New Filtering Approaches to Improve the Classification Capability of Restingstate fMRI Transfer Functions Restingstate functional magnetic resonance imaging (fMRI) uses spontaneous regional brain activity to identify functional networks. Transfer functions (TF) can evaluate the amplification of restingstate fMRI signal frequency components from one brain region to another, but are highly susceptible to noise spikes. Restingstate fMRIxe2x80x99s lowtemporal resolution implies that the high frequency noise characteristics necessary to implement Weiner filtering are not available. We investigated new approaches that replace the standard Weiner filter noise parameter with an alternative outlier suppression parameter (OSP) to identify and remove inaccurate TF estimates. When compared to standard TF approaches, our new filtering approaches shows an improved ability to distinguish optic neuritis (ON) patients from healthy volunteers, as well as patients experiencing ON as a clinically isolated syndrome (CIS) from ON patients with relapsingremitting multiple sclerosis (RRMS).

Just a very expensive breathing training? Risk of respiratory artefacts in functional connectivitybased realtime fMRI neurofeedback Abstract   Realtime functional magnetic resonance imaging neurofeedback (rtfMRI NFB) is a promising method for targeted regulation of pathological brain processes in mental disorders. But most NFB approaches so far have used relatively restricted regional activation as a target, which might not address the complexity of the underlying network changes. Aiming towards advancing novel treatment tools for disorders like schizophrenia, we developed a largescale network functional connectivitybased rtfMRI NFB approach targeting dorsolateral prefrontal cortex and anterior cingulate cortex connectivity with the striatum.  In a doubleblind randomized yokecontrolled singlesession feasibility study with Nxe2x80xafxe2x80xaf38 healthy controls, we identified strong associations between our connectivity estimates and physiological parameters reflecting the rate and regularity of breathing. These undesired artefacts are especially detrimental in rtfMRI NFB, where the same data serves as an online feedback signal and offline analysis target.  To evaluate ways to control for the identified respiratory artefacts, we compared modelbased physiological nuisance regression and global signal regression (GSR) and found that GSR was the most effective method in our data.  Our results strongly emphasize the need to control for physiological artefacts in connectivitybased rtfMRI NFB approaches and suggest that GSR might be a useful method for online data correction for respiratory artefacts.