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A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan time given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan time per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan time are broadly interchangeable up to 20-30 min of data. However, the returns of scan time diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed overhead costs associated with each participant (e.g., recruitment, non-imaging measures), prediction accuracy in many small-scale and some large-scale BWAS might benefit from longer scan time than typically assumed. These results generalize across phenotypic domains, scanners, acquisition protocols, racial groups, mental disorders, age groups, as well as resting-state and task-state functional connectivity. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations maximize sample size, at the expense of scan time, which can result in sub-optimal prediction accuracies and inefficient use of resources. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK., Competing Interests: Conflict of interest DB is shareholder and advisory board member of MindState Design Labs, USA.

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10,085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here, we noninvasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the gamma-aminobutyric acid (GABA) agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in the association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 y old) and Asian (7.2 to 7.9 y old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: Hepatitis delta virus (HDV) coinfects with hepatitis B virus (HBV) causing the most severe form of viral hepatitis. However, its exact global disease burden remains largely obscure. We aim to establish the global epidemiology, infection mode-stratified disease progression, and clinical outcome of HDV infection., Methods: We conducted a meta-analysis with a random-effects model and performed data synthesis., Results: The pooled prevalence of HDV is 0.80% (95% confidence interval [CI], 0.63-1.00) among the general population and 13.02% (95% CI, 11.96-14.11) among HBV carriers, corresponding to 48-60 million infections globally. Among HBV patients with fulminant hepatitis, cirrhosis, or hepatocellular carcinoma, HDV prevalence is 26.75% (95% CI, 19.84-34.29), 25.77% (95% CI, 20.62-31.27), and 19.80% (95% CI, 10.97-30.45), respectively. The odds ratio (OR) of HDV infection among HBV patients with chronic liver disease compared with asymptomatic controls is 4.55 (95% CI, 3.65-5.67). Hepatitis delta virus-coinfected patients are more likely to develop cirrhosis than HBV-monoinfected patients with OR of 3.84 (95% CI, 1.79-8.24). Overall, HDV infection progresses to cirrhosis within 5 years and to hepatocellular carcinoma within 10 years, on average., Conclusions: Findings suggest that HDV poses a heavy global burden with rapid progression to severe liver diseases, urging effective strategies for screening, prevention, and treatment., (© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America.)

Hepatitis delta virus (HDV), as a defective sub-virus that co-infects with hepatitis B virus, imposes an emerging global health burden. However, genetic characteristics and molecular classification of HDV remain under investigated. In this study, we have systematically retrieved and analysed a large set of HDV full-length genome sequences and identified novel recombinants. Based on phylogenetic and genetic analyses, we have established an updated classification system for HDV when recombinants were excluded. Furthermore, we have mapped the global distribution of different genotypes and subtypes. Finally, we have compiled a complete set of reference genomes for each subtype and proposed criteria for future identification of novel genotypes and subtypes. Of note, the global distribution map indicates that currently available HDV genetic data remain limited, and thus our proposed classification will likely evolve as future epidemiological data will accumulate. These results will facilitate the future research on the diagnosis, screening, epidemiology, evolution, prevention and clinical management of HDV infection., (© 2018 The Authors. Journal of Viral Hepatitis Published by John Wiley & Sons Ltd.)

Hepatitis E virus (HEV) infection has emerged as a global health problem. However, no approved medication is available, and the infection biology remains largely elusive. Electron transport chain (ETC), a key component of the mitochondria, is the main site that produces ATP and reactive oxygen species (ROS). By profiling the role of the different complexes of the mitochondrial ETC, we found that pharmacological inhibition of complex III, a well-defined drug target for the treatment of malaria and Pneumocystis pneumonia, potently restricts HEV replication. This effect demonstrated in our HEV models is equivalent to the anti-HEV potency of ribavirin, a widely used off-label treatment for patients with chronic HEV. Mechanistically, we found that this effect is independent of ATP production, ROS level, and pyridine depletion. By using pharmacological inhibitors and genetic approaches, we found that mitochondrial permeability transition pore (MPTP), a newly identified component of ETC, provides basal defense against HEV infection. HEV interferes with the opening of the MPTP. Furthermore, inhibition of the MPTP attenuated the anti-HEV effect of complex III inhibitors, suggesting that the MPTP mediates the antiviral effects of these inhibitors. These findings reveal new insights on HEV-host interactions and provide viable anti-HEV targets for therapeutic development.-Qu, C., Zhang, S., Wang, W., Li, M., Wang, Y., van der Heijde-Mulder, M., Shokrollahi, E., Hakim, M. S., Raat, N. J. H., Peppelenbosch, M. P., Pan, Q. Mitochondrial electron transport chain complex III sustains hepatitis E virus replication and represents an antiviral target.