Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms

Cao, Meng; Martin, Elizabeth; Li, Xiaobo

doi:10.1038/s41398-023-02536-w

Cited by 20 publications

(3 citation statements)

References 149 publications

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“…Attention-Deficit Hyperactivity Disorder Distribution (ADHD) Dataset (Cao et al, 2023 ): The ADHD Dataset offers a comprehensive exploration into ADHD, encompassing an extensive cohort of over 7,400 subjects. This dataset extends beyond simple ADHD symptomatology to include biosamples critical for genetic and biological research, shedding light on ADHD's hereditary aspects through family studies and enhancing our understanding of its genetic underpinnings.…”

Section: Methodsmentioning

confidence: 99%

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Yu,

Fang

2024

Front. Neurosci.

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Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.

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Section: Methodsmentioning

confidence: 99%

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Yu,

Fang

2024

Front. Neurosci.

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“…ML algorithms can learn from big datasets automatically, enabling them to detect trends, correlations, and risk factors that humans might overlook [ 5 ]. By understanding the complex interactions among these risk factors, ML models can accurately predict which children are at a higher risk of dying before their fifth birthday [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset

Samuel,

Zewotir,

North

2024

BMC Med Inform Decis Mak

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Background Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors. Methods The study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew’s correlation coefficient, and the Area Under the Receiver Operating Characteristics. Results The study found that machine learning models can accurately predict under-five mortality, with the Random Forest and Artificial Neural Network algorithms emerging as the best models, both achieving an accuracy of 89.47% and an AUROC of 96%. The results show that under-five mortality rates vary significantly across different characteristics, with wealth index, maternal education, antenatal visits, place of delivery, employment status of the woman, number of children ever born, and region found to be the top determinants of under-five mortality in Nigeria. Conclusions The findings suggest that machine learning models can be useful in predicting U5M in Nigeria with high accuracy. The study emphasizes the importance of addressing social, economic, and demographic disparities among the population in Nigeria. The study’s findings can inform policymakers and health workers about developing targeted interventions to reduce under-five mortality in Nigeria.

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“…This adaptability extends to providing real-time feedback based on ongoing brain activity, enabling adaptive interventions or training protocols. Additionally, ML techniques can automate feature extraction from fMRI data, reducing the need for manual selection and potentially uncovering novel biomarkers or patterns of brain activity associated with specific states [10]. Consequently, the integration of ML with fMRI facilitates efficient and accurate real-time detection of brain states, with broad applications spanning cognitive neuroscience research to clinical interventions for neurological and psychiatric disorders.…”

Section: Introductionmentioning

confidence: 99%

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Gnanajeyaraman Rajaram

2024

jes

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Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

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Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms

Cited by 20 publications

References 149 publications

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

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