1
|
Zhu J, Garin CM, Qi XL, Machado A, Wang Z, Hamed SB, Stanford TR, Salinas E, Whitlow CT, Anderson AW, Zhou XM, Calabro FJ, Luna B, Constantinidis C. Brain structure and activity predicting cognitive maturation in adolescence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.23.608315. [PMID: 39229176 PMCID: PMC11370567 DOI: 10.1101/2024.08.23.608315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Cognitive abilities of primates, including humans, continue to improve through adolescence 1,2. While a range of changes in brain structure and connectivity have been documented 3,4, how they affect neuronal activity that ultimately determines performance of cognitive functions remains unknown. Here, we conducted a multilevel longitudinal study of monkey adolescent neurocognitive development. The developmental trajectory of neural activity in the prefrontal cortex accounted remarkably well for working memory improvements. While complex aspects of activity changed progressively during adolescence, such as the rotation of stimulus representation in multidimensional neuronal space, which has been implicated in cognitive flexibility, even simpler attributes, such as the baseline firing rate in the period preceding a stimulus appearance had predictive power over behavior. Unexpectedly, decreases in brain volume and thickness, which are widely thought to underlie cognitive changes in humans 5 did not predict well the trajectory of neural activity or cognitive performance changes. Whole brain cortical volume in particular, exhibited an increase and reached a local maximum in late adolescence, at a time of rapid behavioral improvement. Maturation of long-distance white matter tracts linking the frontal lobe with areas of the association cortex and subcortical regions best predicted changes in neuronal activity and behavior. Our results provide evidence that optimization of neural activity depending on widely distributed circuitry effects cognitive development in adolescence.
Collapse
Affiliation(s)
- Junda Zhu
- Program in Neuroscience, Vanderbilt University, Nashville TN 37235 USA
| | - Clément M Garin
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN 37235 USA
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, 69675 Bron Cedex, France
| | - Xue-Lian Qi
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston Salem, NC 27203, USA
| | - Anna Machado
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN 37235 USA
| | - Zhengyang Wang
- Program in Neuroscience, Vanderbilt University, Nashville TN 37235 USA
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, 69675 Bron Cedex, France
| | - Terrence R Stanford
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston Salem, NC 27203, USA
| | - Emilio Salinas
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston Salem, NC 27203, USA
| | - Christopher T Whitlow
- Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC 27203, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN 37235 USA
| | - Xin Maizie Zhou
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN 37235 USA
| | - Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA 15213 USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA 15213 USA
| | - Christos Constantinidis
- Program in Neuroscience, Vanderbilt University, Nashville TN 37235 USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN 37235 USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville TN 37232, USA
| |
Collapse
|
2
|
Osong B, Hasannejadasl H, van der Poel H, Vanneste B, van Roermund J, Aben K, Van Soest J, Van Oort I, Hochstenbach L, Bloemen- van Gurp EJ, Dekker A, Fijten RR. The value of PROMs for predicting erectile dysfunction in prostate cancer patients with Bayesian network. Tech Innov Patient Support Radiat Oncol 2024; 31:100234. [PMID: 39188594 PMCID: PMC11345401 DOI: 10.1016/j.tipsro.2024.100234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/03/2023] [Accepted: 01/04/2024] [Indexed: 08/28/2024] Open
Abstract
Purpose This study aims to develop and externally validate a clinically plausible Bayesian network structure to predict one-year erectile dysfunction in prostate cancer patients by combining expert knowledge with evidence from data using clinical and Patient-reported outcome measures (PROMs) data. In addition, compare and contrast structures that stem from PROM information and routine clinical data. Summary of background For men with localized prostate cancer, choosing the optimal treatment can be challenging since each option comes with different side effects, such as erectile dysfunction, which negatively impacts their quality of life. Our previous findings demonstrate that logistic regression models are able to identify patients at high risk of erectile dysfunction. However, methods such as Bayesian networks may be more successful, as they intricately represent the causal relations between the variables. Patients and methods 946 prostate cancer patients from 65 Dutch hospitals were considered to develop the Bayesian network structure. Continuous variables were discretized before analysis based on expert opinions and literature. Patients with missing information and variables with more than 25% of missing information were excluded. Prostate cancer treating physicians first determined the relationships (arcs) between the available variables. The structures were then modified based on algorithmically derived structures using the hill-climbing algorithm. Structural Performance was evaluated based on the area under the curve (AUC) values and calibration plots on the training and test data. Results BMI and prostate volume via MRI were excluded from this analysis due to their high percentage of missingness (>45 %). The final cohort was reduced to 505 and 216 after excluding 157 and 68 patients with missing information, respectively. The AUC of the PROM structure was better than the clinical structure in both the train and test data. The structure that combined both sources of information had an AUC value of 0.94 (0.92 - 0.96) and 0.84171 (0.77 91) in the train and test data, respectively. Conclusion Bayesian network structures derived from PROM information by complimenting expert knowledge with evidence from the data produce a clinically plausible structure that is more performant than structures from clinical data. Our study supports the growing global recognition of incorporating the patient's perspective in outcomes research for better decision-making and optimal outcomes. However, a structure that combines both sources of information gives a more holistic view of the patient with actionable insights and improved discriminative power.
Collapse
Affiliation(s)
- Biche Osong
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Hajar Hasannejadasl
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Henk van der Poel
- Department of Urology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ben Vanneste
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Joep van Roermund
- Department of Urology, Maastricht University Medical Center, the Netherlands
| | - Katja Aben
- Department of Research Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Johan Van Soest
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Inge Van Oort
- Department of Urology, Radboud university Medical Center, Nijmegen, the Netherlands
| | - Laura Hochstenbach
- Department of Urology, Maastricht University Medical Center, the Netherlands
| | - Esther J. Bloemen- van Gurp
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
- Department of Urology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Urology, Maastricht University Medical Center, the Netherlands
- Department of Research Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Rianne R.R. Fijten
- Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| |
Collapse
|
3
|
Su K, Huang Z, Li Q, Fan M, Li T, Yin D. Dissociable functional responses along the posterior-anterior gradient of the frontal and parietal cortices revealed by parametric working memory and training. Brain Struct Funct 2024; 229:1681-1696. [PMID: 38995366 DOI: 10.1007/s00429-024-02834-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: 03/05/2024] [Accepted: 07/06/2024] [Indexed: 07/13/2024]
Abstract
While the storage capacity is limited, accumulating studies have indicated that working memory (WM) can be improved by cognitive training. However, understanding how exactly the brain copes with limited WM capacity and how cognitive training optimizes the brain remains inconclusive. Given the hierarchical functional organization of WM, we hypothesized that the activation profiles along the posterior-anterior gradient of the frontal and parietal cortices characterize WM load and training effects. To test this hypothesis, we recruited 51 healthy volunteers and adopted a parametric WM paradigm and training method. In contrast to exclusively strengthening the activation of posterior areas, a broader range of activation concurrently occurred in the anterior areas to cope with increased memory load for all subjects at baseline. Moreover, there was an imbalance in the responses of the posterior and anterior areas to the same increment of 1 item at different load levels. Although a general decrease in activation after adaptive training, the changes in the posterior and anterior areas were distinct at different memory loads. Particularly, we found that the activation gradient between the posterior and anterior areas was significantly increased at load 4-back after adaptive training, and the changes were correlated with improvement in WM performance. Together, our results demonstrate a shift in the predominant role of posterior and anterior areas in the frontal and parietal cortices when approaching WM capacity limits. Additionally, the training-induced performance improvement likely benefits from the elevated neural efficiency reflected in the increased activation gradient between the posterior and anterior areas.
Collapse
Affiliation(s)
- Kaiqiang Su
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhong-Shan Road, Shanghai, 200062, China
| | - Ziyi Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhong-Shan Road, Shanghai, 200062, China
| | - Qianwen Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, 200062, China
| | - Mingxia Fan
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, 200062, China
| | - Ting Li
- Shanghai Changning Mental Health Center, Shanghai, 200335, China
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhong-Shan Road, Shanghai, 200062, China.
- Shanghai Changning Mental Health Center, Shanghai, 200335, China.
- Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei, 241002, China.
| |
Collapse
|
4
|
Zhao W, Su K, Zhu H, Kaiser M, Fan M, Zou Y, Li T, Yin D. Activity flow under the manipulation of cognitive load and training. Neuroimage 2024; 297:120761. [PMID: 39069226 DOI: 10.1016/j.neuroimage.2024.120761] [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: 03/04/2024] [Revised: 06/11/2024] [Accepted: 07/26/2024] [Indexed: 07/30/2024] Open
Abstract
Flexible cognitive functions, such as working memory (WM), usually require a balance between localized and distributed information processing. However, it is challenging to uncover how local and distributed processing specifically contributes to task-induced activity in a region. Although the recently proposed activity flow mapping approach revealed the relative contribution of distributed processing, few studies have explored the adaptive and plastic changes that underlie cognitive manipulation. In this study, we recruited 51 healthy volunteers (31 females) and investigated how the activity flow and brain activation of the frontoparietal systems was modulated by WM load and training. While the activation of both executive control network (ECN) and dorsal attention network (DAN) increased linearly with memory load at baseline, the relative contribution of distributed processing showed a linear response only in the DAN, which was prominently attributed to within-network activity flow. Importantly, adaptive training selectively induced an increase in the relative contribution of distributed processing in the ECN and also a linear response to memory load, which were predominantly due to between-network activity flow. Furthermore, we demonstrated a causal effect of activity flow prediction through training manipulation on connectivity and activity. In contrast with classic brain activation estimation, our findings suggest that the relative contribution of distributed processing revealed by activity flow prediction provides unique insights into neural processing of frontoparietal systems under the manipulation of cognitive load and training. This study offers a new methodological framework for exploring information integration versus segregation underlying cognitive processing.
