Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI)
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental problem in children. Resting state functional magnetic resonance imaging (rs-fMRI) provides an important tool in understanding the aberrant functional mechanisms in ADHD patients and assist in clinical diagnosis. Recently...
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| Format: | Conference Paper |
| Language: | English |
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2019
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| Online Access: | https://hdl.handle.net/10356/105632 http://hdl.handle.net/10220/50238 http://dx.doi.org/10.1109/EMBC.2018.8513522 |
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| author | Sundararajan, Narasimhan Aradhya, Abhay M S Subbaraju, Vigneshwaran Sundaram, Suresh |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Sundararajan, Narasimhan Aradhya, Abhay M S Subbaraju, Vigneshwaran Sundaram, Suresh |
| author_sort | Sundararajan, Narasimhan |
| collection | NTU |
| description | Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental problem in children. Resting state functional magnetic resonance imaging (rs-fMRI) provides an important tool in understanding the aberrant functional mechanisms in ADHD patients and assist in clinical diagnosis. Recently, spatio-temporal decomposition via spatial filtering (Fukunaga-Koontz transform, ICA) have gained attention in the analysis of fMRI time-series data. Their ability to decompose
the blood oxygen level dependent (BOLD) rs-fMRI time series data into discriminative spatial and temporal components have resulted in better classification accuracy and the ability to isolate the important brain circuits responsible for the observed differences in brain activity. However, they are prone to errors in the estimation of covariance matrices due to the significant presence of atypical samples in the ADHD dataset. In this paper, we present a regularization framework to obtain a
robust estimation of the covariance matrices such that the effect of atypical samples is reduced. The resulting approach called as regularized spatial filtering method (R-SFM) further uses Mahalanobis whitening to lower the effect of two-way correlations while preserving the spatial arrangement of the
data in the feature extraction process. R-SFM was evaluated on the benchmark ADHD200 dataset and not only obtained a 6% improvement in classification accuracy, but also a 66.66% decrease in standard deviation over the previously developed SFM approach. Also R-SFM produces higher specificity which results in lower misclassification of ADHD, thereby reducing the risk of misdiagnosis. These results clearly show that RSFM provides an accurate and reliable tool for detection of ADHD from BOLD rs-fMRI time series data. |
| first_indexed | 2025-02-19T03:46:26Z |
| format | Conference Paper |
| id | ntu-10356/105632 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:46:26Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1056322019-12-06T21:54:57Z Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) Sundararajan, Narasimhan Aradhya, Abhay M S Subbaraju, Vigneshwaran Sundaram, Suresh School of Electrical and Electronic Engineering 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Engineering::Electrical and electronic engineering Attention Deficit Hyperactivity Disorder (ADHD) Regularized Spatial Filtering Method (R-SFM) Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental problem in children. Resting state functional magnetic resonance imaging (rs-fMRI) provides an important tool in understanding the aberrant functional mechanisms in ADHD patients and assist in clinical diagnosis. Recently, spatio-temporal decomposition via spatial filtering (Fukunaga-Koontz transform, ICA) have gained attention in the analysis of fMRI time-series data. Their ability to decompose the blood oxygen level dependent (BOLD) rs-fMRI time series data into discriminative spatial and temporal components have resulted in better classification accuracy and the ability to isolate the important brain circuits responsible for the observed differences in brain activity. However, they are prone to errors in the estimation of covariance matrices due to the significant presence of atypical samples in the ADHD dataset. In this paper, we present a regularization framework to obtain a robust estimation of the covariance matrices such that the effect of atypical samples is reduced. The resulting approach called as regularized spatial filtering method (R-SFM) further uses Mahalanobis whitening to lower the effect of two-way correlations while preserving the spatial arrangement of the data in the feature extraction process. R-SFM was evaluated on the benchmark ADHD200 dataset and not only obtained a 6% improvement in classification accuracy, but also a 66.66% decrease in standard deviation over the previously developed SFM approach. Also R-SFM produces higher specificity which results in lower misclassification of ADHD, thereby reducing the risk of misdiagnosis. These results clearly show that RSFM provides an accurate and reliable tool for detection of ADHD from BOLD rs-fMRI time series data. Accepted version 2019-10-23T05:53:04Z 2019-12-06T21:54:57Z 2019-10-23T05:53:04Z 2019-12-06T21:54:57Z 2018 Conference Paper Aradhya, A. M S, Subbaraju, V., Sundaram, S., & Sundararajan, N. (2018). Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI). 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/EMBC.2018.8513522 https://hdl.handle.net/10356/105632 http://hdl.handle.net/10220/50238 http://dx.doi.org/10.1109/EMBC.2018.8513522 en © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/EMBC.2018.8513522 4 p. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Attention Deficit Hyperactivity Disorder (ADHD) Regularized Spatial Filtering Method (R-SFM) Sundararajan, Narasimhan Aradhya, Abhay M S Subbaraju, Vigneshwaran Sundaram, Suresh Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) |
| title | Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) |
| title_full | Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) |
| title_fullStr | Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) |
| title_full_unstemmed | Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) |
| title_short | Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI) |
| title_sort | regularized spatial filtering method r sfm for detection of attention deficit hyperactivity disorder adhd from resting state functional magnetic resonance imaging rs fmri |
| topic | Engineering::Electrical and electronic engineering Attention Deficit Hyperactivity Disorder (ADHD) Regularized Spatial Filtering Method (R-SFM) |
| url | https://hdl.handle.net/10356/105632 http://hdl.handle.net/10220/50238 http://dx.doi.org/10.1109/EMBC.2018.8513522 |
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