- Email: [email protected]
Modelling the influence of thalamo-cortical projections on prefrontal activity
Neurocomputing 44–46 (2002) 869 – 873
www.elsevier.com/locate/neucom
Modelling the in"uence of thalamo-cortical projections on prefrontal activity Lars Kampera , Ahmet Bozkurta , Pernille Nielsenb , Jonas Dyhrfjeld-Johnsena , Klaas E. Stephana; c , Rolf K5ottera; d; ∗ a Computational
Systems Neuroscience Group, C. & O. Vogt Brain Research Institute, Heinrich Heine University, 40225 D%usseldorf, Germany b Niels Bohr Institute of Astrophysics, Physics and Geophysics, University of Copenhagen, Denmark c Department of Psychology, University of Newcastle upon Tyne, NE1 7RU, Newcastle, UK d Institute of Morphological Endocrinology and Histochemistry, Heinrich Heine University, 40225 D%usseldorf, Germany
Abstract Altered connections between the thalamus and the prefrontal cortex are often implicated in theories concerning the pathogenesis of schizophrenia. Using a model based on anatomically realistic connection patterns between thalamus and prefrontal cortex, we analyzed changes of prefrontal activity resulting from stimulation of the mediodorsal thalamus. Here we demonstrate that the speci9c in"uence of thalamo-cortical projections is smaller than the importance of intra-prefrontal connectivity in shaping prefrontal activity patterns. These results might be helpful in explaining experimentally observed hypo- and hyperactive states of prefrontal cortex c 2002 Elsevier Science B.V. All rights reserved. in schizophrenic patients. Keywords: Schizophrenia; Thalamus; Prefrontal cortex; Connectivity; Computer model
1. Introduction Experimental studies on the pathophysiology of schizophrenia have provided strong evidence that dysfunction in the thalamo-prefrontal cortical network is a fundamental feature of this disease. For example, neuropathological studies of the mediodorsal thalamic nucleus (MD), a main source of subcortical input to prefrontal cortex (PFC), have ∗
Corresponding author. Tel.: +49-211-81-12095; fax: +49-21181-12336. E-mail addresses: [email protected] (L. Kamper), [email protected] (A. Bozkurt), [email protected] (P. Nielsen), [email protected] (J. Dyhrfjeld-Johnsen), [email protected] (K.E. Stephan), [email protected] (R. K5otter). c 2002 Elsevier Science B.V. All rights reserved. 0925-2312/02/$ - see front matter PII: S 0 9 2 5 - 2 3 1 2 ( 0 2 ) 0 0 4 8 5 - X
870
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
consistently demonstrated loss of speci9c types of neurons in schizophrenic patients [7,11]. These putative defects in the thalamo-prefrontal projections might explain wellknown observations of ‘hypofrontality’ [3] in schizophrenic patients [5]. To investigate this possibility more systematically, we compared the speci9c eKects of MD input on prefrontal activation patterns to random changes in thalamo-cortical and intraprefrontal connectivity in a computational model of thalamo-prefrontal networks. 2. Methods Using the primate connectivity database CoCoMac (www.cocomac.org and [8]), we systematically collated data from almost all published experiments tracing anatomical projections between the thalamus and PFC in the macaque monkey. Objective relational transformation (ORT; [9]) was used to map the data into the schemes of Olszewski [6] for the thalamus, and Walker [10] for PFC (Fig. 1). Propagation and patterns of activity in PFC were investigated by using a simpli9ed network model implemented in the general neural simulation system (GENESIS, [1]). Areas were represented as integrate-and-9re units extended with inhibitory autapses and connected according to their thalamo-prefrontal connectivity obtained from CoCoMac. Compartment and synaptic parameters were taken from the visual network model of K5otter et al. [4]. The anatomically realistic model (REAL) was compared with three models characterized by randomized connectional properties. For this purpose, we shuPed the original connectivity matrix in three diKerent ways (Fig. 1): randomizations of (i) thalamic (randomTHA), (ii) intra-prefrontal (randomPFC), and (iii) separately both
Fig. 1. Input matrix for the model as derived from CoCoMac. Missing values were regarded as zeros (italic). Connection strengths are coded by 0 (absent), 1 (weak), 2 (medium) and 3 (strong).
