- Email: [email protected]
The structural basis of information transfer from medial temporal lobe to prefrontal cortex in the macaque monkey
Neurocomputing 44–46 (2002) 753 – 758
www.elsevier.com/locate/neucom
The structural basis of information transfer from medial temporal lobe to prefrontal cortex in the macaque monkey Ahmet Bozkurta , Lars Kampera , Klaas E. Stephanc; d , Rolf K1ottera; b; ∗ a Computational
Systems Neuroscience Group, C. & O. Vogt Brain Research Institute, Heinrich Heine University, Universitatsstr. 1, D-40225 Dusseldorf, Germany b Department of Anatomy II, Heinrich Heine University, Universitatsstr. 1, D-40225 Dusseldorf, Germany c Institute of Medicine, Research Centre Julich, 52425 Julich, Germany d Department of Psychology, University of Newcastle-upon-Jyne, NE1 7RU Newcastle, UK
Abstract A variety of functional studies have emphasized the importance of the medial temporal lobe (MTL) and prefrontal cortex (PFC) for episodic and working memory, respectively. We investigated the structural basis of information transfer from MTL to PFC in primate cerebral cortex using the CoCoMac database (www.cocomac.org), coordinate-independent mapping procedures, and multivariate statistics. In our meta-analysis, we found a clearly decreasing gradient of MTL projections to PFC: strongest from periallocortical regions and weakest from hippocampus proper. These projections roughly divide the PFC into medial and lateral groups of areas. c 2002 Elsevier Science B.V. All rights reserved. Keywords: Database; Connectivity; Multivariate analyses; Primate; Memory
1. Introduction The cortical memory system is divided into several speci>c subsystems: for example, medial temporal lobe structures (MTL) are associated with episodic memory [4], whereas the prefrontal cortex (PFC) is part of the working memory network [7,10]. A ∗
Corresponding author. C. & O. Vogt Brain Research Institute, Heinrich Heine University, Universit1atsstr. 1, D-40225 Dusseldorf, Germany. Tel.: +49-211-81-12095; fax: +49-211-81-12336. E-mail address: [email protected] (R. K1otter). 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 6 8 - X
754
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
challenging question is how these diLerent modalities of memory are integrated in various cognitive contexts, including conscious selection of items from long-term episodic memory in working memory tasks [2]. A >rst step towards understanding the relation between episodic and working memory is to unravel the neuroanatomical basis of information transfer from MTL to PFC. Therefore, we investigated the detailed structure of MTL projections to the prefrontal cortex. In this article, we present the meta-analysis of a comprehensive large-scale network of anatomical projections from MTL to PFC by means of advanced database methods [11] and multivariate statistics [3,5,6].
2. Methods Using the macaque connectivity database CoCoMac ([11], see www.cocomac.org), we systematically collated data from published articles comprising tracer injections in MTL or PFC in macaque monkeys. The MTL structures included in our analysis were hippocampus proper (CA1–CA4 and DG), subicular complex (ProS, Sub, PreS, ParaS), parahippocampal (TF, TH, and TG), entorhinal (E) and perirhinal (35 and 36) areas. Since anatomical reports used diLerent and partially incompatible maps, Objective Relational Transformation (ORT) [12] was used to map all data into a common space, namely the parcellations proposed by Amaral et al. [1] and Rosene & Van Hoesen [9] for MTL, and Walker [13] for PFC. Since relative strengths of connections were scarcely reported, we evaluated projections in a binary fashion. We did not distinguish between unknown and absent projections. Vectors of projection patterns were correlated using the simple matching algorithm for binary data (existing or absent projections). Subsequently, correlation vectors were analyzed by two independent multivariate statistical techniques, multidimensional scaling and hierarchical clustering, to reveal the overall topography of similarity in connectional patterns between areas. Hierarchical clustering (HCA) successively amalgamates areas according to hierarchical degrees of similarity between their connectivity patterns. This procedure was performed for eLerent projections from MTL to PFC. Multidimensional scaling (MDS) interprets the correlations between inter-areal connectivity vectors as proximities in high-dimensional space. The resulting con>gurations are projected into two dimensions under maximal preservation of the proximity ranking. Both MDS and HCA have previously been used for the analysis of neural connectivity (e.g. 6 –8). In the present analysis, SYSTAT 9.0 (SPSS Inc.) under Windows NT 4.0 was used, applying Kruskal’s STRESS formula 1 to MDS and an Euclidean metric with complete linkage in the case of HCA (Table 1).
