The statistical neuroanatomy of frontal networks in the macaque

PLoS Comput Biol. 2008 Apr 4;4(4):e1000050. doi: 10.1371/journal.pcbi.1000050.


We were interested in gaining insight into the functional properties of frontal networks based upon their anatomical inputs. We took a neuroinformatics approach, carrying out maximum likelihood hierarchical cluster analysis on 25 frontal cortical areas based upon their anatomical connections, with 68 input areas representing exterosensory, chemosensory, motor, limbic, and other frontal inputs. The analysis revealed a set of statistically robust clusters. We used these clusters to divide the frontal areas into 5 groups, including ventral-lateral, ventral-medial, dorsal-medial, dorsal-lateral, and caudal-orbital groups. Each of these groups was defined by a unique set of inputs. This organization provides insight into the differential roles of each group of areas and suggests a gradient by which orbital and ventral-medial areas may be responsible for decision-making processes based on emotion and primary reinforcers, and lateral frontal areas are more involved in integrating affective and rational information into a common framework.

MeSH terms

  • Animals
  • Computer Simulation
  • Data Interpretation, Statistical
  • Frontal Lobe / anatomy & histology*
  • Frontal Lobe / physiology*
  • Macaca
  • Models, Anatomic*
  • Models, Neurological*
  • Models, Statistical
  • Nerve Net / anatomy & histology*
  • Nerve Net / physiology*
  • Neural Pathways / anatomy & histology
  • Neural Pathways / physiology
  • Neuroanatomy / methods*