Collapse
Affiliation(s)
- Wanyun Zhao
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Kaiqiang Su
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Hengcheng Zhu
- Division of Biostatistics, University of Minnesota, Minneapolis 55455, MN, USA
| | - Marcus Kaiser
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom; School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Mingxia Fan
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
| | - Yong Zou
- Institute of Theoretical Physics, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
| | - Ting Li
- Shanghai Changning Mental Health Center, Shanghai 200335, China
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; Shanghai Changning Mental Health Center, Shanghai 200335, China.
| |
Collapse
|
5
|
Alleman M, Panichello M, Buschman TJ, Johnston WJ. The neural basis of swap errors in working memory. Proc Natl Acad Sci U S A 2024; 121:e2401032121. [PMID: 39102534 PMCID: PMC11331092 DOI: 10.1073/pnas.2401032121] [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: 01/16/2024] [Accepted: 06/03/2024] [Indexed: 08/07/2024] Open
Abstract
When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once-such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally and theoretical explanations have been proposed, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms by analyzing neural population recordings from monkeys performing two multistimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location or will be shown at the corresponding location. Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results provide evidence that swap errors emerge during selection of correctly remembered information from working memory, and highlight this selection as a crucial-yet surprisingly brittle-neural process.
Collapse
Affiliation(s)
- Matteo Alleman
- Department of Neuroscience, Center for Theoretical Neuroscience and Zuckerman Mind, Brain, and Behavior Institute, Columbia University, New York, NY10027
| | - Matthew Panichello
- Department of Neurobiology, Stanford University, Stanford, CA94305
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ08544
| | - Timothy J. Buschman
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ08544
| | - W. Jeffrey Johnston
- Department of Neuroscience, Center for Theoretical Neuroscience and Zuckerman Mind, Brain, and Behavior Institute, Columbia University, New York, NY10027
| |
Collapse
|
6
|
Tye KM, Miller EK, Taschbach FH, Benna MK, Rigotti M, Fusi S. Mixed selectivity: Cellular computations for complexity. Neuron 2024; 112:2289-2303. [PMID: 38729151 PMCID: PMC11257803 DOI: 10.1016/j.neuron.2024.04.017] [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: 12/11/2023] [Revised: 03/08/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024]
Abstract
The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.
Collapse
Affiliation(s)
- Kay M Tye
- Salk Institute for Biological Studies, La Jolla, CA, USA; Howard Hughes Medical Institute, La Jolla, CA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Kavli Institute for Brain and Mind, San Diego, CA, USA.
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Felix H Taschbach
- Salk Institute for Biological Studies, La Jolla, CA, USA; Biological Science Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Marcus K Benna
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | | | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
| |
Collapse
|
7
|
Fritz BA, King CR, Abdelhack M, Chen Y, Kronzer A, Abraham J, Tripathi S, Abdallah AB, Kannampallil T, Budelier TP, Helsten D, Montes de Oca A, Mehta D, Sontha P, Higo O, Kerby P, Gregory SH, Wildes TS, Avidan MS. Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.22.24307754. [PMID: 38826471 PMCID: PMC11142290 DOI: 10.1101/2024.05.22.24307754] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration ClinicalTrials.gov NCT05042804.
Collapse
Affiliation(s)
- Bradley A Fritz
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Christopher R King
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Mohamed Abdelhack
- Department of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, USA
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, USA
| | - Alex Kronzer
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, USA
| | - Sandhya Tripathi
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, USA
| | - Thaddeus P Budelier
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Daniel Helsten
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Arianna Montes de Oca
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Divya Mehta
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Pratyush Sontha
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Omokhaye Higo
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Paul Kerby
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Stephen H. Gregory
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Troy S. Wildes
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| |
Collapse
|
8
|
Abstract
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or 'iconic' memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
Collapse
Affiliation(s)
- Ivan Tomić
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
- Department of Psychology, Faculty of Humanities and Social Sciences, University of ZagrebZagrebCroatia
| | - Paul M Bays
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| |
Collapse
|
9
|
Fritz BA, Pugazenthi S, Budelier TP, Tellor Pennington BR, King CR, Avidan MS, Abraham J. User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications. Anesth Analg 2024; 138:804-813. [PMID: 37339083 PMCID: PMC10730770 DOI: 10.1213/ane.0000000000006577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
BACKGROUND Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Joanna Abraham
- From the Department of Anesthesiology
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri
| |
Collapse
|
10
|
Bardon AG, Ballesteros JJ, Brincat SL, Roy JE, Mahnke MK, Ishizawa Y, Brown EN, Miller EK. Convergent effects of different anesthetics are due to changes in phase alignment of cortical oscillations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585943. [PMID: 38562734 PMCID: PMC10983946 DOI: 10.1101/2024.03.20.585943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Many different anesthetics cause loss of responsiveness despite having diverse underlying molecular and circuit actions. To explore the convergent effects of these drugs, we examined how ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, and dexmedetomidine, an α2 adrenergic receptor agonist, affected neural oscillations in the prefrontal cortex of nonhuman primates. Previous work has shown that anesthesia increases phase locking of low-frequency local field potential activity across cortex. We observed similar increases with anesthetic doses of ketamine and dexmedetomidine in the ventrolateral and dorsolateral prefrontal cortex, within and across hemispheres. However, the nature of the phase locking varied between regions. We found that oscillatory activity in different prefrontal subregions within each hemisphere became more anti-phase with both drugs. Local analyses within a region suggested that this finding could be explained by broad cortical distance-based effects, such as a large traveling wave. By contrast, homologous areas across hemispheres increased their phase alignment. Our results suggest that the drugs induce strong patterns of cortical phase alignment that are markedly different from those in the awake state, and that these patterns may be a common feature driving loss of responsiveness from different anesthetic drugs.
Collapse
|
11
|
Jahn CI, Markov NT, Morea B, Daw ND, Ebitz RB, Buschman TJ. Learning attentional templates for value-based decision-making. Cell 2024; 187:1476-1489.e21. [PMID: 38401541 DOI: 10.1016/j.cell.2024.01.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/18/2023] [Accepted: 01/25/2024] [Indexed: 02/26/2024]
Abstract
Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.
Collapse
Affiliation(s)
- Caroline I Jahn
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA.
| | - Nikola T Markov
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Britney Morea
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Nathaniel D Daw
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Princeton, NJ 08540, USA
| | - R Becket Ebitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Department of Neurosciences, Université de Montréal, Montréal, QC H3C 3J7, Canada
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Princeton, NJ 08540, USA.
| |
Collapse
|
12
|
Santo-Angles A, Temudo A, Babushkin V, Sreenivasan KK. Effective connectivity of working memory performance: a DCM study of MEG data. Front Hum Neurosci 2024; 18:1339728. [PMID: 38501039 PMCID: PMC10944968 DOI: 10.3389/fnhum.2024.1339728] [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: 11/16/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Visual working memory (WM) engages several nodes of a large-scale network that includes frontal, parietal, and visual regions; however, little is understood about how these regions interact to support WM behavior. In particular, it is unclear whether network dynamics during WM maintenance primarily represent feedforward or feedback connections. This question has important implications for current debates about the relative roles of frontoparietal and visual regions in WM maintenance. In the current study, we investigated the network activity supporting WM using MEG data acquired while healthy subjects performed a multi-item delayed estimation WM task. We used computational modeling of behavior to discriminate correct responses (high accuracy trials) from two different types of incorrect responses (low accuracy and swap trials), and dynamic causal modeling of MEG data to measure effective connectivity. We observed behaviorally dependent changes in effective connectivity in a brain network comprising frontoparietal and early visual areas. In comparison with high accuracy trials, frontoparietal and frontooccipital networks showed disrupted signals depending on type of behavioral error. Low accuracy trials showed disrupted feedback signals during early portions of WM maintenance and disrupted feedforward signals during later portions of maintenance delay, while swap errors showed disrupted feedback signals during the whole delay period. These results support a distributed model of WM that emphasizes the role of visual regions in WM storage and where changes in large scale network configurations can have important consequences for memory-guided behavior.
Collapse
Affiliation(s)
- Aniol Santo-Angles
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Ainsley Temudo
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Vahan Babushkin
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Kartik K. Sreenivasan
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| |
Collapse
|
13
|
Cobos MI, Melcón M, Rodríguez-San Esteban P, Capilla A, Chica AB. The role of brain oscillations in feature integration. Psychophysiology 2024; 61:e14467. [PMID: 37990794 DOI: 10.1111/psyp.14467] [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: 01/17/2023] [Revised: 09/04/2023] [Accepted: 10/05/2023] [Indexed: 11/23/2023]
Abstract
Our sensory system is able to build a unified perception of the world, which although rich, is limited and inaccurate. Sometimes, features from different objects are erroneously combined. At the neural level, the role of the parietal cortex in feature integration is well-known. However, the brain dynamics underlying correct and incorrect feature integration are less clear. To explore the temporal dynamics of feature integration, we studied the modulation of different frequency bands in trials in which feature integration was correct or incorrect. Participants responded to the color of a shape target, surrounded by distractors. A calibration procedure ensured that accuracy was around 70% in each participant. To explore the role of expectancy in feature integration, we introduced an unexpected feature to the target in the last blocks of trials. Results demonstrated the contribution of several frequency bands to feature integration. Alpha and beta power was reduced for hits compared to illusions. Moreover, gamma power was overall larger during the experiment for participants who were aware of the unexpected target presented during the last blocks of trials (as compared to unaware participants). These results demonstrate that feature integration is a complex process that can go wrong at different stages of information processing and is influenced by top-down expectancies.
Collapse
Affiliation(s)
- M I Cobos
- Brain, Mind, and Behavior Research Center (CIMCYC), University of Granada (UGR), Granada, Spain
- Department of Experimental Psychology, University of Granada (UGR), Granada, Spain
| | - M Melcón
- Department of Biological and Health Psychology, Autonomous University of Madrid (UAM), Madrid, Spain
| | - P Rodríguez-San Esteban
- Brain, Mind, and Behavior Research Center (CIMCYC), University of Granada (UGR), Granada, Spain
- Department of Experimental Psychology, University of Granada (UGR), Granada, Spain
| | - A Capilla
- Department of Biological and Health Psychology, Autonomous University of Madrid (UAM), Madrid, Spain
| | - A B Chica
- Brain, Mind, and Behavior Research Center (CIMCYC), University of Granada (UGR), Granada, Spain
- Department of Experimental Psychology, University of Granada (UGR), Granada, Spain
| |
Collapse
|
14
|
Ceccarelli F, Ferrucci L, Londei F, Ramawat S, Brunamonti E, Genovesio A. Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex. Nat Commun 2023; 14:8325. [PMID: 38097560 PMCID: PMC10721651 DOI: 10.1038/s41467-023-43712-2] [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: 03/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.
