In attractor networks, a reduction in the depth of the basins of attraction of cortical attractor states destabilizes the activity at the network level owing to the constant statistical fluctuations that are caused by the stochastic spiking of neurons.
A decrease in the NMDA (N-methyl-D-aspartate)-receptor (NMDAR) conductances, which reduces the depth of the attractor basins, reduces the stability of short-term memory states and increases distractibility. These effects decrease the signal-to-noise ratio of the networks.
The cognitive symptoms of schizophrenia, such as distractibility, working-memory deficits and poor attention, could be caused by such instability of attractor states in prefrontal cortical networks.
Information processing in patients with schizophrenia is characterized by a diminished cortical signal-to-noise ratio during tasks that require the allocation of attention and short-term memory (as suggested by, for example, electrophysiological recordings and functional MRI).
In patients with schizophrenia, reduced dopamine D1 receptor activation in the prefrontal cortex can decrease the signal-to-noise ratio, at least in part by reducing NMDAR-activated synaptic currents. The underlying reason for this might be the decrease in the stability of the cortical attractor networks that is produced by a reduction in NMDAR-activated currents: this reduction decreases the depth of the basins of attraction, making short-term memory and attention unstable in the context of the spiking-related noise in cortical networks.
This computational approach enables us to link factors that modulate currents in synapses to the effects of this modulation on the global performance of a network — for example, to implement cognitive processes such as short-term memory and attention.
A reduction of NMDAR-activated synaptic conductances produces lower firing rates in neurons; in the orbitofrontal and anterior cingulate cortices this could account for the negative symptoms of schizophrenia, including reduced emotions.
Decreasing the GABA (g-aminobutyric acid) and the NMDA conductances produces not only switches between the attractor states, but also jumps from spontaneous activity into one of the attractor states. This might be related to the positive symptoms of schizophrenia, including delusions, paranoia and hallucinations: these symptoms could arise because the basins of attraction are shallow and there is instability in temporal lobe semantic-memory networks, leading thoughts to move too freely around the attractor energy landscape.
Computational neuroscience models can be used to understand the diminished stability and noisy neurodynamical behaviour of prefrontal cortex networks in schizophrenia. These neurodynamical properties can be captured by simulated neural networks with randomly spiking neurons that introduce noise into the system and produce trial-by-trial variation of postsynaptic potentials. Theoretical and experimental studies have aimed to understand schizophrenia in relation to noise and signal-to-noise ratio, which are promising concepts for understanding the symptoms that characterize this heterogeneous illness. Simulations of biologically realistic neural networks show how the functioning of NMDA (N-methyl-D-aspartate), GABA (g-aminobutyric acid) and dopamine receptors is connected to the concepts of noise and variability, and to related neurophysiological findings and clinical symptoms in schizophrenia.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 16 August 2023
npj Parkinson's Disease Open Access 01 April 2023
Scientific Reports Open Access 01 December 2022
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Liddle, P. F. The symptoms of chronic schizophrenia. A re-examination of the positive–negative dichotomy. Br. J. Psychiatry 151, 145–151 (1987).
Green, M. F. What are the functional consequences of neurocognitive deficits in schizophrenia? Am. J. Psychiatry 153, 321–330 (1996).
Mueser, K. T. & McGurk, S. R. Schizophrenia. Lancet 363, 2063–2072 (2004). This paper reviewed the clinical aspects of schizophrenia.
Wang, X. J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587–9603 (1999).
Compte, A., Brunel, N., Goldman-Rakic, P. S. & Wang, X. J. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000).
Durstewitz, D., Seamans, J. K. & Sejnowski, T. J. Neurocomputational models of working memory. Nature Neurosci. 3 (Suppl. 1),184–191 (2000).
Brunel, N. & Wang, X. J. Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. J. Comput. Neurosci. 11, 63–85 (2001). This paper described an integrate-and-fire attractor network for working memory with a consistent mean-field analysis.
Wang, X. J. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455–463 (2001).
Durstewitz, D., Seamans, J. K. & Sejnowski, T. J. Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. J. Neurophysiol. 83, 1733–1750 (2000).
