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The relationship between cognitive ability and BOLD activation across sleep-wake states. Brain Imaging Behav 2021; 16:305-315. [PMID: 34432229 DOI: 10.1007/s11682-021-00504-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2021] [Indexed: 10/20/2022]
Abstract
The sleep spindle, a waxing and waning oscillation in the sigma frequency range, has been shown to correlate with fluid intelligence; i.e. the ability to use logic, learn novel rules/patterns, and solve problems. Using simultaneous EEG and fMRI, we previously identified the neural correlates of this relationship, including activation of the thalamus, bilateral putamen, medial frontal gyrus, middle cingulate cortex, and precuneus. However, research to date has focussed primarily on non-rapid eye movement (NREM) sleep, and spindles per se, thus overlooking the possibility that brain activity that occurs in other sleep-wake states might also be related to cognitive abilities. In our current study, we sought to investigate whether brain activity across sleep/wake states is also related to human intelligence in N = 29 participants. During NREM sleep, positive correlations were observed between fluid intelligence and blood oxygen level dependent (BOLD) activations in the bilateral putamen and the paracentral lobule/precuneus, as well as between short-term memory (STM) abilities and activity in the medial frontal cortex and inferior frontal gyrus. During wake, activity in bilateral postcentral gyri and occipital lobe was positively correlated with short-term memory abilities. In participants who experienced REM sleep in the scanner, fluid intelligence was positively associated with midbrain activation, and verbal intelligence was associated with right postcentral gyrus activation. These findings provide evidence that the relationship between sleep and intellectual abilities exists beyond sleep spindles.
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Stokes MG, Muhle-Karbe PS, Myers NE. Theoretical distinction between functional states in working memory and their corresponding neural states. VISUAL COGNITION 2020; 28:420-432. [PMID: 33223922 PMCID: PMC7655036 DOI: 10.1080/13506285.2020.1825141] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 09/10/2020] [Indexed: 12/15/2022]
Abstract
Working memory (WM) is important for guiding behaviour, but not always for the next possible action. Here we define a WM item that is currently relevant for guiding behaviour as the functionally "active" item; whereas items maintained in WM, but not immediately relevant to behaviour, are defined as functionally "latent". Traditional neurophysiological theories of WM proposed that content is maintained via persistent neural activity (e.g., stable attractors); however, more recent theories have highlighted the potential role for "activity-silent" mechanisms (e.g., short-term synaptic plasticity). Given these somewhat parallel dichotomies, functionally active and latent cognitive states of WM have been associated with storage based on persistent-activity and activity-silent neural mechanisms, respectively. However, in this article we caution against a one-to-one correspondence between functional and activity states. We argue that the principal theoretical requirement for active and latent WM is that the corresponding neural states play qualitatively different functional roles. We consider a number of candidate solutions, and conclude that the neurophysiological mechanisms for functionally active and latent WM items are theoretically independent of the distinction between persistent activity-based and activity-silent forms of WM storage.
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Affiliation(s)
- Mark G. Stokes
- Wellcome Centre for Integrative Neuroimaging and Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Paul S. Muhle-Karbe
- Wellcome Centre for Integrative Neuroimaging and Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Nicholas E. Myers
- Wellcome Centre for Integrative Neuroimaging and Department of Experimental Psychology, University of Oxford, Oxford, UK
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3
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Lorenc ES, Vandenbroucke ARE, Nee DE, de Lange FP, D'Esposito M. Dissociable neural mechanisms underlie currently-relevant, future-relevant, and discarded working memory representations. Sci Rep 2020; 10:11195. [PMID: 32641712 PMCID: PMC7343803 DOI: 10.1038/s41598-020-67634-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 06/09/2020] [Indexed: 11/19/2022] Open
Abstract
In daily life, we use visual working memory (WM) to guide our actions. While attending to currently-relevant information, we must simultaneously maintain future-relevant information, and discard information that is no longer relevant. However, the neural mechanisms by which unattended, but future-relevant, information is maintained in working memory, and future-irrelevant information is discarded, are not well understood. Here, we investigated representations of these different information types, using functional magnetic resonance imaging in combination with multivoxel pattern analysis and computational modeling based on inverted encoding model simulations. We found that currently-relevant WM information in the focus of attention was maintained through representations in visual, parietal and posterior frontal brain regions, whereas deliberate forgetting led to suppression of the discarded representations in early visual cortex. In contrast, future-relevant information was neither inhibited nor actively maintained in these areas. These findings suggest that different neural mechanisms underlie the WM representation of currently- and future-relevant information, as compared to information that is discarded from WM.
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Affiliation(s)
- Elizabeth S Lorenc
- University of California, Berkeley, CA, USA.
- University of Texas at Austin, Austin, TX, USA.
| | - Annelinde R E Vandenbroucke
- University of California, Berkeley, CA, USA
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Derek E Nee
- Florida State University, Tallahassee, FL, USA
| | - Floris P de Lange
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
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Peters B, Rahm B, Kaiser J, Bledowski C. Differential trajectories of memory quality and guessing across sequential reports from working memory. J Vis 2019; 19:3. [PMID: 31287857 DOI: 10.1167/19.7.3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Working memory enables the storage of few items for a short period of time. Previous research has shown that items in working memory cannot be accessed equally well, indicating that they are held in at least two different states with different capacity limitations. However, it is unclear whether differences between states are due to limitations of the number of items that can be stored, or the quality with which items are stored. We employed a sequential whole-report procedure where participants reported the remembered orientation of each of two or four encoded Gabor patches. In addition, they rated their memory confidence prior to each report. Participants performed 600 trials per condition, allowing us to obtain reliable subjective ratings and estimates of precision, guessing, and misreport using a mixture model, separately for each sequential report. Different measures of memory quality consistently showed discontinuous trajectories across reports with a steep drop from the first to the second remembered item but only slight decreases thereafter. In contrast, both reported and modeled guessing changed continuously across reports. Our results support the notion of two states in working memory and show that they are distinguished by memory quality rather than quantity.
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Affiliation(s)
- Benjamin Peters
- Institute of Medical Psychology, Goethe University, Frankfurt am Main, Germany.,Zuckerman Institute, Columbia University, New York, USA
| | - Benjamin Rahm
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jochen Kaiser
- Institute of Medical Psychology, Goethe University, Frankfurt am Main, Germany
| | - Christoph Bledowski
- Institute of Medical Psychology, Goethe University, Frankfurt am Main, Germany
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5
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Zuo N, Salami A, Yang Y, Yang Z, Sui J, Jiang T. Activation-based association profiles differentiate network roles across cognitive loads. Hum Brain Mapp 2019; 40:2800-2812. [PMID: 30854745 DOI: 10.1002/hbm.24561] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 02/14/2019] [Accepted: 02/15/2019] [Indexed: 01/03/2023] Open
Abstract
Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level-dependent (BOLD) response across several large-scale brain networks, the unique and shared contributions of each large-scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared-association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared-association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2-back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared-associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared-associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.
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Affiliation(s)
- Nianming Zuo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Chinese Institute for Brain Research, Beijing, China
| | - Alireza Salami
- Aging Research Center, Karolinska Institute and Stockholm University, Stockholm, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Wallenberg Center for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jing Sui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
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Thomas RM, De Sanctis T, Gazzola V, Keysers C. Where and how our brain represents the temporal structure of observed action. Neuroimage 2018; 183:677-697. [PMID: 30165253 PMCID: PMC6215330 DOI: 10.1016/j.neuroimage.2018.08.056] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 08/20/2018] [Accepted: 08/23/2018] [Indexed: 12/30/2022] Open
Abstract
Reacting faster to the behaviour of others provides evolutionary advantages. Reacting to unpredictable events takes hundreds of milliseconds. Understanding where and how the brain represents what actions are likely to follow one another is, therefore, important. Everyday actions occur in predictable sequences, yet neuroscientists focus on how brains respond to unexpected, individual motor acts. Using fMRI, we show the brain encodes sequence-related information in the motor system. Using EEG, we show visual responses are faster and smaller for predictable sequences. We hope this paradigm encourages the field to shift its focus from single acts to motor sequences. It sheds light on how we adapt to the actions of others and suggests that the motor system may implement perceptual predictive coding.
