2011
DOI: 10.1016/j.neuroimage.2011.04.046
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Biological parametric mapping with robust and non-parametric statistics

Abstract: Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modal… Show more

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Cited by 31 publications
(34 citation statements)
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“…We used Robust Biological Parametric Mapping (BPM), version 2.1(26, 27), to determine voxel-wise DVR-CBF and DVR-R 1 relationships. Robust BPM is based on BPM, which allows voxel-wise regression of data from two imaging modalities using least-squares regression.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…We used Robust Biological Parametric Mapping (BPM), version 2.1(26, 27), to determine voxel-wise DVR-CBF and DVR-R 1 relationships. Robust BPM is based on BPM, which allows voxel-wise regression of data from two imaging modalities using least-squares regression.…”
Section: Methodsmentioning
confidence: 99%
“…Robust BPM is based on BPM, which allows voxel-wise regression of data from two imaging modalities using least-squares regression. (28) Robust BPM extends this approach by using robust regression (M-estimation) (29) to account for outliers in the imaging data (26). …”
Section: Methodsmentioning
confidence: 99%
“…Also, the integration of complementary information through a multimodal approach will be very useful to overcome the shortcomings of each single protocol, requiring advanced analysis tools which are able to integrate information from different protocols into the same processing pipeline. Similar approaches are likely to aid in better discrimination and staging of AD [8,[246][247][248]. In this context, information from different modalities may be simultaneously combined using the support of machine learning algorithms enabling the classification of a single subject into a predefined group while dealing with any type of input features (e.g.…”
Section: Functional Mri Markersmentioning
confidence: 99%
“…In order to assess the influence of gray matter atrophy on the results, we used the voxel-wise regression routine available in the robust Biological Parametric Mapping toolbox (Yang et al, 2011). This routine allows to enter an imaging dependent variable, while regressing out the effect of several imaging and non-imaging regressors in a voxel-by-voxel analysis.…”
Section: Influence Of Gray Matter Atrophy On Age Effects: Bpm Analysesmentioning
confidence: 99%