Collapse
Affiliation(s)
- Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
- PhD program in Behavioral Neuroscience, Sapienza University, Rome, Italy
| | - Surabhi Ramawat
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Emiliano Brunamonti
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy.
| |
Collapse
|
15
|
Apostel A, Panichello M, Buschman TJ, Rose J. Corvids optimize working memory by categorizing continuous stimuli. Commun Biol 2023; 6:1122. [PMID: 37932494 PMCID: PMC10628182 DOI: 10.1038/s42003-023-05442-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023] Open
Abstract
Working memory (WM) is a crucial element of the higher cognition of primates and corvid songbirds. Despite its importance, WM has a severely limited capacity and is vulnerable to noise. In primates, attractor dynamics mitigate the effect of noise by discretizing continuous information. Yet, it remains unclear whether similar dynamics are seen in avian brains. Here, we show jackdaws (Corvus monedula) have similar behavioral biases as humans; memories are less precise and more biased as memory demands increase. Model-based analysis reveal discrete attractors are evenly spread across the stimulus space. Altogether, our comparative approach suggests attractor dynamics in primates and corvids mitigate the effect of noise by systematically drifting towards specific attractors. By demonstrating this effect in an evolutionary distant species, our results strengthen attractor dynamics as general, adaptive biological principle to efficiently use WM.
Collapse
Affiliation(s)
- Aylin Apostel
- Neural Basis of Learning, Department of Psychology, Ruhr University Bochum, Bochum, Germany.
| | | | - Timothy J Buschman
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Jonas Rose
- Neural Basis of Learning, Department of Psychology, Ruhr University Bochum, Bochum, Germany.
| |
Collapse
|
16
|
Pusch R, Packheiser J, Azizi AH, Sevincik CS, Rose J, Cheng S, Stüttgen MC, Güntürkün O. Working memory performance is tied to stimulus complexity. Commun Biol 2023; 6:1119. [PMID: 37923920 PMCID: PMC10624839 DOI: 10.1038/s42003-023-05486-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/18/2023] [Indexed: 11/06/2023] Open
Abstract
Working memory is the cognitive capability to maintain and process information over short periods. Behavioral and computational studies have shown that visual information is associated with working memory performance. However, the underlying neural correlates remain unknown. To identify how visual information affects working memory performance, we conducted behavioral experiments in pigeons (Columba livia) and single unit recordings in the avian prefrontal analog, the nidopallium caudolaterale (NCL). Complex pictures featuring luminance, spatial and color information, were associated with higher working memory performance compared to uniform gray pictures in conjunction with distinct neural coding patterns. For complex pictures, we found a multiplexed neuronal code displaying visual and value-related features that switched to a representation of the upcoming choice during a delay period. When processing gray stimuli, NCL neurons did not multiplex and exclusively represented the choice already during stimulus presentation and throughout the delay period. The prolonged representation possibly resulted in a decay of the memory trace ultimately leading to a decrease in performance. In conclusion, we found that high stimulus complexity is associated with neuronal multiplexing of the working memory representation possibly allowing a facilitated read-out of the neural code resulting in enhancement of working memory performance.
Collapse
Affiliation(s)
- Roland Pusch
- Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, D-44780, Bochum, Germany.
| | - Julian Packheiser
- Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, D-44780, Bochum, Germany
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Amir Hossein Azizi
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Karaj, Iran
| | - Celil Semih Sevincik
- Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, D-44780, Bochum, Germany
| | - Jonas Rose
- Neural Basis of Learning, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, D-44780, Bochum, Germany
| | - Sen Cheng
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Universitätsstraße 150, D-44780, Bochum, Germany
| | - Maik C Stüttgen
- Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg University, Duesbergweg 6, D-55128, Mainz, Germany
| | - Onur Güntürkün
- Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, D-44780, Bochum, Germany
- Research Center One Health Ruhr, Research Alliance Ruhr, Ruhr University Bochum, Bochum, Germany
| |
Collapse
|
17
|
Alleman M, Panichello M, Buschman TJ, Johnston WJ. The neural basis of swap errors in working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561584. [PMID: 37873433 PMCID: PMC10592761 DOI: 10.1101/2023.10.09.561584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once - such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms through trial-by-trial analysis of neural population recordings from posterior and frontal brain regions while monkeys perform two multi-stimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location (requiring selection from working memory) or will be shown at the corresponding location (requiring attention to a position). Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected incorrectly from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results suggest an alternative to established views on the neural origins of swap errors, and highlight selection from and manipulation in working memory as crucial - yet surprisingly brittle - neural processes.
Collapse
Affiliation(s)
- Matteo Alleman
- Center for Theoretical Neuroscience
- Mortimer B. Zuckerman Mind, Brain, and Behavior Institute Columbia University, New York, NY, USA
| | - Matthew Panichello
- Princeton Neuroscience Institute and Department of Psychology Princeton University, Princeton, NJ, USA
- Department of Neurobiology Stanford University, Stanford, CA, USA
| | - Timothy J. Buschman
- Princeton Neuroscience Institute and Department of Psychology Princeton University, Princeton, NJ, USA
| | - W. Jeffrey Johnston
- Center for Theoretical Neuroscience
- Mortimer B. Zuckerman Mind, Brain, and Behavior Institute Columbia University, New York, NY, USA
| |
Collapse
|
18
|
Li S, Rosen MC, Chang S, David S, Freedman DJ. Alterations of neural activity in the prefrontal cortex associated with deficits in working memory performance. Front Behav Neurosci 2023; 17:1213435. [PMID: 37915531 PMCID: PMC10616307 DOI: 10.3389/fnbeh.2023.1213435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023] Open
Abstract
Working memory (WM), a core cognitive function, enables the temporary holding and manipulation of information in mind to support ongoing behavior. Neurophysiological recordings conducted in nonhuman primates have revealed neural correlates of this process in a network of higher-order cortical regions, particularly the prefrontal cortex (PFC). Here, we review the circuit mechanisms and functional importance of WM-related activity in these areas. Recent neurophysiological data indicates that the absence of these neural correlates at different stages of WM is accompanied by distinct behavioral deficits, which are characteristic of various disease states/normal aging and which we review here. Finally, we discuss emerging evidence of electrical stimulation ameliorating these WM deficits in both humans and non-human primates. These results are important for a basic understanding of the neural mechanisms supporting WM, as well as for translational efforts to developing therapies capable of enhancing healthy WM ability or restoring WM from dysfunction.
Collapse
Affiliation(s)
- Sihai Li
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Matthew C. Rosen
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Suha Chang
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Samuel David
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - David J. Freedman
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
- Neuroscience Institute, The University of Chicago, Chicago, IL, United States
| |
Collapse
|
19
|
Teng C, Kaplan SM, Shomstein S, Kravitz DJ. Assessing the interaction between working memory and perception through time. Atten Percept Psychophys 2023; 85:2196-2209. [PMID: 37740152 DOI: 10.3758/s13414-023-02785-3] [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] [Accepted: 08/31/2023] [Indexed: 09/24/2023]
Abstract
Content maintained in visual working memory changes concurrent visual processing, suggesting that visual working memory may recruit an overlapping neural representation with visual perception. However, it remains unclear whether visual working memory representations persist as a sensory code through time, or are recoded later into an abstract code. Here, we directly contrasted a temporal decay + visual code account and a temporal decay + abstract code account within the temporal dynamics of the interaction between working memory and perception. By manipulating the ISI (inter-stimulus interval) between working memory encoding and a perceptual discrimination task, we found that task-relevant and therefore actively maintained perceptual information parametrically altered participants' ability to discriminate perceptual stimuli even 4 s after encoding, whereas task-irrelevant information caused only an acutely transient effect. While continuously present, the size of this shift in discrimination thresholds gradually decreased over time. Concomitantly, the size of the bias in working memory reports increased over time. The opposing directions of threshold and bias effects are consistent with the local maintenance of information in perceptual areas, explained by a temporal decay + visual code account. As the maintained representation decays over time, its ability to alter incoming perceptual signals decreases (reduced threshold effects) while its likelihood of being impacted by those same signals increases (increased bias effects). Altogether, these results suggest that the readout of working memory relies on a sensory representation at a cost of increased interference by ongoing perception.
Collapse
Affiliation(s)
- Chunyue Teng
- Department of Neuroscience, Lawrence University, Appleton, WI, USA.
| | - Simon M Kaplan
- Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA
| | - Sarah Shomstein
- Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA
| | - Dwight J Kravitz
- Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA
- Directorate for Social, Behavioral, and Economic Sciences, National Science Foundation, Arlington, VA, USA
| |
Collapse
|
20
|
Ai H, Cui Y, Chen N. A "Bandwidth" in cortical representations of multiple faces. Cereb Cortex 2023; 33:10028-10035. [PMID: 37522262 DOI: 10.1093/cercor/bhad262] [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: 04/27/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 08/01/2023] Open
Abstract
The human ability to process multiple items simultaneously can be constrained by the extent to which those items are represented by distinct neural populations. In the current study, we used fMRI to investigate the cortical representation of multiple faces. We found that the addition of a second face to occupy both visual hemifields led to an increased response, whereas a further addition of faces within the same visual hemifield resulted in a decreased response. This pattern was widely observed in the occipital visual cortex, the intraparietal sulcus, and extended to the posterior inferotemporal cortex. A parallel trend was found in a behavioral change-detection task, revealing a perceptual "bandwidth" of multiface processing. The sensitivity to face clutter gradually decreased along the ventral pathway, supporting the notion of a buildup of clutter-tolerance representation. These cortical response patterns to face clutters suggest that adding signals with nonoverlapping cortical representation enhanced perception, while adding signals that competed for representation resources impaired perception.
