BACKGROUND: There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain. METHODS: A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n = 110) or diagnosed with bipolar disorder (n = 40), attention-deficit/hyperactivity disorder (n = 37), schizophrenia (n = 29), or schizoaffective disorder (n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components. RESULTS: Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensorymotor network and its connectivity with subcortical structures and cortical executive networks. CONCLUSIONS: We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

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The envelope glycoprotein located at the outermost surface of the flavivirus particle mediates entry of virus into host cells. In this study, the involvement of domain III of West Nile virus (WNV-DIII) envelope protein in binding to host cell surface was investigated. WNV-DIII was first expressed as a recombinant protein and purified after a solubilization and refolding procedure. The refolded WNV-DIII protein displays a content of b-sheets consistent with known homologous structures of other flavivirus envelope DIII, shown by using circular dichroism analysis. Purified recombinant WNV-DIII protein was able to inhibit WNV entry into Vero cells and C6/36 mosquito cells. Recombinant WNV-DIII only partially blocked the entry of dengue-2 (Den 2) virus into Vero cells. However, entry of Den 2 virus into C6/36 was blocked effectively by recombinant WNV-DIII. Murine polyclonal serum produced against recombinant WNV-DIII protein inhibited infection with WNV and to a much lesser extent with Den 2 virus, as demonstrated by plaque neutralization assays. Together these results provided strong evidence that immunoglobulin-like DIII of WNV envelope protein is responsible for binding to receptor on the surface of host cells. The data also suggest that similar attachment molecule(s) or receptor(s) were used by WNV and Den 2 virus for entry into C6/36 mosquito cells.

The global signal in resting-state functional MRI data is considered to be dominated by physiological noise and artifacts, yet a growing literature suggests that it also carries information about widespread neural activity. The biological relevance of the global signal remains poorly understood. Applying principal component analysis to a large neuroimaging dataset, we found that individual variation in global signal topography recapitulates well-established patterns of large-scale functional brain networks. Using canonical correlation analysis, we delineated relationships between individual differences in global signal topography and a battery of phenotypes. The first canonical variate of the global signal, resembling the frontoparietal control network, was significantly related to an axis of positive and negative life outcomes and psychological function. These results suggest that the global signal contains a rich source of information related to trait-level cognition and behavior. This work has significant implications for the contentious debate over artifact removal practices in neuroimaging.

The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).