Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review
Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals
Background The most prevalent neuropsychiatric disorder among children is attention deficit
hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders …
hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders …
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
Individualized prediction models in ADHD: a systematic review and meta-regression
There have been increasing efforts to develop prediction models supporting personalised
detection, prediction, or treatment of ADHD. We overviewed the current status of prediction …
detection, prediction, or treatment of ADHD. We overviewed the current status of prediction …
Classification accuracy of neuroimaging biomarkers in attention-deficit/hyperactivity disorder: effects of sample size and circular analysis
Background Motivated by an inconsistency between reports of high diagnosis-classification
accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this …
accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this …
EEG for diagnosis of adult ADHD: a systematic review with narrative analysis
M Adamou, T Fullen, SL Jones - Frontiers in Psychiatry, 2020 - frontiersin.org
Background Attention deficit hyperactivity disorder is a common neurodevelopmental
disorder characterized by symptoms of inattention, hyperactivity and or impulsivity. Since the …
disorder characterized by symptoms of inattention, hyperactivity and or impulsivity. Since the …
Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning
S Hassin-Baer, OS Cohen, S Israeli-Korn, G Yahalom… - Plos one, 2022 - journals.plos.org
Objective The purpose of this study is to explore the possibility of developing a biomarker
that can discriminate early-stage Parkinson's disease from healthy brain function using …
that can discriminate early-stage Parkinson's disease from healthy brain function using …
Cognitive Working Memory Training (CWMT) in adolescents suffering from Attention-Deficit/Hyperactivity Disorder (ADHD): A controlled trial taking into account …
Although, cognitive working memory training (CWMT) has been reported to enhance
working memory functioning in youths with attention-deficit/hyperactivity disorder (ADHD) …
working memory functioning in youths with attention-deficit/hyperactivity disorder (ADHD) …
Comparative study of attention-related features on attention monitoring systems with a single EEG channel
Z Liang, X Wang, J Zhao, X Li - Journal of Neuroscience Methods, 2022 - Elsevier
The easy-to-use attention monitoring systems usually detect the participant's attentional
status via processing electroencephalogram (EEG) data recorded from a single FPz …
status via processing electroencephalogram (EEG) data recorded from a single FPz …