Collapse
Affiliation(s)
- Hailin Ai
- Department of Psychology, School of Social Sciences, Tsinghua University, Haidian District, Beijing, 100084, P. R. China
| | - Yuwei Cui
- Department of Psychology, School of Social Sciences, Tsinghua University, Haidian District, Beijing, 100084, P. R. China
| | - Nihong Chen
- Department of Psychology, School of Social Sciences, Tsinghua University, Haidian District, Beijing, 100084, P. R. China
- THU-IDG/McGovern Institute for Brain Research, Tsinghua University, Haidian District, Beijing, 100084, P. R. China
| |
Collapse
|
21
|
Pinotsis DA, Miller EK. In vivo ephaptic coupling allows memory network formation. Cereb Cortex 2023; 33:9877-9895. [PMID: 37420330 PMCID: PMC10472500 DOI: 10.1093/cercor/bhad251] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/20/2023] [Accepted: 06/23/2023] [Indexed: 07/09/2023] Open
Abstract
It is increasingly clear that memories are distributed across multiple brain areas. Such "engram complexes" are important features of memory formation and consolidation. Here, we test the hypothesis that engram complexes are formed in part by bioelectric fields that sculpt and guide the neural activity and tie together the areas that participate in engram complexes. Like the conductor of an orchestra, the fields influence each musician or neuron and orchestrate the output, the symphony. Our results use the theory of synergetics, machine learning, and data from a spatial delayed saccade task and provide evidence for in vivo ephaptic coupling in memory representations.
Collapse
Affiliation(s)
- Dimitris A Pinotsis
- Department of Psychology, Centre for Mathematical Neuroscience and Psychology, University of London, London EC1V 0HB, United Kingdom
- The Picower Institute for Learning & Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| |
Collapse
|
22
|
Watanabe K, Kadohisa M, Kusunoki M, Buckley MJ, Duncan J. Cycles of goal silencing and reactivation underlie complex problem-solving in primate frontal and parietal cortex. Nat Commun 2023; 14:5054. [PMID: 37598206 PMCID: PMC10439911 DOI: 10.1038/s41467-023-40676-1] [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: 08/30/2022] [Accepted: 08/03/2023] [Indexed: 08/21/2023] Open
Abstract
While classic views proposed that working memory (WM) is mediated by sustained firing, recent evidence suggests a contribution of activity-silent states. Within WM, human neuroimaging studies suggest a switch between attentional foreground and background, with only the foregrounded item represented in active neural firing. To address this process at the cellular level, we recorded prefrontal (PFC) and posterior parietal (PPC) neurons in a complex problem-solving task, with monkeys searching for one or two target locations in a first cycle of trials, and retaining them for memory-guided revisits on subsequent cycles. When target locations were discovered, neither frontal nor parietal neurons showed sustained goal-location codes continuing into subsequent trials and cycles. Instead there were sequences of timely goal silencing and reactivation, and following reactivation, sustained states until behavioral response. With two target locations, goal representations in both regions showed evidence of transitions between foreground and background, but the PFC representation was more complete, extending beyond the current trial to include both past and future selections. In the absence of unbroken sustained codes, different neuronal states interact to support maintenance and retrieval of WM representations across successive trials.
Collapse
Affiliation(s)
- Kei Watanabe
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK.
- Graduate School of Frontier Biosciences, Osaka University, Osaka, 565-0871, Japan.
- Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan.
| | - Mikiko Kadohisa
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB2 7EF, Cambridge, UK
| | - Makoto Kusunoki
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB2 7EF, Cambridge, UK
| | - Mark J Buckley
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
| | - John Duncan
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB2 7EF, Cambridge, UK
| |
Collapse
|
23
|
Weber J, Iwama G, Solbakk AK, Blenkmann AO, Larsson PG, Ivanovic J, Knight RT, Endestad T, Helfrich R. Subspace partitioning in the human prefrontal cortex resolves cognitive interference. Proc Natl Acad Sci U S A 2023; 120:e2220523120. [PMID: 37399398 PMCID: PMC10334727 DOI: 10.1073/pnas.2220523120] [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: 12/02/2022] [Accepted: 05/31/2023] [Indexed: 07/05/2023] Open
Abstract
The human prefrontal cortex (PFC) constitutes the structural basis underlying flexible cognitive control, where mixed-selective neural populations encode multiple task features to guide subsequent behavior. The mechanisms by which the brain simultaneously encodes multiple task-relevant variables while minimizing interference from task-irrelevant features remain unknown. Leveraging intracranial recordings from the human PFC, we first demonstrate that competition between coexisting representations of past and present task variables incurs a behavioral switch cost. Our results reveal that this interference between past and present states in the PFC is resolved through coding partitioning into distinct low-dimensional neural states; thereby strongly attenuating behavioral switch costs. In sum, these findings uncover a fundamental coding mechanism that constitutes a central building block of flexible cognitive control.
Collapse
Affiliation(s)
- Jan Weber
- Hertie Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, 72076Tübingen, Germany
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, 72076Tübingen, Germany
| | - Gabriela Iwama
- Hertie Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, 72076Tübingen, Germany
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, 72076Tübingen, Germany
| | - Anne-Kristin Solbakk
- Department of Psychology, University of Oslo, 0373Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, 0373Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, 0372Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, 8657Mosjøen, Norway
| | - Alejandro O. Blenkmann
- Department of Psychology, University of Oslo, 0373Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, 0373Oslo, Norway
| | - Pal G. Larsson
- Department of Neurosurgery, Oslo University Hospital, 0372Oslo, Norway
| | - Jugoslav Ivanovic
- Department of Neurosurgery, Oslo University Hospital, 0372Oslo, Norway
| | - Robert T. Knight
- Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA94720
- Department of Psychology, UC Berkeley, Berkeley, CA94720
| | - Tor Endestad
- Department of Psychology, University of Oslo, 0373Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, 0373Oslo, Norway
| | - Randolph Helfrich
- Hertie Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, 72076Tübingen, Germany
| |
Collapse
|
24
|
Yan C, Christophel TB, Allefeld C, Haynes JD. Categorical working memory codes in human visual cortex. Neuroimage 2023; 274:120149. [PMID: 37191658 DOI: 10.1016/j.neuroimage.2023.120149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 04/24/2023] [Accepted: 04/29/2023] [Indexed: 05/17/2023] Open
Abstract
Working memory contents are represented in neural activity patterns across multiple regions of the cortical hierarchy. A division of labor has been proposed where more anterior regions harbor increasingly abstract and categorical representations while the most detailed representations are held in primary sensory cortices. Here, using fMRI and multivariate encoding modeling, we demonstrate that for color stimuli categorical codes are already present at the level of extrastriate visual cortex (V4 and VO1), even when subjects are neither implicitly nor explicitly encouraged to categorize the stimuli. Importantly, this categorical coding was observed during working memory, but not during perception. Thus, visual working memory is likely to rely at least in part on categorical representations. SIGNIFICANCE STATEMENT: Working memory is the representational basis for human cognition. Recent work has demonstrated that numerous regions across the human brain can represent the contents of working memory. We use fMRI brain scanning and machine learning methods to demonstrate that different regions can represent the same content differently during working memory. Reading out the neural codes used to store working memory contents, we show that already in sensory cortex, areas V4 and VO1 represent color in a categorical format rather than a purely sensory fashion. Thereby, we provide a better understanding of how different regions of the brain might serve working memory and cognition.
Collapse
Affiliation(s)
- Chang Yan
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging and Clinic for Neurology, Charité Universitätsmedizin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Philippstraße 13, Haus 6, 10115, Germany
| | - Thomas B Christophel
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging and Clinic for Neurology, Charité Universitätsmedizin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Philippstraße 13, Haus 6, 10115, Germany; Department of Psychology, Humboldt Universität zu Berlin, Rudower Chaussee 18, Berlin, 12489, Germany.
| | - Carsten Allefeld
- Department of Psychology, City, University of London, London EC1V 0HB, United Kingdom
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging and Clinic for Neurology, Charité Universitätsmedizin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Philippstraße 13, Haus 6, 10115, Germany; Department of Psychology, Humboldt Universität zu Berlin, Rudower Chaussee 18, Berlin, 12489, Germany; Berlin School of Mind and Brain, Humboldt Universität, Berlin, Luisenstraße 56, Haus 1, Berlin, 10099, Germany; Cluster of Excellence NeuroCure, Charité Universitätsmedizin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Charitéplatz 1, Hufelandweg 14, Berlin, 10117, Germany; SFB 940 Volition and Cognitive Control, Technische Universität Dresden, Zellescher Weg 17, 01069 Dresden, Germany
| |
Collapse
|
25
|
Katz LN, Yu G, Herman JP, Krauzlis RJ. Correlated variability in primate superior colliculus depends on functional class. Commun Biol 2023; 6:540. [PMID: 37202508 DOI: 10.1038/s42003-023-04912-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Correlated variability in neuronal activity (spike count correlations, rSC) can constrain how information is read out from populations of neurons. Traditionally, rSC is reported as a single value summarizing a brain area. However, single values, like summary statistics, stand to obscure underlying features of the constituent elements. We predict that in brain areas containing distinct neuronal subpopulations, different subpopulations will exhibit distinct levels of rSC that are not captured by the population rSC. We tested this idea in macaque superior colliculus (SC), a structure containing several functional classes (i.e., subpopulations) of neurons. We found that during saccade tasks, different functional classes exhibited differing degrees of rSC. "Delay class" neurons displayed the highest rSC, especially during saccades that relied on working memory. Such dependence of rSC on functional class and cognitive demand underscores the importance of taking functional subpopulations into account when attempting to model or infer population coding principles.
