Individual Thresholding of Voxel-based Functional Connectivity Maps. Estimation of Random Errors by Means of Surrogate Time Series.

Abstract

This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Neural Signals and Images". Voxel-based functional connectivity analysis is a common method for resting state fMRI data. However, correlations between the seed and other brain voxels are corrupted by random estimate errors yielding false connections within the functional connectivity map (FCmap). These errors must be taken into account for a correct interpretation of single-subject results. We estimated the statistical range of random errors and propose two methods for an individual setting of correlation threshold for FCmaps. We assessed the amount of random errors by means of surrogate time series and described its distribution within the brain. On the basis of these results, the FCmaps of the posterior cingulate cortex (PCC) from 15 healthy subjects were thresholded with two innovative methods: the first one consisted in the computation of a unique (global) threshold value to be applied to all brain voxels, while the second method is to set a different (local) threshold of each voxel of the FCmap. The distribution of random errors within the brain was observed to be homogeneous and, after thresholding with both methods, the default mode network areas were well identifiable. The two methods yielded similar results, however the application of a global threshold to all brain voxels requires a reduced computational load. The inter-subject variability of the global threshold was observed to be very low and not correlated with age. Global threshold values are also almost independent from the number of surrogates used for their computation, so the analyses can be optimized using a reduced number of surrogate time series. We demonstrated the efficacy of FCmaps thresholding based on random error estimation. This method can be used for a reliable single-subject analysis and could also be applied in clinical setting, to compute individual measures of disease progression or quantitative response to pharmacological or rehabilitation treatments.