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Liu J, Younk R, Drahos LM, Nagrale SS, Yadav S, Widge AS, Shoaran M. Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597165. [PMID: 38895388 PMCID: PMC11185693 DOI: 10.1101/2024.06.06.597165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Objective Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors. Approach We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance. Main results Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference. Significance Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.
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Affiliation(s)
- Jinhan Liu
- Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland
- Neuro-X Institute, EPFL, Geneva, Switzerland
| | - Rebecca Younk
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Lauren M Drahos
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Sumedh S Nagrale
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Shreya Yadav
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- These authors jointly supervised this work
| | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland
- Neuro-X Institute, EPFL, Geneva, Switzerland
- These authors jointly supervised this work
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2
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Liu F, Meamardoost S, Gunawan R, Komiyama T, Mewes C, Zhang Y, Hwang E, Wang L. Deep learning for neural decoding in motor cortex. J Neural Eng 2022; 19. [PMID: 36148535 DOI: 10.1088/1741-2552/ac8fb5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/06/2022] [Indexed: 11/12/2022]
Abstract
Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons.Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain.Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders.Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.
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Affiliation(s)
- Fangyu Liu
- Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
| | - Saber Meamardoost
- Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, United States of America
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, United States of America
| | - Takaki Komiyama
- Department of Neurobiology, Center for Neural Circuits and Behavior, and Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, United States of America
| | - Claudia Mewes
- Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, United States of America
| | - Ying Zhang
- Department of Cell and Molecular Biology, University of Rhode Island, Kingston, RI 02881, United States of America
| | - EunJung Hwang
- Department of Neurobiology, Center for Neural Circuits and Behavior, and Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, United States of America.,Cell Biology and Anatomy Discipline, Center for Brain Function and Repair, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, United States of America
| | - Linbing Wang
- Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
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3
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Zhou Y, Rosen MC, Swaminathan SK, Masse NY, Zhu O, Freedman DJ. Distributed functions of prefrontal and parietal cortices during sequential categorical decisions. eLife 2021; 10:e58782. [PMID: 34491201 PMCID: PMC8423442 DOI: 10.7554/elife.58782] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/13/2021] [Indexed: 12/19/2022] Open
Abstract
Comparing sequential stimuli is crucial for guiding complex behaviors. To understand mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC), the lateral intraparietal (LIP), and medial intraparietal (MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads M/NM decisions, whereas LIP and MIP appear more involved in stimulus evaluation and motor planning, respectively. Compared to LIP, PFC showed greater nonlinear integration of currently visible and remembered stimuli, which correlated with the monkeys' M/NM decisions. Furthermore, multi-module recurrent networks trained on the same task exhibited key features of PFC and LIP encoding, including nonlinear integration in the PFC-like module, which was causally involved in the networks' decisions. Network analysis found that nonlinear units have stronger and more widespread connections with input, output, and within-area units, indicating putative circuit-level mechanisms for sequential decisions.
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Affiliation(s)
- Yang Zhou
- Department of Neurobiology, The University of ChicagoChicagoUnited States
- School of Psychological and Cognitive Sciences, PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking UniversityBeijingChina
| | - Matthew C Rosen
- Department of Neurobiology, The University of ChicagoChicagoUnited States
| | | | - Nicolas Y Masse
- Department of Neurobiology, The University of ChicagoChicagoUnited States
| | - Ou Zhu
- Department of Neurobiology, The University of ChicagoChicagoUnited States
| | - David J Freedman
- Department of Neurobiology, The University of ChicagoChicagoUnited States
- Neuroscience Institute, The University of ChicagoChicagoUnited States
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4
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Abstract
Remapping is a property of some cortical and subcortical neurons that update their responses around the time of an eye movement to account for the shift of stimuli on the retina due to the saccade. Physiologically, remapping is traditionally tested by briefly presenting a single stimulus around the time of the saccade and looking at the onset of the response and the locations in space to which the neuron is responsive. Here we suggest that a better way to understand the functional role of remapping is to look at the time at which the neural signal emerges when saccades are made across a stable scene. Based on data obtained using this approach, we suggest that remapping in the lateral intraparietal area is sufficient to play a role in maintaining visual stability across saccades, whereas in the frontal eye field, remapped activity carries information that affects future saccadic choices and, in a separate subset of neurons, is used to maintain a map of locations in the scene that have been previously fixated.