Collapse
Affiliation(s)
- Leor N Katz
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA.
| | - Gongchen Yu
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
| | - James P Herman
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, 15219, USA
| | - Richard J Krauzlis
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
| |
Collapse
|
26
|
Pinotsis DA, Fridman G, Miller EK. Cytoelectric Coupling: Electric fields sculpt neural activity and "tune" the brain's infrastructure. Prog Neurobiol 2023; 226:102465. [PMID: 37210066 DOI: 10.1016/j.pneurobio.2023.102465] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023]
Abstract
We propose and present converging evidence for the Cytoelectric Coupling Hypothesis: Electric fields generated by neurons are causal down to the level of the cytoskeleton. This could be achieved via electrodiffusion and mechanotransduction and exchanges between electrical, potential and chemical energy. Ephaptic coupling organizes neural activity, forming neural ensembles at the macroscale level. This information propagates to the neuron level, affecting spiking, and down to molecular level to stabilize the cytoskeleton, "tuning" it to process information more efficiently.
Collapse
Affiliation(s)
- Dimitris A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Gene Fridman
- Departments of Otolaryngology, Biomedical Engineering, and Electrical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| |
Collapse
|
27
|
Abdalaziz M, Redding ZV, Fiebelkorn IC. Rhythmic temporal coordination of neural activity prevents representational conflict during working memory. Curr Biol 2023; 33:1855-1863.e3. [PMID: 37100058 DOI: 10.1016/j.cub.2023.03.088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/27/2023] [Accepted: 03/31/2023] [Indexed: 04/28/2023]
Abstract
Selective attention1 is characterized by alternating states associated with either attentional sampling or attentional shifting, helping to prevent functional conflicts by isolating function-specific neural activity in time.2,3,4,5 We hypothesized that such rhythmic temporal coordination might also help to prevent representational conflicts during working memory.6 Multiple items can be simultaneously held in working memory, and these items can be represented by overlapping neural populations.7,8,9 Traditional theories propose that the short-term storage of to-be-remembered items occurs through persistent neural activity,10,11,12 but when neurons are simultaneously representing multiple items, persistent activity creates a potential for representational conflicts. In comparison, more recent, "activity-silent" theories of working memory propose that synaptic changes also contribute to short-term storage of to-be-remembered items.13,14,15,16 Transient bursts in neural activity,17 rather than persistent activity, could serve to occasionally refresh these synaptic changes. Here, we used EEG and response times to test whether rhythmic temporal coordination helps to isolate neural activity associated with different to-be-remembered items, thereby helping to prevent representational conflicts. Consistent with this hypothesis, we report that the relative strength of different item representations alternates over time as a function of the frequency-specific phase. Although RTs were linked to theta (∼6 Hz) and beta (∼25 Hz) phases during a memory delay, the relative strength of item representations only alternated as a function of the beta phase. The present findings (1) are consistent with rhythmic temporal coordination being a general mechanism for preventing functional or representational conflicts during cognitive processes and (2) inform models describing the role of oscillatory dynamics in organizing working memory.13,18,19,20,21.
Collapse
Affiliation(s)
- Miral Abdalaziz
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA
| | - Zach V Redding
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA
| | - Ian C Fiebelkorn
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA.
| |
Collapse
|
28
|
Hanganu-Opatz IL, Klausberger T, Sigurdsson T, Nieder A, Jacob SN, Bartos M, Sauer JF, Durstewitz D, Leibold C, Diester I. Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective. Neuron 2023; 111:1020-1036. [PMID: 37023708 DOI: 10.1016/j.neuron.2023.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/15/2023] [Accepted: 03/10/2023] [Indexed: 04/08/2023]
Abstract
The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.
Collapse
Affiliation(s)
- Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Thomas Klausberger
- Center for Brain Research, Division of Cognitive Neurobiology, Medical University of Vienna, Vienna, Austria
| | - Torfi Sigurdsson
- Institute of Neurophysiology, Goethe University, Frankfurt, Germany
| | - Andreas Nieder
- Animal Physiology Unit, Institute of Neurobiology, University of Tübingen, 72076 Tübingen, Germany
| | - Simon N Jacob
- Translational Neurotechnology Laboratory, Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marlene Bartos
- Institute for Physiology I, Medical Faculty, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jonas-Frederic Sauer
- Institute for Physiology I, Medical Faculty, University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health & Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Christian Leibold
- Faculty of Biology, Bernstein Center Freiburg, BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany
| | - Ilka Diester
- Optophysiology - Optogenetics and Neurophysiology, IMBIT // BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany.
| |
Collapse
|
29
|
Nomoto M, Murayama E, Ohno S, Okubo-Suzuki R, Muramatsu SI, Inokuchi K. Hippocampus as a sorter and reverberatory integrator of sensory inputs. Nat Commun 2022; 13:7413. [PMID: 36539403 PMCID: PMC9768143 DOI: 10.1038/s41467-022-35119-2] [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: 03/13/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022] Open
Abstract
The hippocampus must be capable of sorting and integrating multiple sensory inputs separately but simultaneously. However, it remains to be elucidated how the hippocampus executes these processes simultaneously during learning. Here we found that synchrony between conditioned stimulus (CS)-, unconditioned stimulus (US)- and future retrieval-responsible cells occurs in the CA1 during the reverberatory phase that emerges after sensory inputs have ceased, but not during CS and US inputs. Mutant mice lacking N-methyl-D-aspartate receptors (NRs) in CA3 showed a cued-fear memory impairment and a decrease in synchronized reverberatory activities between CS- and US-responsive CA1 cells. Optogenetic CA3 silencing at the reverberatory phase during learning impaired cued-fear memory. Thus, the hippocampus uses reverberatory activity to link CS and US inputs, and avoid crosstalk during sensory inputs.
Collapse
Affiliation(s)
- Masanori Nomoto
- grid.267346.20000 0001 2171 836XResearch Centre for Idling Brain Science, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XDepartment of Biochemistry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XCREST, JST, University of Toyama, Toyama, 930−0194 Japan
| | - Emi Murayama
- grid.267346.20000 0001 2171 836XResearch Centre for Idling Brain Science, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XDepartment of Biochemistry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XCREST, JST, University of Toyama, Toyama, 930−0194 Japan
| | - Shuntaro Ohno
- grid.267346.20000 0001 2171 836XResearch Centre for Idling Brain Science, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XDepartment of Biochemistry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XCREST, JST, University of Toyama, Toyama, 930−0194 Japan
| | - Reiko Okubo-Suzuki
- grid.267346.20000 0001 2171 836XResearch Centre for Idling Brain Science, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XDepartment of Biochemistry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XCREST, JST, University of Toyama, Toyama, 930−0194 Japan
| | - Shin-ichi Muramatsu
- grid.410804.90000000123090000Division of Neurology, Department of Medicine, Jichi Medical University, Tochigi, 329−0498 Japan ,grid.26999.3d0000 0001 2151 536XCenter for Gene and Cell Therapy, The Institute of Medical Science, The University of Tokyo, Tokyo, 108−8639 Japan
| | - Kaoru Inokuchi
- grid.267346.20000 0001 2171 836XResearch Centre for Idling Brain Science, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XDepartment of Biochemistry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, 930−0194 Japan ,grid.267346.20000 0001 2171 836XCREST, JST, University of Toyama, Toyama, 930−0194 Japan
| |
Collapse
|
30
|
Kirschhock ME, Nieder A. Number selective sensorimotor neurons in the crow translate perceived numerosity into number of actions. Nat Commun 2022; 13:6913. [PMID: 36376297 PMCID: PMC9663431 DOI: 10.1038/s41467-022-34457-5] [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: 03/04/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
Translating a perceived number into a matching number of self-generated actions is a hallmark of numerical reasoning in humans and animals alike. To explore this sensorimotor transformation, we trained crows to judge numerical values in displays and to flexibly plan and perform a matching number of pecks. We report number selective sensorimotor neurons in the crow telencephalon that signaled the impending number of self-generated actions. Neuronal population activity during the sensorimotor transformation period predicted whether the crows mistakenly planned fewer or more pecks than instructed. During sensorimotor transformation, both a static neuronal code characterized by persistently number-selective neurons and a dynamic code originating from neurons carrying rapidly changing numerical information emerged. The findings indicate there are distinct functions of abstract neuronal codes supporting the sensorimotor number system.
Collapse
Affiliation(s)
- Maximilian E. Kirschhock
- grid.10392.390000 0001 2190 1447Animal Physiology Unit, Institute of Neurobiology, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Andreas Nieder
- grid.10392.390000 0001 2190 1447Animal Physiology Unit, Institute of Neurobiology, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| |
Collapse
|
31
|
Hahn LA, Balakhonov D, Lundqvist M, Nieder A, Rose J. Oscillations without cortex: Working memory modulates brainwaves in the endbrain of crows. Prog Neurobiol 2022; 219:102372. [DOI: 10.1016/j.pneurobio.2022.102372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/21/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
|
32
|
Dudyrev E, Semenkov I, Kuznetsov SO, Gusev G, Sharp A, Pianykh OS. Human knowledge models: Learning applied knowledge from the data. PLoS One 2022; 17:e0275814. [PMID: 36264864 PMCID: PMC9584406 DOI: 10.1371/journal.pone.0275814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 09/25/2022] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of "Human Knowledge Models" (HKMs), designed to reproduce human computational abilities. Departing from a vast body of cognitive research, we formalized the definition of HKMs into a new form of machine learning. Then, by training the models with human processing capabilities, we learned human-like knowledge, that humans can not only understand, but also compute, modify, and apply. We used several datasets from different applied fields to demonstrate the advantages of HKMs, including their high predictive power and resistance to noise and overfitting. Our results proved that HKMs can efficiently mine knowledge directly from the data and can compete with complex AI models in explaining the main data patterns. As a result, our study reveals the great potential of HKMs, particularly in the decision-making applications where "black box" models cannot be accepted. Moreover, this improves our understanding of how well human decision-making, modeled by HKMs, can approach the ideal solutions in real-life problems.
