Literature DB >> 16908198

Classification of functional brain images with a spatio-temporal dissimilarity map.

Svetlana V Shinkareva1, Hernando C Ombao, Bradley P Sutton, Aprajita Mohanty, Gregory A Miller.   

Abstract

Classification of subjects into predefined groups, such as patient vs. control, based on their functional MRI data is a potentially useful procedure for clinical diagnostic purposes. This paper presents an automated method for classifying subjects into groups based on their functional MRI data. The proposed methodology provides general framework using preprocessed time series for the whole brain volume. Using a training set of two groups of subjects, the new methodology identifies spatio-temporal features that distinguish the groups and uses these features to categorize new subjects. We demonstrate the method using simulations and a clinical application that classifies individuals into schizotypy and control groups.

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Year:  2006        PMID: 16908198     DOI: 10.1016/j.neuroimage.2006.06.032

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  16 in total

1.  Automated classification of fMRI during cognitive control identifies more severely disorganized subjects with schizophrenia.

Authors:  Jong H Yoon; Danh V Nguyen; Lindsey M McVay; Paul Deramo; Michael J Minzenberg; J Daniel Ragland; Tara Niendham; Marjorie Solomon; Cameron S Carter
Journal:  Schizophr Res       Date:  2012-01-25       Impact factor: 4.939

2.  Exploring commonalities across participants in the neural representation of objects.

Authors:  Svetlana V Shinkareva; Vicente L Malave; Marcel Adam Just; Tom M Mitchell
Journal:  Hum Brain Mapp       Date:  2011-05-12       Impact factor: 5.038

3.  Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

Authors:  Kristoffer H Madsen; Laerke G Krohne; Xin-Lu Cai; Yi Wang; Raymond C K Chan
Journal:  Schizophr Bull       Date:  2018-10-15       Impact factor: 9.306

4.  Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task.

Authors:  Laerke Gebser Krohne; Yi Wang; Jesper L Hinrich; Morten Moerup; Raymond C K Chan; Kristoffer H Madsen
Journal:  Hum Brain Mapp       Date:  2019-08-12       Impact factor: 5.038

5.  A New Approach for Functional Connectivity via Alignment of Blood Oxygen Level-Dependent Signals.

Authors:  Chun-Jui Chen; Jane-Ling Wang
Journal:  Brain Connect       Date:  2019-06-20

6.  ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements.

Authors:  Matthew R G Brown; Gagan S Sidhu; Russell Greiner; Nasimeh Asgarian; Meysam Bastani; Peter H Silverstone; Andrew J Greenshaw; Serdar M Dursun
Journal:  Front Syst Neurosci       Date:  2012-09-28

7.  A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia.

Authors:  Honghui Yang; Jingyu Liu; Jing Sui; Godfrey Pearlson; Vince D Calhoun
Journal:  Front Hum Neurosci       Date:  2010-10-25       Impact factor: 3.169

8.  A Review of Challenges in the Use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from Multi-site fMRI Schizophrenia Study.

Authors:  Oguz Demirci; Vincent P Clark; Vincent A Magnotta; Nancy C Andreasen; John Lauriello; Kent A Kiehl; Godfrey D Pearlson; Vince D Calhoun
Journal:  Brain Imaging Behav       Date:  2008-09-01       Impact factor: 3.978

9.  Functional Magnetic Resonance Imaging - Implications for Detection of Schizophrenia.

Authors:  Oguz Demirci; Vince D Calhoun
Journal:  Eur Neurol Rev       Date:  2009-12

10.  A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia.

Authors:  Oguz Demirci; Vincent P Clark; Vince D Calhoun
Journal:  Neuroimage       Date:  2008-02-15       Impact factor: 6.556

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