Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI

Aradhya, Abhay M S; Sundaram, Suresh; Pratama, Mahardhika

doi:10.1109/embc44109.2020.9176547

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…They also categorized these ROIs into medial temporal, subcortical, occipital, frontal, temporal, and parietal motor lobes. Aradhya et al 35 annotated the data using the AAL template (with 90 subregions) in their study. The brain was divided according to the AAL atlas (with 90 subregions) in another study by Riaz et al 35 Their results also showed that the frontal lobe is the most discriminative brain region for the detection of ADHD.…”

Section: Network and Atlas Selectionmentioning

confidence: 99%

“…Aradhya et al 35 annotated the data using the AAL template (with 90 subregions) in their study. The brain was divided according to the AAL atlas (with 90 subregions) in another study by Riaz et al 35 Their results also showed that the frontal lobe is the most discriminative brain region for the detection of ADHD. The AAL atlas (with 90 subregions) was used in the study by Shao et al 37 They calculated the average time series by using all voxels in each one of the 90 regions.…”

Section: Network and Atlas Selectionmentioning

confidence: 99%

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A review of ADHD detection studies with machine learning methods using rsfMRI data

Taspinar,

Ozkurt

2024

NMR in Biomedicine

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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.

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Section: Network and Atlas Selectionmentioning

confidence: 99%

Section: Network and Atlas Selectionmentioning

confidence: 99%

A review of ADHD detection studies with machine learning methods using rsfMRI data

Taspinar,

Ozkurt

2024

NMR in Biomedicine

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“…The majority of the studies were on human participants and included age-matched controls for the ADHD participants to allow for non-biased comparison. Studies using existing databases ( 31 , 43 , 45 ) did not report age range. Some studies did not specify age ranges, but attempted to match the sample size of ADHD and control participants ( 28 , 35 , 38 , 77 , 80 , 95 ).…”

Section: Potential Biomarkersmentioning

confidence: 99%

“…They identified ADHD-specific changes in the neuronal connectivity in the cingulate gyrus and paracingulate gyrus in images of ADHD brains, which may be potentially developed into a diagnostic tool with high accuracy ( 43 ). Using the same database, another group used a different algorithm called “metaheuristic spatial transformation” to analyse the resting fMRI images, which significantly increased the accuracy of ADHD diagnosis, especially distinguishing it from autism ( 45 ). It can be predicted that artificial intelligence will be applied to such imaging based diagnoses in the near future, not limited to ADHD.…”

Section: Potential Biomarkersmentioning

confidence: 99%

Can biomarkers be used to diagnose attention deficit hyperactivity disorder?

et al. 2023

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Currently, the diagnosis of attention deficit hyperactivity disorder (ADHD) is solely based on behavioral tests prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). However, biomarkers can be more objective and accurate for diagnosis and evaluating treatment efficacy. Thus, this review aimed to identify potential biomarkers for ADHD. Search terms “ADHD,” and “biomarker” combined with one of “protein,” “blood/serum,” “gene,” and “neuro” were used to identify human and animal studies in PubMed, Ovid Medline, and Web of Science. Only papers in English were included. Potential biomarkers were categorized into radiographic, molecular, physiologic, or histologic markers. The radiographic analysis can identify specific activity changes in several brain regions in individuals with ADHD. Several molecular biomarkers in peripheral blood cells and some physiologic biomarkers were found in a small number of participants. There were no published histologic biomarkers for ADHD. Overall, most associations between ADHD and potential biomarkers were properly controlled. In conclusion, a series of biomarkers in the literature are promising as objective parameters to more accurately diagnose ADHD, especially in those with comorbidities that prevent the use of DSM-5. However, more research is needed to confirm the reliability of the biomarkers in larger cohort studies.

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“…Resting-state functional magnetic resonance imaging (RS-fMRI) is a functional imaging technique based on the detection of blood oxygen level-dependent (BOLD) signals in the cerebral blood vessels [ 9 ]. The RS-fMRI examination does not require any peculiar stimulation to the patient but only requires the patient to remain calm and awake.…”

Section: Introductionmentioning

confidence: 99%

Image Features of Resting‐State Functional Magnetic Resonance Imaging in Evaluating Poor Emotion and Sleep Quality in Patients with Chronic Pain under Artificial Intelligence Algorithm

Yang

Bie

Wang

et al. 2022

Contrast Media & Molecular Imaging

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The balanced iterative reducing and clustering using hierarchies (BIRCH) method was adopted to optimize the results of the resting-state functional magnetic resonance imaging (RS-fMRI) to analyze the changes in the brain function of patients with chronic pain accompanied by poor emotion or abnormal sleep quality in this study, so as to provide data support for the prevention and treatment of clinical chronic pain with poor emotion or sleep quality. 159 patients with chronic pain who visited the hospital were selected as the research objects, and they were grouped according to the presence or absence of abnormalities in emotion and sleep. The patients without poor emotion and sleep quality were set as the control group (60 cases), and the patients with the above symptoms were defined in the observation group (90 cases). The brain function was detected by RS-fMRI technology based on the BIRCH algorithm. The results showed that the rand index (RI), adjustment of RI (ARI), and Fowlkes–Mallows index (FMI) results in the k-means, flow cytometry (FCM), and BIRCH algorithms were 0.82, 0.71, and 0.88, respectively. The scores of Hamilton Depression Scale (HAHD), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were 7.26 ± 3.95, 7.94 ± 3.15, and 8.03 ± 4.67 in the observation group and 4.03 ± 1.95, 5.13 ± 2.35, and 4.43 ± 2.07 in the control group; the higher proportion of RS-fMRI was with abnormal brain signal connections. A score of 7 or more meant that the number of brain abnormalities was more than 90% and that of less than 7 was less than 40%, showing a statistically obvious difference in contrast P < 0.05 . Therefore, the BIRCH clustering algorithm showed reliable value in the optimization of RS-fMRI images, and RS-fMRI showed high application value in evaluating the emotion and sleep quality of patients with chronic pain.

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Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI

Cited by 8 publications

References 16 publications

A review of ADHD detection studies with machine learning methods using rsfMRI data

A review of ADHD detection studies with machine learning methods using rsfMRI data

Can biomarkers be used to diagnose attention deficit hyperactivity disorder?

Image Features of Resting‐State Functional Magnetic Resonance Imaging in Evaluating Poor Emotion and Sleep Quality in Patients with Chronic Pain under Artificial Intelligence Algorithm

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