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Interactions between category signals in prefrontal and parietal cortex were significant at lag 1 but not lag 0, providing evidence that one cortical area drove the other, rather than both being driven by common input

Prefrontal neurons transmit signals to parietal neurons that reflect executive control of cognition

NATURE NEUROSCIENCE, no. 10 (2013): 1484.0-+

Cited: 78|Views45
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Abstract

Prefrontal cortex influences behavior largely through its connections with other association cortices; however, the nature of the information conveyed by prefrontal output signals and what effect these signals have on computations performed by target structures is largely unknown. To address these questions, we simultaneously recorded the...More

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Introduction
  • Prefrontal cortex is anatomically situated at the center of a complex array of projections that link it to other cortical association areas, one of which is the posterior parietal cortex[1,2,3,4].
  • The authors recorded neural activity in parietal and prefrontal cortex simultaneously and used pattern classification to decode spatial category from firing rates in a sequence of 50-ms time bins
  • This produced a time series of posterior probabilities quantifying the strengths of category representation in the two areas.
  • The authors provide evidence that physiological signals encoding rule-dependent categories are transmitted selectively in a topdown direction from prefrontal to parietal neurons
  • This identifies a neural mechanism by which prefrontal output could rapidly adapt computations performed by distributed cortical networks to changing environmental demands
Highlights
  • Prefrontal cortex is anatomically situated at the center of a complex array of projections that link it to other cortical association areas, one of which is the posterior parietal cortex[1,2,3,4]
  • To determine whether neural signals encoding rule-dependent spatial categories were transmitted between prefrontal and parietal neurons during the trial, we evaluated whether rapid fluctuations in the strength of these signals were correlated between cortical areas over time
  • We found evidence that rule signals were transmitted in a top-down direction from prefrontal to parietal neurons (Supplementary Fig. 9b), as based on the activity of neurons that varied as a function of the rule only and not spatial category
  • Evaluating neural signals encoding the position of the sample in parietal and prefrontal cortex, we found evidence that position signals covaried strongly and significantly between cortical areas at lag 0 (Supplementary Fig. 9c), an effect that peaked in the delay period following the offset of the sample stimulus. (Our analysis had limited ability to resolve transmission earlier in the trial, during the cue period, when feedforward transmission might be expected, because data had to be aggregated over a sequence of bins longer than the cue period before transmission could be detected.)
  • Interactions between category signals in prefrontal and parietal cortex were significant at lag 1 but not lag 0, providing evidence that one cortical area drove the other, rather than both being driven by common input
  • Our results identify a specific neural signal related to the executive control of cognition that is transmitted from prefrontal to parietal cortex
Methods
  • Methods and any associated references are available in the online version of the paper.

