Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

From gene networks to brain networks

Abstract

The brain's structural organization is so complex that 2,500 years of analysis leaves pervasive uncertainty about (i) the identity of its basic parts (regions with their neuronal cell types and pathways interconnecting them), (ii) nomenclature, (iii) systematic classification of the parts with respect to topographic relationships and functional systems and (iv) the reliability of the connectional data itself. Here we present a prototype knowledge management system (http://brancusi.usc.edu/bkms/) for analyzing the architecture of brain networks in a systematic, interactive and extendable way. It supports alternative interpretations and models, is based on fully referenced and annotated data and can interact with genomic and functional knowledge management systems through web services protocols.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Portraits of discovery.
Figure 2: Borders of brain regions are defined by recognizable changes in cell type distribution patterns.
Figure 3: Neuronal cell types defined by connections.
Figure 4: Alternative brain region parcelling schemes.
Figure 5: Inferring possible networks between two brain regions or cell types with one, two or three intermediate connections.

Similar content being viewed by others

References

  1. Watson, J.D. & Crick, F.H.C. Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid. Nature 171, 737–738 (1953).

    Article  CAS  Google Scholar 

  2. Davidson, E.H. Genomic Regulatory Systems: Development and Evolution (Academic, San Diego, 2001).

    Google Scholar 

  3. Hasty, J., McMillen, D. & Collins, J.J. Engineered gene circuits. Nature 420, 224–230 (2002).

    Article  CAS  Google Scholar 

  4. Swanson, L.W. Brain Architecture: Understanding the Basic Plan (Oxford Univ. Press, Oxford, 2003).

    Google Scholar 

  5. Meyer, A. Historical Aspects of Cerebral Anatomy (Oxford Univ. Press, London, 1971).

    Google Scholar 

  6. Swanson, L.W. A history of neuroanatomical mapping. in Brain Mapping: the Systems (eds. Toga, A.W. & Mazziotta, J.C.) 77–109 (Academic, San Diego, 2000).

    Chapter  Google Scholar 

  7. Swanson, L.W. Cerebral hemisphere regulation of motivated behavior. Brain Res. 886, 113–164 (2000).

    Article  CAS  Google Scholar 

  8. Nauta, W.J.H. & Ryan, L.F. Selective silver impregnation of degenerating axons in the central nervous system. Stain Technol. 27, 175–179 (1952).

    Article  CAS  Google Scholar 

  9. Swanson, L.W. The neuroanatomy revolution of the 1970s and the hypothalamus. Brain Res. Bull. 50, 397 (1999).

    Article  CAS  Google Scholar 

  10. Kandel, E.R., Schwartz, J.H. & Jessell, T.M. Principles of Neural Science 4th edn. (McGraw-Hill, New York, 2000).

    Google Scholar 

  11. Brodal, A. Neurological Anatomy (Oxford Univ. Press, London, 1948).

    Google Scholar 

  12. Steno, N. Lecture on the Anatomy of the Brain (facsimile and translation of the 1669 original, published by A. Busck, Copenhagen, 1965).

    Google Scholar 

  13. Golgi, C. Sulla struttura della sostanza grigia dell cervello. Gazz. Med. Ital. 33, 244–246 (1873).

    Google Scholar 

  14. Swanson, L.W. Mapping the human brain: past, present, and future. Trends Neurosci. 18, 471–474 (1995).

    Article  CAS  Google Scholar 

  15. Swanson, L.W. What is the brain? Trends Neurosci. 23, 519–527 (2000).

    Article  CAS  Google Scholar 

  16. Bota, M. & Arbib, M.A. Integrating databases and expert systems for the analysis of brain structures, connections, and homologies. Neuroinformatics (in press).

  17. Finger, S. Origins of Neuroscience: a History of Explorations into Brain Function (Oxford Univ. Press, New York, 1994).

