Dynamic layer-specific processing in the prefrontal cortex during working memory.

Humans Memory, Short-Term / physiology Male Magnetic Resonance Imaging Female Adult Young Adult Prefrontal Cortex / physiology Dorsolateral Prefrontal Cortex / physiology Brain Mapping / methods

Journal

Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179

Informations de publication

Date de publication:
14 Sep 2024
Historique:
received: 22 11 2023
accepted: 26 08 2024
medline: 15 9 2024
pubmed: 15 9 2024
entrez: 14 9 2024
Statut: epublish

Résumé

The dorsolateral prefrontal cortex (dlPFC) is reliably engaged in working memory (WM) and comprises different cytoarchitectonic layers, yet their functional role in human WM is unclear. Here, participants completed a delayed-match-to-sample task while undergoing functional magnetic resonance imaging (fMRI) at ultra-high resolution. We examine layer-specific activity to manipulations in WM load and motor response. Superficial layers exhibit a preferential response to WM load during the delay and retrieval periods of a WM task, indicating a lamina-specific activation of the frontoparietal network. Multivariate patterns encoding WM load in the superficial layer dynamically change across the three periods of the task. Last, superficial and deep layers are non-differentially involved in the motor response, challenging earlier findings of a preferential deep layer activation. Taken together, our results provide new insights into the functional laminar circuitry of the dlPFC during WM and support a dynamic account of dlPFC coding.

Identifiants

DOI: 10.1038/s42003-024-06780-8 PMID: 39277694
pubmed: 39277694
doi: 10.1038/s42003-024-06780-8
pii: 10.1038/s42003-024-06780-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1140

Subventions

Organisme : Klaus Tschira Stiftung (Klaus Tschira Foundation)
ID : GSO/KT18

Informations de copyright

© 2024. The Author(s).

Références

Curtis, C. E. & D’Esposito, M. Persistent activity in the prefrontal cortex during working memory. Trends Cogn. Sci. 7, 415–423 (2003).
pubmed: 12963473 doi: 10.1016/S1364-6613(03)00197-9
Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989).
pubmed: 2918358 doi: 10.1152/jn.1989.61.2.331
Fuster, J. M. & Alexander, G. E. Neuron activity related to short-term memory. Science 173, 652–654 (1971).
pubmed: 4998337 doi: 10.1126/science.173.3997.652
Sreenivasan, K. K., Vytlacil, J. & D’Esposito, M. Distributed and dynamic storage of working memory stimulus information in extrastriate. Cortex. J. Cogn. Neurosci. 26, 1141–1153 (2014).
pubmed: 24392897 doi: 10.1162/jocn_a_00556
Harrison, S. A. & Tong, F. Decoding reveals the contents of visual working memory in early visual areas. Nature 458, 632–635 (2009).
pubmed: 19225460 pmcid: 2709809 doi: 10.1038/nature07832
Christophel, T. B., Hebart, M. N. & Haynes, J.-D. Decoding the contents of visual short-term memory from human visual and parietal cortex. J. Neurosci. 32, 12983–12989 (2012).
pubmed: 22993415 pmcid: 6621473 doi: 10.1523/JNEUROSCI.0184-12.2012
Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R. & Haynes, J.-D. The distributed nature of working memory. Trends Cogn. Sci. 21, 111–124 (2017).
pubmed: 28063661 doi: 10.1016/j.tics.2016.12.007
Druzgal, T. J. & D’Esposito, M. Dissecting contributions of prefrontal cortex and fusiform face area to face working memory. J. Cogn. Neurosci. 15, 771–784 (2003).
pubmed: 14511531 doi: 10.1162/089892903322370708
Emrich, S. M., Riggall, A. C., LaRocque, J. J. & Postle, B. R. Distributed patterns of activity in sensory cortex reflect the precision of multiple items maintained in visual short-term memory. J. Neurosci. 33, 6516–6523 (2013).
pubmed: 23575849 pmcid: 3664518 doi: 10.1523/JNEUROSCI.5732-12.2013
Todd, J. J. & Marois, R. Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428, 751–754 (2004).
pubmed: 15085133 doi: 10.1038/nature02466
Ranganath, C., DeGutis, J. & D’Esposito, M. Category-specific modulation of inferior temporal activity during working memory encoding and maintenance. Cogn. Brain Res. 20, 37–45 (2004).
doi: 10.1016/j.cogbrainres.2003.11.017
D’Esposito, M., Postle, B. R., Ballard, D. & Lease, J. Maintenance versus manipulation of information held in working memory: an event-related fMRI study. Brain Cogn 41, 66–86 (1999).
pubmed: 10536086 doi: 10.1006/brcg.1999.1096
Duncan, J. The Structure of Cognition: Attentional Episodes in Mind and Brain. Neuron 80, 35–50 (2013).
pubmed: 24094101 pmcid: 3791406 doi: 10.1016/j.neuron.2013.09.015
Duncan, J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn. Sci. 14, 172–179 (2010).
pubmed: 20171926 doi: 10.1016/j.tics.2010.01.004
Soreq, E., Leech, R. & Hampshire, A. Dynamic network coding of working-memory domains and working-memory processes. Nat. Commun. 10, 936 (2019).
pubmed: 30804436 pmcid: 6389921 doi: 10.1038/s41467-019-08840-8
Pinotsis, D. A., Buschman, T. J. & Miller, E. K. Working Memory Load Modulates Neuronal Coupling. Cereb. Cortex 29, 1670–1681 (2019).
pubmed: 29608671 doi: 10.1093/cercor/bhy065
Eryilmaz, H. et al. Working memory load-dependent changes in cortical network connectivity estimated by machine learning. NeuroImage 217, 116895 (2020).
pubmed: 32360929 doi: 10.1016/j.neuroimage.2020.116895
Bastos, A. M., Loonis, R., Kornblith, S., Lundqvist, M. & Miller, E. K. Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory. Proc. Natl. Acad. Sci. USA 115, 1117–1122 (2018).
pubmed: 29339471 pmcid: 5798320 doi: 10.1073/pnas.1710323115
Bastos, A. M., Lundqvist, M., Waite, A. S., Kopell, N. & Miller, E. K. Layer and rhythm specificity for predictive routing. Proc. Natl. Acad. Sci. 117, 31459–31469 (2020).
pubmed: 33229572 pmcid: 7733827 doi: 10.1073/pnas.2014868117
Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995).
pubmed: 7695894 doi: 10.1016/0896-6273(95)90304-6
Arnsten, A. F. T., Wang, M. J. & Paspalas, C. D. Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses. Neuron 76, 223–239 (2012).
pubmed: 23040817 pmcid: 3488343 doi: 10.1016/j.neuron.2012.08.038
Wang, M., Vijayraghavan, S. & Goldman-Rakic, P. S. Selective D2 receptor actions on the functional circuitry of working memory. Science 303, 853–856 (2004).
pubmed: 14764884 doi: 10.1126/science.1091162
Lawrence, S. J. D. et al. Laminar organization of working memory signals in human visual cortex. Curr. Biol. 28, 3435–3440.e4 (2018).
pubmed: 30344121 doi: 10.1016/j.cub.2018.08.043
Lawrence, S. J. D., Norris, D. G. & de Lange, F. P. Dissociable laminar profiles of concurrent bottom-up and top-down modulation in the human visual cortex. eLife 8, e44422 (2019).
pubmed: 31063127 pmcid: 6538372 doi: 10.7554/eLife.44422
Muckli, L. et al. Contextual Feedback to Superficial Layers of V1. Curr. Biol. 25, 2690–2695 (2015).
pubmed: 26441356 pmcid: 4612466 doi: 10.1016/j.cub.2015.08.057
Kok, P., Bains, L. J., van Mourik, T., Norris, D. G. & de Lange, F. P. Selective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback. Curr. Biol. 26, 371–376 (2016).
pubmed: 26832438 doi: 10.1016/j.cub.2015.12.038
De Martino, F. et al. Frequency preference and attention effects across cortical depths in the human primary auditory cortex. Proc. Natl. Acad. Sci. 112, 16036–16041 (2015).
pubmed: 26668397 pmcid: 4702984 doi: 10.1073/pnas.1507552112
