Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis

Pulini, Alfredo A.; Kerr, Wesley T.; Loo, Sandra K.; Lenartowicz, Agatha

doi:10.1016/j.bpsc.2018.06.003

Cited by 64 publications

(80 citation statements)

References 107 publications

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“…Recently, machine learning algorithms have used EEG spectral power, including TBR, to generate classification models that distinguish between childhood ADHD and neuro-typical controls (31). In adults, studies report 68% accuracy for relative theta power (32) and 76% accuracy for power in all frequency bands (18).…”

Section: Introductionmentioning

confidence: 99%

“…In adults, studies report 68% accuracy for relative theta power (32) and 76% accuracy for power in all frequency bands (18). However, Pulini et al (31) concluded that methodological factors can lead to inflated accuracy estimates in machine-learning studies using neuroimaging data to classify ADHD.…”

Section: Introductionmentioning

confidence: 99%

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Methods.

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confidence: 99%

“…However, a recent review (Pulini et al 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behavior (mediational endophenotypes).…”

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EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

Kiiski

Bennett

Rueda‐Delgado

et al. 2019

Preprint

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Background. Adults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR).However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behavior (mediational endophenotypes). Methods. Resting-state eyes-open and eyes-closed EEG were measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis. Results. Eyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = .71-.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = .68-.72), suggesting that these features may be a mediational endophenotype for adult ADHD.Conclusions. Resting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…

Methods.

…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

Kiiski

Bennett

Rueda‐Delgado

et al. 2019

Preprint

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“…In the 300 study by Tamboer and colleagues (2016), classification performance dropped to around 60% 301 when the trained classifier was applied to an independent sample. We only performed 302 classification within the available dataset and it would be important to assess the current 303 classifier's performance within additional datasets (Pulini et al 2018). In addition, to further 304 advance on the relation between EEG network features and cognitive deficits in dyslexia, 305 longitudinal studies should examine whether the current approach could be used to predict 306 reading improvements or treatment outcomes.…”

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confidence: 99%

Machine learning Classification of Dyslexic Children based on EEG Local Network Features

Rezvani

Zare

Žarić

et al. 2019

Preprint

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16Machine learning can be used to find meaningful patterns characterizing individual 17 differences. Deploying a machine learning classifier fed by local features derived from graph 18 analysis of electroencephalographic (EEG) data, we aimed at designing a neurobiologically-19 based classifier to differentiate two groups of children, one group with and the other group 20 without dyslexia, in a robust way. We used EEG resting-state data of 29 dyslexics and 15 typical 21 readers in grade 3, and calculated weighted connectivity matrices for multiple frequency bands 22 using the phase lag index (PLI). From the connectivity matrices, we derived weighted 23 connectivity graphs. A number of local network measures were computed from those graphs, and 24 37 False Discovery Rate (FDR) corrected features were selected as input to a Support Vector 25 Machine (SVM) and a common K Nearest Neighbors (KNN) classifier. Cross validation was 26 employed to assess the machine-learning performance and random shuffling to assure the 27 performance appropriateness of the classifier and avoid features overfitting. The best 28 performance was for the SVM using a polynomial kernel. Children were classified with 95% 29 accuracy based on local network features from different frequency bands. The automatic 30 classification techniques applied to EEG graph measures showed to be both robust and reliable in 31 distinguishing between typical and dyslexic readers. 32

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Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Zhang-James

Buitelaar

Rooij³

et al. 2021

JCPP Advances

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Background: Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable. Methods:In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.5-64; average age: 15.6; male/female ratio: 4.2:1). Results:The highest classification accuracy on a hold-out test set was achieved using a stacked Extra Tree Classifier. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.62 (95% confidence interval [CI]: 0.57, 0.68) and the area under the precision-recall curve was 0.58. Learning curve analysis showed the good fit of the model and suggests that more training examples will not likely benefit model performance. Conclusions:Our results suggest that sMRI volumetric and cortical thickness data alone may not provide clinically sufficient useful diagnostic biomarkers for ASD.Developing clinically useful imaging classifiers for ASD will benefit from combining other data modalities or feature types, such as functional MRI data and raw images that can leverage other machine learning (ML) techniques such as convolutional neural networks.

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Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis

Cited by 64 publications

References 107 publications

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

Machine learning Classification of Dyslexic Children based on EEG Local Network Features

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

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