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Affiliation(s)
- James W Bisley
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Jules Stein Eye Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Department of Psychology and the Brain Research Institute, UCLA, Los Angeles, CA, USA
| | - Koorosh Mirpour
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Yelda Alkan
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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5
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Yao JD, Gimoto J, Constantinople CM, Sanes DH. Parietal Cortex Is Required for the Integration of Acoustic Evidence. Curr Biol 2020; 30:3293-3303.e4. [PMID: 32619478 DOI: 10.1016/j.cub.2020.06.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/12/2020] [Accepted: 06/04/2020] [Indexed: 01/31/2023]
Abstract
Sensory-driven decisions are formed by accumulating information over time. Although parietal cortex activity is thought to represent accumulated evidence for sensory-based decisions, recent perturbation studies in rodents and non-human primates have challenged the hypothesis that these representations actually influence behavior. Here, we asked whether the parietal cortex integrates acoustic features from auditory cortical inputs during a perceptual decision-making task. If so, we predicted that selective inactivation of this projection should impair subjects' ability to accumulate sensory evidence. We trained gerbils to perform an auditory discrimination task and obtained measures of integration time as a readout of evidence accumulation capability. Minimum integration time was calculated behaviorally as the shortest stimulus duration for which subjects could discriminate the acoustic signals. Direct pharmacological inactivation of parietal cortex increased minimum integration times, suggesting its role in the behavior. To determine the specific impact of sensory evidence, we chemogenetically inactivated the excitatory projections from auditory cortex to parietal cortex and found this was sufficient to increase minimum behavioral integration times. Our signal-detection-theory-based model accurately replicated behavioral outcomes and indicated that the deficits in task performance were plausibly explained by elevated sensory noise. Together, our findings provide causal evidence that parietal cortex plays a role in the network that integrates auditory features for perceptual judgments.
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Affiliation(s)
- Justin D Yao
- Center for Neural Science, New York University, New York, NY 10003, USA.
| | - Justin Gimoto
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Christine M Constantinople
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute, NYU Langone Medical Center, New York University, New York, NY 10016, USA
| | - Dan H Sanes
- Center for Neural Science, New York University, New York, NY 10003, USA; Department of Psychology, New York University, New York, NY 10003, USA; Department of Biology, New York University, New York, NY 10003, USA; Neuroscience Institute, NYU Langone Medical Center, New York University, New York, NY 10016, USA
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6
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Machine Learning for Neural Decoding. eNeuro 2020; 7:ENEURO.0506-19.2020. [PMID: 32737181 PMCID: PMC7470933 DOI: 10.1523/eneuro.0506-19.2020] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 07/01/2020] [Accepted: 07/03/2020] [Indexed: 01/11/2023] Open
Abstract
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems. We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. We also provide detailed comparisons of the performance of various methods at the task of decoding spiking activity in motor cortex, somatosensory cortex, and hippocampus. Modern methods, particularly neural networks and ensembles, significantly outperform traditional approaches, such as Wiener and Kalman filters. Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help to advance engineering applications such as brain–machine interfaces. Our code package is available at github.com/kordinglab/neural_decoding.
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7
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De A, El-Shamayleh Y, Horwitz GD. Fast and reversible neural inactivation in macaque cortex by optogenetic stimulation of GABAergic neurons. eLife 2020; 9:52658. [PMID: 32452766 PMCID: PMC7329331 DOI: 10.7554/elife.52658] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 05/24/2020] [Indexed: 12/21/2022] Open
Abstract
Optogenetic techniques for neural inactivation are valuable for linking neural activity to behavior but they have serious limitations in macaques. To achieve powerful and temporally precise neural inactivation, we used an adeno-associated viral (AAV) vector carrying the channelrhodopsin-2 gene under the control of a Dlx5/6 enhancer, which restricts expression to GABAergic neurons. We tested this approach in the primary visual cortex, an area where neural inactivation leads to interpretable behavioral deficits. Optical stimulation modulated spiking activity and reduced visual sensitivity profoundly in the region of space represented by the stimulated neurons. Rebound firing, which can have unwanted effects on neural circuits following inactivation, was not observed, and the efficacy of the optogenetic manipulation on behavior was maintained across >1000 trials. We conclude that this inhibitory cell-type-specific optogenetic approach is a powerful and spatiotemporally precise neural inactivation tool with broad utility for probing the functional contributions of cortical activity in macaques.