    Note: Any Supplementary Information and Source Data files are available in the online version of the paper.
  • When the rule cue was a vertical line, it instructed the horizontal categorization rule and divided the circular sample array into the spatial categories ‘left’ and ‘right’ on opposite sides of the boundary (Fig. 1a,c).
  • When the rule cue was a horizontal line, it instructed the vertical (‘above’ or ‘below’) categorization rule, requiring monkeys to reassign the same set of sample positions to a new set of spatial categories (Fig. 1b,d).
  • If the choice stimulus that was visible at the time of the motor response matched the spatial category of the sample, defined by the sample location and the orientation of the rule cue, the monkey was rewarded with a drop of juice
Results
  • The authors trained two monkeys to perform a rule-based spatial categorization task[18] (Fig. 1).
  • In this task, monkeys viewed a spot visual stimulus.
  • Decode accuracy Proportion correct a Sample Delay 1 Rule cue Choice 1.
  • Task, behavioral performance and network representation of spatial categories.
  • (a) Stimulus sequence on a trial employing Gaze target Left Right.
  • Response the horizontal categorization rule
  • Task, behavioral performance and network representation of spatial categories. (a) Stimulus sequence on a trial employing Gaze target Left Right
Conclusion
  • The authors obtained evidence that neural signals encoding rule-dependent spatial categories, and reflecting the executive control of a cognitive process, were selectively transmitted in a top-down direction from prefrontal to parietal neurons.
  • Transmission was directional and selective for the nature of the transmitted information, depended on simultaneously recorded neural activity in the two areas, occurred at a restricted time scale and was modulated as a function of behavior.
  • These data effectively translate the information content of a physiological signal transmitted from prefrontal to parietal neurons during cognitive processing.
  • Interactions between category signals in prefrontal and parietal cortex were significant at lag 1 but not lag 0, providing evidence that one cortical area drove the other, rather than both being driven by common input
Funding
  • Supported by the US National Institutes of Health (grant R01 MH077779 and R24MH069675), the Department of Veterans Affairs and the American Legion Brain Sciences Chair
  • Blackman was supported by US National Institutes of Health grant T32 GM008244
Reference
  • Medalla, M. & Barbas, H. Diversity of laminar connections linking periarcuate and lateral intraparietal areas depends on cortical structure. Eur. J. Neurosci. 23, 161–179 (2006).
    Google ScholarLocate open access versionFindings
  • Schwartz, M.L. & Goldman-Rakic, P.S. Callosal and intrahemispheric connectivity of the prefrontal association cortex in rhesus monkey: relation between intraparietal and principal sulcal cortex. J. Comp. Neurol. 226, 403–420 (1984).
    Google ScholarLocate open access versionFindings
  • Cavada, C. & Goldman-Rakic, P.S. Posterior parietal cortex in rhesus monkey: II. Evidence for segregated corticocortical networks linking sensory and limbic areas with the frontal lobe. J. Comp. Neurol. 287, 422–445 (1989).
    Google ScholarLocate open access versionFindings
  • Andersen, R.A., Asanuma, C., Essick, G. & Siegel, R.M. Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule. J. Comp. Neurol. 296, 65–113 (1990).
    Google ScholarLocate open access versionFindings
  • Goldman-Rakic, P.S. Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. in. Handbook of Physiology: The Nervous System: Higher Functions of the Brain (eds. Mountcastle, V.B., Plum, F. & Geiger, S.R.) 373–417 (Am. Physiol. Soc., Bethesda, Maryland, USA, 1987).
    Google ScholarFindings
  • Andersen, R.A. & Cui, H. Intention, action planning, and decision making in parietalfrontal circuits. Neuron 63, 568–583 (2009).
    Google ScholarLocate open access versionFindings
  • Qi, X.L. et al. Comparison of neural activity related to working memory in primate dorsolateral prefrontal and posterior parietal cortex. Front Syst. Neurosci 4, 12 (2010).
    Google ScholarLocate open access versionFindings
  • Chafee, M.V. & Goldman-Rakic, P.S. Matching patterns of activity in primate prefrontal area 8a and parietal area 7ip neurons during a spatial working memory task. J. Neurophysiol. 79, 2919–2940 (1998).
    Google ScholarLocate open access versionFindings
  • Funahashi, S., Chafee, M.V. & Goldman-Rakic, P.S. Prefrontal neuronal activity in rhesus monkeys performing a delayed anti-saccade task. Nature 365, 753–756 (1993).
    Google ScholarLocate open access versionFindings
  • Gnadt, J.W. & Andersen, R.A. Memory related motor planning activity in posterior parietal cortex of macaque. Exp. Brain Res. 70, 216–220 (1988).
    Google ScholarLocate open access versionFindings
