gain, amplifying stimulus-evoked neural responses by multiplicatively scaling the contrast-response function (CRF), fMRI appears to be insensitive to these multiplicative effects. Instead, fMRI studies typically find that attention produces an additive baseline shift in the BOLD signal. These findings suggest that attentional effects measured with fMRI reflect top-down inputs to visual cortex, rather than the modulation of sensory gain. If true, this drastically limits what fMRI can tell us about how attention improves sensory coding. Here, we examined whether fMRI is sensitive to multiplicative effects of attention using a feature-based attention paradigm designed to preclude any possible additive effects. We measured BOLD activity evoked by a probe stimulus in one visual hemifield while participants (6 male, 6 female) attended to the probe orientation (attended condition), or to an orthogonal orientation (unattended condition), in the other hemifield. To measure CRFs in visual areas V1-V3, we parametrically varied the contrast of the probe stimulus. In all three areas, feature-based attention increased contrast gain, improving sensitivity by shifting CRFs toward lower contrasts. In V2 and V3, we also found an increase in response gain, an increase in the responsivity of the CRF, that was greatest at inner eccentricities. These results provide clear evidence that the fMRI-BOLD signal is sensitive to multiplicative effects of attention.SIGNIFICANCE STATEMENT fMRI plays a central role in the study of attention because it allows researchers to precisely and noninvasively characterize the effects of attention throughout the brain. Electrophysiological studies have shown that attention increases sensory gain, amplifying stimulus-evoked neural responses. However, a growing body of work suggests that the BOLD signal that is measured with fMRI is not sensitive to these multiplicative effects of attention, calling into question what we can learn from fMRI about how attention improves sensory codes. Here, using a feature-based attention paradigm, we provide evidence that the BOLD signal can pick up multiplicative effects of attention.
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Affiliation(s)
- Joshua J Foster
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts 02215
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215
| | - Sam Ling
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts 02215
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215
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9
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Ding J, Ye Z, Xu F, Hu X, Yu H, Zhang S, Tu Y, Zhang Q, Sun Q, Hua T, Lu ZL. Effects of top-down influence suppression on behavioral and V1 neuronal contrast sensitivity functions in cats. iScience 2022; 25:103683. [PMID: 35059603 PMCID: PMC8760559 DOI: 10.1016/j.isci.2021.103683] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/16/2021] [Accepted: 12/20/2021] [Indexed: 02/09/2023] Open
Abstract
To explore the relative contributions of higher-order and primary visual cortex (V1) to visual perception, we compared cats' behavioral and V1 neuronal contrast sensitivity functions (CSF) and threshold versus external noise contrast (TvC) functions before and after top-down influence of area 7 (A7) was modulated with transcranial direct current stimulation (tDCS). We found that suppressing top-down influence of A7 with cathode-tDCS, but not sham-tDCS, reduced behavioral and neuronal contrast sensitivity in the same range of spatial frequencies and increased behavioral and neuronal contrast thresholds in the same range of external noise levels. The neuronal CSF and TvC functions were highly correlated with their behavioral counterparts both before and after the top-down suppression. Analysis of TvC functions using the Perceptual Template Model (PTM) indicated that top-down influence of A7 increased both behavioral and V1 neuronal contrast sensitivity by reducing internal additive noise and the impact of external noise. Top-down suppression lowers both behavioral and V1 neuronal CSF functions Top-down suppression raises both behavioral and V1 neuronal TvC functions The neuronal CSFs and TvCs are highly correlated with their behavioral counterparts Top-down influence lowers internal additive noise and impact of external noise in V1
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Affiliation(s)
- Jian Ding
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Zheng Ye
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Fei Xu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Xiangmei Hu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Hao Yu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Shen Zhang
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Yanni Tu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Qiuyu Zhang
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Qingyan Sun
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Tianmiao Hua
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Zhong-Lin Lu