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Affiliation(s)
- Abhishek De
- Graduate Program in Neuroscience, University of Washington, Seattle, United States.,Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, United States
| | - Yasmine El-Shamayleh
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States
| | - Gregory D Horwitz
- Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, United States
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8
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Lakshminarasimhan KJ, Avila E, Neyhart E, DeAngelis GC, Pitkow X, Angelaki DE. Tracking the Mind's Eye: Primate Gaze Behavior during Virtual Visuomotor Navigation Reflects Belief Dynamics. Neuron 2020; 106:662-674.e5. [PMID: 32171388 PMCID: PMC7323886 DOI: 10.1016/j.neuron.2020.02.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/24/2019] [Accepted: 02/19/2020] [Indexed: 01/02/2023]
Abstract
To take the best actions, we often need to maintain and update beliefs about variables that cannot be directly observed. To understand the principles underlying such belief updates, we need tools to uncover subjects' belief dynamics from natural behavior. We tested whether eye movements could be used to infer subjects' beliefs about latent variables using a naturalistic navigation task. Humans and monkeys navigated to a remembered goal location in a virtual environment that provided optic flow but lacked explicit position cues. We observed eye movements that appeared to continuously track the goal location even when no visible target was present there. Accurate goal tracking was associated with improved task performance, and inhibiting eye movements in humans impaired navigation precision. These results suggest that gaze dynamics play a key role in action selection during challenging visuomotor behaviors and may possibly serve as a window into the subject's dynamically evolving internal beliefs.
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Affiliation(s)
- Kaushik J Lakshminarasimhan
- Center for Neural Science, New York University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
| | - Eric Avila
- Center for Neural Science, New York University, New York, NY, USA
| | - Erin Neyhart
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | | | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Dora E Angelaki
- Center for Neural Science, New York University, New York, NY, USA; Tandon School of Engineering, New York University, New York, NY, USA
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9
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Zhou Y, Freedman DJ. Posterior parietal cortex plays a causal role in perceptual and categorical decisions. SCIENCE (NEW YORK, N.Y.) 2020; 365:180-185. [PMID: 31296771 DOI: 10.1126/science.aaw8347] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 06/18/2019] [Indexed: 01/14/2023]
Abstract
Posterior parietal cortex (PPC) activity correlates with monkeys' decisions during visual discrimination and categorization tasks. However, recent work has questioned whether decision-correlated PPC activity plays a causal role in such decisions. That study focused on PPC's contribution to motor aspects of decisions (deciding where to move), but not sensory evaluation aspects (deciding what you are looking at). We employed reversible inactivation to compare PPC's contributions to motor and sensory aspects of decisions. Inactivation affected both aspects of behavior, but preferentially impaired decisions when visual stimuli, rather than motor response targets, were in the inactivated visual field. This demonstrates a causal role for PPC in decision-making, with preferential involvement in evaluating attended task-relevant sensory stimuli compared with motor planning.
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Affiliation(s)
- Yang Zhou
- Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA.
| | - David J Freedman
- Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA.
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10
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Freedman DJ, Ibos G. An Integrative Framework for Sensory, Motor, and Cognitive Functions of the Posterior Parietal Cortex. Neuron 2019; 97:1219-1234. [PMID: 29566792 DOI: 10.1016/j.neuron.2018.01.044] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 01/12/2018] [Accepted: 01/23/2018] [Indexed: 11/28/2022]
Abstract
Throughout the history of modern neuroscience, the parietal cortex has been associated with a wide array of sensory, motor, and cognitive functions. The use of non-human primates as a model organism has been instrumental in our current understanding of how areas in the posterior parietal cortex (PPC) modulate our perception and influence our behavior. In this Perspective, we highlight a series of influential studies over the last five decades examining the role of the PPC in visual perception and motor planning. We also integrate long-standing views of PPC functions with more recent evidence to propose a more general model framework to explain integrative sensory, motor, and cognitive functions of the PPC.