Usher, M. & Niebur, E. Modelling the temporal dynamics of IT neurons in visual search: a mechanism for top-down selective attention. J. Cogn. Neurosci. 8, 311–327 (1996).
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).
Deco, G. & Rolls, E. T. Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex. Eur. J. Neurosci. 18, 2374–2390 (2003).
Wang, X. J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).
Bogacz, R. Optimal decision-making theories: linking neurobiology with behaviour. Trends Cogn. Sci. 11, 118–125 (2007).
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).
Rolls, E. T. Memory, Attention, and Decision-Making: A Unifying Computational Neuroscience Approach (Oxford Univ. Press, Oxford, 2008). This book describes computational neuroscience and how it can be applied in a multidisciplinary approach to many aspects of brain function, including not only memory, attention and decision making, but also emotion and visual-object recognition.
Winterer, G. et al. Schizophrenia: reduced signal-to-noise ratio and impaired phase-locking during information processing. Clin. Neurophysiol. 111, 837–849 (2000). This paper was the first report that abnormal electrophysiological phase synchrony (noise) is increased in schizophrenia and predicts cortical activation abnormalities in schizophrenia with high diagnostic specificity.
Winterer, G. et al. Prefrontal broadband noise, working memory, and genetic risk for schizophrenia. Am. J. Psychiatry 161, 490–500 (2004). This was the first report on abnormal electrophysiological noise as a genetically determined risk factor for schizophrenia and cognitive deficits.
Braitenberg, V. & Schütz, A. Anatomy of the Cortex (Springer, Berlin, 1991).
Abeles, M. Corticonics — Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press, New York, 1991).
Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995).
Rolls, E. T. & Deco, G. Computational Neuroscience of Vision (Oxford Univ. Press, Oxford, 2002).
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Amit, D. J. Modeling Brain Function (Cambridge Univ. Press, Cambridge, 1989).
Hertz, J., Krogh, A. & Palmer, R. G. An Introduction to the Theory of Neural Computation (Addison–Wesley, Wokingham, 1991).
Goldman-Rakic, P. S. The prefrontal landscape: implications of functional architecture for understanding human mentation and the central executive. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, 1445–1453 (1996).
Fuster, J. M. Executive frontal functions. Exp. Brain Res. 133, 66–70 (2000).
Fuster, J. M. & Alexander, G. E. Neuron activity related to short-term memory. Science 173, 652–654 (1971).
Kubota, K. & Niki, H. Prefrontal cortical unit activity and delayed alternation performance in monkeys. J. Neurophysiol. 34, 337–347 (1971).
Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. Mnemonic coding of visual space in monkey dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989).
Fuster, J. M. Memory in the Cerebral Cortex (MIT Press, Cambridge, Massachusetts, 1995).
Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995).
Deco, G. & Rolls, E. T. Attention, short-term memory, and action selection: a unifying theory. Prog. Neurobiol. 76, 236–256 (2005).
Goldman-Rakic, P. S. & Leung, H.-C. in Principles of Frontal Lobe Function (eds Stuss, D. T. & Knight, R. T.) 85–95 (Oxford Univ. Press, New York, 2002).
Seidman, L. J. et al. Relationship of prefrontal and temporal lobe MRI measures to neuropsychological performance in chronic schizophrenia. Biol. Psychiatry 35, 235–246 (1994).
Weinberger, D. R. & Berman, K. F. Prefrontal function in schizophrenia: confounds and controversies. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, 1495–503 (1996).
Frith, C. & Dolan, R. J. Brain mechanisms associated with top-down processes in perception. Philos. Trans. R. Soc. Lond. B Biol. Sci. 352, 1221–1230 (1997).
Loh, M., Rolls, E. T. & Deco, G. A dynamical systems hypothesis of schizophrenia. PLoS Comput. Biol. 3, e228 (2007). This study took an attractor-based approach that linked alterations in glutamate function to the different symptoms of schizophrenia.
