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Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large-scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5-17 years of age) from six different centers. Six data quality metrics-contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)-and four diffusion measures-fractional anisotropy, mean diffusivity, tract density, and length-were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between-site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group-wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

BACKGROUND: The aim was to describe the incidence and risk factors of barotrauma in patients with the Coronavirus disease 2019 (COVID-19) on invasive mechanical ventilation, during the outbreak in our region (Lombardy, Italy). METHODS: The study was an electronic survey open from March 27th to May 2nd, 2020. Patients with COVID-19 who developed barotrauma while on invasive mechanical ventilation from 61 hospitals of the COVID-19 Lombardy Intensive Care Unit network were involved. RESULTS: The response rate was 38/61 (62%). The incidence of barotrauma was 145/2041 (7.1%; 95%-CI: 6.1-8.3%). Only a few cases occurred with ventilatory settings that may be considered non-protective such as a plateau airway pressure >35 cmH2O (2/113 [2%]), a driving airway pressure >15 cmH2O (30/113 [27%]), or a tidal volume >8 mL/kg of ideal body weight and a plateau airway pressure >30 cmH2O (12/134 [9%]). CONCLUSIONS: Within the limits of a survey, patients with COVID-19 might be at high risk for barotrauma during invasive (and allegedly lung-protective) mechanical ventilation.