More Web Proxy on the site http://driver.im/

research-article

Classification of Schizophrenia versus normal subjects using deep learning

Authors:

Priya Aggarwal,

Anubha GuptaAuthors Info & Claims

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 28, Pages 1 - 6

https://doi.org/10.1145/3009977.3010050

Published: 18 December 2016 Publication History

Abstract

Motivated by deep learning approaches to classify normal and neuro-diseased subjects in functional Magnetic Resonance Imaging (fMRI), we propose stacked autoencoder (SAE) based 2-stage architecture for disease diagnosis. In the proposed architecture, a separate 4-hidden layer autoencoder is trained in unsupervised manner for feature extraction corresponding to every brain region. Thereafter, these trained autoencoders are used to provide features on class-labeled input data for training a binary support vector machine (SVM) based classifier. In order to design a robust classifier, noisy or inactive gray matter voxels are filtered out using a proposed covariance based approach. We applied the proposed methodology on a public dataset, namely, 1000 Functional Connectomes Project Cobre dataset consisting of fMRI data of normal and Schizophrenia subjects. The proposed architecture is able to classify normal and Schizophrenia subjects with 10-fold cross-validation accuracy of 92% that is better compared to the existing methods used on the same dataset.

References

[1]

A. Anderson and M. S. Cohen. Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial, 2013.

[2]

P. Baldi. Autoencoders, unsupervised learning, and deep architectures. ICML unsupervised and transfer learning, 27(37--50):1, 2012.

[3]

B. Biswal, F. Zerrin Yetkin, V. M. Haughton, and J. S. Hyde. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic resonance in medicine, 34(4):537--541, 1995.

[4]

H. Cheng, S. Newman, J. Goñi, J. S. Kent, J. Howell, A. Bolbecker, A. Puce, B. F. O'Donnell, and W. P. Hetrick. Nodal centrality of functional network in the differentiation of Schizophrenia. Schizophrenia research, 168(1):345--352, 2015.

[5]

D. Chyzhyk, A. Savio, and M. Graña. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Networks, 68:23--33, 2015.

Digital Library

[6]

D. Cordes, V. M. Haughton, K. Arfanakis, G. J. Wendt, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand. Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiology, 21(9):1636--1644, 2000.

[7]

C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273--297, 1995.

[8]

M. D. Fox and M. Greicius. Clinical applications of resting state functional connectivity. Frontiers in systems neuroscience, 4:19, 2010.

[9]

T.-H. Hsieh, M.-J. Sun, and S.-F. Liang. Diagnosis of Schizophrenia patients based on brain network complexity analysis of resting-state fMRI. In The 15th International Conference on Biomedical Engineering, pages 203--206. Springer, 2014.

[10]

S. Huang, J. Li, L. Sun, J. Ye, A. Fleisher, T. Wu, K. Chen, E. Reiman, A. D. N. Initiative, et al. Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. NeuroImage, 50(3):935--949, 2010.

[11]

D. T. Jones, P. Vemuri, M. C. Murphy, J. L. Gunter, M. L. Senjem, M. M. Machulda, S. A. Przybelski, B. E. Gregg, K. Kantarci, D. S. Knopman, et al. Non-stationarity in the resting brain's modular architecture. PloS one, 7(6):e39731, 2012.

[12]

J. Kim, V. D. Calhoun, E. Shim, and J.-H. Lee. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. NeuroImage, 124:127--146, 2016.

[13]

D. Kuang, X. Guo, X. An, Y. Zhao, and L. He. Discrimination of ADHD based on fMRI data with Deep Belief Network. In International Conference on Intelligent Computing, pages 225--232. Springer, 2014.

[14]

N. Leonardi, J. Richiardi, M. Gschwind, S. Simioni, J.-M. Annoni, M. Schluep, P. Vuilleumier, and D. Van De Ville. Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. NeuroImage, 83:937--950, 2013.

