Differentiation between resting-state fMRI data from ADHD and normal subjects: Based on functional connectivity and machine learning

Research output: Contribution to conferencePaper

8 Citations (Scopus)

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder which is quite common in childhood, with an estimated prevalence of 5-8%, and often persists into adolescence and adulthood. It is further characterized as inappropriate developmentally symptoms of inattention, impulsiveness, motor over-activity and restlessness. The aim of this study is to evaluate the feasibility of diagnosing ADHD by analyzing the resting-state functional magnetic resonance imaging (fMRI) data. In addition to confirming the previously observed three areas including anterior cingulate cortex (ACC), posterior cingulated cortex (PCC) and ventro medial prefrontal cortex (vmPFC), we also found significant differences in cerebellum, motor cortex and temporal lobe between ADHD and normal humans based on regional homogeneity analysis of the dataset from 73 children with ADHD and 76 normal children. Extracting features from these seven brain areas and utilizing the LDA classifier, the average accuracy of distinguishing normal and ADHD children reaches 80.08% though 50 times of 2-fold validation. Experimental results demonstrate the feasibility of ADHD diagnosis based on the combination of functional connectivity of resting-state fMRI and machine learning technique.

Original languageEnglish
Pages294-298
Number of pages5
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012 - Taichung, Taiwan
Duration: 2012 Nov 162012 Nov 18

Other

Other2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012
CountryTaiwan
CityTaichung
Period12-11-1612-11-18

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Functional Magnetic Resonance Imaging
Disorder
Machine Learning
Connectivity
Cortex
Cerebellum
Homogeneity
Fold
Classifier
Evaluate
Experimental Results
Demonstrate
Children

All Science Journal Classification (ASJC) codes

  • Logic

Cite this

Liang, S-F., Hsieh, T. H., Chen, P. T., Wu, M-L., Kung, C-C., Lin, C-Y., & Shaw, F-Z. (2012). Differentiation between resting-state fMRI data from ADHD and normal subjects: Based on functional connectivity and machine learning. 294-298. Paper presented at 2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012, Taichung, Taiwan. https://doi.org/10.1109/iFUZZY.2012.6409719
Liang, Sheng-Fu ; Hsieh, Tsung Hao ; Chen, Pin Tzu ; Wu, Ming-Long ; Kung, Chun-Chia ; Lin, Chun-Yu ; Shaw, Fu-Zen. / Differentiation between resting-state fMRI data from ADHD and normal subjects : Based on functional connectivity and machine learning. Paper presented at 2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012, Taichung, Taiwan.5 p.
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Liang, S-F, Hsieh, TH, Chen, PT, Wu, M-L, Kung, C-C, Lin, C-Y & Shaw, F-Z 2012, 'Differentiation between resting-state fMRI data from ADHD and normal subjects: Based on functional connectivity and machine learning' Paper presented at 2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012, Taichung, Taiwan, 12-11-16 - 12-11-18, pp. 294-298. https://doi.org/10.1109/iFUZZY.2012.6409719

Differentiation between resting-state fMRI data from ADHD and normal subjects : Based on functional connectivity and machine learning. / Liang, Sheng-Fu; Hsieh, Tsung Hao; Chen, Pin Tzu; Wu, Ming-Long; Kung, Chun-Chia; Lin, Chun-Yu; Shaw, Fu-Zen.

2012. 294-298 Paper presented at 2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012, Taichung, Taiwan.

Research output: Contribution to conferencePaper

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AU - Liang, Sheng-Fu

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AU - Chen, Pin Tzu

AU - Wu, Ming-Long

AU - Kung, Chun-Chia

AU - Lin, Chun-Yu

AU - Shaw, Fu-Zen

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N2 - Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder which is quite common in childhood, with an estimated prevalence of 5-8%, and often persists into adolescence and adulthood. It is further characterized as inappropriate developmentally symptoms of inattention, impulsiveness, motor over-activity and restlessness. The aim of this study is to evaluate the feasibility of diagnosing ADHD by analyzing the resting-state functional magnetic resonance imaging (fMRI) data. In addition to confirming the previously observed three areas including anterior cingulate cortex (ACC), posterior cingulated cortex (PCC) and ventro medial prefrontal cortex (vmPFC), we also found significant differences in cerebellum, motor cortex and temporal lobe between ADHD and normal humans based on regional homogeneity analysis of the dataset from 73 children with ADHD and 76 normal children. Extracting features from these seven brain areas and utilizing the LDA classifier, the average accuracy of distinguishing normal and ADHD children reaches 80.08% though 50 times of 2-fold validation. Experimental results demonstrate the feasibility of ADHD diagnosis based on the combination of functional connectivity of resting-state fMRI and machine learning technique.

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Liang S-F, Hsieh TH, Chen PT, Wu M-L, Kung C-C, Lin C-Y et al. Differentiation between resting-state fMRI data from ADHD and normal subjects: Based on functional connectivity and machine learning. 2012. Paper presented at 2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012, Taichung, Taiwan. https://doi.org/10.1109/iFUZZY.2012.6409719