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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. [ABSTRACT FROM AUTHOR]

Introduction: Neurodevelopmental disorders comprise a group of neuropsychiatric conditions. Presently, behavior-based diagnostic approaches are utilized in clinical settings, but the overlapping features among these disorders obscure their recognition and management. Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have common characteristics across various levels, from genes to symptoms. Designing a computational framework based on the neuroimaging findings could provide a discriminative tool for ultimate more efficient treatment. Machine learning approaches, specifically classification methods are among the most applied techniques to reach this goal. Methods: We applied a novel two-level multi-class data maximum dispersion classifier (DMDC) algorithm to classify the functional neuroimaging data (utilizing datasets: ADHD-200 and autism brain imaging data exchange (ABIDE)) into two categories: Neurodevelopmental disorders (ASD and ADHD) or healthy participants, based on calculated functional connectivity values (statistical temporal correlation). Results: Our model achieved a total accuracy of 62% for healthy controls. Specifically, it demonstrated an accuracy of 51% for healthy subjects, 61% for autism spectrum disorder, and 84% for ADHD. The support vector machine (SVM) model achieved an accuracy of 46% for both the healthy control and ASD groups, while the ADHD group classification accuracy was estimated to be 84%. These two models showed similar classification indices for the ADHD group. However, the discrimination power was higher in the ASD class. Conclusion: The method employed in this study demonstrated acceptable performance in classifying disorders and healthy conditions compared to the more commonly used SVM method. Notably, functional connections associated with the cerebellum showed discriminative power. [ABSTRACT FROM AUTHOR]

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Restingstate functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study's primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information. Method: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a twostep hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation. Result: The data-driven method--ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds. Conclusion: This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a datadriven approach to build a model specific to a single data set may be better than combining several data and creating a general model. [ABSTRACT FROM AUTHOR]

Background: Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent childhood-onset neurodevelopmental disorders; however, the underlying neural mechanisms for the inattention symptom remain elusive for children with ADHD. At present, the majority of studies have analyzed the structural MRI (sMRI) with the univariate method, which fails to demonstrate the interregional covarying relationship of gray matter (GM) volumes among brain regions. The scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate method, which can detect more robust brain-behavioral phenotype association compared to the univariate analysis method. This study aims to identify the GM network associated with attention in children with ADHD by applying SSM-PCA to the sMRI. Methods: The sMRI of 209 children with ADHD and 209 typically developing controls (TDCs) aged 7-14 years from the ADHD-200 dataset was used for anatomical computation, and the GM volume in each brain region was acquired. Then, SSM-PCA was applied to the GM volumes of all the subjects to capture the GM network of children with ADHD (i.e., ADHD-related pattern). The relationship between the expression of ADHD-related pattern and inattention symptom was further investigated. Finally, the influence of sample size on the analysis of this study was explored. Results: The ADHD-related pattern mainly included putamen, pallium, caudate, thalamus, right accumbens, superior/middle/inferior frontal cortex, superior occipital cortex, superior parietal cortex, and left middle occipital cortex. In addition, the expression of the ADHD-related pattern was related to inattention scores measured by the Conners' Parent Rating Scale long version (CPRS-LV; r = 0.25, p = 0.0004) and the DuPaul ADHD Rating Scale IV (ADHD-RS; r = 0.18, p = 0.03). Finally, we found that when the sample size was 252, the results of ADHD-related pattern were relatively reliable. Similarly, the sample size needed to be 162 when exploring the relationship between ADHD-related pattern and behavioral indicator measured by CPRS-LV. Conclusion: We captured a GM network associated with attention in children with ADHD, which is different from that in adolescents and adults with ADHD. Our findings may shed light on the diverse neural mechanisms of inattention and provide treatment targets for children with ADHD. [ABSTRACT FROM AUTHOR]