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Affiliation(s)
- David J Freedman
- Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA; Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, The University of Chicago, Chicago, IL 60637, USA.
| | - Guilhem Ibos
- Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA; Institut de Neuroscience de la Timone, UMR 7289 CNRS & Aix-Marseille Université, Marseille, France.
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11
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Wang M, Montanède C, Chandrasekaran C, Peixoto D, Shenoy KV, Kalaska JF. Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known. Nat Commun 2019; 10:1793. [PMID: 30996222 PMCID: PMC6470163 DOI: 10.1038/s41467-019-09460-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 03/12/2019] [Indexed: 01/16/2023] Open
Abstract
How deliberation on sensory cues and action selection interact in decision-related brain areas is still not well understood. Here, monkeys reached to one of two targets, whose colors alternated randomly between trials, by discriminating the dominant color of a checkerboard cue composed of different numbers of squares of the two target colors in different trials. In a Targets First task the colored targets appeared first, followed by the checkerboard; in a Checkerboard First task, this order was reversed. After both cues appeared in both tasks, responses of dorsal premotor cortex (PMd) units covaried with action choices, strength of evidence for action choices, and RTs- hallmarks of decision-related activity. However, very few units were modulated by checkerboard color composition or the color of the chosen target, even during the checkerboard deliberation epoch of the Checkerboard First task. These findings implicate PMd in the action-selection but not the perceptual components of the decision-making process in these tasks.
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Affiliation(s)
- Megan Wang
- Neurosciences Graduate Program, Stanford University, Stanford, CA, 94305, USA
| | - Christéva Montanède
- Département de Neurosciences, Pavillon Paul-G.-Desmarais, Faculté de Médecine, Université de Montréal, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada
| | - Chandramouli Chandrasekaran
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA
- Department of Anatomy and Neurobiology, Boston University, Boston, MA, 02118, USA
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | - Diogo Peixoto
- Department of Neurobiology, Stanford University, Stanford, CA, 94305, USA
- Champalimaud Neuroscience Programme, 1400-038, Lisbon, Portugal
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA
- Department of Neurobiology, Stanford University, Stanford, CA, 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Bio-X Program, Stanford University, Stanford, CA, 94305, USA
- Stanford Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA
| | - John F Kalaska
- Département de Neurosciences, Pavillon Paul-G.-Desmarais, Faculté de Médecine, Université de Montréal, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada.
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12
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Distinct roles of prefrontal and parietal areas in the encoding of attentional priority. Proc Natl Acad Sci U S A 2018; 115:E8755-E8764. [PMID: 30154164 DOI: 10.1073/pnas.1804643115] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
When searching for an object in a crowded scene, information about the similarity of stimuli to the target object is thought to be encoded in spatial priority maps, which are subsequently used to guide shifts of attention and gaze to likely targets. Two key cortical areas that have been described as holding priority maps are the frontal eye field (FEF) and the lateral intraparietal area (LIP). However, little is known about their distinct contributions in priority encoding. Here, we compared neuronal responses in FEF and LIP during free-viewing visual search. Although saccade selection signals emerged earlier in FEF, information about the target emerged at similar latencies in distinct populations within the two areas. Notably, however, effects in FEF were more pronounced. Moreover, LIP neurons encoded the similarity of stimuli to the target independent of saccade selection, whereas in FEF, encoding of target similarity was strongly modulated by saccade selection. Taken together, our findings suggest hierarchical processing of saccade selection signals and parallel processing of feature-based attention signals within the parietofrontal network with FEF having a more prominent role in priority encoding. Furthermore, they suggest discrete roles of FEF and LIP in the construction of priority maps.
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