Background Accumulating evidence suggests the COVID-19 pandemic has negative effects on public mental health. Digital interventions that have been developed and evaluated in recent years may be used to mitigate the negative consequences of the pandemic. However, evidence-based recommendations on the use of existing telemedicine and internet-based (eHealth) and app-based mobile health (mHealth) interventions are lacking. Objective The aim of this study was to investigate the theoretical and empirical base, user perspective, safety, effectiveness, and cost-effectiveness of digital interventions related to public mental health provision (ie, mental health promotion, prevention, and treatment of mental disorders) that may help to reduce the consequences of the COVID-19 pandemic. Methods A rapid meta-review was conducted. The MEDLINE, PsycINFO, and CENTRAL databases were searched on May 11, 2020. Study inclusion criteria were broad and considered systematic reviews and meta-analyses that investigated digital tools for health promotion, prevention, or treatment of mental health conditions and determinants likely affected by the COVID-19 pandemic. Results Overall, 815 peer-reviewed systematic reviews and meta-analyses were identified, of which 83 met the inclusion criteria. Our findings suggest that there is good evidence on the usability, safety, acceptance/satisfaction, and effectiveness of eHealth interventions. Evidence on mHealth apps is promising, especially if social components (eg, blended care) and strategies to promote adherence are incorporated. Although most digital interventions focus on the prevention or treatment of mental disorders, there is some evidence on mental health promotion. However, evidence on process quality, cost-effectiveness, and long-term effects is very limited. Conclusions There is evidence that digital interventions are particularly suited to mitigating psychosocial consequences at the population level. In times of physical distancing, quarantine, and restrictions on social contacts, decision makers should develop digital strategies for continued mental health care and invest time and efforts in the development and implementation of mental health promotion and prevention programs.

While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce. In previous work we presented tract orientation mapping (TOM) as a novel concept for bundle-specific tractography. It is based on a learned mapping from the original fiber orientation distribution function (FOD) peaks to tract specific peaks, called tract orientation maps. Each tract orientation map represents the voxelwise principal orientation of one tract. Here, we present an extension of this approach that combines TOM with accurate segmentations of the tract outline and its start and end region. We also introduce a custom probabilistic tracking algorithm that samples from a Gaussian distribution with fixed standard deviation centered on each peak thus enabling more complete trackings on the tract orientation maps than deterministic tracking. These extensions enable the automatic creation of bundle-specific tractograms with previously unseen accuracy.We show for 72 different bundles on high quality, low quality and phantom data that our approach runs faster and produces more accurate bundle-specific tractograms than 7 state of the art benchmark methods while avoiding cumbersome processing steps like whole brain tractography, non-linear registration, clustering or manual dissection. Moreover, we show on 17 datasets that our approach generalizes well to datasets acquired with different scanners and settings as well as with pathologies.

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