Markus Ullsperger and Stefan Debener (eds)
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195372731
- eISBN:
- 9780199776283
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195372731.001.0001
- Subject:
- Neuroscience, Techniques
Systemic interactions in brain networks have been successfully studied in vivo using non-invasive methods such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), for ...
More
Systemic interactions in brain networks have been successfully studied in vivo using non-invasive methods such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), for many years. The rate-limiting step, however, is that each method can only map selective aspects of brain function, while missing other key aspects. Furthermore, the inferences on neuronal processes and information flow are often rather indirect. By simultaneously combining both methods, the researcher is better able to make optimal use of their specific advantages while compensating for their disadvantages. In recent years, research has shifted and expanded, from demonstrating technical feasibility, to methodological issues of artifact control, new ways of analyzing and integrating data, and to applications for scientific and clinical questions. Combined EEG and fMRI methods now cover everything from physiological questions on the bases of the two recorded signals, to more specific questions on the mechanisms of certain cognitive and pathological functions like epileptic brain activity. Simultaneous EEG and fMRI provides the interested researcher with the tools to establish a simultaneous EEG-fMRI laboratory, as well as for those scientists who are interested in integrating electrophysiological and hemodynamic data. As evidenced by the diversity of topics presented, this is a dynamically developing field in which several approaches are being tested, validated, and compared. Chapters are dedicated to the physiological bases of the measured signals, technical setup, sources of artifacts and data de-noising, various approaches of data analysis and fusion, as well as applications. In addition, open questions and directions for future research are outlined.Less
Systemic interactions in brain networks have been successfully studied in vivo using non-invasive methods such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), for many years. The rate-limiting step, however, is that each method can only map selective aspects of brain function, while missing other key aspects. Furthermore, the inferences on neuronal processes and information flow are often rather indirect. By simultaneously combining both methods, the researcher is better able to make optimal use of their specific advantages while compensating for their disadvantages. In recent years, research has shifted and expanded, from demonstrating technical feasibility, to methodological issues of artifact control, new ways of analyzing and integrating data, and to applications for scientific and clinical questions. Combined EEG and fMRI methods now cover everything from physiological questions on the bases of the two recorded signals, to more specific questions on the mechanisms of certain cognitive and pathological functions like epileptic brain activity. Simultaneous EEG and fMRI provides the interested researcher with the tools to establish a simultaneous EEG-fMRI laboratory, as well as for those scientists who are interested in integrating electrophysiological and hemodynamic data. As evidenced by the diversity of topics presented, this is a dynamically developing field in which several approaches are being tested, validated, and compared. Chapters are dedicated to the physiological bases of the measured signals, technical setup, sources of artifacts and data de-noising, various approaches of data analysis and fusion, as well as applications. In addition, open questions and directions for future research are outlined.
Markus Ullsperger
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195372731
- eISBN:
- 9780199776283
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195372731.003.0010
- Subject:
- Neuroscience, Techniques
This chapter gives an overview of data integration methods for simultaneous EEG-fMRI, in which EEG features are extracted and used to parametrically model the fMRI data. Up to now, variants of ...
More
This chapter gives an overview of data integration methods for simultaneous EEG-fMRI, in which EEG features are extracted and used to parametrically model the fMRI data. Up to now, variants of EEG-informed fMRI analysis have been most widely and successfully applied. After a brief discussion of the rationale of this approach, its variants for ongoing and event-related EEG phenomena are explained. Studies applying EEG-informed fMRI are reviewed. The advantage of denoising methods such as independent component analysis allowing single-trial quantifications of the EEG phenomena of interest is discussed. To allow clear interpretations of covariations between electrophysiological and hemodynamic measures, further dependent variables such as behavioral data should be taken into account. The chapter closes with an outlook on future questions and ongoing methodological developments.Less
This chapter gives an overview of data integration methods for simultaneous EEG-fMRI, in which EEG features are extracted and used to parametrically model the fMRI data. Up to now, variants of EEG-informed fMRI analysis have been most widely and successfully applied. After a brief discussion of the rationale of this approach, its variants for ongoing and event-related EEG phenomena are explained. Studies applying EEG-informed fMRI are reviewed. The advantage of denoising methods such as independent component analysis allowing single-trial quantifications of the EEG phenomena of interest is discussed. To allow clear interpretations of covariations between electrophysiological and hemodynamic measures, further dependent variables such as behavioral data should be taken into account. The chapter closes with an outlook on future questions and ongoing methodological developments.