Collapse
Affiliation(s)
| | - Ilia Semenkov
- HSE University, Moscow, Russia
- Artificial Intelligence Research Institute, Moscow, Russia
| | | | - Gleb Gusev
- Artificial Intelligence Research Institute, Moscow, Russia
| | - Andrew Sharp
- Medical Analytics Group, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Oleg S. Pianykh
- Medical Analytics Group, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
| |
Collapse
|
33
|
Bocincova A, Buschman TJ, Stokes MG, Manohar SG. Neural signature of flexible coding in prefrontal cortex. Proc Natl Acad Sci U S A 2022; 119:e2200400119. [PMID: 36161948 PMCID: PMC9546590 DOI: 10.1073/pnas.2200400119] [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: 01/09/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
The ability of prefrontal cortex to quickly encode novel associations is crucial for adaptive behavior and central to working memory. Fast Hebbian changes in synaptic strength permit forming new associations, but neuronal signatures of this have been elusive. We devised a trialwise index of pattern similarity to look for rapid changes in population codes. Based on a computational model of working memory, we hypothesized that synaptic strength-and consequently, the tuning of neurons-could change if features of a subsequent stimulus need to be "reassociated," i.e., if bindings between features need to be broken to encode the new item. As a result, identical stimuli might elicit different neural responses. As predicted, neural response similarity dropped following rebinding, but only in prefrontal cortex. The history-dependent changes were expressed on top of traditional, fixed selectivity and were not explainable by carryover of previous firing into the current trial or by neural adaptation.
Collapse
Affiliation(s)
- Andrea Bocincova
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Timothy J. Buschman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540
- Department of Psychology, Princeton University, Princeton, NJ 08540
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Sanjay G. Manohar
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
| |
Collapse
|
34
|
Dynamic and stable population coding of attentional instructions coexist in the prefrontal cortex. Proc Natl Acad Sci U S A 2022; 119:e2202564119. [PMID: 36161937 DOI: 10.1073/pnas.2202564119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A large body of recent work suggests that neural representations in prefrontal cortex (PFC) are changing over time to adapt to task demands. However, it remains unclear whether and how such dynamic coding schemes depend on the encoded variable and are influenced by anatomical constraints. Using a cued attention task and multivariate classification methods, we show that neuronal ensembles in PFC encode and retain in working memory spatial and color attentional instructions in an anatomically specific manner. Spatial instructions could be decoded both from the frontal eye field (FEF) and the ventrolateral PFC (vlPFC) population, albeit more robustly from FEF, whereas color instructions were decoded more robustly from vlPFC. Decoding spatial and color information from vlPFC activity in the high-dimensional state space indicated stronger dynamics for color, across the cue presentation and memory periods. The change in the color code was largely due to rapid changes in the network state during the transition to the delay period. However, we found that dynamic vlPFC activity contained time-invariant color information within a low-dimensional subspace of neural activity that allowed for stable decoding of color across time. Furthermore, spatial attention influenced decoding of stimuli features profoundly in vlPFC, but less so in visual area V4. Overall, our results suggest that dynamic population coding of attentional instructions within PFC is shaped by anatomical constraints and can coexist with stable subspace coding that allows time-invariant decoding of information about the future target.
Collapse
|
35
|
Fritz B, King C, Chen Y, Kronzer A, Abraham J, Ben Abdallah A, Kannampallil T, Budelier T, Montes de Oca A, McKinnon S, Tellor Pennington B, Wildes T, Avidan M. Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study. F1000Res 2022; 11:653. [PMID: 37547785 PMCID: PMC10397896 DOI: 10.12688/f1000research.122286.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/22/2022] [Indexed: 08/08/2023] Open
Abstract
Background: More than four million people die each year in the month following surgery, and many more experience complications such as acute kidney injury. Some of these outcomes may be prevented through early identification of at-risk patients and through intraoperative risk mitigation. Telemedicine has revolutionized the way at-risk patients are identified in critical care, but intraoperative telemedicine services are not widely used in anesthesiology. Clinicians in telemedicine settings may assist with risk stratification and brainstorm risk mitigation strategies while clinicians in the operating room are busy performing other patient care tasks. Machine learning tools may help clinicians in telemedicine settings leverage the abundant electronic health data available in the perioperative period. The primary hypothesis for this study is that anesthesiology clinicians can predict postoperative complications more accurately with machine learning assistance than without machine learning assistance. Methods: This investigation is a sub-study nested within the TECTONICS randomized clinical trial (NCT03923699). As part of TECTONICS, study team members who are anesthesiology clinicians working in a telemedicine setting are currently reviewing ongoing surgical cases and documenting how likely they feel the patient is to experience 30-day in-hospital death or acute kidney injury. For patients who are included in this sub-study, these case reviews will be randomized to be performed with access to a display showing machine learning predictions for the postoperative complications or without access to the display. The accuracy of the predictions will be compared across these two groups. Conclusion: Successful completion of this study will help define the role of machine learning not only for intraoperative telemedicine, but for other risk assessment tasks before, during, and after surgery. Registration: ORACLE is registered on ClinicalTrials.gov: NCT05042804; registered September 13, 2021.
Collapse
Affiliation(s)
- Bradley Fritz
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Christopher King
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University McKelvey School of Engineering, St. Louis, Missouri, 63130, USA
| | - Alex Kronzer
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Thaddeus Budelier
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Arianna Montes de Oca
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Sherry McKinnon
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Bethany Tellor Pennington
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Troy Wildes
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Michael Avidan
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| |
Collapse
|
36
|
Chen YT, van Ede F, Kuo BC. Alpha Oscillations Track Content-Specific Working Memory Capacity. J Neurosci 2022; 42:7285-7293. [PMID: 35995565 PMCID: PMC9512572 DOI: 10.1523/jneurosci.2296-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 11/21/2022] Open
Abstract
Although the neural basis of working memory (WM) capacity is often studied by exploiting interindividual differences, capacity may also differ across memory materials within a given individual. Here, we exploit the content dependence of WM capacity as a novel approach to investigate the oscillatory correlates of WM capacity, focusing on posterior 9-12 Hz alpha activity during retention. We recorded scalp electroencephalography (EEG) while male and female human participants performed WM tasks with varying memory loads (two vs. four items) and materials (English letters vs. regular shapes vs. abstract shapes). First, behavioral data confirmed that memory capacity was fundamentally content dependent; capacity for abstract shapes plateaued at around two, whereas the participants could remember more letters and regular shapes. Critically, content-specific capacity was paralleled in the degree of attenuation of EEG-alpha activity that plateaued in a similar content-specific manner. Although we observed greater alpha attenuation for higher loads for all materials, we found larger load effects for letters and regular shapes than for abstract shapes, which is consistent with our behavioral data showing a lower capacity plateau for abstract shapes. Moreover, when only considering two-item trials, alpha attenuation was greater for abstract shapes where two items were close to the capacity plateau than for other materials. Multivariate decoding of alpha activity patterns reinforced these findings. Finally, for each material, load effects on capacity (K) and alpha attenuation were correlated across individuals. Our results demonstrate that alpha oscillations track memory capacity in a content-specific manner and track not just the number of items but also their complexity.SIGNIFICANCE STATEMENT WM is limited in its capacity. We show that capacity is not fixed for an individual but is rather memory-content dependent. Moreover, we used this as a novel approach to investigate the neural basis of WM capacity with EEG. We found that both behavioral capacity estimates and neural oscillations in the alpha band varied with memory loads and materials. The critical finding is a capacity plateau of approximately two items only for the more complex materials, accompanied by a similar plateau in the EEG alpha attenuation. The load effects on capacity and alpha attenuation were furthermore correlated across individuals for each of the materials. Our results demonstrate that alpha oscillations track the content-specific nature of WM capacity.
Collapse
Affiliation(s)
- Ya-Ting Chen
- Department of Psychology, National Taiwan University, Taipei 10617, Taiwan
| | - Freek van Ede
- Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, 1081BT Amsterdam, The Netherlands
| | - Bo-Cheng Kuo
- Department of Psychology, National Taiwan University, Taipei 10617, Taiwan
| |
Collapse
|
37
|
Erez Y, Kadohisa M, Petrov P, Sigala N, Buckley MJ, Kusunoki M, Duncan J. Integrated neural dynamics for behavioural decisions and attentional competition in the prefrontal cortex. Eur J Neurosci 2022; 56:4393-4410. [PMID: 35781352 DOI: 10.1111/ejn.15757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 11/27/2022]
Abstract
In the behaving monkey, complex neural dynamics in the prefrontal cortex contribute to context-dependent decisions and attentional competition. We used demixed principal component analysis to track prefrontal activity dynamics in a cued target detection task. In this task, the animal combined identity of a visual object with a prior instruction cue to determine a target/nontarget decision. From population activity, we extracted principal components for each task feature and examined their time course and sensitivity to stimulus and task variations. For displays containing a single choice object in left or right hemifield, object identity, cue identity and decision were all encoded in population activity, with different dynamics and lateralisation. Object information peaked at 100-200 ms from display onset and was largely confined to the contralateral hemisphere. Cue information was weaker and present even prior to display onset. Integrating information from cue and object, decision information arose more slowly and was bilateral. Individual neurons contributed independently to coding of the three task features. The analysis was then extended to displays with a target in one hemifield and a competing distractor in the other. In this case, the data suggest that each hemisphere initially encoded the identity of the contralateral object. The distractor representation was then rapidly suppressed, with the final target decision again encoded bilaterally. The results show how information is coded along task-related dimensions while competition is resolved and suggest how information flows within and across frontal lobes to implement a learned behavioural decision.
