Journal: Human Brain Mapping

Article Title: Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

doi: 10.1002/hbm.25448

Figure Lengend Snippet: Differential identifiability framework ( I f ). (a) For each subject, two functional connectomes (FC) matrices (restA and restB) were estimated for each half of the fMRI time‐series. (b) FC matrices were vectorized (upper triangular) and placed into a group FC matrix. (c) Principal component analysis (PCA) decomposition was performed on the group FC matrix. Each PC can be arranged as a matrix in the FC domain. (d) Individual FCs were reconstructed using different number of PCs. (e) I diff was estimated for different number of PCs (in order of explained variance) and the number of PCs maximizing I diff found

Article Snippet: We used T1‐weighted MPRAGE scans and EPI fMRI scans from the initial visit in ADNI2/GO (Philips Platforms, TR/TE = 3000/30 ms, 140 volumes, 3.3 mm thickness, see www.adni-info.org for detailed protocols) for estimation of whole‐brain FCs. fMRI scans were processed with an in‐house MATLAB and FSL based pipeline (Amico et al., ).

Techniques: Functional Assay