Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.
about
Cortical-Subcortical Interactions in Depression: From Animal Models to Human PsychopathologyNetwork neuroscience.Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.Neurofeedback training for major depressive disorder: recent developments and future directions.Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root TraitsLarge-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder.Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children.Graph Theoretic Analysis of Resting State Functional MR Imaging.Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.Graph theory applied to the analysis of motor activity in patients with schizophrenia and depression.Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.Structural neuroimaging as clinical predictor: A review of machine learning applicationsClassification and Prediction of Brain Disorders Using Functional Connectivity: Promising but ChallengingSupport Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging
P2860
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P2860
Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.
description
2015 nî lūn-bûn
@nan
2015 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
Support vector machine classif ...... neuroimaging and graph theory.
@ast
Support vector machine classif ...... neuroimaging and graph theory.
@en
type
label
Support vector machine classif ...... neuroimaging and graph theory.
@ast
Support vector machine classif ...... neuroimaging and graph theory.
@en
prefLabel
Support vector machine classif ...... neuroimaging and graph theory.
@ast
Support vector machine classif ...... neuroimaging and graph theory.
@en
P2093
P2860
P356
P1476
Support vector machine classif ...... neuroimaging and graph theory.
@en
P2093
Gautam Prasad
Ian H Gotlib
Lara C Foland-Ross
Matthew D Sacchet
P2860
P356
10.3389/FPSYT.2015.00021
P577
2015-02-18T00:00:00Z