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
871
the intra-prefrontal and thalamic projections in one matrix (randomALL). Overall, we created 60 randomized matrices for the three control simulations (i.e. 20 randomized matrices for each category). Finally, we analyzed the spike frequencies of the model units between simulations with two diKerent statistical methods. First, for each cortical area, the spike frequency in the REAL-model was compared with the corresponding results from randomTHA, randomPFC, and randomALL, respectively, in a descriptive manner. That is, for each random model, we computed the distance between the mean of the spiking frequency distribution across the 20 simulations, in terms of standard deviations, and the frequency delivered by the REAL simulation. Second, we used a multifactorial analysis of variance (SPSS, v.9.0, SPSS, Inc.) to assess diKerences between frequencies produced by the three connectionally randomized models (randomTHA, randomPFC, randomALL). Post hoc pairwise comparisons were conducted by Bonferroni-corrected t-tests for each of the 12 cortical areas included in the model, testing for signi9cant eKects of connectional structure on the spike frequency within each area. 3. Results Stimulation of thalamo-cortical activation based on the anatomical connectivity produced a characteristic prefrontal cortical activation pattern. In contrast, simulations based on randomized connectional properties produced a markedly diKerent activation pattern of most prefrontal target areas (Fig. 2). Comparing the spike frequencies of each cortical area between REAL and randomized models, we highlighted those areas whose frequencies in REAL diKered more than two standard deviations from the mean frequencies in the random models (Fig. 3). For the randomALL model these areas were W11 and W45, for the
Fig. 2. Spike frequencies in Hz (y-axis; mean+standard deviation) of prefrontal area models (x-axis) for the three diKerent random categories and the anatomical network (REAL-model).
872
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
Fig. 3. Distances (in units of standard deviations) between the frequencies of areas in the REAL simulation and the mean of the frequencies produced by the 20 simulations for each random model.
randomPFC model it was W45 only, and for the last category randomTHA no frequency diKered by more than two standard deviations from the REAL activation pattern. Within the context of the multifactorial analysis of variance, pairwise comparisons between the eKect of the random models on spiking frequency in each area revealed signi9cant diKerences between all three categories in W10, W11, W25, and W46 (all p ¡ 0:016). Signi9cant diKerences between the randomTHA and the remaining two categories were found in W12, W24, W45 and W9 (all p ¡ 0:0005). Area W14 and W8A showed signi9cant diKerences only between simulations with matrices of the categories randomTHA and randomALL (all p ¡ 0:035). Finally, we obtained no signi9cant diKerences between any categories in W13 and W8B.
4. Discussion Previous observations of ‘hypofrontality’ in schizophrenic patients have been explained by putative defects in the thalamo-prefrontal projections [3]. Our simulations only partially support this view by showing that changes in connectivity patterns (while maintaining an identical overall number and density of connectivity) between thalamus and prefrontal cortex do lead to regionally speci9c diKerences of activity patterns—but that major changes were only observed if cortico-cortical connections were included in the randomization procedure. In contrast, shuPing of the thalamo-prefrontal connections alone was not suRcient to produce signi9cant deviations in the activation patterns compared to the anatomically realistic model (REAL). The more pronounced impact of changing intra-prefrontal connections as compared to altering thalamo-prefrontal connections was particularly evident for orbitofrontal area 11 and ventrolateral area 45 (Fig. 3).
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
873
Furthermore, the concept of impaired thalamo-cortical connectivity as a main mechanism underlying schizophrenia is diRcult to reconcile, with more recent 9ndings of condition-speci9c hyperactivation of dorsolateral prefrontal cortex (DLPFC) in functional imaging studies [2]. An alternative explanation, which is more compatible with our simulation results, is that the loss of thalamo-cortical aKerents and thalamo-cortical excitation may result in various degrees of compensatory hyperexcitability of the aKected cortical target areas. As a consequence, thalamic input may lead to almost normal or decreased cortical activation. If this hyperexcitability lacks selectivity to thalamo-cortical aKerents, then cortico-cortical aKerents would lead to enhanced responses in the aKected areas. We will explore this possibility by future simulations based on realistic and schizophreniaspeci9c thalamo-cortical connectivity. Acknowledgements L.K. and A.B. thank Alexander Dreiling and Thomas SpliethoK for helpful discussions on statistical methods and analyses. References [1] J.M. Bower, D. Beeman, The book of Genesis, 2nd Edition, Springer, New York, 1997. [2] J.H. Callicott, A. Bertolino, V.S. Mattay, F.J. Langheim, J. Duyn, R. Coppola, T.E. Goldberg, D.R. Weinberger, Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited, Cereb. Cortex 10 (2000) 1078–1092. [3] G. Franzen, D.H. Ingvar, Absence of activation in frontal structures during psychological testing of chronic schizophrenics, J. Neurol. Neurosurg. Psychiatry 38 (1975) 1027–1032. [4] R. K5otter, P. Nielsen, J. Dyhrfjeld-Johnsen, F.T. Sommer, G. NorthoK, Multi-level neuron and network modeling in computational neuroanatomy, in: G. Ascoli (Ed.), Computational Neuroanatomy: Principles and Methods, Totowa NJ, Humana, 2002. [5] D.A. Lewis, J.A. Lieberman, Catching up on schizophrenia: Natural history and neurobiology, Neuron 28 (2000) 325–334. [6] J. Olszewski, The Thalamus of the Macaca Mulatta, S. Karger, New York, 1952. [7] G.J. Popken, W.E. Bunney Jr., S.G. Potkin, E.G. Jones, Subnucleus-speci9c loss of neurons in medial thalamus of schizophrenics, Proc. Natl. Acad. Sci. USA 97 (2000) 9276–9280. [8] K.E. Stephan, L. Kamper, A. Bozkurt, G.A. Burns, M.P. Young, R. Kotter, Advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac), Philos. Trans. Roy. Soc. London B 356 (2001) 1159–1186. [9] K.E. Stephan, K. Zilles, R. Kotter, Coordinate independent mapping of structural and functional cortical data by objective relational transformation ORT, Philos. Trans. Roy. Soc. London B 355 (2000) 37–54. [10] A.E. Walker, A cytoarchitectural study of the prefrontal area of the macaque monkey, J. Comp. Neurol. 98 (1940) 59–86. [11] K.A. Young, K.F. Manaye, C. Liang, P.B. Hicks, D.C. German, Reduced number of mediodorsal and anterior thalamic neurons in schizophrenia, Biol. Psychiatry 47 (2000) 944–953.
www.elsevier.com/locate/neucom
Modelling the in"uence of thalamo-cortical projections on prefrontal activity Lars Kampera , Ahmet Bozkurta , Pernille Nielsenb , Jonas Dyhrfjeld-Johnsena , Klaas E. Stephana; c , Rolf K5ottera; d; ∗ a Computational
Systems Neuroscience Group, C. & O. Vogt Brain Research Institute, Heinrich Heine University, 40225 D%usseldorf, Germany b Niels Bohr Institute of Astrophysics, Physics and Geophysics, University of Copenhagen, Denmark c Department of Psychology, University of Newcastle upon Tyne, NE1 7RU, Newcastle, UK d Institute of Morphological Endocrinology and Histochemistry, Heinrich Heine University, 40225 D%usseldorf, Germany
Abstract Altered connections between the thalamus and the prefrontal cortex are often implicated in theories concerning the pathogenesis of schizophrenia. Using a model based on anatomically realistic connection patterns between thalamus and prefrontal cortex, we analyzed changes of prefrontal activity resulting from stimulation of the mediodorsal thalamus. Here we demonstrate that the speci9c in"uence of thalamo-cortical projections is smaller than the importance of intra-prefrontal connectivity in shaping prefrontal activity patterns. These results might be helpful in explaining experimentally observed hypo- and hyperactive states of prefrontal cortex c 2002 Elsevier Science B.V. All rights reserved. in schizophrenic patients. Keywords: Schizophrenia; Thalamus; Prefrontal cortex; Connectivity; Computer model
1. Introduction Experimental studies on the pathophysiology of schizophrenia have provided strong evidence that dysfunction in the thalamo-prefrontal cortical network is a fundamental feature of this disease. For example, neuropathological studies of the mediodorsal thalamic nucleus (MD), a main source of subcortical input to prefrontal cortex (PFC), have ∗
Corresponding author. Tel.: +49-211-81-12095; fax: +49-21181-12336. E-mail addresses: [email protected] (L. Kamper), [email protected] (A. Bozkurt), [email protected] (P. Nielsen), [email protected] (J. Dyhrfjeld-Johnsen), [email protected] (K.E. Stephan), [email protected] (R. K5otter). c 2002 Elsevier Science B.V. All rights reserved. 0925-2312/02/$ - see front matter PII: S 0 9 2 5 - 2 3 1 2 ( 0 2 ) 0 0 4 8 5 - X