3. Results The anatomical projection patterns were derived from 16 studies including more than 90 tracer injections and 780 resulting labeled sizes.
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
755
Table 1 “0”, “1”, and no entry represent absent, existing, and unknown projections, respectively. Projection strengths were not coded in this binary matrix
Source areas
E 35 36 TF TG TH CA1 CA2 CA3 CA4 DG ProS Sub PreS ParaS
Target areas W10
W11
W12
W13
1 1
1 0 0 1 1 1 1 0 0 0 0 0 0 0 0
0 0 1 1 1 0 0 0 0 0 0 1 1
1 1
0 1 0
1
1 1 0 0 0 0 1 0 1 0
W14
W24
W25
1 1 1 1 1 1 1 0 0 0 0 1 1 1 0
0 1 1 1 1 1 0 0 0 0 0 1 1 0
1 1 0 1 1 0 1 0 0 0 0 1 1 1 0
W8B
1 0 1
W46
1 1 1 0 0 0 0 0 0 0 1 1
W9 0 1 0 1 1 1
Using MDS and HCA, projections of MTL structures to the PFC could be grouped according to the similarity of sending and recipient brain regions, respectively. Among MTL areas (Fig. 1), both statistical procedures showed a cluster consisting of areas DG, CA2-CA4, PreS, and ParaS, which have no or a unique pattern of projections to PFC. Other areas were sequentially arranged. Among these, TF and TH formed a second group characterized by the most extensive projections to the PFC. The remaining areas with sparser projections formed clusters that varied between MDS and HCA, respectively. MDS produced two subgroups, i.e. 35, E, and Sub, on the one hand, and 36, CA1, and ProS, on the other hand. HCA con>rmed the close association of areas 36 and ProS, but slightly regrouped the other areas (subgroups with E + CA1 and 35 + Sub, respectively). MTL projections also grouped their prefrontal destination areas (Fig. 2): Orbito-medial areas 10, 13, 14, 24, and 25 were contrasted by another group of areas that comprised 8B, 9, 11, 12, and 46. This distinction roughly divides PFC into medial and lateral regions. 4. Discussion Multivariate statistical analyses provided insights into the structural organization of projections from the MTL to the PFC. Concerning the MTL, there was a notable absence of any projection from the hippocampus proper with the only exception of the hippocampal sub>eld CA1, which is the most peripheral of its areas. By contrast,
756
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
Multidimensional scaling
Hierarchical cluster analysis TH TF TG 35 Sub E CA1 ProS 36 DG CA3 CA2 CA4 ParaS PreS
2
Dimension-2
1
TH ParaS TF
PreS
0 TG
Sub
E
35
-1
-2 -2
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Distances
DG CA4 CA3 CA2
-1
36 CA1 ProS
0 1 Dimension-1
2
Fig. 1. Hierarchical cluster analyses (HCA, left) and multidimensional scaling (MDS, right) of medial temporal areas according to similarity of their projections to the prefrontal cortex (PFC).
Multidimensional scaling
Hierarchical cluster analysis 2
W10 W25 W13
1 Dimension-2
W14 W24 W12 W46 W9 W8B
W10 W11
0
W14 W25
W9
W8B
W24 W46
W12 W13
-1
W11
0.0
0.1
0.2
0.3
0.4
Distances
0.5
0.6
-2 -2
-1
0 1 Dimension-1
2
Fig. 2. Hierarchical cluster analyses (left) and multidimensional scaling (right) of prefrontal areas according to similarity of projections from MTL.
those MTL areas that project most extensively to the PFC (TF, TG, TH) are positioned in its periallocortical and proviocortical rim. The remaining areas of the MTL show intermediate numbers of projections. Thus, we >nd a clear gradient of projections to PFC as we proceed from proviocortical via periallocortical regions to the allocortex. The distinguishing feature of the pre- and parasubiculum from other moderately projecting MTL areas (35, 36, Sub, ProS, E, CA1) is the demonstration of projections to lateral prefrontal area 46, and their absence to orbitomedial area 14. Thus, besides a gradient, we >nd evidence for a specialisation of MTL structures in their information transfer to the PFC with a prominent role of pre- and parasubiculum.
