Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

The study examined the hypothesis that hypoperfusion in brain areas known to be involved in emotional disturbances in primary psychiatric disorders is also linked to emotional difficulties in systemic lupus erythematosus (SLE) and that these are not secondary to the physical and social burden incurred by the disease. Nineteen SLE patients without overt neuropsychiatric manifestations (non-NPSLE), 31 NPSLE patients, and 23 healthy controls were examined. Dynamic susceptibility contrast MRI was used and cerebral blood flow and cerebral blood volume values were estimated in six manually selected regions of interest of brain regions suspected to play a role in anxiety and depression (dorsolateral prefrontal cortex, ventromedial prefrontal cortex, anterior cingulate cortex, hippocampi, caudate nuclei and putamen). NPSLE patients reported high rates of anxiety and depression symptomatology. Significantly reduced cerebral blood flow and cerebral blood volume values were detected in the NPSLE group compared to healthy controls in the dorsolateral prefrontal cortex and ventromedial prefrontal cortex, bilaterally. Within the NPSLE group, anxiety symptomatology was significantly associated with lower perfusion in frontostriatal regions and in the right anterior cingulate gyrus. Importantly, the latter associations appeared to be specific to anxiety symptoms, as they persisted after controlling for depression symptomatology and independent of the presence of visible lesions on conventional MRI. In conclusion, hypoperfusion in specific limbic and frontostriatal regions is associated with more severe anxiety symptoms in the context of widespread haemodynamic disturbances in NPSLE.

It has been widely assumed that internal forward models use efference copies to create predictions about the sensory consequences of our own actions. While these predictions have frequently been associated with a reduced blood oxygen level dependent (BOLD) response in sensory cortices, the timing and duration of the hemodynamic response for the processing of video feedback of self‐generated (active) versus externally generated (passive) movements is poorly understood. In the present study, we tested the hypothesis that predictive mechanisms for self‐generated actions lead to early and shorter neural processing compared with externally generated movements. We investigated active and passive movements using a custom‐made fMRI‐compatible movement device. Visual video feedback of the active and passive movements was presented in real time or with variable delays. Participants had to judge whether the feedback was delayed. Timing and duration of BOLD impulse response was calculated using a first (temporal derivative [TD]) and second‐order (dispersion derivative [DD]) Taylor approximation. Our reanalysis confirmed our previous finding of reduced BOLD response for active compared to passive movements. Moreover, we found positive effects of the TD and DD in the supplementary motor area, cerebellum, visual cortices, and subcortical structures, indicating earlier and shorter hemodynamic responses for active compared to passive movements. Furthermore, earlier activation in the putamen for active compared to passive conditions was associated with reduced delay detection performance. These findings indicate that efference copy‐based predictive mechanisms enable earlier processing of action feedback, which might have reduced the ability to detect short delays between action and feedback.

It has been widely assumed that internal forward models use efference copies to create predictions about the sensory consequences of our own actions. While these predictions had been frequently associated with reduced neural processing in sensory cortices, the timing and duration of the hemodynamic response of self-generated as opposed to externally generated movements is poorly investigated. In the present study we tested the hypothesis that predictive mechanisms for self-generated actions lead to early and shorter neural processing compared with externally generated movements. Using a first and second-order Taylor approximation in terms of the temporal (TD) and dispersion (DD) derivatives of a canonical hemodynamic response function, we investigated the timing and duration of activation for self-generated and externally generated movements using a custom-made fMRI-compatible movement device. Visual video feedback of the active and passive hand movements were presented in real time or with variable delays (0 - 417 ms). Participants had to judge, whether the feedback was delayed. We found earlier feedback processing for self-generated compared to externally generated movements in several regions including the supplementary motor area, cerebellum, subcortical structures such as the putamen and visual cortices. Shorter processing was found in areas, which show also lower blood oxygen level dependent (BOLD) amplitudes, such as the SMA, occipital and parietal cortex. Specifically, earlier activation in the putamen, of self-generated movements was associated with worse performance in detecting delays. These findings support our hypothesis, that efference copy based predictive mechanisms enable earlier processing of action feedback, as potential source for behavioral effects.