Spiro P. Pantazatos

Accurate estimates of the rates of COVID vaccine-induced severe adverse events and deaths per some standard of population size are critical for risk-benefit ratio analyses of vaccination and boosters against SARS-CoV-2 coronavirus in different age groups. However, existing surveillance studies are not designed to reliably estimate life-threatening events, or vaccine-induced mortality risks. Here, regional variations in the vaccination rates were used to predict all-cause mortality and non-COVID deaths in subsequent time periods using two independent, publicly available datasets from the US and Europe (month- and week-level resolutions, respectively). Vaccination correlated negatively with European mortality 6-20 weeks post-injection, while vaccination predicted all-cause mortality 0-5 weeks post-injection in almost all age groups and with an age-related temporal pattern consistent with the US vaccine rollout. Results from fitted regression slopes (p < 0.05 corrected for false discovery rate) suggest a US national average vaccination mortality rate (VMR) of 0.04% (0.0244, 0.0474 95% CI) and higher VMR with age (lower bound estimates of VMR=0.005% (0.0028, 0.0080 95% CI) in ages 0-17 increasing to 0.06% (0.0108, 0.0859 95% CI) in ages >75 years), and 146K to 187K vaccine-associated US deaths between February and August, 2021. Notably, adult vaccination correlated with increased subsequent mortality of unvaccinated younger people (<18, US; <15, Europe), possibly reflecting adverse effects indirectly caused by shedding of vaccine components. Comparing our estimates with the CDC-reported vaccine-induced mortality risk (0.002%) suggests VAERS deaths are underreported by a factor of 20, consistent with known VAERS under-ascertainment bias. Comparing our age-stratified VMRs with published age-stratified coronavirus infection fatality rates suggests the risks of the COVID vaccines and boosters outweigh the benefits in children, young adults, and older adults with low occupational risk, or with previous coronavirus exposure. Our findings raise important questions about current COVID mass vaccination strategies and warrant further investigation and review.

Brain activity in response to food cues following Roux-En-Y Gastric Bypass (RYGB) in binge eating (BE) or non-binge eating (NB) individuals is understudied. Here, 15 RYGB (8 BE; 7 NB) and 13 no treatment (NT) (7 BE; 6 NB) women with obesity underwent fMRI imaging while viewing high and low energy density food (HEF and LEF, respectively) and non-food (NF) visual cues. A region of interest (ROI) analysis compared BE participants to NB participants in those undergoing RYGB surgery pre-surgery and 4 months post. Results were corrected for multiple comparisons using liberal (p < 0.006 uncorrected) and stringent (p < 0.05 FDR corrected) thresholds. Four months following RYGB (vs. no treatment (NT) control), both BE and NB participants showed greater reductions in blood oxygen level-dependent (BOLD) signals (a proxy of local brain activity) in the dorsomedial prefrontal cortex in response to HEF (vs. LEF) cues (p < 0.006). BE (vs. NB) participants showed greater increases in the precuneus (p < 0.006) and thalamic regions (p < 0.05 corrected) to food (vs. NF). For RYGB (vs. NT) participants, BE participants, but not NB participants, showed lower BOLD signal in the middle occipital gyrus (p < 0.006), whilst NB participants, but not BE participants, showed lower signal in inferior frontal gyrus (p < 0.006) in response to HEF (vs. LEF). Results suggest distinct neural mechanisms of RGYB in BE and may help lead to improved clinical treatments.

Previous research suggests anxious individuals demonstrate hypervigilance for threatening stimuli. Recent controversial studies suggested people's bodies (presentiment) and brain (precognition, Bem, 2011; Bem et al., 2015) can 'predict' biologically salient events, such as threatening stimuli, milliseconds to seconds before they occur. If true, this suggests a possible mechanism for hypervigilance towards threatening stimuli in anxious individuals. We sought to replicate Bem's precognitive avoidance experiment in an independent cohort of adolescents (n = 144), tested whether performance ability is associated with state or trait anxiety, and examined sex-differences in these variables. We hypothesized that trait anxiety would correlate positively with precognitive avoidance performance. We were unable to replicate the 2011 Bem findings in our sample (precognitive avoidance hit rate = 48.95%, p = 0.825), and neither trait nor state anxiety was correlated with precognitive avoidance. We did not detect a sex difference in precognitive avoidance (females > males: t = 1.36, p = 0.175, two sample t-test). We found higher levels of trait anxiety in females in line with previous research. Contrary to our hypotheses, we failed to detect evidence for precognitive avoidance in our sample, possibly because our study was underpowered. Implications are discussed with respect to related literature.

