Abstract:
:Standard procedures to achieve quality assessment (QA) of functional magnetic resonance imaging (fMRI) data are of great importance. A standardized and fully automated procedure for QA is presented that allows for classification of data quality and the detection of artifacts by inspecting temporal variations. The application of the procedure on phantom measurements was used to check scanner and stimulation hardware performance. In vivo imaging data were checked efficiently for artifacts within the standard fMRI post-processing procedure by realignment. Standardized and routinely carried out QA is essential for extensive data amounts as collected in fMRI, especially in multicenter studies. Furthermore, for the comparison of two different groups, it is important to ensure that data quality is approximately equal to avoid possible misinterpretations. This is shown by example, and criteria to quantify differences of data quality between two groups are defined.
journal_name
Hum Brain Mappjournal_title
Human brain mappingauthors
Stöcker T,Schneider F,Klein M,Habel U,Kellermann T,Zilles K,Shah NJdoi
10.1002/hbm.20096keywords:
subject
Has Abstractpub_date
2005-06-01 00:00:00pages
237-46issue
2eissn
1065-9471issn
1097-0193journal_volume
25pub_type
杂志文章abstract::Previous studies on crossmodal spatial orienting typically used simple and stereotyped stimuli in the absence of any meaningful context. This study combined computational models, behavioural measures and functional magnetic resonance imaging to investigate audiovisual spatial interactions in naturalistic settings. We ...
journal_title:Human brain mapping
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