Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification ...

@article{1364839,
   author = {Janoušová, Eva and Montana, Giovanni and Kašpárek, Tomáš and Schwarz, Daniel},
   article_location = {Lausanne},
   article_number = {AUG},
   doi = {http://dx.doi.org/10.3389/fnins.2016.00392},
   keywords = {computational neuroanatomy; pattern recognition; classification; penalized linear discriminant analysis; support vector machines; cross-validation; magnetic resonance imaging; schizophrenia},
   language = {eng},
   issn = {1662-453X},
   journal = {Frontiers in Neuroscience},
   title = {Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research},
   volume = {10},
   year = {2016}
}

TY  - JOUR
ID  - 1364839
AU  - Janoušová, Eva - Montana, Giovanni - Kašpárek, Tomáš - Schwarz, Daniel
PY  - 2016
TI  - Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research
JF  - Frontiers in Neuroscience
VL  - 10
IS  - AUG
SP  - 1-15
EP  - 1-15
PB  - Frontiers Media S.A.
SN  - 1662453X
KW  - computational neuroanatomy
KW  - pattern recognition
KW  - classification
KW  - penalized linear discriminant analysis
KW  - support vector machines
KW  - cross-validation
KW  - magnetic resonance imaging
KW  - schizophrenia
N2  - We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies.
ER  - 

JANOUŠOVÁ, Eva, Giovanni MONTANA, Tomáš KAŠPÁREK a Daniel SCHWARZ. Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research. \textit{Frontiers in Neuroscience}. Lausanne: Frontiers Media S.A., 2016, roč.~10, AUG, s.~1-15. ISSN~1662-453X. Dostupné z: https://dx.doi.org/10.3389/fnins.2016.00392.