Paper Info
Title
Classification of schizophrenia patients on lattice computing resting-state fMRI features.
Abstract
Currently there are many efforts to find neurological biomarkers that can be extracted from resting state fMRI data. In this paper we concentrate on a study about the discrimination between schizophrenia patients and healthy control, as well as the discrimination of subpopulations of schizophrenia patients with and without auditory hallucinations. Specifically, we compute scalar measures of the high dimensional fMRI voxel time series, carrying out feature selection, feature extraction and classification by SVM over them. The dimensionality reduction is formalized as a supervised h-function proposed in recent Multivariate Mathematical Morphology approaches. It is computed as the recall error of a Lattice Auto-Associative Memory. Using as background and foreground seeds the average time series of regions of interest extracted from the brain ventricles and auditory cortex, respectively. Results on a database of healthy controls and schizophrenia patients with and without auditory hallucinations show that the approach can provide accurate discrimination between these populations.
Year
DOI
Venue
2015
10.1016/j.neucom.2014.09.075
Neurocomputing
Keywords
Field
DocType
rs-fMRI,Schizophrenia classification,Lattice computing,Functional connectivity,Multivariate mathematical morphology,SVM
Auditory cortex,Voxel,Dimensionality reduction,Pattern recognition,Feature selection,Resting state fMRI,Feature extraction,Artificial intelligence,Recall,Machine learning,Mathematics,Schizophrenia
Journal
Volume
ISSN
Citations 
151
0925-2312
2
PageRank 
References 
Authors
0.36
11
2
Name
Order
Citations
PageRank
Darya Chyzhyk113710.82
Manuel Graña21367156.11