Collapse
Affiliation(s)
- Yaara Erez
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Mikiko Kadohisa
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Philippe Petrov
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Natasha Sigala
- Brighton and Sussex Medical School, Brighton, UK
- Sackler Center for Consciousness Science, University of Sussex, Brighton, UK
| | - Mark J Buckley
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Makoto Kusunoki
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| |
Collapse
|
38
|
Jiang LX, Huang GD, Wang HL, Zhang C, Yu X. The protocol for assessing olfactory working memory capacity in mice. Brain Behav 2022; 12:e2703. [PMID: 35849713 PMCID: PMC9392537 DOI: 10.1002/brb3.2703] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/04/2022] [Accepted: 06/27/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Working memory capacity (WMC) is the ability to maintain information over a few seconds. Although it has been extensively studied in healthy subjects and neuropsychiatric patients, few tasks have been developed to measure such changes in rodents. Many procedures have been used to measure WM in rodents, including the radial arm maze, the WM version of the Morris swimming task, and various delayed matching and nonmatching-to-sample tasks. It should be noted, however, that the memory components assessed in these procedures do not include memory capacity. METHODS We developed an olfactory working memory capacity (OWMC) paradigm to assess the WMC of 3-month-old 5×FAD mice, a mouse model of Alzheimer's disease. The task is divided into five phases: context adaptation, digging training, rule learning for nonmatching to a single sample odor (NMSS), rule learning for nonmatching to multiple sample odors (NMMS), and capacity testing. RESULTS In the NMSS rule-learning phase, there was no difference between wild-type (WT) mice and 5×FAD mice in the performance correct rate, correct option rate, and correct rejection rate. The WT mice and 5×FAD mice showed similar memory capacity in the NMMS rule-learning phase. After capacity test, we found that the WMC was significantly diminished in 5×FAD mice. As the memory load increased, 5×FAD mice also made significantly more errors than WT mice. CONCLUSION The OWMC task, based on a nonmatch-to-sample rule, is a sensitive and robust behavioral assay that we validated as a reliable method for measuring WMC and exploring different components of memory in mice.
Collapse
Affiliation(s)
- Li-Xin Jiang
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China.,National Clinical Research Center for Mental Disorders & NHC Key Laboratory of Mental Health (Peking University), Beijing, China.,Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
| | - Geng-Di Huang
- Peking University Shenzhen Graduate School, Shenzhen, China.,Shenzhen Kangning Hospital & Shenzhen Mental Health Center, Shenzhen, China
| | - Hua-Li Wang
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China.,National Clinical Research Center for Mental Disorders & NHC Key Laboratory of Mental Health (Peking University), Beijing, China.,Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
| | - Chen Zhang
- Capital Medical University, Youanmenwai, Beijing, China
| | - Xin Yu
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China.,National Clinical Research Center for Mental Disorders & NHC Key Laboratory of Mental Health (Peking University), Beijing, China.,Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
| |
Collapse
|
39
|
Arlt C, Barroso-Luque R, Kira S, Bruno CA, Xia N, Chettih SN, Soares S, Pettit NL, Harvey CD. Cognitive experience alters cortical involvement in goal-directed navigation. eLife 2022; 11:76051. [PMID: 35735909 PMCID: PMC9259027 DOI: 10.7554/elife.76051] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Neural activity in the mammalian cortex has been studied extensively during decision tasks, and recent work aims to identify under what conditions cortex is actually necessary for these tasks. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same navigation decision task, revealing past learning as a critical determinant of whether cortex is necessary for goal-directed navigation. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multiarea calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron–neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in goal-directed navigation.
Collapse
Affiliation(s)
- Charlotte Arlt
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | | | - Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Carissa A Bruno
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Ningjing Xia
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Selmaan N Chettih
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Sofia Soares
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Noah L Pettit
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | | |
Collapse
|
40
|
Vedmurthy P, Pinto ALR, Lin DDM, Comi AM, Ou Y. Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber. BMJ Open 2022; 12:e053103. [PMID: 35121603 PMCID: PMC8819809 DOI: 10.1136/bmjopen-2021-053103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 01/13/2022] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Secondary analysis of hospital-hosted clinical data can save time and cost compared with prospective clinical trials for neuroimaging biomarker development. We present such a study for Sturge-Weber syndrome (SWS), a rare neurovascular disorder that affects 1 in 20 000-50 000 newborns. Children with SWS are at risk for developing neurocognitive deficit by school age. A critical period for early intervention is before 2 years of age, but early diagnostic and prognostic biomarkers are lacking. We aim to retrospectively mine clinical data for SWS at two national centres to develop presymptomatic biomarkers. METHODS AND ANALYSIS We will retrospectively collect clinical, MRI and neurocognitive outcome data for patients with SWS who underwent brain MRI before 2 years of age at two national SWS care centres. Expert review of clinical records and MRI quality control will be used to refine the cohort. The merged multisite data will be used to develop algorithms for abnormality detection, lesion-symptom mapping to identify neural substrate and machine learning to predict individual outcomes (presence or absence of seizures) by 2 years of age. Presymptomatic treatment in 0-2 years and before seizure onset may delay or prevent the onset of seizures by 2 years of age, and thereby improve neurocognitive outcomes. The proposed work, if successful, will be one of the largest and most comprehensive multisite databases for the presymptomatic phase of this rare disease. ETHICS AND DISSEMINATION This study involves human participants and was approved by Boston Children's Hospital Institutional Review Board: IRB-P00014482 and IRB-P00025916 Johns Hopkins School of Medicine Institutional Review Board: NA_00043846. Participants gave informed consent to participate in the study before taking part. The Institutional Review Boards at Kennedy Krieger Institute and Boston Children's Hospital approval have been obtained at each site to retrospectively study this data. Results will be disseminated by presentations, publication and sharing of algorithms generated.
Collapse
Affiliation(s)
- Pooja Vedmurthy
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Anna L R Pinto
- Department of Neurology, Division of Epilepsy, Harvard Medical School, Boston, Massachusetts, USA
| | - Doris D M Lin
- Neuroradiology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Anne M Comi
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology and Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital; Harvard Medical School, Boston, MA, USA
| |
Collapse
|
41
|
Zhang H, Zhen Y, Yu S, Long T, Zhang B, Jiang X, Li J, Fang W, Sigman M, Dehaene S, Wang L. Working Memory for Spatial Sequences: Developmental and Evolutionary Factors in Encoding Ordinal and Relational Structures. J Neurosci 2022; 42:850-864. [PMID: 34862186 PMCID: PMC8808738 DOI: 10.1523/jneurosci.0603-21.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 11/08/2021] [Accepted: 11/19/2021] [Indexed: 11/21/2022] Open
Abstract
Sequence learning is a ubiquitous facet of human and animal cognition. Here, using a common sequence reproduction task, we investigated whether and how the ordinal and relational structures linking consecutive elements are acquired by human adults, children, and macaque monkeys. While children and monkeys exhibited significantly lower precision than adults for spatial location and temporal order information, only monkeys appeared to exceedingly focus on the first item. Most importantly, only humans, regardless of age, spontaneously extracted the spatial relations between consecutive items and used a chunking strategy to compress sequences in working memory. Monkeys did not detect such relational structures, even after extensive training. Monkey behavior was captured by a conjunctive coding model, whereas a chunk-based conjunctive model explained more variance in humans. These age- and species-related differences are indicative of developmental and evolutionary mechanisms of sequence encoding and may provide novel insights into the uniquely human cognitive capacities.SIGNIFICANCE STATEMENT Sequence learning, the ability to encode the order of discrete elements and their relationships presented within a sequence, is a ubiquitous facet of cognition among humans and animals. By exploring sequence-processing abilities at different human developmental stages and in nonhuman primates, we found that only humans, regardless of age, spontaneously extracted the spatial relations between consecutive items and used an internal language to compress sequences in working memory. The findings provided insights into understanding the origins of sequence capabilities in humans and how they evolve through development to identify the unique aspects of human cognitive capacity, which includes the comprehension, learning, and production of sequences, and perhaps, above all, language processing.
Collapse
Affiliation(s)
- He Zhang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Yanfen Zhen
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Shijing Yu
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Tenghai Long
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Bingqian Zhang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, People's Republic of China
| | - Xinjian Jiang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Junru Li
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Wen Fang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Mariano Sigman
- Laboratory Neuroscience, Universidad Torcuato di Tella, C1428 Buenos Aires, Argentina
- School of Language and Education, Universidad Nebrija, 28015 Madrid, Spain
| | - Stanislas Dehaene
- Collège de France, 75231 Paris Cedex 05, France
- Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, NeuroSpin Center, Université Paris Sud/Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Liping Wang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| |
Collapse
|
42
|
Kutter EF, Boström J, Elger CE, Nieder A, Mormann F. Neuronal codes for arithmetic rule processing in the human brain. Curr Biol 2022; 32:1275-1284.e4. [DOI: 10.1016/j.cub.2022.01.054] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 12/20/2021] [Accepted: 01/20/2022] [Indexed: 11/25/2022]
|
43
|
Rezayat E, Clark K, Dehaqani MRA, Noudoost B. Dependence of Working Memory on Coordinated Activity Across Brain Areas. Front Syst Neurosci 2022; 15:787316. [PMID: 35095433 PMCID: PMC8792503 DOI: 10.3389/fnsys.2021.787316] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/06/2021] [Indexed: 11/15/2022] Open
Abstract
Neural signatures of working memory (WM) have been reported in numerous brain areas, suggesting a distributed neural substrate for memory maintenance. In the current manuscript we provide an updated review of the literature focusing on intracranial neurophysiological recordings during WM in primates. Such signatures of WM include changes in firing rate or local oscillatory power within an area, along with measures of coordinated activity between areas based on synchronization between oscillations. In comparing the ability of various neural signatures in any brain area to predict behavioral performance, we observe that synchrony between areas is more frequently and robustly correlated with WM performance than any of the within-area neural signatures. We further review the evidence for alteration of inter-areal synchrony in brain disorders, consistent with an important role for such synchrony during behavior. Additionally, results of causal studies indicate that manipulating synchrony across areas is especially effective at influencing WM task performance. Each of these lines of research supports the critical role of inter-areal synchrony in WM. Finally, we propose a framework for interactions between prefrontal and sensory areas during WM, incorporating a range of experimental findings and offering an explanation for the observed link between intra-areal measures and WM performance.