Coyle, J. T. Glutamate and schizophrenia: beyond the dopamine hypothesis. Cell. Mol. Neurobiol. 26, 365–384 (2006). This paper gave an account of how glutamate function might be related to schizophrenia.
Rolls, E. T., Loh, M. & Deco, G. An attractor hypothesis of obsessive-compulsive disorder. Eur. J. Neurosci. 28, 782–793 (2008).
Brunel, N. & Hakim, V. Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput. 11, 1621–1671 (1999).
Mattia, M. & Del Giudice, P. Population dynamics of interacting spike neurons. Phys. Rev. E 66, 51917–51919 (2002).
Mattia, M. & Del Giudice, P. Finite-size dynamics of inhibitory and excitatory interacting spiking neurons. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 70, 052903 (2004).
Deco, G. & Rolls, E. T. Decision-making and Weber's law: a neurophysiological model. Eur. J. Neurosci. 24, 901–916 (2006).
Faisal, A. A., Selen, L. P. & Wolpert, D. M. Noise in the nervous system. Nature Rev. Neurosci. 9, 292–303 (2008).
Benes, F. M. Emerging principles of altered neural circuitry in schizophrenia. Brain Res. Brain Res. Rev. 31, 251–269 (2000).
Hashimoto, T. et al. Alterations in GABA-related transcriptome in the dorsolateral prefrontal cortex of subjects with schizophrenia. Mol. Psychiatry 13, 147–161 (2008).
Lisman, J. E., Fellous, J. M. & Wang, X. J. A role for NMDA-receptor channels in working memory. Nature Neurosci. 1, 273–275 (1998).
Tegner, J., Compte, A. & Wang, X. J. The dynamical stability of reverberatory neural circuits. Biol. Cybern. 87, 471–481 (2002).
Hoffman, R. E. & Dobscha, S. K. Cortical pruning and the development of schizophrenia: a computer model. Schizophr. Bull. 15, 477–490 (1989).
Hoffman, R. E. Neural network simulations, cortical connectivity, and schizophrenic psychosis. MD Comput. 14, 200–208 (1997).
Hoffman, R. E. & McGlashan, T. H. Neural network models of schizophrenia. Neuroscientist 7, 441–454 (2001).
Treves, A. Are spin-glass effects relevant to understanding realistic auto-associative networks. J. Phys. A 24, 2645–2654 (1991).
Rolls, E. T. & Tovee, M. J. Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. J. Neurophysiol. 73, 713–726 (1995).
McGlashan, T. H. & Hoffman, R. E. Schizophrenia as a disorder of developmentally reduced synaptic connectivity. Arch. Gen. Psychiatry 57, 637–648 (2000).
O'Kane, D. & Treves, A. Why the simplest notion of neocortex as an autoassociative memory would not work. Network 3, 379–384 (1992).
Rolls, E. T. & Treves, A. Neural Networks and Brain Function (Oxford Univ. Press, Oxford, 1998).
Stephan, K. E., Baldeweg, T. & Friston, K. J. Synaptic plasticity and dysconnection in schizophrenia. Biol. Psychiatry 59, 929–939 (2006).
Friston, K. J. Dysfunctional connectivity in schizophrenia. World Psychiatry 1, 66–71 (2002).
Cohen, J. D., Braver, T. S. & O'Reilly, R. C. A computational approach to prefrontal cortex, cognitive control and schizophrenia: recent developments and current challenges. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, 1515–1527 (1996).
Braver, T. S., Barch, D. M. & Cohen, J. D. Cognition and control in schizophrenia: a computational model of dopamine and prefrontal function. Biol. Psychiatry 46, 312–328 (1999).
Cohen, J. D. & Servan-Schreiber, D. Context, cortex, and dopamine: a connectionist approach to behavior and biology in schizophrenia. Psychol. Rev. 99, 45–77 (1992).
Braver, T. S. & Cohen, J. D. in Disorders of Brain, Behavior, and Cognition: the Neurocomputational Perspective (eds Reggia, J. A., Ruppin, E. & Glanzman, D. L.) 327–350 (Elsevier, New York, 1999).