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
757
Previous studies of intrinsic connectivity in the PFC [5] have shown a clear division into medial and lateral networks. The orbital cortex, however, appeared fairly homogeneous when analysed using the parcellation scheme of Walker. Investigation of connections in a more detailed map of orbito-medial prefrontal cortex, however, revealed further diLerences between medial and orbital networks in relation to their visceromotor and sensory functions, respectively [8]. In the context of information transfer from episodic to working memory it is noticeable that a specialization exists both among MTL and PFC areas. For episodic memory, a common distinction is between anterior and posterior parts of the MTL [4]. As far as working memory is elaborated in prefrontal cortical networks there is a preferential involvement of dorsolateral prefrontal areas (DLPFC, areas 8A and 46) [7,10]. As shown by our meta-analysis, the MTL structures that are known to project to DLPFC are PreS and ParaS, as well as areas TF, TG, and TH. Thus, it would be expected that these areas, most prominently ParaS, which is known only to project to DLPFC, are responsible for the transfer of information from episodic to working memory. Whether these areas are the neural substrates of episodic memory themselves, or whether they relay the information from other MTL structures including the hippocampus proper, remains unclear and will require additional detailed investigation of the connectivity within the temporal lobe. Acknowledgements Ahmet Bozkurt is indebted to the ‘German National Merit Foundation’ (Studienstiftung des deutschen Volkes) for their support. This study was supported by the DFG (LIS 4-554 95 (2) D1usseldorf).
References [1] D.G. Amaral, R. Insausti, W.M. Cowan, The entorhinal cortex of the monkey: I. Cytoarchitectonic organization, J. Comput. Neurol. 264 (1987) 326–355. [2] A. Baddeley, The episodic buLer: a new component of working memory? Trends Cogn. Sci. 4 (2000) 417–423. [3] A. Bozkurt, L. Kamper, K.E. Stephan, R. K1otter, Organization of primate amygdalo-prefrontal projections, Neurocomputing 38– 40 (2001) 1135–1140. [4] B. Desgranges, J.-C. Baron, F. Eustache, The functional neuroanatomy of episodic memory: the role of the frontal lobes, the hippocampal formation, and other areas, Neuroimage 8 (1998) 198–213. [5] R. K1otter, C.C. Hilgetag, K.E. Stephan, Connectional characteristics of areas in Walker’s map of primate prefrontal cortex, Neurocomputing 38– 40 (2001) 741–746. [6] R. K1otter, K.E. Stephan, N. Palomero-Gallagher, S. Geyer, A. Schleicher, K. Zilles, Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data, Anat. Embryol. 204 (2001) 333–350. [7] R. Levy, P.S. Goldman-Rakic, Association of storage and processing functions in the dorsolateral prefrontal cortex of the nonhuman primate, J. Neurosci. 19 (1999) 5149–5158. 1 ur, J.L. Price, The organization of networks within the orbital and medial prefrontal cortex of [8] D. Ong1 rats, monkeys and humans, Cerb. Cortex 10 (2000) 206–219.
758
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
[9] D.L. Rosene, G.W. Van Hoesen, The hippocampal formation of the primate brain—a review of some comparative aspects of the cytoarchitecture and connections, in: E.G. Jones, A. Peters (Eds.), Cerebral Cortex 6, Plenum Press, New York, 1987, pp. 345–456. [10] E.E. Smith, J. Jonides, Storage and executive processes in the frontal lobes, Science 283 (1999) 1657–1661. [11] K.E. Stephan, L. Kamper, A. Bozkurt, G.A.P.C. Burns, M. Young, R. K1otter, Advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac), Philos. Trans. Roy Soc. London Ser. B 356 (2001) 1159–1186. [12] K.E. Stephan, K. Zilles, R. K1otter, Coordinate-independent mapping of structural and functional cortical data by objective relational transformation (ORT), Philos. Trans. Roy Soc. London Ser. B 355 (2000) 37–54. [13] A.E. Walker, A cytoarchitectural study of the prefrontal area of macaque monkey, J. Comput. Neurol. 73 (1940) 59–86.
