LEADING EIGENVECTOR DYNAMICS ANALYSIS (LEiDA) The LEiDA consists of computing instantaneous BOLD phase coherence matrices and clustering the corresponding leading eigenvectors into a set of patterns that can be visualized on the cortical surface or in matrix format. This repository includes the codes and data to replicate the analysis reported in the manuscript: Fine-grained analysis of functional connectivity dynamics links cognitive performance in healthy aging to spontaneous switching between brain states J Cabral, D Vidaurre, P Marques, R Magalhães, P Silva Moreira, JM Soares, G Deco, N Sousa and ML Kringelbach The first function is LEiDA_data.m that loads the BOLD data from Aging_data.mat, computes the instantaneous phases and saves the leading eigenvector at each time point. On a second step, LEiDA_Cluster.m applies a kmeans clustering algorithm to the leading eigenvectors and saves the optimal solution. Finally, LEiDA_analysis.m loads the results and plots the optimal set of vectors on the cortical surface and compares the FC patterns with the average BOLD FC in Static_FC.mat LEiDA needs the SPM functions spm_vol() and spm_slice_vol(), so SPM needs be added to the Matlab pathway before running