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We suggest using the pMVS parameter to deal with modulations of RS-fMRI fluctuations due to MVS. MVS-induced variance can easily be accounted by using high-resolution anatomical imaging of the inner ear and including the proposed pMVS parameter in fMRI group-level analysis.Background Coronavirus disease 2019 (COVID-19) has become a global pandemic, affecting millions of people. see more However, clinical research on its neurological manifestations is thus far limited. In this study, we aimed to systematically collect and investigate the clinical manifestations and evidence of neurological involvement in COVID-19. Methods Three medical (Medline, Embase, and Scopus) and two preprints (BioRxiv and MedRxiv) databases were systematically searched for all published articles on neurological involvement in COVID-19 since the outbreak. All included studies were systematically reviewed, and selected clinical data were collected for meta-analysis via random-effects. Results A total of 41 articles were eligible and included in this review, showing a wide spectrum of neurological manifestations in COVID-19. The meta-analysis for unspecific neurological symptoms revealed that the most common manifestations were fatigue (33.2% [23.1-43.3]), anorexia (30.0% [23.2-36.9]), dyspnea/shortness of breath (26.9% [19.2-34.6]), and malaise (26.7% [13.3-40.1]). The common specific neurological symptoms included olfactory (35.7-85.6%) and gustatory (33.3-88.8%) disorders, especially in mild cases. Guillain-Barré syndrome and acute inflammation of the brain, spinal cord, and meninges were repeatedly reported after COVID-19. Laboratory, electrophysiological, radiological, and pathological evidence supported neurologic involvement of COVID-19. Conclusions Neurological manifestations are various and prevalent in COVID-19. Emerging clinical evidence suggests neurological involvement is an important aspect of the disease. The underlying mechanisms can include both direct invasion and maladaptive inflammatory responses. More studies should be conducted to explore the role of neurological manifestations in COVID-19 progression and to verify their underlying mechanisms.Disgust might be elicited by various sensory channels, including the sense of smell. It has been previously demonstrated that unpleasant odors emitted by an external source are more disgusting than those emitted by oneself (the source effect). As disgust’s main purpose is to help organisms avoid potentially dangerous, contaminating objects, individuals with visual or hearing sensory impairment (thus, with an impeded ability to detect cues indicating pathogen threat) might have developed an increased levels of olfactory disgust sensitivity (modality compensation in disgust sensitivity). We set out to investigate disgust sensitivity in olfaction using the Body Odor Disgust Scale (BODS) on a large sample of 74 deaf and 98 blind participants, with comparison to control groups without sensory impairment (N = 199 in total). The results did not support the hypothesis of modality compensation in disgust sensitivity. Contrary to previous research, neither sex nor age influenced the outcomes. Evidence for the source effect was found. Acquired data are interpreted in the light of social desirability. The emphasis put on the olfaction by blind and deaf individuals is discussed.There has been a recent increase in individual differences research within the field of audiovisual perception (Spence & Squire, 2003, Current Biology, 13(13), R519-R521), and furthering the understanding of audiovisual integration capacity with an individual differences approach is an important facet within this line of research. Across four experiments, participants were asked to complete an audiovisual integration capacity task (cf. Van der Burg, Awh, & Olivers, 2013, Psychological Science, 24(3), 345-351; Wilbiks & Dyson, 2016, PLOS ONE 11(12), e0168304; 2018, Journal of Experimental Psychology Human Perception and Performance, 44(6), 871-884), along with differing combinations of additional perceptual tasks. Experiment 1 employed a multiple object tracking task and a visual working memory task. Experiment 2 compared performance on the capacity task with that of the Attention Network Test. Experiment 3 examined participants’ focus in space through a Navon task and vigilance through time. Having completed this exploratory work, in Experiment 4 we collected data again from the tasks that were found to correlate significantly across the first three experiments and entered them into a regression model to predict capacity. The current research provides a preliminary explanation of the vast individual differences seen in audiovisual integration capacity in previous research, showing that by considering an individual’s multiple object tracking span, focus in space, and attentional factors, we can account for up to 34.3% of the observed variation in capacity. Future research should seek to examine higher-level differences between individuals that may contribute to audiovisual integration capacity, including neurodevelopmental and mental health differences.Over the last decade, researchers have explored the influence of visual working-memory (WM) load on selective attention in general, by focusing on the modulation of visual WM load on distractor processing in perception. However, there were three distinct hypotheses (perceptual-load hypothesis, resolution hypothesis, and domain-specific hypothesis) with different predictions. While the perceptual-load hypothesis suggests that visual WM capacity load serves as a type of perceptual load, the latter two hypotheses consider visual WM capacity load acting as a type of central executive load, with a constraint that the domain-specific hypothesis claimed that only a content overlap existed between WM load and the perceptual task. By adding a flanker task into the maintenance phase of visual WM, here we attempted to understand the influence of visual WM load on distractor processing. We systematically manipulated the parameters of the task setting between WM and flanker tasks (Experiments 1-4), the perceptual load of flanker task (Experiment 5), the settings of the flanker stimuli and the WM load (Experiment 6), and the content overlap between WM task and flanker task and the exposure time of flanker task (Experiments 7, 8, and 9).