www.elsevier.com/locate/neucom
The structural basis of information transfer from medial temporal lobe to prefrontal cortex in the macaque monkey Ahmet Bozkurta , Lars Kampera , Klaas E. Stephanc; d , Rolf K1ottera; b; ∗ a Computational
Systems Neuroscience Group, C. & O. Vogt Brain Research Institute, Heinrich Heine University, Universitatsstr. 1, D-40225 Dusseldorf, Germany b Department of Anatomy II, Heinrich Heine University, Universitatsstr. 1, D-40225 Dusseldorf, Germany c Institute of Medicine, Research Centre Julich, 52425 Julich, Germany d Department of Psychology, University of Newcastle-upon-Jyne, NE1 7RU Newcastle, UK
Abstract A variety of functional studies have emphasized the importance of the medial temporal lobe (MTL) and prefrontal cortex (PFC) for episodic and working memory, respectively. We investigated the structural basis of information transfer from MTL to PFC in primate cerebral cortex using the CoCoMac database (www.cocomac.org), coordinate-independent mapping procedures, and multivariate statistics. In our meta-analysis, we found a clearly decreasing gradient of MTL projections to PFC: strongest from periallocortical regions and weakest from hippocampus proper. These projections roughly divide the PFC into medial and lateral groups of areas. c 2002 Elsevier Science B.V. All rights reserved. Keywords: Database; Connectivity; Multivariate analyses; Primate; Memory
1. Introduction The cortical memory system is divided into several speci>c subsystems: for example, medial temporal lobe structures (MTL) are associated with episodic memory [4], whereas the prefrontal cortex (PFC) is part of the working memory network [7,10]. A ∗
Corresponding author. C. & O. Vogt Brain Research Institute, Heinrich Heine University, Universit1atsstr. 1, D-40225 Dusseldorf, Germany. Tel.: +49-211-81-12095; fax: +49-211-81-12336. E-mail address: [email protected] (R. K1otter). 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 6 8 - X
754
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
challenging question is how these diLerent modalities of memory are integrated in various cognitive contexts, including conscious selection of items from long-term episodic memory in working memory tasks [2]. A >rst step towards understanding the relation between episodic and working memory is to unravel the neuroanatomical basis of information transfer from MTL to PFC. Therefore, we investigated the detailed structure of MTL projections to the prefrontal cortex. In this article, we present the meta-analysis of a comprehensive large-scale network of anatomical projections from MTL to PFC by means of advanced database methods [11] and multivariate statistics [3,5,6].
2. Methods Using the macaque connectivity database CoCoMac ([11], see www.cocomac.org), we systematically collated data from published articles comprising tracer injections in MTL or PFC in macaque monkeys. The MTL structures included in our analysis were hippocampus proper (CA1–CA4 and DG), subicular complex (ProS, Sub, PreS, ParaS), parahippocampal (TF, TH, and TG), entorhinal (E) and perirhinal (35 and 36) areas. Since anatomical reports used diLerent and partially incompatible maps, Objective Relational Transformation (ORT) [12] was used to map all data into a common space, namely the parcellations proposed by Amaral et al. [1] and Rosene & Van Hoesen [9] for MTL, and Walker [13] for PFC. Since relative strengths of connections were scarcely reported, we evaluated projections in a binary fashion. We did not distinguish between unknown and absent projections. Vectors of projection patterns were correlated using the simple matching algorithm for binary data (existing or absent projections). Subsequently, correlation vectors were analyzed by two independent multivariate statistical techniques, multidimensional scaling and hierarchical clustering, to reveal the overall topography of similarity in connectional patterns between areas. Hierarchical clustering (HCA) successively amalgamates areas according to hierarchical degrees of similarity between their connectivity patterns. This procedure was performed for eLerent projections from MTL to PFC. Multidimensional scaling (MDS) interprets the correlations between inter-areal connectivity vectors as proximities in high-dimensional space. The resulting con>gurations are projected into two dimensions under maximal preservation of the proximity ranking. Both MDS and HCA have previously been used for the analysis of neural connectivity (e.g. 6 –8). In the present analysis, SYSTAT 9.0 (SPSS Inc.) under Windows NT 4.0 was used, applying Kruskal’s STRESS formula 1 to MDS and an Euclidean metric with complete linkage in the case of HCA (Table 1).
3. Results The anatomical projection patterns were derived from 16 studies including more than 90 tracer injections and 780 resulting labeled sizes.
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
755
Table 1 “0”, “1”, and no entry represent absent, existing, and unknown projections, respectively. Projection strengths were not coded in this binary matrix
Source areas
E 35 36 TF TG TH CA1 CA2 CA3 CA4 DG ProS Sub PreS ParaS
Target areas W10
W11
W12
W13
1 1
1 0 0 1 1 1 1 0 0 0 0 0 0 0 0
0 0 1 1 1 0 0 0 0 0 0 1 1
1 1
0 1 0
1
1 1 0 0 0 0 1 0 1 0
W14
W24
W25
1 1 1 1 1 1 1 0 0 0 0 1 1 1 0
0 1 1 1 1 1 0 0 0 0 0 1 1 0
1 1 0 1 1 0 1 0 0 0 0 1 1 1 0
W8B
1 0 1
W46
1 1 1 0 0 0 0 0 0 0 1 1
W9 0 1 0 1 1 1
Using MDS and HCA, projections of MTL structures to the PFC could be grouped according to the similarity of sending and recipient brain regions, respectively. Among MTL areas (Fig. 1), both statistical procedures showed a cluster consisting of areas DG, CA2-CA4, PreS, and ParaS, which have no or a unique pattern of projections to PFC. Other areas were sequentially arranged. Among these, TF and TH formed a second group characterized by the most extensive projections to the PFC. The remaining areas with sparser projections formed clusters that varied between MDS and HCA, respectively. MDS produced two subgroups, i.e. 35, E, and Sub, on the one hand, and 36, CA1, and ProS, on the other hand. HCA con>rmed the close association of areas 36 and ProS, but slightly regrouped the other areas (subgroups with E + CA1 and 35 + Sub, respectively). MTL projections also grouped their prefrontal destination areas (Fig. 2): Orbito-medial areas 10, 13, 14, 24, and 25 were contrasted by another group of areas that comprised 8B, 9, 11, 12, and 46. This distinction roughly divides PFC into medial and lateral regions. 4. Discussion Multivariate statistical analyses provided insights into the structural organization of projections from the MTL to the PFC. Concerning the MTL, there was a notable absence of any projection from the hippocampus proper with the only exception of the hippocampal sub>eld CA1, which is the most peripheral of its areas. By contrast,
756
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
Multidimensional scaling
Hierarchical cluster analysis TH TF TG 35 Sub E CA1 ProS 36 DG CA3 CA2 CA4 ParaS PreS
2
Dimension-2
1
TH ParaS TF
PreS
0 TG
Sub
E
35
-1
-2 -2
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Distances
DG CA4 CA3 CA2
-1
36 CA1 ProS
0 1 Dimension-1
2
Fig. 1. Hierarchical cluster analyses (HCA, left) and multidimensional scaling (MDS, right) of medial temporal areas according to similarity of their projections to the prefrontal cortex (PFC).
Multidimensional scaling
Hierarchical cluster analysis 2
W10 W25 W13
1 Dimension-2
W14 W24 W12 W46 W9 W8B
W10 W11
0
W14 W25
W9
W8B
W24 W46
W12 W13
-1
W11
0.0
0.1
0.2
0.3
0.4
Distances
0.5
0.6
-2 -2
-1
0 1 Dimension-1
2
Fig. 2. Hierarchical cluster analyses (left) and multidimensional scaling (right) of prefrontal areas according to similarity of projections from MTL.
those MTL areas that project most extensively to the PFC (TF, TG, TH) are positioned in its periallocortical and proviocortical rim. The remaining areas of the MTL show intermediate numbers of projections. Thus, we >nd a clear gradient of projections to PFC as we proceed from proviocortical via periallocortical regions to the allocortex. The distinguishing feature of the pre- and parasubiculum from other moderately projecting MTL areas (35, 36, Sub, ProS, E, CA1) is the demonstration of projections to lateral prefrontal area 46, and their absence to orbitomedial area 14. Thus, besides a gradient, we >nd evidence for a specialisation of MTL structures in their information transfer to the PFC with a prominent role of pre- and parasubiculum.
