Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity

Kim, Sungkean; Baek, Ji Hyun; Kwon, Young Joon; Lee, Hwa Young; Yoo, Jae Hyun; Shim, Se-Hoon; Kim, Ji Sun

doi:10.1038/s41398-021-01604-3

Cited by 22 publications

(7 citation statements)

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The finding that greater endorsement of inattention is linked to smaller caudal anterior cingulate gray matter volume is in line with prior empirical studies acknowledging the anterior cingulate's potential importance in the pathophysiology of ADHD (Bernanke et al, 2022 ; Kim et al, 2021 ; Seidman et al, 2006 ). Dysfunction or alterations to the anterior cingulate are associated with impaired attention allocation and sustainment, domains important to ADHD's inattentive phenotype (Wu et al, 2017 ).…”

Section: Discussionsupporting

confidence: 88%

Gray matter volume associations in youth with ADHD features of inattention and hyperactivity/impulsivity

Reimann,

Jeong,

Durham

et al. 2024

Human Brain Mapping

View full text Add to dashboard Cite

BackgroundPrior research has shown smaller cortical and subcortical gray matter volumes among individuals with attention‐deficit/hyperactivity disorder (ADHD). However, neuroimaging studies often do not differentiate between inattention and hyperactivity/impulsivity, which are distinct core features of ADHD. The present study uses an approach to disentangle overlapping variance to examine the neurostructural heterogeneity of inattention and hyperactivity/impulsivity dimensions.MethodsWe analyzed data from 10,692 9‐ to 10‐year‐old children from the Adolescent Brain Cognitive Development (ABCD) Study. Confirmatory factor analysis was used to derive factors representing inattentive and hyperactive/impulsive traits. We employed structural equation modeling to examine these factors' associations with gray matter volume while controlling for the shared variance between factors.ResultsGreater endorsement of inattentive traits was associated with smaller bilateral caudal anterior cingulate and left parahippocampal volumes. Greater endorsement of hyperactivity/impulsivity traits was associated with smaller bilateral caudate and left parahippocampal volumes. The results were similar when accounting for socioeconomic status, medication, and in‐scanner motion. The magnitude of these findings increased when accounting for overall volume and intracranial volume, supporting a focal effect in our results.ConclusionsInattentive and hyperactivity/impulsivity traits show common volume deficits in regions associated with visuospatial processing and memory while at the same time showing dissociable differences, with inattention showing differences in areas associated with attention and emotion regulation and hyperactivity/impulsivity associated with volume differences in motor activity regions. Uncovering such biological underpinnings within the broader disorder of ADHD allows us to refine our understanding of ADHD presentations.

show abstract

Section: Discussionsupporting

confidence: 88%

Gray matter volume associations in youth with ADHD features of inattention and hyperactivity/impulsivity

Reimann,

Jeong,

Durham

et al. 2024

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Our approach has achieved better performance than the existing approaches in Chen et al (2019) , Altınkaynak et al (2020) , Ekhlasi et al (2021) , Kim et al (2021) , Parashar et al (2021) , Maniruzzaman et al (2022) , and Alim and Imtiaz (2023) . The approaches in Chen et al (2019) , Altınkaynak et al (2020) , and Kim et al (2021) have performed experiments on different datasets, while the approaches in Ekhlasi et al (2021) , Parashar et al (2021) , Maniruzzaman et al (2022) , and Alim and Imtiaz (2023) have performed experiments on the same dataset as ours. Chen et al (2019) performed four distinct methods: relative spectral power, spectral power ratio, complexity analyses, and bicoherence for resting-state EEG feature extraction.…”

Section: Resultsmentioning

confidence: 83%

“…The classifier constructed by selecting features from all four methods obtained an Acc of 85% on data acquired from 108 subjects. Kim et al (2021) investigated the mismatch negativity (MMN) features, exploring both sensor-level attributes such as amplitude, latency, and source-level characteristics across various brain regions and achieved an Acc of 81%. It should be noted that authors have collected data from only 79 subjects.…”

Section: Resultsmentioning

confidence: 99%

Gabor filter-based statistical features for ADHD detection

Sathiya,

Rao,

Kumar

2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Attention deficit/hyperactivity disorder (ADHD) is a neuropsychological disorder that occurs in children and is characterized by inattention, impulsivity, and hyperactivity. Early and accurate diagnosis of ADHD is very important for effective intervention. The aim of this study is to develop a computer-aided approach to detecting ADHD using electroencephalogram (EEG) signals. Specifically, we explore a Gabor filter-based statistical features approach for the classification of EEG signals into ADHD and healthy control (HC). The EEG signal is processed by a bank of Gabor filters to obtain narrow-band signals. Subsequently, a set of statistical features is extracted. The computed features are then subjected to feature selection. Finally, the obtained feature vector is given to a classifier to detect ADHD and HC. Our approach achieves the highest classification accuracy of 96.4% on a publicly available dataset. Furthermore, our approach demonstrates better classification accuracy than the existing methods.

show abstract

“…Tenev et al [54] investigated the changes in the characteristics of mismatch negativity in adults with ADHD and healthy controls using ML-based algorithms and SVM obtained a classification accuracy of 82.3%. Kim et al [62] performed SVM with linear kernel for classification and its performance was evaluated using classification accuracy. Khoshnoud et al [51] recorded EEG data from 12 children with ADHD and 12 healthy controls during eyes-closed resting.…”

Section: Introductionmentioning

confidence: 99%

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

et al. 2023

View full text Add to dashboard Cite

A TTENTION deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach is very important for the early detection and classification of children with ADHD. However, diagnosing children with ADHD using full EEG channels with all features may lead to computational complexity and overfitting problems.To solve these problems, machine learning (ML)-based ADHD detection was designed by identifying optimal channels and its significant features. In this work, support vector machine and t-test based, two separate approaches were devised to select optimal channels individually and then proposed a hybrid channel selection approach by combining these two channel selection methods in order to select the optimal channels. After that, LASSO logistic regression-based model was used to select the important features from the selected channels. Finally, six ML-based classifiers, like Gaussian process classification (GPC), random forest, k-nearest neighbors, multilayer perceptron, decision tree, and logistic regression were applied for the detection of children with ADHD and evaluated their performances using accuracy and area under the curve (AUC). This study utilized a total of one hundred twenty-one children, with sixty-one children with ADHD, aged 7-12 years, and had nineteen channels. Ten different channels were selected by SVM based and an independent t-test-based approach separately and six overlapping channels were identified from both channel selection methods. Then, we selected twenty-eight features from selected six channels using LASSO. Using only six channels and twenty-eight features, GP-based classifier achieved an accuracy rate of 97.53% and AUC of 0.999. This is an improvement of 3% over previously published values in the literature. This study illustrated that LASSO with GP-based system performed outstanding performance in distinguishing children with ADHD from healthy children. This proposed system will be helpful to doctors and physicians in order to detect children with ADHD at an early stage and take the necessary steps for the patients to access appropriate healthcare services, receive effective treatment, and be more conscious of maintaining their lives.

show abstract

Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity

Cited by 22 publications

References 98 publications

Gray matter volume associations in youth with ADHD features of inattention and hyperactivity/impulsivity

Gray matter volume associations in youth with ADHD features of inattention and hyperactivity/impulsivity

Gabor filter-based statistical features for ADHD detection

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

Contact Info

Product

Resources

About