  18. Clarke, E. & O'Malley, C.D. The Human Brain and Spinal Cord 2nd edn. (Norman, San Francisco, 1996).

    Google Scholar 

  19. Swanson, L.W. Brain Maps: Structure of the Rat Brain 2nd edn. (Elsevier, Amsterdam, 1999).

    Google Scholar 

  20. Wilder, B.G. Neural terms, international and national. J. Comp. Neurol. 6, 216–352 (1896).

    Article  Google Scholar 

  21. Rasmussen, A.T. Some Trends in Neuroanatomy (Brown, Dubuque, 1947).

    Google Scholar 

  22. Dashti, A.E., Ghandeharizadeh, S., Stone, J., Swanson, L.W. & Thompson, R.H. Database challenges and solutions in neuroscientific applications. Neuroimage 5, 97–115 (1997).

    Article  CAS  Google Scholar 

  23. Swanson, L.W. Interactive brain maps and atlases. in Computing the Brain: a Guide to Neuroinformatics (eds. Arbib, M.A. & Grethe, J.G.) 167–177 (Academic, San Diego, 2001).

    Chapter  Google Scholar 

  24. Stephan, K.E., Zilles, K. & Kötter, R. Coordinate-independent mapping of structural and functional data by objective relational transformation (ORT). Phil. Trans. R. Soc. Lond. B Biol. Sci. 335, 37–54 (2000).

    Article  Google Scholar 

  25. Stephan, K.E. et al. Advanced database methodology for the collation of connectivity data on the Macaque brain (CoCoMac). Phil. Trans. R. Soc. Lond. B Biol. Sci. 356, 1159–1186 (2001).

    Article  CAS  Google Scholar 

  26. Burns, G.A.P.C. Knowledge management of the neuroscientific literature: the data model and underlying strategy of the NeuroScholar system. Phil. Trans. R. Soc. Lond. B Biol. Sci. 356, 1187–1208 (2001).

    Article  CAS  Google Scholar 

  27. Egenhoffer, M. & Franzosa, R. Point-set topological spatial relations. Int. J. Geogr. Inf. Sci. 5, 161–174 (1991).

    Article  Google Scholar 

  28. Burns, G.A.P.C. Neural Connectivity of the Rat: Theory, Methods and Applications. Thesis, Oxford Univ. (1997).

  29. Burns, G.A.P.C. & Young, M.P. Analysis of the connectional organisation of neural systems associated with the hippocampus in rats. Phil. Trans. R. Soc. Lond. B Biol. Sci. 355, 55–70 (2000).

    Article  CAS  Google Scholar 

  30. Csete, M.E. & Doyle, J.C. Reverse engineering of biological complexity. Science 295, 1664–1669 (2002).

    Article  CAS  Google Scholar 

  31. Clark, W.E.L. Morphological aspects of the hypothalamus. in The Hypothalamus: Morphological, Functional, Clinical and Surgical Aspects (eds. Clark, W.E.L., Beattie, J., Riddoch, G. & Dott, N.M.) 2–68 (Oliver and Boyd, Edinburgh, 1938).

    Google Scholar 

  32. Ingram, W.R. Nuclear organization and chief connections of the primate hypothalamus. Res. Publ.Assoc. Res. Nerv. Ment. Dis. 20, 195–244 (1940).

    Google Scholar 

  33. Raisman, G. Neural connexions of the hypothalamus. Brit. Med. Bull. 22, 197–201 (1966).

    Article  CAS  Google Scholar 

  34. Nauta, W.J.H. & Haymaker, W. Hypothalamic nuclei and fiber connections. in The Hypothalamus (eds. Haymaker, W., Anderson, E. & Nauta, W.J.H.) 136–209 (Thomas, Springfield, 1969).

    Google Scholar 

  35. Swanson, L.W. The hypothalamus. in Handbook of Chemical Neuroanatomy Vol. 5 (eds. Björklund, A., Hökfelt, T. & Swanson, L.W.) 1–124 (Elsevier, Amsterdam, 1987).

    Google Scholar 

  36. Raisman, G., Cowan, W.M. & Powell, T.P.S. The extrinsic afferent, commissural and association fibres of the hippocampus. Brain 88, 963–996 (1965).

    Article  Google Scholar 

  37. Raisman, G., Cowan, W.M. & Powell, T.P.S. An experimental analysis of the efferent projection of the hippocampus. Brain 89, 83–108 (1966).

    Article  CAS  Google Scholar 

  38. Cowan, W.M, Raisman, G. & Powell, T.P.S. The connexions of the amygdala. J. Neurol. Neurosurg. Psychiatry 28, 137–151 (1965).

