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Discriminant Spatial Filtering Method (DSFM) for the identification and analysis of abnormal resting state brain activities

Aradhya, AMS and Subbaraju, V and Sundaram, S and Sundararajan, N (2021) Discriminant Spatial Filtering Method (DSFM) for the identification and analysis of abnormal resting state brain activities. In: Expert Systems with Applications, 181 .

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Official URL: https://doi.org/10.1016/j.eswa.2021.115074


Advances in neuroimaging techniques have enabled early diagnosis and better understanding of neuro-psychological disorders using resting state - functional Magnetic Resonance Images (rs-fMRI). However, factors like the large intra-class variability, high homogeneity, limited sample size and varied acquisition methodologies in aggregated datasets have limited their classification performance. These issues especially are highly prevalent in the automatic diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, a Discriminative Spatial Filtering Method (DSFM) is developed to improve the separability between the two classes in the dataset by automatically identifying regions of the brain with abnormal activities. Further, DSFM uses an orthogonal projection to extract highly separable features from raw rs-fMRI data of the identified regions of the brain. Using a projection based learning classifier DSFM achieved a classification accuracy of 73.83 on the publicly available ADHD200 dataset. The results show that, selective inclusion of data from the regions of the brain with discriminant activity identified using DSFM removes the redundant features and improved the classification performance (improvement of 3.5 over baseline). Further, the brain activity maps derived by inverse mapping of the DSFM spatial filters describe the nature of the abnormal brain activities and identify the corresponding regions responsible for the cognitive symptoms in neuro-psychological disorders like ADHD. The results in this paper clearly show that DSFM is a reliable diagnostic tool with interpretable results critical for the accurate understanding the etiological factors of neuro-psychological disorders like ADHD using rs-fMRI. © 2021 Elsevier Ltd

Item Type: Journal Article
Publication: Expert Systems with Applications
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to Elsevier Ltd
Keywords: Beamforming; Brain; Classification (of information); Diagnosis; Feature extraction; Functional neuroimaging; Neurophysiology, Attention deficit hyperactivity disorder; Brain activity; Classification performance; Features selection; Projection based learning; Psychological disorders; Resting state; Resting state - functional magnetic resonance image; Spatial filtering methods; Spatial transformation, Magnetic resonance imaging
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 07 Oct 2021 15:52
Last Modified: 07 Oct 2021 15:52
URI: http://eprints.iisc.ac.in/id/eprint/69582

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