AIMC Topic: Attention Deficit Disorder with Hyperactivity

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Drug grouping learning for improving evidence-based treatment recommendations.

Computers in biology and medicine
Clinical practice guidelines (CPGs) are essential tools that facilitate the translation of the growing body of scientific evidence into clinical practice by providing clinicians with evidence-based recommendations. The first step of CPG development i...

Detection of cortical arousals in sleep using multimodal wearable sensors and machine learning.

Scientific reports
Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. Although polysomnography is the gold standard for arousal detection, its cost and complexity limit us...

An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD.

Scientific reports
Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with serious long-term effects if untreated, emphasizing the need for early treatment given its neurobiological heterogeneity. This study introduces a novel expla...

Inferences on the Watts-Strogatz Model: A Study on Brain Functional Connectivity.

Neuroinformatics
Modelling real-world networks allows investigating the structure and the dynamics of such networks, which led to significant developments in various scientific fields. One of the most used models in these investigations is the Watts-Strogatz, with a ...

Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.

Translational psychiatry
Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder diagnosed and subtyped solely based on clinical traits, which are prone to subjective judgment and lack of reliability. Also, the clinical subtyping does...

Visual processing oscillates differently through time for adults with ADHD.

PloS one
ADHD is a neurodevelopmental disorder affecting 3-4% of Canadian adults and 2.6% of adults worldwide. Its symptoms include inattention, hyperactivity and impulsivity. Though ADHD is known to affect several brain functions and cognitive processes, lit...

Deep adversarial learning identifies ADHD-specific associations between apoptotic genes and white matter microstructure in frontal-striatum-cerebellum circuit.

Translational psychiatry
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by genetic predisposition and alterations in brain structural connectivity. While existing studies have established associations between genetic variants a...

Use of computer vision analysis for labeling inattention periods in EEG recordings with visual stimuli.

Scientific reports
Electroencephalography (EEG) recordings with visual stimuli require detailed coding to determine the periods of participant's attention. Here we propose to use a supervised machine learning model and off-the-shelf video cameras only. We extract compu...

Entropy-based risk network identification in adolescent self-injurious behavior using machine learning and network analysis.

Translational psychiatry
Adolescent Self-Injurious Behavior (SIB) is a significant global public health issue, with a lifetime prevalence rate of approximately 13.7%. As awareness of SIB rises, there is an urgent need for effective prediction mechanisms to enable early ident...