  • Buschman, T.J. & Miller, E.K. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315, 1860–1862 (2007).
    Google ScholarLocate open access versionFindings
  • Nieder, A. & Miller, E.K. A parieto-frontal network for visual numerical information in the monkey. Proc. Natl. Acad. Sci. USA 101, 7457–7462 (2004).
    Google ScholarLocate open access versionFindings
  • Nieder, A., Freedman, D.J. & Miller, E.K. Representation of the quantity of visual items in the primate prefrontal cortex. Science 297, 1708–1711 (2002).
    Google ScholarLocate open access versionFindings
  • Vallentin, D. & Nieder, A. Representations of visual proportions in the primate posterior parietal and prefrontal cortices. Eur. J. Neurosci. 32, 1380–1387 (2010).
    Google ScholarLocate open access versionFindings
  • Tudusciuc, O. & Nieder, A. Contributions of primate prefrontal and posterior parietal cortices to length and numerosity representation. J. Neurophysiol. 101, 2984–2994 (2009).
    Google ScholarLocate open access versionFindings
  • Swaminathan, S.K. & Freedman, D.J. Preferential encoding of visual categories in parietal cortex compared with prefrontal cortex. Nat. Neurosci. 15, 315–320 (2012).
    Google ScholarLocate open access versionFindings
  • Merchant, H., Crowe, D.A., Robertson, M.S., Fortes, A.F. & Georgopoulos, A.P. Top-down spatial categorization signal from prefrontal to posterior parietal cortex in the primate. Front Syst. Neurosci 5, 69 (2011).
    Google ScholarLocate open access versionFindings
  • Goodwin, S.J., Blackman, R.K., Sakellaridi, S. & Chafee, M.V. Executive control over cognition: stronger and earlier rule-based modulation of spatial category signals in prefrontal cortex relative to parietal cortex. J. Neurosci. 32, 3499–3515 (2012).
    Google ScholarLocate open access versionFindings
  • Box, G.E.P., Jenkins, G.M. & Reinsel, G.C. Time Series Analysis: Forecasting and Control (Prentice-Hall, Upper Saddle River, New Jersey, USA, 1994).
    Google ScholarFindings
  • Granger, C.W.J. & Newbold, P. Forecasting Economic Time Series (Academic, New York, 1977).
    Google ScholarFindings
  • Christova, P., Lewis, S.M., Jerde, T.A., Lynch, J.K. & Georgopoulos, A.P. True associations between resting fMRI time series based on innovations. J. Neural Eng. 8, 046025 (2011).
    Google ScholarLocate open access versionFindings
  • Sugrue, L.P., Corrado, G.S. & Newsome, W.T. Matching behavior and the representation of value in the parietal cortex. Science 304, 1782–1787 (2004).
    Google ScholarLocate open access versionFindings
  • Gottlieb, J. & Balan, P. Attention as a decision in information space. Trends Cogn. Sci. 14, 240–248 (2010).
    Google ScholarLocate open access versionFindings
  • Peck, C.J., Jangraw, D.C., Suzuki, M., Efem, R. & Gottlieb, J. Reward modulates attention independently of action value in posterior parietal cortex. J. Neurosci. 29, 11182–11191 (2009).
    Google ScholarLocate open access versionFindings
  • Platt, M.L. & Glimcher, P.W. Neural correlates of decision variables in parietal cortex. Nature 400, 233–238 (1999).
    Google ScholarLocate open access versionFindings
  • Barraclough, D.J., Conroy, M.L. & Lee, D. Prefrontal cortex and decision making in a mixed-strategy game. Nat. Neurosci. 7, 404–410 (2004).
    Google ScholarLocate open access versionFindings
  • Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995).
    Google ScholarLocate open access versionFindings
  • Fitzgerald, J.K. et al. Biased associative representations in parietal cortex. Neuron 77, 180–191 (2013).
    Google ScholarLocate open access versionFindings
  • Nee, D.E. & Brown, J.W. Dissociable frontal-striatal and frontal-parietal networks involved in updating hierarchical contexts in working memory. Cereb. Cortex 23, 2146–2158 (2013).
    Google ScholarLocate open access versionFindings
  • Gregoriou, G.G., Gotts, S.J., Zhou, H. & Desimone, R. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324, 1207–1210 (2009).
    Google ScholarLocate open access versionFindings
  • Gregoriou, G.G., Gotts, S.J. & Desimone, R. Cell-type-specific synchronization of neural activity in FEF with V4 during attention. Neuron 73, 581–594 (2012).
    Google ScholarLocate open access versionFindings
  • Chafee, M.V. & Goldman-Rakic, P.S. Inactivation of parietal and prefrontal cortex reveals interdependence of neural activity during memory-guided saccades. J. Neurophysiol. 83, 1550–1566 (2000).
    Google ScholarLocate open access versionFindings
  • Ferraina, S., Pare, M. & Wurtz, R.H. Comparison of cortico-cortical and corticocollicular signals for the generation of saccadic eye movements. J. Neurophysiol. 87, 845–858 (2002).
    Google ScholarLocate open access versionFindings
  • Sherman, S.M. & Guillery, R.W. Distinct functions for direct and transthalamic corticocortical connections. J. Neurophysiol. 106, 1068–1077 (2011).
    Google ScholarLocate open access versionFindings
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