Durstewitz, D., Kelc, M. & Gunturkun, O. A neurocomputational theory of the dopaminergic modulation of working memory functions. J. Neurosci. 19, 2807–2822 (1999).
Seamans, J. K. & Yang, C. R. The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Prog. Neurobiol. 74, 1–58 (2004).
Durstewitz, D. A few important points about dopamine's role in neural network dynamics. Pharmacopsychiatry 39 (Suppl. 1), S72–S75 (2006).
Durstewitz, D. in Monoaminergic Modulation of Cortical Excitability (eds Tseng, K. Y. & Atzori, M.) 217–234 (Springer, Berlin, 2007).
Winterer, G. & Weinberger, D. R. Genes, dopamine and cortical signal-to-noise ratio in schizophrenia. Trends Neurosci. 27, 683–690 (2004). This paper provides a comprehensive overview of the noise concept in schizophrenia and of how different molecular mechanisms (dopamine, GABA and glutamate) contribute to cortical microcircuit stability by changing neural synchrony (noise).
Wang, X. J. Toward a prefrontal microcircuit model for cognitive deficits in schizophrenia. Pharmacopsychiatry 39 (Suppl. 1), S80–S87 (2006).
Wang, X. J., Tegner, J., Constantinidis, C. & Goldman-Rakic, P. S. Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory. Proc. Natl Acad. Sci. USA 101, 1368–1373 (2004).
Lewis, D. A., Hashimoto, T. & Volk, D. W. Cortical inhibitory neurons and schizophrenia. Nature Rev. Neurosci. 6, 312–324 (2005).
Rolls, E. T. Emotion Explained (Oxford Univ. Press, Oxford, 2005).
Loh, M., Rolls, E. T. & Deco, G. Statistical fluctuations in attractor networks related to schizophrenia. Pharmacopsychiatry 40, S78–S84 (2007).
Coyle, J. T., Tsai, G. & Goff, D. Converging evidence of NMDA receptor hypofunction in the pathophysiology of schizophrenia. Ann. NY Acad. Sci. 1003, 318–327 (2003).
Goldman-Rakic, P. S. Working memory dysfunction in schizophrenia. J. Neuropsychiatry Clin. Neurosci. 6, 348–357 (1994).
Goldman-Rakic, P. S. The physiological approach: functional architecture of working memory and disordered cognition in schizophrenia. Biol. Psychiatry 46, 650–661 (1999).
Durstewitz, D. & Seamans, J. K. The computational role of dopamine D1 receptors in working memory. Neural Netw. 15, 561–572 (2002).
Bilder, R. M. et al. Neurocognitive effects of clozapine, olanzapine, risperidone, and haloperidol in patients with chronic schizophrenia or schizoaffective disorder. Am. J. Psychiatry 159, 1018–1028 (2002).
Delawalla, Z. et al. Factors mediating cognitive deficits and psychopathology among siblings of individuals with schizophrenia. Schizophr. Bull. 32, 525–537 (2006).
Jacobs, J., Kahana, M. J., Ekstrom, A. D. & Fried, I. Brain oscillations control timing of single-neuron activity in humans. J. Neurosci. 27, 3839–3844 (2007).
Gallinat, J. et al. Frontal and temporal dysfunction of auditory stimulus processing in schizophrenia. Neuroimage 17, 110–127 (2002).
Rolls, E. T. The Brain and Emotion (Oxford Univ. Press, Oxford, 1999).
Paus, T. Primate anterior cingulate cortex: where motor control, drive and cognition interface. Nature Rev. Neurosci. 2, 417–424 (2001).
Winterer, G., Adams, C. M., Jones, D. W. & Knutson, B. Volition to action — an event-related fMRI study. Neuroimage 17, 851–858 (2002).
Rolls, E. T. in The Orbitofrontal Cortex (eds Zald, D. H. & Rauch, S. L.) 95–124 (Oxford Univ. Press, Oxford, 2006).
