Ricerc@Sapienza

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions.

While the role of individual differences in shaping primary appraisals of psychosocial working conditions has been well investigated, less is known about how objective characteristics of the employee profile (e.g., age) are associated with different perceptions of psychosocial risk factors. Moreover, previous research on the link between employment status (i.e., work contract) and such perceptions has provided mixed results, leading to contradictory conclusions.