Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. Highthroughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

atients with COVID-19 exhibit a broad spectrum of disease progression, with 81% showing mild, moderate or no symptoms; 14% showing severe symptoms; and 5% experiencing critical disease with high mortality risk 1 . The risk of developing severe or critical disease has been associated with advanced age 1,2 , comorbidities 1,2 , hyperactivation of the immune system 3,4 , sex 1,2 and other factors. However, an understanding of these risk factors at the molecular and cellular levels is in its infancy.In this study, we investigated the immune response in patients with COVID-19 by single-cell RNA sequencing (scRNA-seq) of nasopharyngeal and bronchial samples to identify molecular correlates of disease severity. Two recent studies applied scRNA-seq to bronchioalveolar lavage fluid samples from patients with COVID-19 and provide an extensive characterization of the inflammatory immune phenotype in the lower respiratory tract 5,6 . However, SARS-CoV-2 and other coronaviruses infect and replicate in both COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis

Peritumoral mapping of the motor cortex by nTMS agreed well with the gold standard of DCS. Thus, nTMS is a reliable tool for preoperative mapping of motor function.