Identifying individuals with attention‐deficit/hyperactivity disorder based on multisite resting‐state functional magnetic resonance imaging: A radiomics analysis

Abstract

Attention‐deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age‐inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting‐state functional magnetic resonance (rs‐fMRI) have more discriminative power for the diagnosis of ADHD. The rs‐fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD‐200 Consortium. A total of four preprocessed rs‐fMRI images including regional homogeneity (ReHo), amplitude of low‐frequency fluctuation (ALFF), voxel‐mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs‐fMRI information to distinguish ADHD from healthy controls. The rs‐fMRI‐based radiomics features have the potential to be neuroimaging biomarkers for ADHD.

The Use of Transcranial Magnetic Stimulation in Attention Optimization Research: A Review from Basic Theory to Findings in Attention-Deficit/Hyperactivity Disorder and Depression

“…The combined application of brain neuroimaging and TMS holds promise for the clinical treatment of ADHD by providing insights into its neural underpinnings and offering targeted interventions [ 97 , 98 ]. Advanced neuroimaging techniques like fMRI can identify specific brain regions and networks associated with ADHD [ 99 , 100 ]. These imaging findings can serve as neural markers for diagnosis and treatment planning.…”

Study on classification of attention deficit hyperactivity disorder based on dual-channel autoencoder

“…Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in childhood characterized by symptoms of inattention, hyperactivity, and impulsivity. 1 Its estimated prevalence typically ranges from 3% to 5%, with a four-to-one male-to-female ratio. This neuropsychiatric disorder affects ∼366 million individuals worldwide, which accounts for roughly 5% of the global population.…”

“…Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in childhood characterized by symptoms of inattention, hyperactivity, and impulsivity 1 . Its estimated prevalence typically ranges from 3% to 5%, with a four-to-one male-to-female ratio.…”

Causal Forest Machine Learning Analysis of Parkinson’s Disease in Resting-State Functional Magnetic Resonance Imaging

“…Approaches such as Radiomics are based on extracting many descriptors and then selecting the most useful ones to train the ML algorithms [ 7 ]. During rs-fMRI analysis, some of the most used techniques to reduce dimensionality and enhance ML performance include linear dimensionality reduction like PCA [ 8 ], non-linear dimensionality reduction like Laplacian eigenmaps [ 9 ], and ML-based dimensionality reduction like Support Vector Machines [ 10 , 11 ], LASSO [ 12 , 13 , 14 ], and tree-based algorithms [ 15 ].…”