Article

Integrating Structural Brain MRI and Clinical Phenotypes for Automated ADHD Diagnosis: A Multimodal Deep Learning Approach

2026-01-08

Abstract excerpt

<title>Abstract</title> <p>Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis is largely based on subjective evaluations of behavior, which highlights the requirement for objective, neurobiologically based biomarkers. Structural magnetic resonance imaging (MRI) has pointed to very delicate neuroanatomical changes related to ADHD; however, the high variability between subjects makes its reliability doubtful...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 28bed4b4-8c67-5961-84b0-80fd8618504a
DOI: 10.21203/rs.3.rs-8501240/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Integrating Structural Brain MRI and Clinical Phenotypes for Automated ADHD Diagnosis: A Multimodal Deep Learning ApproachDOI 10.21203/rs.3.rs-8501240/v1Read

Interpretable ADHD Classification using Hybrid CatBoost-MLP with Early Fusion of Structural MRI and Clinical Variables Using SHAP-Guided Ensemble MethodsSemantic proximity Read

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?Semantic proximity Read

Multimodal Magnetic Resonance Imaging-based neuromarkers trace longitudinal changes in cognitive functioning in Attention Deficit/Hyperactivity DisorderSemantic proximity Read

Predicting Cognitive Functioning in ADHD Using Population-Based MRI Across Large and Small SamplesSemantic proximity Read

Differential Diagnosis of Cognitive Disorders using Deep Learning Techniques based on Neuroimaging DataSemantic proximity Read

NOVEL CONTRIBUTIONS OF THE MULTI-MODAL DEEP LEARNING FRAMEWORK INTEGRATING MRI AND GENETIC DATA FOR ENHANCED ALZHEIMER 'S DISEASE DIAGNOSISSemantic proximity Read

Deep Learning-based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s DiseaseSemantic proximity Read

eTSC-Net: A Parameter-efficient Convolutional Neural Network for Drug Treatment Outcome Studies of Pediatric EpilepsySemantic proximity Read

Three-dimensional convolutional autoencoder extracts features of structural brain images with a “diagnostic label-free” approach: Application to schizophrenia datasetsSemantic proximity Read

Intelligent assessment method for mental disordersby integrating EEG and MRI multimodalhigh-dimensional dataSemantic proximity Read

Genomic patterns linked to gray matter alterations underlying working memory deficits in adults and adolescents with attention-deficit/hyperactivity disorderSemantic proximity Read

Multimodal CNN-PD: A Parkinson's Disease Diagnostics Framework Using Multimodal Convolutional Neural NetworkSemantic proximity Read