The primary claim of the Richiardi et. al. 2015 Science article1 is that a measure of correlated gene expression, significant strength fraction (SSF), is related to resting state fMRI (rsfMRI) networks. However, there is still debate about this claim and whether spatial proximity, in the form of contiguous clusters, accounts entirely, or only partially, for SSF2,3. Here, thirteen distributed networks were simulated by combining 34 contiguous clusters randomly placed throughout cortex, with resulting edge distance distributions similar to rsfMRI networks. Cluster size was modulated (6-15mm radius) to test its influence on SSF false positive rate (SSF-FPR) among simulated ‘noise’ networks. The contribution of rsfMRI networks on SSF-FPR was examined by comparing simulations using: 1) all cortical samples 2) all samples with non-rsfMRI cluster centers and 3) only non-rsfMRI samples. Results show that SSF-FPR was influenced only by cluster size (r>0.9, p<0.001), not by rsfMRI samples. Simulations using 14mm radius clusters most resembled rsfMRI networks. When thresholding at p<10-4, the SSF-FPR was 0.47. Features that maximize SF have high global spatial autocorrelation. In conclusion, SSF is unrelated to rsfMRI networks. The main conclusion of Richiardi et. al. 2015 is based on a finding that is ~50% likely to be a false positive, not less than 0.01% as originally reported in the article1. We discuss why distance corrections alone and external face validity are insufficient to establish a trustworthy relationship between correlated gene expression measures and rsfMRI networks, and propose more rigorous approaches to preclude common pitfalls in related studies.

Magnetic resonance (MR) images of the brain are of immense clinical and research utility. At the atomic and subatomic levels, the sources of MR signals are well understood. However, at the macromolecular scale, we lack a comprehensive understanding of what determines MR signal contrast. To address this gap, we used genome-wide measurements to correlate gene expression with MR signal intensity across the cortex in the Allen Human Brain Atlas. We focused on the ratio of T1-weighted and T2-weighted intensities (T1-w/T2-w) which is considered to be a useful proxy for myelin content. Positive correlations between myelin-associated genes and the ratio supported its use as a myelin marker. However, stronger correlations were observed for neurofilaments, and genes linked to the production of formaldehyde (which cross-links protein to create larger molecules). There was also an association with protein mass, with genes coding for heavier proteins expressed in regions with high T1-w/T2-w values. Oligodendrocyte gene markers were strongly correlated but this was not driven by myelin-associated genes, suggesting this signal is from non-myelinating satellite oligodendrocytes. We find the strongest support for the previous finding of high aerobic glycolysis in regions with low T1-w/T2-w ratio. Specifically, many mitochondrial genes were negatively correlated with T1-w/T2-w ratio. Genes up-regulated by pH in the brain were also highly correlated with the ratio, suggesting the pH gradient in mitochondria may explain the aerobic glycolysis association. Expression of protease subunit genes was also inversely associated with the ratio, in agreement with the protein mass correlation. While we corroborate associations with myelin and synaptic plasticity, differences in the T1-w/T2-w ratio appear to be more attributable to molecule size, satellite oligodendrocyte proportion, mitochondrial number, alkalinity, and axon caliber. Using disease-associated gene lists, we observed an enrichment of negative T1-w/T2-w ratio correlations with human immunodeficiency virus (HIV) associated genes. Expanding our analysis to the whole brain results in strong positive T1-w/T2-w associations for immune system, inflammatory disease, and microglial genes. In contrast, neuron markers and synaptic plasticity genes are negatively enriched. Lastly, our results vary little when our analysis is performed on T1-w or inverted T2-w intensities alone, possibly because the noise reduction properties of the ratio are not needed for postmortem brain scans. These results provide a molecular characterization of MR contrast that will aid interpretation of future MR studies of the brain.

A recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI) can be recapitulated with correlated gene expression (i.e. high within-network tissue-tissue “strength fraction”, SF) (1). However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether “null networks” (i.e. clusters with center coordinates randomly placed throughout cortex) also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in (1), reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.

Summary: A recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI) can be recapitulated with correlated gene expression (i.e. high within-network tissue-tissue “strength fraction”, SF) (Richiardi et al., 2015). However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether “null networks” (i.e. clusters with center coordinates randomly placed throughout cortex) also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in (Richiardi et al., 2015), reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.