Seamans, J. K., Durstewitz, D., Christie, B. R., Stevens, C. F. & Sejnowski, T. J. Dopamine D1/D5 receptor modulation of excitatory synaptic inputs to layer V prefrontal cortex neurons. Proc. Natl Acad. Sci. USA 98, 301–306 (2001).
Friston, K. J. The labile brain. III. Transients and spatio-temporal receptive fields. Philos. Trans. R. Soc. Lond. B Biol. Sci. 355, 253–265 (2000).
Friston, K. J. The labile brain. II. Transients, complexity and selection. Philos. Trans. R. Soc. Lond. B Biol. Sci. 355, 237–252 (2000).
Friston, K. J. The labile brain. I. Neuronal transients and nonlinear coupling. Philos. Trans. R Soc. Lond. B Biol. Sci. 355, 215–236 (2000).
Fries, P., Roelfsema, P. R., Engel, A. K., Konig, P. & Singer, W. Synchronization of oscillatory responses in visual cortex. Proc. Natl Acad. Sci. USA 94, 12699–12704 (1997).
Franco, L., Rolls, E. T., Aggelopoulos, N. C. & Treves, A. The use of decoding to analyze the contribution to the information of the correlations between the firing of simultaneously recorded neurons. Exp. Brain Res. 155, 370–384 (2004).
Aggelopoulos, N. C., Franco, L. & Rolls, E. T. Object perception in natural scenes: encoding by inferior temporal cortex simultaneously recorded neurons. J. Neurophysiol. 93, 1342–1357 (2005).
Softky, W. R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).
Shadlen, M. N. & Newsome, W. T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569–579 (1994).
Durstewitz, D. & Gabriel, T. Dynamical basis of irregular spiking in NMDA-driven prefrontal cortex neurons. Cereb. Cortex 17, 894–908 (2007).
Niessing, J. et al. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309, 948–951 (2005).
Chawla, D., Lumer, E. D. & Friston, K. J. The relationship between synchronization among neuronal populations and their mean activity levels. Neural. Comput. 11, 1389–1411 (1999).
Mitzdorf, U. & Singer, W. Prominent excitatory pathways in the cat visual cortex (A 17 and A 18): a current source density analysis of electrically evoked potentials. Exp. Brain Res. 33, 371–394 (1978).
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).
Kwon, J. S. et al. Gamma frequency-range abnormalities to auditory stimulation in schizophrenia. Arch. Gen. Psychiatry 56, 1001–1005 (1999).
Uhlhaas, P. J. & Singer, W. Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron 52, 155–168 (2006).
Izhikevich, E. M. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (MIT Press, Cambridge, Massachusetts, 2007).
Winterer, G. et al. Cortical activation, signal-to-noise ratio and stochastic resonance during information processing in man. Clin. Neurophysiol. 110, 1193–1203 (1999).
Tateno, T., Harsch, A. & Robinson, H. P. Threshold firing frequency-current relationships of neurons in rat somatosensory cortex: type 1 and type 2 dynamics. J. Neurophysiol. 92, 2283–2294 (2004).
Ward, L. M., Doesburg, S. M., Kitajo, K., MacLean, S. E. & Roggeveen, A. B. Neural synchrony in stochastic resonance, attention, and consciousness. Can. J. Exp. Psychol. 60, 319–326 (2006).
Melloni, L. et al. Synchronization of neural activity across cortical areas correlates with conscious perception. J. Neurosci. 27, 2858–2865 (2007).
Winterer, G. et al. Prefrontal electrophysiologic “noise” and catechol-O-methyltransferase genotype in schizophrenia. Biol. Psychiatry 60, 578–584 (2006). This study demonstrated that dopamine reduces electrophysiological noise.
Krishnan, G. P. et al. Steady state visual evoked potential abnormalities in schizophrenia. Clin. Neurophysiol. 116, 614–624 (2005).
Kim, D. et al. A method for multi-group inter-participant correlation: abnormal synchrony in patients with schizophrenia during auditory target detection. Neuroimage 39, 1129–1141 (2008).