Aitken, F. et al. Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex. PLOS Biol 18, e3001023 (2020).
pubmed: 33284791 pmcid: 7746273 doi: 10.1371/journal.pbio.3001023
Finn, E. S., Huber, L., Jangraw, D. C., Molfese, P. J. & Bandettini, P. A. Layer-dependent activity in human prefrontal cortex during working memory. Nat. Neurosci. 22, 1687–1695 (2019).
pubmed: 31551596 pmcid: 6764601 doi: 10.1038/s41593-019-0487-z
Spaak, E., Watanabe, K., Funahashi, S. & Stokes, M. G. Stable and dynamic coding for working memory in primate prefrontal cortex. J. Neurosci 37, 6503–6516 (2017).
pubmed: 28559375 pmcid: 5511881 doi: 10.1523/JNEUROSCI.3364-16.2017
Stokes, M. G. et al. Dynamic Coding for Cognitive Control in Prefrontal Cortex. Neuron 78, 364–375 (2013).
pubmed: 23562541 pmcid: 3898895 doi: 10.1016/j.neuron.2013.01.039
Cavanagh, S. E., Towers, J. P., Wallis, J. D., Hunt, L. T. & Kennerley, S. W. Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex. Nat. Commun. 9, 3498 (2018).
pubmed: 30158519 pmcid: 6115433 doi: 10.1038/s41467-018-05873-3
Miller, E. K., Lundqvist, M. & Bastos, A. M. Working Memory 2.0. Neuron 100, 463–475 (2018).
pubmed: 30359609 pmcid: 8112390 doi: 10.1016/j.neuron.2018.09.023
Meyers, E. M., Freedman, D. J., Kreiman, G., Miller, E. K. & Poggio, T. Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex. J. Neurophysiol. 100, 1407–1419 (2008).
pubmed: 18562555 pmcid: 2544466 doi: 10.1152/jn.90248.2008
Iamshchinina, P. et al. Benchmarking GE-BOLD, SE-BOLD, and SS-SI-VASO sequences for depth-dependent separation of feedforward and feedback signals in high-field MRI (Neuroscience) (2021).
Iamshchinina, P. et al. Perceived and mentally rotated contents are differentially represented in cortical depth of V1. Commun. Biol. 4, 1069 (2021).
pubmed: 34521987 pmcid: 8440580 doi: 10.1038/s42003-021-02582-4
Adam, K. C. S., Vogel, E. K. & Awh, E. Multivariate analysis reveals a generalizable human electrophysiological signature of working memory load. Psychophysiology 57, e13691 (2020).
pubmed: 33040349 pmcid: 7722086 doi: 10.1111/psyp.13691
Thyer, W., et al Storage in Visual Working Memory Recruits a Content-Independent Pointer System. Psychol. Sci., 095679762210909. (2022).
Majerus, S. et al. Cross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory. Cereb. Cortex 26, 166–179 (2016).
pubmed: 25146374 doi: 10.1093/cercor/bhu189
Majerus, S., Péters, F., Bouffier, M., Cowan, N. & Phillips, C. The Dorsal Attention Network Reflects Both Encoding Load and Top–down Control during Working Memory. J. Cogn. Neurosci. 30, 144–159 (2018).
pubmed: 28984526 doi: 10.1162/jocn_a_01195
Wolff, M. J., Jochim, J., Akyürek, E. G., Buschman, T. J. & Stokes, M. G. Drifting codes within a stable coding scheme for working memory. PLOS Biol 18, e3000625 (2020).
pubmed: 32119658 pmcid: 7067474 doi: 10.1371/journal.pbio.3000625
Li, H.-H. & Curtis, C. E. Neural population dynamics of human working memory. Curr. Biol. 33, 3775–3784.e4 (2023).
pubmed: 37595590 pmcid: 10528783 doi: 10.1016/j.cub.2023.07.067
Christophel, T. B., Iamshchinina, P., Yan, C., Allefeld, C. & Haynes, J.-D. Cortical specialization for attended versus unattended working memory. Nat. Neurosci. 21, 494–496 (2018).
pubmed: 29507410 doi: 10.1038/s41593-018-0094-4
Rademaker, R. L., Chunharas, C. & Serences, J. T. Coexisting representations of sensory and mnemonic information in human visual cortex. Nat. Neurosci. 22, 1336–1344 (2019).
pubmed: 31263205 pmcid: 6857532 doi: 10.1038/s41593-019-0428-x
D’Esposito, M., Postle, B. R. & Rypma, B. Prefrontal cortical contributions to working memory: evidence from event-related fMRI studies. Exp. Brain Res. 133, 3–11 (2000).
pubmed: 10933205 doi: 10.1007/s002210000395
Rypma, B., Berger, J. S. & D’Esposito, M. The Influence of Working-Memory Demand and Subject Performance on Prefrontal Cortical Activity. J. Cogn. Neurosci 14, 721–731 (2002).
pubmed: 12167257 doi: 10.1162/08989290260138627
Lorenz, R. et al. The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI. NeuroImage 129, 320–334 (2016).
pubmed: 26804778 doi: 10.1016/j.neuroimage.2016.01.032
Lorenz, R. et al. Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization. Nat. Commun. 9, 1227 (2018).
pubmed: 29581425 pmcid: 5964320 doi: 10.1038/s41467-018-03657-3
Lorenz, R. et al. A Bayesian optimization approach for rapidly mapping residual network function in stroke. Brain 144, 2120–2134 (2021).
pubmed: 33725125 pmcid: 8370405 doi: 10.1093/brain/awab109
Miller, E. K. & Cohen, J. D. An Integrative Theory of Prefrontal Cortex Function. Annu. Rev. Neurosci. 24, 167–202 (2001).
pubmed: 11283309 doi: 10.1146/annurev.neuro.24.1.167
O’Reilly, R. C. & Frank, M. J. Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput 18, 283–328 (2006).
pubmed: 16378516 doi: 10.1162/089976606775093909
Friedman, N. P. & Robbins, T. W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology 47, 72–89 (2022).
pubmed: 34408280 doi: 10.1038/s41386-021-01132-0
Hussar, C. & Pasternak, T. Trial-to-trial variability of the prefrontal neurons reveals the nature of their engagement in a motion discrimination task. Proc. Natl. Acad. Sci. USA 107, 21842–21847 (2010).
pubmed: 21098286 pmcid: 3003075 doi: 10.1073/pnas.1009956107
Buschman, T. J. & Miller, E. K. Working memory is complex and dynamic, like your thoughts. J. Cogn. Neurosci 35, 17–23 (2022).
