Fusion of fMRI and non-imaging data for ADHD classification-Reference-Cited by-同舟云学术

Fusion of fMRI and non-imaging data for ADHD classification

Published:2018-04 Issue: Volume:65 Page:115-128
ISSN:0895-6111
Container-title:Computerized Medical Imaging and Graphics
language:en
Short-container-title:Computerized Medical Imaging and Graphics

Author:

Riaz Atif,Asad Muhammad,Alonso Eduardo,Slabaugh Greg

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference37 articles.

1. Adhd-200 preprocessed. http://preprocessed-connectomes-project.org/adhd200/.

2. Adhd-200 sample. http://fcon_1000.projects.nitrc.org/indi/adhd200/.

3. Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects;Bohland;Front. Syst. Neurosci.,2012

4. SMOTE: synthetic minority over-sampling technique;Chawla;J. Artif. Intell. Res.,2002

5. Frontal lobe functions;Chayer;Curr. Neurol. Neurosci. Rep.,2001

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