Rolls, E. T., Thorpe, S. J., Boytim, M., Szabo, I. & Perrett, D. I. Responses of striatal neurons in the behaving monkey. 3. Effects of iontophoretically applied dopamine on normal responsiveness. Neuroscience 12, 1201–1212 (1984).
Brozoski, T. J., Brown, R. M., Rosvold, H. E. & Goldman, P. S. Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey. Science 205, 929–932 (1979).
Sawaguchi, T., Matsumura, M. & Kubota, K. Dopamine enhances the neuronal activity of spatial short-term memory task in the primate prefrontal cortex. Neurosci. Res. 5, 465–473 (1988).
Sawaguchi, T., Matsumura, M. & Kubota, K. Catecholaminergic effects on neuronal activity related to a delayed response task in monkey prefrontal cortex. J. Neurophysiol. 63, 1385–1400 (1990).
Sawaguchi, T. & Goldman-Rakic, P. S. The role of D1-dopamine receptor in working memory: local injections of dopamine antagonists into the prefrontal cortex of rhesus monkeys performing an oculomotor delayed-response task. J. Neurophysiol. 71, 515–528 (1994).
Sawaguchi, T. & Goldman-Rakic, P. S. D1 dopamine receptors in prefrontal cortex: involvement in working memory. Science 251, 947–950 (1991).
Goldman-Rakic, P. S., Muly, E. C. & Williams, G. V. D1 receptors in prefrontal cells and circuits. Brain Res. Brain Res. Rev. 31, 295–301 (2000).
Williams, G. V. & Goldman-Rakic, P. S. Modulation of memory fields by dopamine D1 receptors in prefrontal cortex. Nature 376, 572–575 (1995).
Vijayraghavan, S., Wang, M., Birnbaum, S. G., Williams, G. V. & Arnsten, A. F. Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory. Nature Neurosci. 10, 376–384 (2007).
Castner, S. A., Williams, G. V. & Goldman-Rakic, P. S. Reversal of antipsychotic-induced working memory deficits by short-term dopamine D1 receptor stimulation. Science 287, 2020–2022 (2000).
Floresco, S. B., Magyar, O., Ghods-Sharifi, S., Vexelman, C. & Tse, M. T. Multiple dopamine receptor subtypes in the medial prefrontal cortex of the rat regulate set-shifting. Neuropsychopharmacology 31, 297–309 (2006).
Stemme, A., Deco, G. & Busch, A. The neuronal dynamics underlying cognitive flexibility in set shifting tasks. J. Comput. Neurosci. 23, 313–331 (2007).
Akil, M. et al. Lamina-specific alterations in the dopamine innervation of the prefrontal cortex in schizophrenic subjects. Am. J. Psychiatry 156, 1580–1589 (1999).
Okubo, Y., Suhara, T., Sudo, Y. & Toru, M. Possible role of dopamine D1 receptors in schizophrenia. Mol. Psychiatry 2, 291–292 (1997).
Okubo, Y. et al. Decreased prefrontal dopamine D1 receptors in schizophrenia revealed by PET. Nature 385, 634–636 (1997).
Abi-Dargham, A. et al. Prefrontal dopamine D1 receptors and working memory in schizophrenia. J. Neurosci. 22, 3708–3719 (2002).
Egan, M. F. et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc. Natl Acad. Sci. USA 98, 6917–6922 (2001).
Winterer, G. et al. Instability of prefrontal signal processing in schizophrenia. Am. J. Psychiatry 163, 1960–1968 (2006). This was the first report on abnormal noise in schizophrenia, as measured with fMRI.
Winterer, G. et al. COMT genotype predicts BOLD signal and noise characteristics in prefrontal circuits. Neuroimage 32, 1722–1732 (2006). This study demonstrated that dopamine reduces cortical noise, as measured with fMRI.
Durstewitz, D. & Seamans, J. K. The dual-state theory of prefrontal cortex dopamine function with relevance to COMT genotypes and schizophrenia. Biol. Psychiatry 11 Jul 2008 (doi:10.1016/j.biopsych.2008.05.015). This recent paper analysed the role of dopamine in the computations that are performed by cortical networks, and its relevance to schizophrenia.
