Information processing in large-scale cerebral networks: the causal connectivity approach

Biol Cybern. 2000 Jan;82(1):49-59. doi: 10.1007/PL00007961.


Today, cognitive functions are considered to be the offspring of the activity of large-scale networks of functionally interconnected cerebral regions. The interpretation of cerebral activation data provided by functional imaging has therefore recently moved to the search for the effective connectivity of activated regions, which aims at understanding the role of anatomical links in the activation propagation. Our assumption is that only causal connectivity can offer a real understanding of the links between brain and mind. Causal connectivity is based on the anatomical connection pattern, the information processing within cerebral regions and the causal influences that connected regions exert on each other. In our approach, the information processing within a region is implemented by a causal network of functional primitives, which are the interpretation of integrated biological properties. Our choice of a qualitative representation of information reflects the fact that cerebral activation data are only the approximate view, provided by imaging techniques, of the real cerebral activity. This explicit modeling approach allows the formulation and the simulation of functional and physiological assumptions about activation data. Two alternative models explaining results of the striate cortex activation described by Fox and Raichle (Fox PT, Raichle ME (1984) J. Neurophysiol 51:1109-1120; Fox PT, Raichle ME (1985) Ann Neurol 17:303-305) are provided as an example of our approach.

MeSH terms

  • Animals
  • Cerebral Cortex / physiology*
  • Cognition*
  • Humans
  • Models, Biological*
  • Models, Theoretical*
  • Nerve Net*