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
757
Previous studies of intrinsic connectivity in the PFC [5] have shown a clear division into medial and lateral networks. The orbital cortex, however, appeared fairly homogeneous when analysed using the parcellation scheme of Walker. Investigation of connections in a more detailed map of orbito-medial prefrontal cortex, however, revealed further diLerences between medial and orbital networks in relation to their visceromotor and sensory functions, respectively [8]. In the context of information transfer from episodic to working memory it is noticeable that a specialization exists both among MTL and PFC areas. For episodic memory, a common distinction is between anterior and posterior parts of the MTL [4]. As far as working memory is elaborated in prefrontal cortical networks there is a preferential involvement of dorsolateral prefrontal areas (DLPFC, areas 8A and 46) [7,10]. As shown by our meta-analysis, the MTL structures that are known to project to DLPFC are PreS and ParaS, as well as areas TF, TG, and TH. Thus, it would be expected that these areas, most prominently ParaS, which is known only to project to DLPFC, are responsible for the transfer of information from episodic to working memory. Whether these areas are the neural substrates of episodic memory themselves, or whether they relay the information from other MTL structures including the hippocampus proper, remains unclear and will require additional detailed investigation of the connectivity within the temporal lobe. Acknowledgements Ahmet Bozkurt is indebted to the ‘German National Merit Foundation’ (Studienstiftung des deutschen Volkes) for their support. This study was supported by the DFG (LIS 4-554 95 (2) D1usseldorf).
References [1] D.G. Amaral, R. Insausti, W.M. Cowan, The entorhinal cortex of the monkey: I. Cytoarchitectonic organization, J. Comput. Neurol. 264 (1987) 326–355. [2] A. Baddeley, The episodic buLer: a new component of working memory? Trends Cogn. Sci. 4 (2000) 417–423. [3] A. Bozkurt, L. Kamper, K.E. Stephan, R. K1otter, Organization of primate amygdalo-prefrontal projections, Neurocomputing 38– 40 (2001) 1135–1140. [4] B. Desgranges, J.-C. Baron, F. Eustache, The functional neuroanatomy of episodic memory: the role of the frontal lobes, the hippocampal formation, and other areas, Neuroimage 8 (1998) 198–213. [5] R. K1otter, C.C. Hilgetag, K.E. Stephan, Connectional characteristics of areas in Walker’s map of primate prefrontal cortex, Neurocomputing 38– 40 (2001) 741–746. [6] R. K1otter, K.E. Stephan, N. Palomero-Gallagher, S. Geyer, A. Schleicher, K. Zilles, Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data, Anat. Embryol. 204 (2001) 333–350. [7] R. Levy, P.S. Goldman-Rakic, Association of storage and processing functions in the dorsolateral prefrontal cortex of the nonhuman primate, J. Neurosci. 19 (1999) 5149–5158. 1 ur, J.L. Price, The organization of networks within the orbital and medial prefrontal cortex of [8] D. Ong1 rats, monkeys and humans, Cerb. Cortex 10 (2000) 206–219.
758
A. Bozkurt et al. / Neurocomputing 44–46 (2002) 753 – 758
[9] D.L. Rosene, G.W. Van Hoesen, The hippocampal formation of the primate brain—a review of some comparative aspects of the cytoarchitecture and connections, in: E.G. Jones, A. Peters (Eds.), Cerebral Cortex 6, Plenum Press, New York, 1987, pp. 345–456. [10] E.E. Smith, J. Jonides, Storage and executive processes in the frontal lobes, Science 283 (1999) 1657–1661. [11] K.E. Stephan, L. Kamper, A. Bozkurt, G.A.P.C. Burns, M. Young, R. K1otter, Advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac), Philos. Trans. Roy Soc. London Ser. B 356 (2001) 1159–1186. [12] K.E. Stephan, K. Zilles, R. K1otter, Coordinate-independent mapping of structural and functional cortical data by objective relational transformation (ORT), Philos. Trans. Roy Soc. London Ser. B 355 (2000) 37–54. [13] A.E. Walker, A cytoarchitectural study of the prefrontal area of macaque monkey, J. Comput. Neurol. 73 (1940) 59–86.