    Article  CAS  Google Scholar 

  39. Vesalius, A. De Humani corporis Fabrica Libri Septem (Oporinus, Basel, 1543).

    Google Scholar 

  40. Watson, J. The Double Helix: a Personal Account of the Discovery of the Structure of DNA (Atheneum, New York, 1968).

    Google Scholar 

  41. Brodmann, K. Vergleichende Localisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues (Barth, Leipzig, 1909) translated as Brodmann's Localization in the Cerebral Cortex (Smith-Gordon, London, 1994).

    Google Scholar 

  42. Cajal, S.R. Histologie du système nerveux de l'homme et des vertébrés, 2 vols. (Maloine, Paris, 1909–1911) translated as Histology of the Nervous System of Man and Vertebrates (Oxford Univ. Press, New York, 1995).

    Google Scholar 

  43. Paxinos, G. & Watson, C. The Rat Brain in Stereotaxic Coordinates 4th edn. (Academic, San Diego, 1998).

    Google Scholar 

  44. Armstrong, D.M., Saper, C.B., Levey, A.I., Wainer, B.H. & Terry, R.D. Distribution of cholinergic neurons in rat brain: demonstrated by the immunocytochemical localization of choline acetyltransferase. J. Comp. Neurol. 216, 53–68 (1983).

    Article  CAS  Google Scholar 

  45. Gritti, I., Mainville, L. & Jones, B.E. Codistribution of GABA- with acetylcholine-synthesizing neurons in the basal forebrain of the rat. J. Comp. Neurol. 329, 438–457 (1993).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

For invaluable discussions we especially thank M. Arbib, D. Bowden, G. Burns, S. Koslow, S. Subramaniam, and A. Toga. This work was supported by US National Institutes of Health grants PSA-99-060 and NS-16668.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Larry W Swanson.

Supplementary information

Supplementary Fig. 1.

The general object-relationship (OR) schema of BAMS. Each object and relation in the figure can be captured in more than one table. The BAMS OR structure is centered on the object "Brain Part" as defined in different neuroanatomical nomenclatures (atlases). Any Brain Part is uniquely identified in BAMS with four attributes: name, species, atlas, and atlas version. The Brain Part module is constructed in an m:n relation with "Cell Types" module. It permits description of any cell type or subtype in terms of position within the associated brain region, distribution pattern, and numerical data (cell number, cell density, percentage of total cell number, and range of these parameters over multiple experiments). The "Connections" module is in an m:n relation with the Brain Part module because a brain region can send projections to, and receive inputs from, many other brain regions. BAMS can associate any major fiber tract registered in the Brain Part module with many reports of projections, and any neural connection can participate in many fiber tracts. The Connections module has a set of 40+ attributes allowing it to describe comprehensively connectivity reports collated from literature inserted by neuroanatomists. The object "Collator" stores basic information about people with permission to add, delete, or update information in BAMS. Collator is in a 1:n relationship with Brain Part because every brain part record is uniquely identified in BAMS and must therefore be inserted by a single collator. The object "Reference" stores information about sources used to insert data. The object Reference and the Brain Part module are in a 1:n relationship because atlas and atlas version are two attributes that define uniquely any brain record and refer to a single source. The Cell Types and Connections modules are m:n relationships with the objects Collator and Reference because a collator may insert data about many cell types and projections, and information associated with a cell type or projection may be provided by many collators. "Relations" is the fourth module of BAMS and consists of three parts, "Hierarchy", "Topology", and "Nomenclature". The Hierarchy part of BAMS refers to the set of tables and relationships that allow collators to construct ordered sets of brain parts according to different criteria used to organize brain nomenclatures (different taxonomies) inserted in the system. Topology refers to inserted or inferred topological relationships between brain regions defined in different brain nomenclatures. Nomenclature refers to the set of relationships established in BAMS between pairs of brain structures in different neuroanatomical nomenclatures. It includes two types of relationships that can be inserted or inferred in BAMS: identical names, and common sets of references used to identify brain parts. (GIF 11 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bota, M., Dong, HW. & Swanson, L. From gene networks to brain networks. Nat Neurosci 6, 795–799 (2003). https://doi.org/10.1038/nn1096

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1096

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing