NDLI: Accurate classification of schizophrenia patients based on novel resting-state fMRI features

Content Provider	IEEE Xplore Digital Library
Author	Arbabshirani, M.R. Castro, E. Calhoun, V.D.
Copyright Year	2014
Description	Author affiliation: Mind Res. Network, Albuquerque, NM, USA (Castro, E.) \|\| Electr. & Comput. Eng. Dept., Univ. of New Mexico, Albuquerque, NM, USA (Arbabshirani, M.R.; Calhoun, V.D.)
Abstract	There is a growing interest in automatic classification of mental disorders such as schizophrenia based on neuroimaging data. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state fMRI data has not been used much to evaluate discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. In this study, we extract two types of features from resting-state fMRI data: functional network connectivity features that capture internetwork connectivity patterns and autoconnectivity features capturing temporal connectivity of each brain network. Autoconnectivity is a novel concept we have recently proposed. We used minimum redundancy maximum relevancy to select features. Classification results using support vector machine shows that combining these two types of features can improve the classification on a large resting fMRI dataset consisting of 195 patients with schizophrenia and 175 healthy controls. We achieved the accuracy of 85% which is very promising.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	6691
Ending Page	6694
File Size	1093869
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6945163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Support vector machines Independent component analysis Accuracy Magnetic resonance imaging Mathematical model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Classification of borderline personality disorder based on spectral power of resting-state fMRI

Independent component versus Local Sparse Component Analysis in resting state fMRI

Features for cells and nuclei classification

An age estimation method using brain local features for T1-weighted images

Classification of schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM)

EEG auditory steady state responses classification for the novel BCI

Image features of spectral correlation function for arrhythmia classification

Localized Sparse Code Gradient in Alzheimer's disease staging

Boosting specificity of MEG artifact removal by weighted support vector machine

Accurate classification of schizophrenia patients based on novel resting-state fMRI features

Similar Documents

Classification of borderline personality disorder based on spectral power of resting-state fMRI

Independent component versus Local Sparse Component Analysis in resting state fMRI

Features for cells and nuclei classification

An age estimation method using brain local features for T1-weighted images

Classification of schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM)

EEG auditory steady state responses classification for the novel BCI

Image features of spectral correlation function for arrhythmia classification

Localized Sparse Code Gradient in Alzheimer's disease staging

Boosting specificity of MEG artifact removal by weighted support vector machine

Accurate classification of schizophrenia patients based on novel resting-state fMRI features