Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review

2021 ◽  
Vol 69 (6) ◽  
pp. 1518
Author(s):  
R Periyasamy ◽  
VS Vibashan ◽  
GeorgeTom Varghese ◽  
MA Aleem
2003 ◽  
Vol 17 (3) ◽  
pp. 496-506 ◽  
Author(s):  
Dina E. Hill ◽  
Ronald A. Yeo ◽  
Richard A. Campbell ◽  
Blaine Hart ◽  
Janet Vigil ◽  
...  

2002 ◽  
Vol 4 (4) ◽  
pp. 444-448

Neuroimaging techniques are increasingly being applied to the study of attention-deficit/hyperactivity disorder (ADHD). This review focuses on magnetic resonance imaging studies of the brain anatomy of ADHD. Such studies were first conducted over a decade ago, and most focus on frontal-striatal regions and tend to find smaller volumes in ADHD children than in controls. Recently published analyses with the largest sample so far of patients and controls found that ADHD is associated with a statistically significant 3% to 4% global reduction in brain volume in both boys and girls, with abnormally small caudate nuclei only being found in younger patients. After adjusting for global brain differences, only cerebellar hemispheric volumes remained significantly smaller in ADHD, and these differences continued throughout childhood and adolescence. Pathophysiological models of ADHD need take into account cerebellar dysfunction, as well as prefrontal-striatal dysregulation.


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