Collapse
Affiliation(s)
- Ehsan Rezayat
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Kelsey Clark
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Mohammad-Reza A. Dehaqani
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
- *Correspondence: Behrad Noudoost,
| |
Collapse
|
44
|
Lyu N, Hu Y, Zhang J, Lloyd H, Sun YH, Tao Y. Switching costs in stochastic environments drive the emergence of matching behaviour in animal decision-making through the promotion of reward learning strategies. Sci Rep 2021; 11:23593. [PMID: 34880339 PMCID: PMC8654859 DOI: 10.1038/s41598-021-02979-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 11/23/2021] [Indexed: 11/18/2022] Open
Abstract
A principle of choice in animal decision-making named probability matching (PM) has long been detected in animals, and can arise from different decision-making strategies. Little is known about how environmental stochasticity may influence the switching time of these different decision-making strategies. Here we address this problem using a combination of behavioral and theoretical approaches, and show, that although a simple Win-Stay-Loss-Shift (WSLS) strategy can generate PM in binary-choice tasks theoretically, budgerigars (Melopsittacus undulates) actually apply a range of sub-tactics more often when they are expected to make more accurate decisions. Surprisingly, budgerigars did not get more rewards than would be predicted when adopting a WSLS strategy, and their decisions also exhibited PM. Instead, budgerigars followed a learning strategy based on reward history, which potentially benefits individuals indirectly from paying lower switching costs. Furthermore, our data suggest that more stochastic environments may promote reward learning through significantly less switching. We suggest that switching costs driven by the stochasticity of an environmental niche can potentially represent an important selection pressure associated with decision-making that may play a key role in driving the evolution of complex cognition in animals.
Collapse
Affiliation(s)
- Nan Lyu
- Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China.
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China.
| | - Yunbiao Hu
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Jiahua Zhang
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Huw Lloyd
- Department of Natural Sciences, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UK
| | - Yue-Hua Sun
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China.
| | - Yi Tao
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China.
| |
Collapse
|
45
|
Hahn LA, Balakhonov D, Fongaro E, Nieder A, Rose J. Working memory capacity of crows and monkeys arises from similar neuronal computations. eLife 2021; 10:72783. [PMID: 34859781 PMCID: PMC8660017 DOI: 10.7554/elife.72783] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows (Corvus corone) in a change detection task, developed for monkeys (Macaca mulatta), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here, we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.
Collapse
Affiliation(s)
- Lukas Alexander Hahn
- Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Dmitry Balakhonov
- Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Erica Fongaro
- Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Andreas Nieder
- Animal Physiology, Institute of Neurobiology, University of Tübingen, Tübingen, Germany
| | - Jonas Rose
- Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| |
Collapse
|
46
|
Yang AI, Dikecligil GN, Jiang H, Das SR, Stein JM, Schuele SU, Rosenow JM, Davis KA, Lucas TH, Gottfried JA. The what and when of olfactory working memory in humans. Curr Biol 2021; 31:4499-4511.e8. [PMID: 34450088 DOI: 10.1016/j.cub.2021.08.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 06/15/2021] [Accepted: 08/02/2021] [Indexed: 12/31/2022]
Abstract
Encoding and retaining novel sequences of sensory stimuli in working memory is crucial for adaptive behavior. A fundamental challenge for the central nervous system is to maintain each sequence item in an active and discriminable state, while also preserving their temporal context. Nested neural oscillations have been postulated to disambiguate the "what" and "when" of sequences, but the mechanisms by which these multiple streams of information are coordinated in the human brain remain unclear. Drawing from foundational animal studies, we recorded local field potentials from the human piriform cortex and hippocampus during a working memory task in which subjects experienced sequences of three distinct odors. Our data revealed a unique organization of odor memories across multiple timescales of the theta rhythm. During encoding, odors elicited greater gamma at distinct theta phases in both regions, time stamping their positions in the sequence, whereby the robustness of this effect was predictive of temporal order memory. During maintenance, stimulus-driven patterns of theta-coupled gamma were spontaneously reinstated in piriform cortex, recapitulating the order of the initial sequence. Replay events were time compressed across contiguous theta cycles, coinciding with periods of enhanced piriform-hippocampal theta-phase synchrony, and their prevalence forecasted subsequent recall accuracy on a trial-by-trial basis. Our data provide a novel link between endogenous replay orchestrated by the theta rhythm and short-term retention of sequential memories in the human brain.
Collapse
Affiliation(s)
- Andrew I Yang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Gulce N Dikecligil
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heidi Jiang
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sandhitsu R Das
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephan U Schuele
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Joshua M Rosenow
- Department of Neurosurgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jay A Gottfried
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
47
|
Abstract
Working memory (WM) is the ability to maintain and manipulate information in the conscious mind over a timescale of seconds. This ability is thought to be maintained through the persistent discharges of neurons in a network of brain areas centered on the prefrontal cortex, as evidenced by neurophysiological recordings in nonhuman primates, though both the localization and the neural basis of WM has been a matter of debate in recent years. Neural correlates of WM are evident in species other than primates, including rodents and corvids. A specialized network of excitatory and inhibitory neurons, aided by neuromodulatory influences of dopamine, is critical for the maintenance of neuronal activity. Limitations in WM capacity and duration, as well as its enhancement during development, can be attributed to properties of neural activity and circuits. Changes in these factors can be observed through training-induced improvements and in pathological impairments. WM thus provides a prototypical cognitive function whose properties can be tied to the spiking activity of brain neurons. © 2021 American Physiological Society. Compr Physiol 11:1-41, 2021.
Collapse
Affiliation(s)
- Russell J Jaffe
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Christos Constantinidis
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Neuroscience Program, Vanderbilt University, Nashville, Tennessee, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| |
Collapse
|
48
|
Zhao YJ, Kay KN, Tian Y, Ku Y. Sensory Recruitment Revisited: Ipsilateral V1 Involved in Visual Working Memory. Cereb Cortex 2021; 32:1470-1479. [PMID: 34476462 DOI: 10.1093/cercor/bhab300] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022] Open
Abstract
The "sensory recruitment hypothesis" posits an essential role of sensory cortices in working memory, beyond the well-accepted frontoparietal areas. Yet, this hypothesis has recently been challenged. In the present study, participants performed a delayed orientation recall task while high-spatial-resolution 3 T functional magnetic resonance imaging (fMRI) signals were measured in posterior cortices. A multivariate inverted encoding model approach was used to decode remembered orientations based on blood oxygen level-dependent fMRI signals from visual cortices during the delay period. We found that not only did activity in the contralateral primary visual cortex (V1) retain high-fidelity representations of the visual stimuli, but activity in the ipsilateral V1 also contained such orientation tuning. Moreover, although the encoded tuning was faded in the contralateral V1 during the late delay period, tuning information in the ipsilateral V1 remained sustained. Furthermore, the ipsilateral representation was presented in secondary visual cortex (V2) as well, but not in other higher-level visual areas. These results thus supported the sensory recruitment hypothesis and extended it to the ipsilateral sensory areas, which indicated the distributed involvement of visual areas in visual working memory.
Collapse
Affiliation(s)
- Yi-Jie Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Center for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.,Peng Cheng Laboratory, Shenzhen 518055, China.,School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Kendrick N Kay
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yonghong Tian
- Peng Cheng Laboratory, Shenzhen 518055, China.,School of Electronic Engineering and Computer Science, Peking University, Beijing 100871, China
| | - Yixuan Ku
- Center for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.,Peng Cheng Laboratory, Shenzhen 518055, China
| |
Collapse
|
49
|
Hartikainen KM. Emotion-Attention Interaction in the Right Hemisphere. Brain Sci 2021; 11:1006. [PMID: 34439624 PMCID: PMC8394055 DOI: 10.3390/brainsci11081006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/16/2021] [Accepted: 07/19/2021] [Indexed: 11/16/2022] Open
Abstract
Hemispheric asymmetries in affective and cognitive functions have been extensively studied. While both cerebral hemispheres contribute to most affective and cognitive processes, neuroscientific literature and neuropsychological evidence support an overall right hemispheric dominance for emotion, attention and arousal. Emotional stimuli, especially those with survival value such as threat, tend to be prioritized in attentional resource competition. Arousing unpleasant emotional stimuli have prioritized access, especially to right-lateralized attention networks. Interference of task performance may be observed when limited resources are exhausted by task- and emotion-related processing. Tasks that rely on right hemisphere-dependent processing, like attending to the left visual hemifield or global-level visual features, are especially vulnerable to interference due to attention capture by unpleasant emotional stimuli. The aim of this review is to present literature regarding the special role of the right hemisphere in affective and attentional brain processes and their interaction. Furthermore, clinical and technological implications of this interaction will be presented. Initially, the effects of focal right hemisphere lesion or atrophy on emotional functions will be introduced. Neurological right hemisphere syndromes including aprosodia, anosognosia and neglect, which further point to the predominance of the intact right hemisphere in emotion, attention and arousal will be presented. Then there will be a brief review of electrophysiological evidence, as well as evidence from patients with neglect that support attention capture by emotional stimuli in the right hemisphere. Subsequently, experimental work on the interaction of emotion, attention and cognition in the right hemispheres of healthy subjects will be presented. Finally, clinical implications for better understanding and assessment of alterations in emotion-attention interaction due to brain disorder or treatment, such as neuromodulation, that impact affective brain functions will be discussed. It will be suggested that measuring right hemispheric emotion-attention interactions may provide basis for novel biomarkers of brain health. Such biomarkers allow for improved diagnostics in brain damage and disorders and optimized treatments. To conclude, future technological applications will be outlined regarding brain physiology-based measures that reflect engagement of the right hemisphere in affective and attentional processes.
Collapse
Affiliation(s)
- Kaisa M. Hartikainen
- Behavioral Neurology Research Unit, Tampere University Hospital, 33521 Tampere, Finland; or
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland
| |
Collapse
|
50
|
Tokuhara H, Fujita K, Kashimori Y. Neural Mechanisms of Maintenance and Manipulation of Information of Temporal Sequences in Working Memory. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09907-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|