Background: Pneumonia with respiratory failure represents the main cause of death in COVID-19, where hyper inflammation plays an important role in lung damage. This study aims to evaluate if tocilizumab, an anti-soluble IL-6 receptor monoclonal antibody, reduces patients' mortality. Methods: 85 consecutive patients admitted to the Montichiari Hospital (Italy) with COVID-19 related pneumonia and respiratory failure, not needing mechanical ventilation, were included if satisfying at least one among: respiratory rate ≥ 30 breaths/min, peripheral capillary oxygen saturation ≤ 93% or PaO2/FiO2<=300 mmHg. Patients admitted before March 13th (n=23) were prescribed the standard therapy (hydroxychloroquine, lopinavir and ritonavir) and were considered controls. On March 13th tocilizumab was available and patients admitted thereafter (n=62) received tocilizumab once within 4 days from admission, plus the standard care. Results: Patients receiving tocilizumab showed significantly greater survival rate as compared to control patients (hazard ratio for death, 0.035; 95% confidence interval [CI], 0.004 to 0.347; p = 0.004), adjusting for baseline clinical characteristics. Two out of 62 patients of the tocilizumab group and 11 out of 23 in the control group died. 92% and 42.1% of the discharged patients in the tocilizumab and control group respectively, recovered. The respiratory function resulted improved in 64.8% of the observations in tocilizumab patients who were still hospitalized, whereas 100% of controls worsened and needed mechanical ventilation. No infections were reported. Conclusions: Tocilizumab results to have a positive impact if used early during Covid-19 pneumonia with severe respiratory syndrome in terms of increased survival and favorable clinical course.

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Schizophrenia has been conceived as a disorder of brain connectivity, but it is unclear how this network phenotype is related to the underlying genetics. We used morphometric similarity analysis of MRI data as a marker of interareal cortical connectivity in three prior case–control studies of psychosis: in total, n = 185 cases and n = 227 controls. Psychosis was associated with globally reduced morphometric similarity in all three studies. There was also a replicable pattern of case–control differences in regional morphometric similarity, which was significantly reduced in patients in frontal and temporal cortical areas but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case–control differences in morphometric similarity was spatially correlated with cortical expression of a weighted combination of genes enriched for neurobiologically relevant ontology terms and pathways. In addition, genes that were normally overexpressed in cortical areas with reduced morphometric similarity were significantly up-regulated in three prior post mortem studies of schizophrenia. We propose that this combined analysis of neuroimaging and transcriptional data provides insight into how previously implicated genes and proteins as well as a number of unreported genes in their topological vicinity on the protein interaction network may drive structural brain network changes mediating the genetic risk of schizophrenia.

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Schizophrenia has been conceived as a disorder of brain connectivity but it is unclear how this network phenotype is related to the emerging genetics. We used morphometric similarity analysis of magnetic resonance imaging (MRI) data as a marker of inter-areal cortical connectivity in three prior case-control studies of psychosis: in total, N=185 cases and N=227 controls. Psychosis was associated with globally reduced morphometric similarity (MS) in all 3 studies. There was also a replicable pattern of case-control differences in regional MS which was significantly reduced in patients in frontal and temporal cortical areas, but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of casecontrol differences in MS was spatially correlated with cortical expression of a weighted combination of genes enriched for neurobiologically relevant ontology terms and pathways. In addition, genes that were normally over-expressed in cortical areas with reduced MS were significantly up-regulated in a prior post mortem study of schizophrenia. We propose that this combination of neuroimaging and transcriptional data provides new insight into how previously implicated genes and proteins, as well as a number of unreported proteins in their vicinity on the protein interaction network, may interact to drive structural brain network changes in schizophrenia. dysconnectivity | network neuroscience | psychosis | partial least squares | Allen Human Brain AtlasCorrespondence: sem91@cam.ac.uk

Spatial representations are processed in the service of several different cognitive functions. The present study capitalizes on the Activation Likelihood Estimation (ALE) method of meta‐analysis to identify: (a) the shared neural activations among spatial functions to reveal the “core” network of spatial processing; (b) the specific neural activations associated with each of these functions. Following PRISMA guidelines, a total of 133 fMRI and PET studies were included in the meta‐analysis. The overall analysis showed that the core network of spatial processing comprises regions that are symmetrically distributed on both hemispheres and that include dorsal frontoparietal regions, presupplementary motor area, anterior insula, and frontal operculum. The specific analyses revealed the brain regions that are selectively recruited for each spatial function, such as the right temporoparietal junction for shift of spatial attention, the right parahippocampal gyrus, and the retrosplenial cortex for navigation and spatial long‐term memory. The findings are integrated within a systematic review of the neuroimaging literature and a new neurocognitive model of spatial cognition is proposed.

Background Pneumonia with severe respiratory failure represents the principal cause of death in COVID-19, where hyper-inflammation plays an important role in lung damage. An effective treatment aiming at reducing the inflammation without preventing virus clearance is thus urgently needed. Tocilizumab, an anti-soluble IL-6 receptor monoclonal antibody, has been proposed for treatment of patients with COVID-19. Methods A retrospective cohort study at the Montichiari Hospital, Brescia, Italy, was conducted. We included consecutive patients with COVID-19 related pneumonia at the early stage of respiratory failure, all treated with a standard protocol (hydroxychloroquine 400 mg daily, lopinavir 800 mg plus ritonavir 200 mg per day). We compared survival rate and clinical status in a cohort of patients who received additional treatment with tocilizumab once (either 400 mg intravenous or 324 mg subcutaneous) with a retrospective cohort of patients who did not receive tocilizumab (referred to as the standard treatment group). All outcomes were assessed at the end of the follow-up, that correspond to death or complete recovery and discharge from the hospital. Findings 158 patients were included, 90 of which received tocilizumab. 34 out of 68 (50%) patients in the standard treatment group and 7 out of 90 (7.7%) in the tocilizumab group died. Tocilizumab significantly improved survival compared to standard care (multivariate HR: 0.057; 95% C.I = 0.017- 0.187, p < 0.001). No differences between the two administration routes of tocilizumab were observed. No tocilizumab-related infections and/or side effects were observed. Interpretation Early treatment with tocilizumab could be helpful to prevent excessive hyper-inflammation and death in COVID-19 related pneumonia. Low dose administration of tocilizumab is not associated with adverse events. Funding none

The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.