Table 3 A minimal set of markers that are predictive of MCI-to-AD progression. A generalized linear model (GLM) classifier with L1 regularization was utilized to identify a small set of minimally correlated features that can predict MCI-to-AD progression.
CSF | PIB-PET | FDG-PET | SMRI | Genetic | Demographics |
|---|---|---|---|---|---|
Total-Tau | COMPOSITE REFNORM | Angular-Left | Cortical Thickness Average of Insula | CR1 | AGE |
Amyloid Beta | TEMPORAL | Temporal-Left | Cortical Thickness Average of SuperiorFrontal | ||
Phosphorylated-Tau | Cingulum-Bilateral | Cortical Thickness Average of InferiorParietal | |||
Temporal-Right | Cortical Thickness Average of Parahippocampal | ||||
Cortical Thickness Average of MedialOrbitofrontal | |||||
Cortical Thickness Average of CaudalAnteriorCingulate | |||||
Cortical Thickness Average of SuperiorParietal | |||||
Cortical Thickness Average of IsthmusCingulate | |||||
Cortical Thickness Average of ParsTriangularis | |||||
Cortical Thickness Average of PosteriorCingulate | |||||
Hippocampal Subfield Volume of Subiculum |
