Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.
作者:
关键词
18F-FDG-PET, 18F-Fluorodeoxyglucose-Positron Emission TomographyAAL, Automated Anatomical Labeling (atlas)AD, Alzheimer's DiseaseAlzheimer's DiseaseBA, Brodmann's AreaBrain atlasGM, Gray MatterMKL, Multiple Kernel LearningMKL-ROI, MKL based on regions of interestML, Machine LearningMRIMultiple kernel learningNF, number of featuresNSR, Number of Selected RegionsPETPVE, Partial Volume EffectsROI, Region of InterestSPECTSVM, Support Vector MachineT1-MRI, T1-weighted Magnetic Resonance ImagingTN, True Negative (specificity - proportion of healthy controls correctly classified)TP, True Positive (sensitivity - proportion of patients correctly classified)rAUC, Ratio between negative and positive Area Under CurverCBF-SPECT, Regional Cerebral Blood Flow
主题词
老年人(Aged);阿尔茨海默病(Alzheimer Disease);图集(主题)(Atlases as Topic);脑(Brain);脑图(Brain Mapping);女(雌)性(Female);人类(Humans);磁共振成像(Magnetic Resonance Imaging);男(雄)性(Male);正电子发射断层显像术(Positron-Emission Tomography);ROC曲线(ROC Curve);结果可重复性(Reproducibility of Results);体层摄影术, 发射型计算机, 单光子(Tomography, Emission-Computed, Single-Photon)
DOI
10.1016/j.nicl.2017.10.026
PMID
29234599
发布时间
2024-06-05
- 浏览9
NeuroImage. Clinical
2018年17卷
628-641页
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