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Consciousness and Neuroscience

  • Andrea NaniEmail author
  • Stefano Seri
  • Andrea E. Cavanna
Chapter
  • 2.9k Downloads

Abstract

Neuroscience has received a strong impulse from brain imaging techniques. For the first time, neuroimaging made it possible to study the brain in vivo and thereby associate mental processes with distinctive patterns of cerebral activity. Moreover, functional images of the living brain have provided a powerful instrument for the scientific study of consciousness. Advances in imaging techniques have proved invaluable for obtaining high-resolution maps of the functional and anatomical brain connectivity. Over the last few years, the notion that cerebral regions work together to form a functional network at rest was substantiated by functional imaging studies. These resting-state networks show a high level of spontaneous coupling of ongoing neuronal activity. Among the functional networks identified thus far, the so-called default mode network exhibits particularly interesting features, which might play an important role in the promotion and maintenance of conscious states, especially with regard to the level of consciousness (arousal). The objective level of arousal and the subjective contents of awareness appear to be the two planes within which the neural correlates of consciousness can be interpreted. With regard to the content dimension, which has traditionally been the most elusive to scientific exploration, a theory of conscious access should incorporate five essential concepts: a supervisory system, a serial processing, a coherent structure of recurrent neural loops, a global neuronal workspace capable to differentiate and integrate the various contents of experience, and a complex system of topological properties which identifies crucial hub nodes.

Keywords

Diffusion Tensor Imaging Attentional Blink Default Mode Network Neural Correlate Functional Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrea Nani
    • 1
    Email author
  • Stefano Seri
    • 2
  • Andrea E. Cavanna
    • 3
    • 4
  1. 1.Michael Trimble Neuropsychiatry Research GroupBSMHFT and University of BirminghamBirminghamUK
  2. 2.School of Life and Health Sciences, Aston Brain CentreAston UniversityBirminghamUK
  3. 3.Department of Neuropsychiatry, Michael Trimble Neuropsychiatry Research GroupBSMHFT and University of BirminghamBirminghamUK
  4. 4.Sobell Department of Motor Neuroscience and Movement DisordersUCL and Institute of NeurologyLondonUK

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