Functional brain mapping during free viewing of natural scenes

Hum Brain Mapp. 2004 Feb;21(2):75-85. doi: 10.1002/hbm.10153.


Previous imaging studies have used mostly perceptually abstracted, idealized, or static stimuli to show segregation of function in the cerebral cortex. We wanted to learn whether functional segregation is maintained during more natural, complex, and dynamic conditions when many features have to be processed simultaneously, and identify regions whose activity correlates with the perception of specific features. To achieve this, we used functional magnetic resonance imaging (fMRI) to measure brain activity when human observers viewed freely dynamic natural scenes (a James Bond movie). The intensity with which they perceived different features (color, faces, language, and human bodies) was assessed psychometrically in separate sessions. In all subjects different features were perceived with a high degree of independence over time. We found that the perception of each feature correlated with activity in separate, specialized areas whose activity also varied independently. We conclude that even in natural conditions, when many features have to be processed simultaneously, functional specialization is preserved. Our method thus opens a new way of brain mapping, which allows the localization of a multitude of brain areas based on a single experiment using uncontrolled, natural stimuli. Furthermore, our results show that the intensity of activity in a specialized area is linearly correlated with the intensity of its perceptual experience. This leads us to suggest that each specialized area is directly responsible for the creation of a feature-specific conscious percept (a microconsciousness). Hum. Brain Mapp. 21:75-83, 2004.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain Mapping / methods*
  • Cerebral Cortex / physiology*
  • Female
  • Humans
  • Magnetic Resonance Imaging / methods
  • Male
  • Perception / physiology*
  • Photic Stimulation / methods*
  • Regression Analysis
  • Statistics, Nonparametric