Kühn, S. | Search bioRxiv

bioRxiv2018

Evaluation of FlowVR: a virtual reality game for improvement of depressive mood

ObjectiveThis study evaluated the efficacy of FlowVR, a virtual reality (VR) game designed to improve mood and reduce feelings of depression. The aim is to contribute to the question of whether and how VR could be used for depression therapy, as research in this area is quite rare.\n\nMethod18 healthy participants (9 female; Mage = 25.9) underwent three conditions, playing FlowVR in VR with a head-mounted display, playing FlowVR on a tablet or reading a text on a tablet. For each condition, they were tested on a separate day at the same time of day within a two-week period. Before and after every condition participants completed the Becks Depression Inventory II (BDI-II), the state part of the State-Trait-Anxiety-Depression-Inventory (STADI(S)) and the Positive Affect Negative Affect Schedule-Expanded Form (PANAS-X).\n\nResultsWhile the participants showed only a reduction in acute anxiety in the control and the tablet conditions, they showed improved affectivity in all variables measured in the VR condition. In addition, VR had significantly better results than the control condition in improving positive affectivity, negative affectivity and acute feelings of depression. Using a less conservative statistical approach, these significant differences could also be found between the tablet and the VR condition. There were no significant differences between the tablet and the control condition.\n\nConclusionThe results indicate that due to its immersive nature, VR can be used effectively to improve mood and temporarily reduce feelings of depression. Long-term effects of FlowVR on participants with depression must be investigated in consecutive research.

neuroscience↗

bioRxiv2018

Variability and reliability of effective connectivity within the core default mode network: A longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most research applying spectral DCM has focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, behavioural, and physical states. Determining the sources of fluctuations in effective connectivity may yield greater understanding of brain processes and inform clinical applications about potential confounds. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We further investigated how standard procedures for data processing and signal extraction affect this consistency. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample consisted of 20 subjects with 653 resting state fMRI sessions in total. These data allowed to quantify the robustness of connectivity estimates for each subject, and to draw conclusions beyond specific data features. We found that subjects contributing to all datasets showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and reliability of connectivity for the majority of subjects. Bayesian model reduction increased reliability (within-subjects) and stability (between-subjects) of connectivity patterns.

neuroscience↗