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Intraoperative Human Functional Brain Mapping Using Optical Intrinsic Signal Imaging

S. Sheth,Vijay Yanamadala,E. Eskandar

Published 2012 in Unknown venue

ABSTRACT

Functional brain mapping strives to describe the brain’s organization as a mosaic of distinct regions, each of which subserves a particular function. Advances in our understanding of functional brain organization over the past decades have been propelled by the availability of increasingly sophisticated methods for assessing various aspects of neuronal activity in vivo. These methods can be broadly categorized as “direct” or “indirect” measures of neuronal activity (Figure 1). Direct techniques measure changes in electromagnetic fields resulting from neuronal action potentials and synaptic activity. Indirect techniques measure changes in other tissue properties that are related to neural activity. This distinction does not imply the superiority of direct over indirect techniques. Certain disadvantages of direct measures were the very motivation for the development of indirect measures. Indeed, the most widely used functional brain imaging modality currently is functional magnetic resonance imaging (fMRI), an indirect technique. A subset of indirect techniques are based on changes in blood flow subsequent to and produced by neural activity. These perfusiondependent functional brain imaging techniques include fMRI, positron emission tomography (PET), and others. Although they are among the most commonly used methods for investigating brain function, they rely on vascular responses that are not completely understood. In this chapter, we will focus on indirect measures of brain activity, emphasizing the technique of optical intrinsic signal imaging (OISI). We discuss the physical basis of perfusion imaging and OISI, animal and human studies of OISI to date, and its potential as a powerful intraoperative functional brain mapping tool.

PUBLICATION RECORD

  • Publication year

    2012

  • Venue

    Unknown venue

  • Publication date

    2012-02-01

  • Fields of study

    Medicine, Computer Science, Engineering

  • Identifiers
    DOI 10.5772/29255
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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