Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

Aradhya, Abhay M S; Joglekar, Aditya; Sundaram, Suresh; Pratama, Mahardhika

doi:10.1609/aaai.v33i01.33012556

Cited by 17 publications

(6 citation statements)

References 23 publications

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“…The multi-modal joint learning CNN architecture was superior to CNNs using either data modality alone. Aradhya et al (2019) also used a CNN classifier and extracted features using the Deep Transformation Method (DTM).…”

Section: Discussion and Qualitative Reviewmentioning

confidence: 99%

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

et al. 2023

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Objective: Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. Methods: A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered. Results: We found that, although most studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. Conclusion: Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

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Section: Discussion and Qualitative Reviewmentioning

confidence: 99%

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

et al. 2023

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“…The multi-modal joint learning CNN architecture was superior to CNNs using either data modality alone (Zou et al, 2017). Aradhya et al (2019) also used a CNN classifier and extracted features using the Deep Transformation Method (DTM).…”

Section: Discussion and Qualitative Reviewmentioning

confidence: 99%

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

Zhang-James

Hoogman

Franke

et al. 2020

Preprint

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Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. We found that, although most of studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

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“…They used 90 subregions in their study. Following the AAL template, Aradhya et al 32 used 90 brain subregions in the cerebrum. In their study, the brain network analysis demonstrated that the difference in functional activities is very high in the temporal lobe and posterior cingulate cortex (PCC), which implies inattentiveness and impulsivity in ADHD.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, instead of extracting features with tailored methods, the features were learned by data in the layers of deep learning networks. A deep transformation method (DTM) was used by Aradhya et al 32 This method extracted the discriminant latent feature space and projected it in the subsequent layer. Also, DTM utilized the convolutional layers to extract low‐level spatial features and transform them into high‐level features in their study.…”

Section: Methodsmentioning

confidence: 99%

A review of ADHD detection studies with machine learning methods using rsfMRI data

Taspinar,

Ozkurt

2024

NMR in Biomedicine

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Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school‐age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non‐invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)‐based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD‐200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area.

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Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

Cited by 17 publications

References 23 publications

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

A review of ADHD detection studies with machine learning methods using rsfMRI data

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