Muly, E. C., Szigeti, K. & Goldman-Rakic, P. S. D1 receptor in interneurons of macaque prefrontal cortex: distribution and subcellular localization. J. Neurosci. 18, 10553–10565 (1998).
Winterer, G. Prefrontal dopamine signaling in schizophrenia — the corticocentric model. Pharmacopsychiatry 40 (Suppl. 1), S45–S53 (2007).
Lewis, D. A. & Gonzalez-Burgos, G. Pathophysiologically based treatment interventions in schizophrenia. Nature Med. 12, 1016–1022 (2006).
Hebb, D. O. The Organization of Behavior: A Neuropsychological Theory (Wiley, New York, 1949).
This work was supported by the European Union, grant EC005-024; by the Spanish Research Project BFU2007-61710/BFI and CONSOLIDER INGENIO 2010 (G.D.); by two grants from the German Research Foundation (Deutsche Forschungsgemeinschaft): Wi1316/2-1 and Wi1316/2-2 (G.W.); by a Fellowship from the Fogarty Foundation (G.W.); by the Oxford McDonnell Centre for Cognitive Neuroscience (E.T.R.); and by the Boehringer Ingelheim Fonds (M.L.).
The authors declare no competing financial interests.
- Dysexecutive syndrome
A disorder of the planning and organization of actions that is typically produced by damage to the prefrontal cortex.
- Time constant
The time the system takes to reach 1/e of its initial value.
- Attractor networks
Neural networks in which sets of neurons with strong interconnections have stable high-firing-rate states into which they can be attracted by memory-retrieval cues. The strong interconnections are formed during a learning period in which a set of neurons is active. An attractor net can be used to implement a short-term memory.
- Long-term potentiation
(LTP). A long-term increase in synaptic strength.
With a Poisson distribution.
- Hopfield equation
This is a measure of the stability of an attractor state that reflects the depth of the basin of attraction.
- Disconnection hypothesis of schizophrenia
A hypothesis which suggests that brain regions such as the frontal and temporal lobes become relatively disconnected in schizophrenia.
- Gain function
The sensitivity of a working-memory system to external stimuli in some models.
- Multi-compartment Hodgkin–Huxley neurons
Models of neurons with separate biophysical parameters and modelling for the different parts of neurons, including different parts of the dendritic tree.
- Basins of attraction
The shape in state space of the gradients of the low-energy, stable states into which a subset of neurons in an attractor network can be drawn.
- Blood-oxygen-level-dependent (BOLD) brain response
A signal that can be extracted with fMRI and that reflects the change in the amount of deoxyhaemoglobin that is induced by changes in the activity of neurons and their synapses in a region of the brain. The signal thus reflects the activity in a local brain region.
- Local field potentials
The potentials in a local brain area that reflect the activity of many neurons and their synaptic inputs.
- Event-related potentials
The potentials elicited in a brain area by the activity of neurons and their synaptic inputs in response to an event or stimulus.
- Gamma frequency band
The spectral frequency band of the electrical activity of the brain that is close to 40 Hz.
- Phase locking
Time locking of brain oscillations to (sensory) stimuli or (motor) responses.
- Broadband phase synchrony
A measure of whether the energy in different spectral frequency bands of the electrical activity of the brain is in phase.
- Catechol-O-methyl-transferase (COMT) gene
The gene that encodes the COMT enzyme, which provides one of the ways in which dopamine is degraded by methylation and therefore removed from the activity at a synapse. If COMT is too active there are likely to be low levels of dopamine in the prefrontal cortex, and this might be related to the cognitive symptoms of schizophrenia.
About this article
Cite this article
Rolls, E., Loh, M., Deco, G. et al. Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat Rev Neurosci 9, 696–709 (2008). https://doi.org/10.1038/nrn2462
This article is cited by
Nature Communications (2023)
npj Parkinson's Disease (2023)
Dopamine transporter silencing in the rat: systems-level alterations in striato-cerebellar and prefrontal-midbrain circuits
Molecular Psychiatry (2022)
Scientific Reports (2022)