scholarly journals Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment

2018 ◽  
Vol 9 ◽  
Author(s):  
Xia-an Bi ◽  
Qian Xu ◽  
Xianhao Luo ◽  
Qi Sun ◽  
Zhigang Wang
Keyword(s):  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Resting State Fmri ◽  
Support Vector

scholarly journals To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Justine Staal ◽  
Francesco Mattace-Raso ◽  
Hennie A. M. Daniels ◽  
Johannes van der Steen ◽  
Johan J. M. Pel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Support Vector ◽  
Potential Biomarker

BackgroundResearch into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.MethodHere, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.ResultsFair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).Comparison with Existing Method(s)The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.ConclusionThe data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

scholarly journals P3-366: SUPPORT VECTOR MACHINE ANALYSIS OF RESTING-STATE FMRI IN COGNITIVE CONTINUUM

2019 ◽  
Vol 15 ◽  
pp. P1086-P1087
Author(s):  
Li Lin ◽  
Yu Sun ◽  
Wenying Du ◽  
Xuanyu Li ◽  
Yang Zhan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Resting State ◽  
Resting State Fmri ◽  
Support Vector ◽  
Cognitive Continuum ◽  
Machine Analysis

Changes in thalamus connectivity in mild cognitive impairment: Evidence from resting state fMRI

2012 ◽  
Vol 81 (2) ◽  
pp. 277-285 ◽  
Author(s):  
Zhiqun Wang ◽  
Xiuqin Jia ◽  
Peipeng Liang ◽  
Zhigang Qi ◽  
Yanhui Yang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Resting State Fmri

Longitudinal Alteration of Amygdalar Functional Connectivity in Mild Cognitive Impairment Subjects Revealed by Resting-State fMRI

2014 ◽  
Vol 4 (5) ◽  
pp. 361-370 ◽  
Author(s):  
Hongxiang Yao ◽  
Bo Zhou ◽  
Zengqiang Zhang ◽  
Pan Wang ◽  
Yan'e Guo ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri
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