2016
DOI: 10.1016/j.neuroimage.2016.02.033
|View full text |Cite
|
Sign up to set email alerts
|

The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

4
90
0

Year Published

2016
2016
2020
2020

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 105 publications
(94 citation statements)
references
References 36 publications
(67 reference statements)
4
90
0
Order By: Relevance
“…Successful classification of ILD values was observed across a range of conditions, with performance exceeding chance levels by up to 22%. That performance is roughly on par with other attempts at multi-voxel classification in the auditory system (De Martino et al, 2008; Obleser et al, 2010; Kumar et al, 2014; Gardumi et al, 2016). …”
Section: Resultssupporting
confidence: 68%
“…Successful classification of ILD values was observed across a range of conditions, with performance exceeding chance levels by up to 22%. That performance is roughly on par with other attempts at multi-voxel classification in the auditory system (De Martino et al, 2008; Obleser et al, 2010; Kumar et al, 2014; Gardumi et al, 2016). …”
Section: Resultssupporting
confidence: 68%
“…The warping parameters were then separately applied to the functional and structural images to produce normalized images of resolution 2 × 2 × 2 mm 3 and 1 × 1 × 1 mm 3 , respectively. Finally the warped functional images were spatially smoothed with a Gaussian kernel of 4 mm FWHM to improve signal-to-noise ratio while preserving the underlying spatial distribution (Schrouff, Kussé, Wehenkel, Maquet, & Phillips, 2012); this smoothing also diminishes the impact residual head motion can have on MVPA performance, even after head motion correction (Gardumi et al, 2012).…”
Section: Image Preprocessingmentioning
confidence: 99%
“…Hence, we emphasize the importance of thinking about this choice. As an interesting example, Gardumi et al (32) studied the effect of smoothing on their dataset and decided to use the optimal smoothing level only after careful examination.…”
Section: Discussionmentioning
confidence: 99%