Ana Triana (Aalto)
Tuesday 2018-04-10 12.30 – 13.00
Seminar room A346, T-building
Effects of spatial smoothing on group-level differences in the structure of functional brain networks
Brain connectivity with functional Magnetic Resonance Imaging (fMRI) is a popular approach to investigate psychiatric disorders and to detect differences between healthy and clinical populations. In such studies, fMRI time-series must undergo several preprocessing steps to control for artifacts –such as head motion and breathing– to increase the signal-to-noise ratio. Functional networks are created only after preprocessing, thus, choices on these steps may affect the end result. For example, it has been demonstrated that spatial smoothing induces changes in the functional network structure, but its effects on group-level differences in functional brain network structure are still unknown.
Here, we study such effects on the difference between Autism Spectrum Disorder (ASD) patients and healthy controls (TC) using resting-state data from the ABIDE initiative (N=33 matched subject pairs). We apply 4 different levels of spatial smoothing (0, 6, 8, 12mm) and compute functional networks using nodes defined in each subject’s native space. This allows us to reduce possible bias due to the implicit spatial smoothing used at the registration of the individual data to a common brain template.
We find that link weights are affected by smoothing. These effects are diverse, non-trivial and hard to predict, as they affect links differently. In particular, for full weight matrices, the weight distributions of both groups tend to become more similar for the links showing the most significant between-group differences (T-statistic>3). However, for certain links, the effect is the opposite. For links with smaller T-statistics, the effects appear random.
This has important consequences for group comparisons of functional networks: spatial smoothing distorts network differences between groups. Hence, we conclude that spatial smoothing should not be used in the fMRI preprocessing for group-level differences analysis using functional networks.