[15]

N. Leonardi and D. Van De Ville. Identifying network correlates of brain states using tensor decompositions of whole-brain dynamic functional connectivity. In Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on, pages 74--77. IEEE, 2013.

Digital Library

[16]

X. Li, D. Zhu, X. Jiang, C. Jin, X. Zhang, L. Guo, J. Zhang, X. Hu, L. Li, and T. Liu. Dynamic functional connectomics signatures for characterization and differentiation of ptsd patients. Human brain mapping, 35(4):1761--1778, 2014.

[17]

D. Liu, N. Zhong, and Y. Qin. Exploring functional connectivity networks in fMRI data using clustering analysis. In International Conference on Brain Informatics, pages 148--159. Springer, 2011.

Digital Library

[18]

T. M. R. Network and the University of New Mexico. The center for biomedical research excellence (cobre), 2012.

[19]

A. Ng. Sparse autoencoder. CS294A Lecture notes, 72:1--19, 2011.

[20]

Ü. Sakoğlu, G. D. Pearlson, K. A. Kiehl, Y. M. Wang, A. M. Michael, and V. D. Calhoun. A method for evaluating dynamic functional network connectivity and task-modulation: application to Schizophrenia. Magnetic Resonance Materials in Physics, Biology and Medicine, 23(5--6):351--366, 2010.

[21]

A. Savio and M. Graña. Local activity features for computer aided diagnosis of Schizophrenia on resting-state fMRI. Neurocomputing, 164:154--161, 2015.

Digital Library

[22]

T. Starck, J. Nikkinen, J. Rahko, J. Remes, T. Hurtig, H. Haapsamo, K. Jussila, S. Kuusikko-Gauffin, M.-L. Mattila, E. Jansson-Verkasalo, D. L. Pauls, H. Ebeling, I. Moilanen, O. Tervonen, and V. J. Kiviniemi. Resting state fmri reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in asd despite motion scrubbing. Front Hum Neurosci, 7:802, Nov 2013. 24319422{pmid}.

[23]

H.-I. Suk, S.-W. Lee, and D. Shen. A hybrid of deep network and hidden markov model for MCI identification with resting-state fMRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 573--580. Springer, 2015.

Digital Library

[24]

V. Vapnik. The nature of statistical learning theory. Springer Science & Business Media, 2013.

Digital Library

Cited By

Fafrowicz MTutajewski MSieradzki IOchab JCeglarek-Sroka ALewandowska KMarek TSikora-Wachowicz BPodolak IOświęcimka P(2024)Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networksFrontiers in Neuroinformatics10.3389/fninf.2024.148036618Online publication date: 20-Dec-2024
https://doi.org/10.3389/fninf.2024.1480366
Gütlin DMcDermott HGrundei MAuksztulewicz R(2024)Model-Based Approaches to Investigating Mismatch Responses in SchizophreniaClinical EEG and Neuroscience10.1177/1550059424125391056:1(8-21)Online publication date: 15-May-2024
https://doi.org/10.1177/15500594241253910
Huang ZLiu RZhu ZTan K(2024)Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRIIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322517935:6(8161-8175)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3225179
Show More Cited By

Index Terms

Classification of Schizophrenia versus normal subjects using deep learning
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
2. Computing methodologies
  1. Machine learning
    1. Cross-validation

Recommendations

3D-CNN based discrimination of schizophrenia using resting-state fMRI
Highlights
- Very optimistic results for the automated discrimination of schizophrenia using state-of-the-art 3D deep learning architecture.
Abstract Motivation
This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain. Resting-state functional MRI data from a ...
A Novel Approach for Classification of Schizophrenia Patients and Healthy Subjects Using Auditory Oddball Functional MRI
MICAI '14: Proceedings of the 2014 13th Mexican International Conference on Artificial Intelligence

Schizophrenia is a serious psychiatric illness which needs early and accurate diagnosis. Difference in activation patterns of schizophrenia patients and healthy subjects can be identified with the help of functional magnetic resonance imaging (fMRI). ...
fMRI based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection

