Title |
Power spectral aspects of the default mode network in schizophrenia: an MEG study
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Published in |
BMC Neuroscience, September 2014
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DOI | 10.1186/1471-2202-15-104 |
Pubmed ID | |
Authors |
June Sic Kim, Kyung Soon Shin, Wi Hoon Jung, Sung Nyun Kim, Jun Soo Kwon, Chun Kee Chung |
Abstract |
Symptoms of schizophrenia are related to deficits in self-monitoring function, which may be a consequence of irregularity in aspects of the default mode network (DMN). Schizophrenia can also be characterized by a functional abnormality of the brain activity that is reflected in the resting state. Oscillatory analysis provides an important understanding of resting brain activity. However, conventional methods using electroencephalography are restricted because of low spatial resolution, despite their excellent temporal resolution.The aim of this study was to investigate resting brain oscillation and the default mode network based on a source space in various frequency bands such as theta, alpha, beta, and gamma using magnetoencephalography. In addition, we investigated whether these resting and DMN activities could distinguish schizophrenia patients from normal controls. To do this, the power spectral density of each frequency band at rest was imaged and compared on a spatially normalized brain template in 20 patients and 20 controls. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Canada | 2 | 25% |
United Kingdom | 1 | 13% |
Germany | 1 | 13% |
Unknown | 4 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 4 | 50% |
Members of the public | 4 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Hungary | 1 | <1% |
United States | 1 | <1% |
France | 1 | <1% |
Canada | 1 | <1% |
Unknown | 125 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 32 | 25% |
Student > Ph. D. Student | 29 | 22% |
Student > Master | 17 | 13% |
Student > Bachelor | 9 | 7% |
Student > Doctoral Student | 8 | 6% |
Other | 23 | 18% |
Unknown | 11 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 29 | 22% |
Medicine and Dentistry | 25 | 19% |
Neuroscience | 24 | 19% |
Agricultural and Biological Sciences | 8 | 6% |
Engineering | 7 | 5% |
Other | 9 | 7% |
Unknown | 27 | 21% |