3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI

Zou, Liang; Zheng, Jiannan; Miao, Chunyan; McKeown, Martin J.; Wang, Z. Jane

doi:10.1109/access.2017.2762703

Cited by 264 publications

(173 citation statements)

References 33 publications

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“…Although our accuracies were modest (61.2% for children and 62.1% for adults), they were at the high end of prior results with the held-out test sets (Figure 1). Two studies reported higher test accuracies than ours (52, 53). Both studies utilized functional MRI in addition to sMRI data as features.…”

Section: Discussioncontrasting

confidence: 58%

Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis

Zhang-James

Helminen

Liu

et al. 2019

Preprint

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ADHD affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences in the pathophysiology of the disorder, it is not clear if adults with ADHD have similar neuroanatomical impairments as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural abnormalities for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that common to ADHD in childhood and adulthood. These results support the continuity of ADHD's pathophysiology from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity. 0

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Section: Discussioncontrasting

confidence: 58%

Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis

Zhang-James

Helminen

Liu

et al. 2019

Preprint

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“…Also, (Riaz et al 2017) used data from individual acquisition sites in order to validate the results in order to avoid inter-site variability. (Zou et al 2017) made use of the s-MRI and rs-fMRI data to extract six types of 3D features and introduced a 3D CNN based classifier. They achieved a maximum classification accuracy of 69.15%.…”

Section: Resultsmentioning

confidence: 99%

“…They obtained an average accuracy of 62%, with a Support Vector Machine (SVM) classifier. (Zou et al 2017) introduced a multi-modal 3D CNN approach to study ADHD. Here, the encoded structural-MRI and fMRI are jointly extracted as features and achieved a classification accuracy of 69.15%.…”

Section: Introductionmentioning

confidence: 99%

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

Aradhya

Joglekar

Sundaram

et al. 2019

AAAI

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Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

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“…SVM has also been applied to structural MRI and DTI data collected from adults with ADHD and controls, which reported between-group differences in widespread GM and WM regions in cortices, thalamus, and cerebellum (Chaim-Avancini et al, 2017). Meanwhile, neural network-based techniques, including deep belief network, fully connected cascade artificial neural network, convolutional neural network, extreme learning machine, and hierarchical extreme learning machine, have also been utilized to structural MRI and resting-state functional MRI (fMRI) data in children with ADHD and controls (Deshpande et al, 2015; Kuang and He, 2014; Peng et al, 2013; Qureshi et al, 2016; Qureshi et al, 2017; Zou et al, 2017). The most important group discrimination predictors identified by these neural network studies included functional connectivities within cerebellum, functional connectivity, surface area, cortical thickness and/or folding indices of frontal lobe, temporal lobe, occipital lobe and insula (Deshpande et al, 2015; Peng et al, 2013; Qureshi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…These existing studies have either utilized features representing regional/voxel brain properties collected from only single imaging modality, or the combination of two modalities (mostly structural MRI and resting-state fMRI) (Brown et al, 2012; Fair et al, 2012; Hart et al, 2014; Iannaccone et al, 2015; Johnston et al, 2014), or reported poor accuracy (Dai et al, 2012; Sen et al, 2018; Zou et al, 2017). Some studies did not conduct the very necessary step of estimating the most important features that contribute to accurate classifications (Chang et al, 2012; Dai et al, 2012; Kuang and He, 2014; Qureshi et al, 2016; Sen et al, 2018; Tenev et al, 2014; Zou et al, 2017). In this field, systems-level functional and structural features, such as global and regional topological properties from functional brain networks during cognitive processes and WM tract properties have not been considered.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Neuroimaging-based Prediction of Adult Outcomes in Childhood-onset ADHD using Ensemble Learning Techniques

Luo

Alvarez

Halperin

et al. 2019

Preprint

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Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, currently relaying on subjective symptom observations for diagnosis.Machine learning classifiers have been utilized to assist the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, the existing basic model-based studies in ADHD reported suboptimal classification performances and inconclusive results, mainly due to the limited flexibility for each type of basic classifiers to appropriately handle multi-dimensional source features with various properties. In this study, we proposed to apply ensemble learning techniques (ELTs) in multimodal neuroimaging data collected from 72 young adults, including 36 probands (18 remitters and 18 persisters of childhood ADHD) and 36 group-matched controls. All the currently available optimization strategies for ELTs (i.e., voting, bagging, boosting and stacking techniques) were tested in a pool of semi-final classification results generated by seven basic classifiers. The highdimensional neuroimaging features for classification included regional cortical gray matter (GM) thickness and surface area, GM volume of subcortical structures, volume and fractional anisotropy of major white matter fiber tracts, pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process. As a result, the bagging-based ELT with the base model of support vector machine achieved the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD vs. controls, and 0.9 for ADHD persisters vs. remitters). We found that features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. Our study also suggested that considering their solidly improved robustness than the commonly implemented basic classifiers, ELTs may have the potential to identify more reliable neurobiological markers for severe brain disorders.structures estimated from structural MRI data, volume and FA of major WM fiber tracts derived from DTI data, the pair-wise regional connectivity and global/nodal topological properties (i.e., global-, local-, and nodal-efficiency, etc.) of the cue-evoked attention processing network computed from task-based fMRI data. Based on findings from existing studies from our group and other teams, we hypothesized that structural and functional alterations in frontal, parietal, and subcortical areas and their interactions would significantly contribute to accurate discrimination of ADHD probands (adults diag...

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3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI

Cited by 264 publications

References 33 publications

Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis

Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis

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

Multimodal Neuroimaging-based Prediction of Adult Outcomes in Childhood-onset ADHD using Ensemble Learning Techniques

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