Surface values, volumetric measurements, and radiomics of structural MRI for the diagnosis and subtyping of attention‐deficit/hyperactivity disorder

2021 ◽  
Author(s):  
Liting Shi ◽  
Xuechun Liu ◽  
Keqing Wu ◽  
Kui Sun ◽  
Chunsen Lin ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Structural Mri ◽  
Hyperactivity Disorder ◽  
Volumetric Measurements

scholarly journals 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 23626-23636 ◽  
Author(s):  
Liang Zou ◽  
Jiannan Zheng ◽  
Chunyan Miao ◽  
Martin J. Mckeown ◽  
Z. Jane Wang
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Structural Mri ◽  
Automatic Diagnosis ◽  
Hyperactivity Disorder ◽  
3D Cnn

scholarly journals Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

2021 ◽  
Vol 14 ◽  
Author(s):  
Taban Eslami ◽  
Fahad Almuqhim ◽  
Joseph S. Raiker ◽  
Fahad Saeed
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Imaging Techniques ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Hyperactivity Disorder ◽  
Machine Learning Model

Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.

scholarly journals Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanli Zhang-James ◽  
◽  
Emily C. Helminen ◽  
Jinru Liu ◽  
Barbara Franke ◽  
...  
Keyword(s):  
Neural Network ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Adult Adhd ◽  
Structural Mri ◽  
Classification Models ◽  
Hyperactivity Disorder ◽  
Neural Network Classification ◽  
Deep Learning Neural Network ◽  
Structural Brain

AbstractAttention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.

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