pubmed: 36322832 doi: 10.1162/jocn_a_01940
Curtis, C. E. & Sprague, T. C. Persistent activity during working memory from front to back. Front. Neural Circuits 15, 696060 (2021).
pubmed: 34366794 pmcid: 8334735 doi: 10.3389/fncir.2021.696060
Sreenivasan, K. K. & D’Esposito, M. The what, where and how of delay activity. Nat. Rev. Neurosci. 20, 466–481 (2019).
pubmed: 31086326 pmcid: 8801206 doi: 10.1038/s41583-019-0176-7
Constantinidis, C. et al. Persistent spiking activity underlies working memory. J. Neurosci 38, 7020–7028 (2018).
pubmed: 30089641 pmcid: 6083457 doi: 10.1523/JNEUROSCI.2486-17.2018
Stokes, M. G. ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework. Trends Cogn. Sci. 19, 394–405 (2015).
pubmed: 26051384 pmcid: 4509720 doi: 10.1016/j.tics.2015.05.004
Huber, L. et al. Cortical lamina-dependent blood volume changes in human brain at 7 T. NeuroImage 107, 23–33 (2015).
pubmed: 25479018 doi: 10.1016/j.neuroimage.2014.11.046
Huber, L. et al. Techniques for blood volume fMRI with VASO: From low-resolution mapping towards sub-millimeter layer-dependent applications. NeuroImage 164, 131–143 (2018).
pubmed: 27867088 doi: 10.1016/j.neuroimage.2016.11.039
Polimeni, J. R., Fischl, B., Greve, D. N. & Wald, L. L. Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1. NeuroImage 52, 1334–1346 (2010).
pubmed: 20460157 doi: 10.1016/j.neuroimage.2010.05.005
Chaimow, D., Yacoub, E., Uğurbil, K. & Shmuel, A. Spatial specificity of the functional MRI blood oxygenation response relative to neuronal activity. NeuroImage 164, 32–47 (2018).
pubmed: 28882632 doi: 10.1016/j.neuroimage.2017.08.077
Markuerkiaga, I., Marques, J. P., Gallagher, T. E. & Norris, D. G. Estimation of laminar BOLD activation profiles using deconvolution with a physiological point spread function. J. Neurosci. Methods 353, 109095 (2021).
pubmed: 33549635 doi: 10.1016/j.jneumeth.2021.109095
Heinzle, J., Koopmans, P. J., den Ouden, H. E. M., Raman, S. & Stephan, K. E. A hemodynamic model for layered BOLD signals. NeuroImage 125, 556–570 (2016).
pubmed: 26484827 doi: 10.1016/j.neuroimage.2015.10.025
Koopmans, P. J., Barth, M. & Norris, D. G. Layer-specific BOLD activation in human V1. Hum. Brain Mapp. 31, 1297–1304 (2010).
pubmed: 20082333 pmcid: 6870878 doi: 10.1002/hbm.20936
de Hollander, G., van der Zwaag, W., Qian, C., Zhang, P. & Knapen, T. Ultra-high field fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns. NeuroImage 228, 117683 (2021).
pubmed: 33385565 doi: 10.1016/j.neuroimage.2020.117683
Havlicek, M. & Uludağ, K. A dynamical model of the laminar BOLD response. NeuroImage 204, 116209 (2020).
pubmed: 31546051 doi: 10.1016/j.neuroimage.2019.116209
Bergmann J., Morgan A. T., & Muckli L. Two distinct feedback codes in V1 for ‘real’ and ‘imaginary’ internal experiences (Neuroscience). (2019).
Sharoh, D. et al. Laminar specific fMRI reveals directed interactions in distributed networks during language processing. Proc. Natl. Acad. Sci. USA 116, 21185–21190 (2019).
pubmed: 31570628 pmcid: 6800353 doi: 10.1073/pnas.1907858116
Huang, P. et al. Correcting for Superficial Bias in 7T Gradient Echo fMRI. Front. Neurosci. 15 (2021).
Righi, G., Peissig, J. J. & Tarr, M. J. Recognizing disguised faces. Vis. Cogn. 20, 143–169 (2012).
doi: 10.1080/13506285.2012.654624
Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).
pubmed: 22248573 doi: 10.1016/j.neuroimage.2012.01.021
Sriranga Kashyap (2021). srikash/3dMPRAGEise: ondu. Version 1.0 (Zenodo). https://doi.org/10.5281/ZENODO.4626825 .
Gaser, C., Dahnke, R., Thompson, P. M., Kurth, F., Luders, E., and Alzheimer’s Disease Neuroimaging Initiative. CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data (Neuroscience). https://doi.org/10.1101/2022.06.11.495736 . (2022)
Dickie, E. W. et al. Ciftify: A framework for surface-based analysis of legacy MR acquisitions. NeuroImage 197, 818–826 (2019).
pubmed: 31091476 doi: 10.1016/j.neuroimage.2019.04.078
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
pubmed: 27437579 pmcid: 4990127 doi: 10.1038/nature18933
Robinson, E. C. et al. MSM: A new flexible framework for Multimodal Surface Matching. NeuroImage 100, 414–426 (2014).
pubmed: 24939340 doi: 10.1016/j.neuroimage.2014.05.069
Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011).
pubmed: 20851191 doi: 10.1016/j.neuroimage.2010.09.025
Haenelt, D., Chaimow, D., Nasr, S., Weiskopf, N., & Trampel, R. Decoding of columnar-level organization across cortical depth using BOLD- and CBV-fMRI at 7 T (Neuroscience) (2023).
Cox, R. W. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
pubmed: 8812068 doi: 10.1006/cbmr.1996.0014
Huber, L. et al. LayNii: A software suite for layer-fMRI. NeuroImage 237, 118091 (2021).
Coalson, T. S., Van Essen, D. C., and Glasser, M. F. The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc. Natl. Acad. Sci. USA 115. https://doi.org/10.1073/pnas.1801582115 . (2018)
Ji, J. L. et al. Mapping the human brain’s cortical-subcortical functional network organization. NeuroImage 185, 35–57 (2019).
pubmed: 30291974 doi: 10.1016/j.neuroimage.2018.10.006
Chang, C.-C. & Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).
doi: 10.1145/1961189.1961199
Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).
pubmed: 17517438 doi: 10.1016/j.jneumeth.2007.03.024

