A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data
This method, fcHMRF-LIS, controls false discovery rates for voxel-wise testing in neuroimaging by combining Local Index of Significance (LIS) with a fully connected Hidden Markov Random Field (fcHMRF). It models complex spatial dependencies to maintain stable control over error proportions and is computationally scalable for large brain datasets.