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Affiliation(s)
- R M Thomas
- The Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, the Netherlands; Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - T De Sanctis
- The Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, the Netherlands; PharmAccess Foundation, Amsterdam, Netherlands
| | - V Gazzola
- The Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, the Netherlands; Brain & Cognition, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
| | - C Keysers
- The Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, the Netherlands; Brain & Cognition, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
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Sanfratello L, Lundy S, Qualls C, Knoefel J, Adair J, Caprihan A, Stephen J, Aine C. Brain structure and verbal function across adulthood while controlling for cerebrovascular risks. Hum Brain Mapp 2017; 38:3472-3490. [PMID: 28390167 PMCID: PMC5632576 DOI: 10.1002/hbm.23602] [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: 01/22/2016] [Revised: 03/01/2017] [Accepted: 03/26/2017] [Indexed: 11/08/2022] Open
Abstract
The development and decline of brain structure and function throughout adulthood is a complex issue, with cognitive aging trajectories influenced by a host of factors including cerebrovascular risk. Neuroimaging studies of age-related cognitive decline typically reveal a linear decrease in gray matter (GM) volume/density in frontal regions across adulthood. However, white matter (WM) tracts mature later than GM, particularly in regions necessary for executive functions and memory. Therefore, it was predicted that a middle-aged group (MC: 35-45 years) would perform best on a verbal working memory task and reveal greater regional WM integrity, compared with both young (YC: 18-25 years) and elder groups (EC: 60+ years). Diffusion tensor imaging (DTI) and magnetoencephalography (MEG) were obtained from 80 healthy participants. Objective measures of cerebrovascular risk and cognition were also obtained. As predicted, MC revealed best verbal working memory accuracy overall indicating some maturation of brain function between YC and MC. However, contrary to the prediction fractional anisotropy values (FA), a measure of WM integrity, were not greater in MC (i.e., there were no significant differences in FA between YC and MC but both groups showed greater FA than EC). An overall multivariate model for MEG ROIs showed greater peak amplitudes for MC and YC, compared with EC. Subclinical cerebrovascular risk factors (systolic blood pressure and blood glucose) were negatively associated with FA in frontal callosal, limbic, and thalamic radiation regions which correlated with executive dysfunction and slower processing speed, suggesting their contribution to age-related cognitive decline. Hum Brain Mapp 38:3472-3490, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- L. Sanfratello
- The Mind Research Network1101 Yale Blvd. NEAlbuquerqueNew Mexico87106
- Department of RadiologyUniversity of New Mexico Health Sciences CenterAlbuquerqueNew Mexico87131
| | - S.L. Lundy
- Center for Neuropsychological Services, University of New Mexico Health Sciences CenterAlbuquerqueNew Mexico87131
| | - C. Qualls
- Clinical and Translational Science Center (Biostatistics),University of New Mexico Health Sciences CenterAlbuquerqueNew Mexico87131
| | - J.E. Knoefel
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNew Mexico87131
- Department of NeurologyUniversity of New Mexico Health Sciences CenterAlbuquerqueNew Mexico87131
| | - J.C. Adair
- Department of NeurologyUniversity of New Mexico Health Sciences CenterAlbuquerqueNew Mexico87131
- New Mexico VA Health Care SystemAlbuquerqueNew Mexico87108
| | - A. Caprihan
- The Mind Research Network1101 Yale Blvd. NEAlbuquerqueNew Mexico87106
| | - J.M. Stephen
- The Mind Research Network1101 Yale Blvd. NEAlbuquerqueNew Mexico87106
| | - C.J. Aine
- The Mind Research Network1101 Yale Blvd. NEAlbuquerqueNew Mexico87106
- Department of RadiologyUniversity of New Mexico Health Sciences CenterAlbuquerqueNew Mexico87131
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