870
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
consistently demonstrated loss of speci9c types of neurons in schizophrenic patients [7,11]. These putative defects in the thalamo-prefrontal projections might explain wellknown observations of ‘hypofrontality’ [3] in schizophrenic patients [5]. To investigate this possibility more systematically, we compared the speci9c eKects of MD input on prefrontal activation patterns to random changes in thalamo-cortical and intraprefrontal connectivity in a computational model of thalamo-prefrontal networks. 2. Methods Using the primate connectivity database CoCoMac (www.cocomac.org and [8]), we systematically collated data from almost all published experiments tracing anatomical projections between the thalamus and PFC in the macaque monkey. Objective relational transformation (ORT; [9]) was used to map the data into the schemes of Olszewski [6] for the thalamus, and Walker [10] for PFC (Fig. 1). Propagation and patterns of activity in PFC were investigated by using a simpli9ed network model implemented in the general neural simulation system (GENESIS, [1]). Areas were represented as integrate-and-9re units extended with inhibitory autapses and connected according to their thalamo-prefrontal connectivity obtained from CoCoMac. Compartment and synaptic parameters were taken from the visual network model of K5otter et al. [4]. The anatomically realistic model (REAL) was compared with three models characterized by randomized connectional properties. For this purpose, we shuPed the original connectivity matrix in three diKerent ways (Fig. 1): randomizations of (i) thalamic (randomTHA), (ii) intra-prefrontal (randomPFC), and (iii) separately both
Fig. 1. Input matrix for the model as derived from CoCoMac. Missing values were regarded as zeros (italic). Connection strengths are coded by 0 (absent), 1 (weak), 2 (medium) and 3 (strong).
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
871
the intra-prefrontal and thalamic projections in one matrix (randomALL). Overall, we created 60 randomized matrices for the three control simulations (i.e. 20 randomized matrices for each category). Finally, we analyzed the spike frequencies of the model units between simulations with two diKerent statistical methods. First, for each cortical area, the spike frequency in the REAL-model was compared with the corresponding results from randomTHA, randomPFC, and randomALL, respectively, in a descriptive manner. That is, for each random model, we computed the distance between the mean of the spiking frequency distribution across the 20 simulations, in terms of standard deviations, and the frequency delivered by the REAL simulation. Second, we used a multifactorial analysis of variance (SPSS, v.9.0, SPSS, Inc.) to assess diKerences between frequencies produced by the three connectionally randomized models (randomTHA, randomPFC, randomALL). Post hoc pairwise comparisons were conducted by Bonferroni-corrected t-tests for each of the 12 cortical areas included in the model, testing for signi9cant eKects of connectional structure on the spike frequency within each area. 3. Results Stimulation of thalamo-cortical activation based on the anatomical connectivity produced a characteristic prefrontal cortical activation pattern. In contrast, simulations based on randomized connectional properties produced a markedly diKerent activation pattern of most prefrontal target areas (Fig. 2). Comparing the spike frequencies of each cortical area between REAL and randomized models, we highlighted those areas whose frequencies in REAL diKered more than two standard deviations from the mean frequencies in the random models (Fig. 3). For the randomALL model these areas were W11 and W45, for the
Fig. 2. Spike frequencies in Hz (y-axis; mean+standard deviation) of prefrontal area models (x-axis) for the three diKerent random categories and the anatomical network (REAL-model).
872
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
Fig. 3. Distances (in units of standard deviations) between the frequencies of areas in the REAL simulation and the mean of the frequencies produced by the 20 simulations for each random model.
randomPFC model it was W45 only, and for the last category randomTHA no frequency diKered by more than two standard deviations from the REAL activation pattern. Within the context of the multifactorial analysis of variance, pairwise comparisons between the eKect of the random models on spiking frequency in each area revealed signi9cant diKerences between all three categories in W10, W11, W25, and W46 (all p ¡ 0:016). Signi9cant diKerences between the randomTHA and the remaining two categories were found in W12, W24, W45 and W9 (all p ¡ 0:0005). Area W14 and W8A showed signi9cant diKerences only between simulations with matrices of the categories randomTHA and randomALL (all p ¡ 0:035). Finally, we obtained no signi9cant diKerences between any categories in W13 and W8B.