- Divison of Arts and Sciences, NYU Shanghai, Shanghai 200122, China.,Center for Neural Science and Department of Psychology, New York University, New York, NY 10003, USA.,NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai 200062, China
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10
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Goddard E, Carlson TA, Woolgar A. Spatial and Feature-selective Attention Have Distinct, Interacting Effects on Population-level Tuning. J Cogn Neurosci 2021; 34:290-312. [PMID: 34813647 PMCID: PMC7613071 DOI: 10.1162/jocn_a_01796] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Attention can be deployed in different ways: When searching for a taxi in New York City, we can decide where to attend (e.g., to the street) and what to attend to (e.g., yellow cars). Although we use the same word to describe both processes, nonhuman primate data suggest that these produce distinct effects on neural tuning. This has been challenging to assess in humans, but here we used an opportunity afforded by multivariate decoding of MEG data. We found that attending to an object at a particular location and attending to a particular object feature produced effects that interacted multiplicatively. The two types of attention induced distinct patterns of enhancement in occipital cortex, with feature-selective attention producing relatively more enhancement of small feature differences and spatial attention producing relatively larger effects for larger feature differences. An information flow analysis further showed that stimulus representations in occipital cortex were Granger-caused by coding in frontal cortices earlier in time and that the timing of this feedback matched the onset of attention effects. The data suggest that spatial and feature-selective attention rely on distinct neural mechanisms that arise from frontal-occipital information exchange, interacting multiplicatively to selectively enhance task-relevant information.
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Affiliation(s)
- Erin Goddard
- University of New South Wales.,Macquarie University, Sydney, New South Wales, Australia
| | - Thomas A Carlson
- Macquarie University, Sydney, New South Wales, Australia.,University of Sydney
| | - Alexandra Woolgar
- Macquarie University, Sydney, New South Wales, Australia.,University of Cambridge
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Ding J, Hu X, Xu F, Yu H, Ye Z, Zhang S, Pan H, Pan D, Tu Y, Zhang Q, Sun Q, Hua T. Suppression of top-down influence decreases neuronal excitability and contrast sensitivity in the V1 cortex of cat. Sci Rep 2021; 11:16034. [PMID: 34362965 PMCID: PMC8346540 DOI: 10.1038/s41598-021-95407-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 07/26/2021] [Indexed: 12/18/2022] Open
Abstract
How top-down influence affects neuronal activity and information encoding in the primary visual cortex (V1) remains elusive. This study examined changes of neuronal excitability and contrast sensitivity in cat V1 cortex after top-down influence of area 7 (A7) was modulated by transcranial direct current stimulation (tDCS). The neuronal excitability in V1 cortex was evaluated by visually evoked field potentials (VEPs), and contrast sensitivity (CS) was assessed by the inverse of threshold contrast of neurons in response to visual stimuli at different performance accuracy. We found that the amplitude of VEPs in V1 cortex lowered after top-down influence suppression with cathode-tDCS in A7, whereas VEPs in V1 did not change after sham-tDCS in A7 and nonvisual cortical area 5 (A5) or cathode-tDCS in A5 and lesioned A7. Moreover, the mean CS of V1 neurons decreased after cathode-tDCS but not sham-tDCS in A7, which could recover after tDCS effect vanished. Comparisons of neuronal contrast-response functions showed that cathode-tDCS increased the stimulus contrast required to generate the half-maximum response, with a weakly-correlated reduction in maximum response but not baseline response. Therefore, top-down influence of A7 enhanced neuronal excitability in V1 cortex and improved neuronal contrast sensitivity by both contrast gain and response gain.
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Affiliation(s)
- Jian Ding
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Xiangmei Hu
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Fei Xu
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Hao Yu
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Zheng Ye
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Shen Zhang
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Huijun Pan
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Deng Pan
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Yanni Tu
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Qiuyu Zhang
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Qingyan Sun
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China
| | - Tianmiao Hua
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, Anhui, China.
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