Abstract：
:There is a widespread interest in applying pattern recognition methods to anatomical neuroimaging data, but so far, there has been relatively little investigation into how best to derive image features in order to make the most accurate predictions. In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using the ABIDE and COBRE datasets. MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing. We compared classification and regression accuracy using the different sorts of features, and with various degrees of spatial smoothing. Results suggested that feature sets that did not ignore the implicit background tissue class, tended to result in better overall performance, whereas some of the most commonly used feature sets performed relatively poorly.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Monté-Rubio GC,Falcón C,Pomarol-Clotet E,Ashburner Jdoi
10.1016/j.neuroimage.2018.05.065subject
Has Abstractpub_date
2018-09-01 00:00:00pages
753-768eissn
1053-8119issn
1095-9572pii
S1053-8119(18)30491-9journal_volume
178pub_type
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