Auteurs

Jonas Karolis Degutis (JK)

Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany. j.karolis.degutis@maxplanckschools.de.
Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, corporate member of the Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. j.karolis.degutis@maxplanckschools.de.
Max Planck School of Cognition, Leipzig, Germany. j.karolis.degutis@maxplanckschools.de.
ORCID: 0009-0003-8682-3920

Denis Chaimow (D)

Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
ORCID: 0000-0003-4532-3084

Daniel Haenelt (D)

Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
ORCID: 0000-0003-2310-5086

Moataz Assem (M)

MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK.

John Duncan (J)

MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK.
ORCID: 0000-0002-9695-2764

John-Dylan Haynes (JD)

Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, corporate member of the Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Max Planck School of Cognition, Leipzig, Germany.
Research Training Group "Extrospection" and Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
Research Cluster of Excellence "Science of Intelligence", Technische Universität Berlin, Berlin, Germany.
Collaborative Research Center "Volition and Cognitive Control", Technische Universität Dresden, Dresden, Germany.

Nikolaus Weiskopf (N)

Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany.
Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK.
ORCID: 0000-0001-5239-1881

Romy Lorenz (R)

Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. romy.lorenz@tuebingen.mpg.de.
Max Planck Institute for Biological Cybernetics, Tübingen, Germany. romy.lorenz@tuebingen.mpg.de.

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questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Prosencéphale › Télencéphale › Cerveau › Cortex cérébral › Lobe frontal › Cortex préfrontal › Cortex préfrontal dorsolatéral › Dynamic layer-specific processing in the...
questionsmedicales.fr › Techniques d'investigation › Neuroimagerie › Neuroimagerie fonctionnelle › Cartographie cérébrale › Dynamic layer-specific processing in the...