Paper Info
Title
Discrimination Of Resting-State Fmri For Schizophrenia Patients With Lattice Computing Based Features
Abstract
Resting state fMRI data can be used to find biomarkers of specific neurological conditions, such as schizophrenia. In this paper we report results on the discrimination between schizophrenia patients and healthy control, as well as the discrimination of subpopulations of schizophrenia patients with and without auditory hallucinations. Data features for classification are obtained as follows: a Multivariate reduced ordering based on a h-function constructed from Lattice Autoassociative Memories recall. The Pearson correlation coefficient between the h-function values and the categorical variable at each voxel site allows to identify the most informative voxel sites. Feature vectors are constructed as the h-function values at these sites. 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
2013
10.1007/978-3-642-40846-5_48
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS
Field
DocType
Volume
Voxel,Feature vector,Pearson product-moment correlation coefficient,Pattern recognition,Computer science,Categorical variable,Resting state fMRI,Auditory hallucination,Artificial intelligence,Recall,Schizophrenia
Conference
8073
ISSN
Citations 
PageRank 
0302-9743
2
0.38
References 
Authors
8
2
Name
Order
Citations
PageRank
Darya Chyzhyk113710.82
Manuel Graña21367156.11