4. Discussion Previous observations of ‘hypofrontality’ in schizophrenic patients have been explained by putative defects in the thalamo-prefrontal projections [3]. Our simulations only partially support this view by showing that changes in connectivity patterns (while maintaining an identical overall number and density of connectivity) between thalamus and prefrontal cortex do lead to regionally speci9c diKerences of activity patterns—but that major changes were only observed if cortico-cortical connections were included in the randomization procedure. In contrast, shuPing of the thalamo-prefrontal connections alone was not suRcient to produce signi9cant deviations in the activation patterns compared to the anatomically realistic model (REAL). The more pronounced impact of changing intra-prefrontal connections as compared to altering thalamo-prefrontal connections was particularly evident for orbitofrontal area 11 and ventrolateral area 45 (Fig. 3).
L. Kamper et al. / Neurocomputing 44–46 (2002) 869 – 873
873
Furthermore, the concept of impaired thalamo-cortical connectivity as a main mechanism underlying schizophrenia is diRcult to reconcile, with more recent 9ndings of condition-speci9c hyperactivation of dorsolateral prefrontal cortex (DLPFC) in functional imaging studies [2]. An alternative explanation, which is more compatible with our simulation results, is that the loss of thalamo-cortical aKerents and thalamo-cortical excitation may result in various degrees of compensatory hyperexcitability of the aKected cortical target areas. As a consequence, thalamic input may lead to almost normal or decreased cortical activation. If this hyperexcitability lacks selectivity to thalamo-cortical aKerents, then cortico-cortical aKerents would lead to enhanced responses in the aKected areas. We will explore this possibility by future simulations based on realistic and schizophreniaspeci9c thalamo-cortical connectivity. Acknowledgements L.K. and A.B. thank Alexander Dreiling and Thomas SpliethoK for helpful discussions on statistical methods and analyses. References [1] J.M. Bower, D. Beeman, The book of Genesis, 2nd Edition, Springer, New York, 1997. [2] J.H. Callicott, A. Bertolino, V.S. Mattay, F.J. Langheim, J. Duyn, R. Coppola, T.E. Goldberg, D.R. Weinberger, Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited, Cereb. Cortex 10 (2000) 1078–1092. [3] G. Franzen, D.H. Ingvar, Absence of activation in frontal structures during psychological testing of chronic schizophrenics, J. Neurol. Neurosurg. Psychiatry 38 (1975) 1027–1032. [4] R. K5otter, P. Nielsen, J. Dyhrfjeld-Johnsen, F.T. Sommer, G. NorthoK, Multi-level neuron and network modeling in computational neuroanatomy, in: G. Ascoli (Ed.), Computational Neuroanatomy: Principles and Methods, Totowa NJ, Humana, 2002. [5] D.A. Lewis, J.A. Lieberman, Catching up on schizophrenia: Natural history and neurobiology, Neuron 28 (2000) 325–334. [6] J. Olszewski, The Thalamus of the Macaca Mulatta, S. Karger, New York, 1952. [7] G.J. Popken, W.E. Bunney Jr., S.G. Potkin, E.G. Jones, Subnucleus-speci9c loss of neurons in medial thalamus of schizophrenics, Proc. Natl. Acad. Sci. USA 97 (2000) 9276–9280. [8] K.E. Stephan, L. Kamper, A. Bozkurt, G.A. Burns, M.P. Young, R. Kotter, Advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac), Philos. Trans. Roy. Soc. London B 356 (2001) 1159–1186. [9] K.E. Stephan, K. Zilles, R. Kotter, Coordinate independent mapping of structural and functional cortical data by objective relational transformation ORT, Philos. Trans. Roy. Soc. London B 355 (2000) 37–54. [10] A.E. Walker, A cytoarchitectural study of the prefrontal area of the macaque monkey, J. Comp. Neurol. 98 (1940) 59–86. [11] K.A. Young, K.F. Manaye, C. Liang, P.B. Hicks, D.C. German, Reduced number of mediodorsal and anterior thalamic neurons in schizophrenia, Biol. Psychiatry 47 (2000) 944–953.