Functional magnetic resonance imaging (fMRI) is a useful technique for capturing deformities in brain activity patterns of several disorders. Schizophrenia is one such serious psychiatric disorder that, in absence of any standard diagnostic tests, is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

December 2016

743 pages

ISBN:9781450347532

DOI:10.1145/3009977

Program Chairs:
Dhruv Batra
Georgia Tech
,
Michael Brown
York University, Canada
,
Vijay Natarajan
Indian Institute of Science, Bangalore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Google Inc.
QI: Qualcomm Inc.
Tata Consultancy Services
NVIDIA
MathWorks: The MathWorks, Inc.
Microsoft Research: Microsoft Research

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Ministry of Communication and IT, Govt. of India

Conference

ICVGIP '16

Sponsor:

QI
MathWorks
Microsoft Research

ICVGIP '16: Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2016

Assam, Guwahati, India

Acceptance Rates

ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;

Overall Acceptance Rate 95 of 286 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
685
Total Downloads

Downloads (Last 12 months)63
Downloads (Last 6 weeks)7

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Fafrowicz MTutajewski MSieradzki IOchab JCeglarek-Sroka ALewandowska KMarek TSikora-Wachowicz BPodolak IOświęcimka P(2024)Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networksFrontiers in Neuroinformatics10.3389/fninf.2024.148036618Online publication date: 20-Dec-2024
https://doi.org/10.3389/fninf.2024.1480366
Gütlin DMcDermott HGrundei MAuksztulewicz R(2024)Model-Based Approaches to Investigating Mismatch Responses in SchizophreniaClinical EEG and Neuroscience10.1177/1550059424125391056:1(8-21)Online publication date: 15-May-2024
https://doi.org/10.1177/15500594241253910
Huang ZLiu RZhu ZTan K(2024)Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRIIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322517935:6(8161-8175)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3225179
Sarkar JHajdu A(2024)Comparative Analysis of Deep Learning Methods for Schizophrenia Classification from fMRI Scans2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS61543.2024.00020(69-74)Online publication date: 26-Jun-2024
https://doi.org/10.1109/CBMS61543.2024.00020
Tripathi AAhmed RTiwari A(2024)Review of Deep Learning Techniques for Neurological Disorders DetectionWireless Personal Communications10.1007/s11277-024-11464-x137:2(1277-1311)Online publication date: 14-Jul-2024
https://doi.org/10.1007/s11277-024-11464-x
Yousefian AShayegh FMaleki Z(2023)Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signalsFrontiers in Systems Neuroscience10.3389/fnsys.2022.90477016Online publication date: 2-Feb-2023
https://doi.org/10.3389/fnsys.2022.904770
Swathi NPrabha S(2023)Survey on Structural Neuro Imaging for the Identification of Brain Abnormalities in SchizophreniaCurrent Medical Imaging Reviews10.2174/221155520466622013111263919:2(115-125)Online publication date: Feb-2023
https://doi.org/10.2174/2211555204666220131112639
Sharma MPatel RGarg ASanTan RAcharya U(2023)Automated detection of schizophrenia using deep learning: a review for the last decadePhysiological Measurement10.1088/1361-6579/acb24d44:3(03TR01)Online publication date: 6-Mar-2023
https://doi.org/10.1088/1361-6579/acb24d
Chaki JWoźniak M(2023)Deep learning for neurodegenerative disorder (2016 to 2022): A systematic reviewBiomedical Signal Processing and Control10.1016/j.bspc.2022.10422380(104223)Online publication date: Feb-2023
https://doi.org/10.1016/j.bspc.2022.104223
Nallusamy KEaswarakumar K(2023)Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detectionNetwork Modeling Analysis in Health Informatics and Bioinformatics10.1007/s13721-023-00415-412:1Online publication date: 4-Apr-2023
https://doi.org/10.1007/s13721-023-00415-4
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents