William Akhtar (personamount5)

Alzheimer's disease is a neurodegenerative disease in which the degree of cortical atrophy in specific structures of the brain serves as a useful imaging biomarker. Recent approaches using linear mixed effects (LME) models in longitudinal neuroimaging have been powerful and flexible in investigating the temporal trajectories of cortical thickness. However, massive-univariate analysis, a simplified approach that obtains a summary statistic (e.g., a p-value) for every vertex along the cortex, is insufficient to model cortical atrophy because it does not account for spatial similarities of the signals in neighboring locations. In this article, we develop a permutation-based inference procedure to detect spatial clusters of vertices showing statistically significant differences in the rates of cortical atrophy. The proposed method, called SpLoc, uses spatial information to combine the signals adaptively across neighboring vertices, yielding high statistical power while controlling family-wise error rate (FWER) accurately. When we reject the global null hypothesis, we use a cluster selection algorithm to detect the spatial clusters of significant vertices. We validate our method using simulation studies and apply it to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data to show its superior performance over existing methods. An R package for implementing SpLoc is publicly available.The coronavirus disease 2019 (COVID-19) outbreak introduced unprecedented health-risks, as well as pressure on the economy, society, and psychological well-being due to the response to the outbreak. In a preregistered study, we hypothesized that the intense experience of the outbreak potentially induced stress-related brain modifications in the healthy population, not infected with the virus. We examined volumetric changes in 50 participants who underwent MRI scans before and after the COVID-19 outbreak and lockdown in Israel. Their scans were compared with those of 50 control participants who were scanned twice prior to the pandemic. Following COVID-19 outbreak and lockdown, the test group participants uniquely showed volumetric increases in bilateral amygdalae, putamen, and the anterior temporal cortices. Changes in the amygdalae diminished as time elapsed from lockdown relief, suggesting that the intense experience associated with the pandemic induced transient volumetric changes in brain regions commonly associated with stress and anxiety. The current work utilizes a rare opportunity for real-life natural experiment, showing evidence for brain plasticity following the COVID-19 global pandemic. These findings have broad implications, relevant both for the scientific community as well as the general public.Zebrafish possess hair cells on the body surface similar to that of mammals inner hear, in particular in the neuromasts, and due to its ability in regenerating damaged hair cells, is regularly used as a powerful animal model to study in vivo cytotoxicity. Among the factors leading to hair cell disruption, metal ions are of particular concern since they are important environmental pollutants. To date, several studies on zebrafish hair cell regeneration after metal exposure exist, while no data on regeneration during continuous metal exposure are available. In the present study, neuromast hair cell disruption and regeneration were assessed in zebrafish larvae for the first time during zinc (Zn) and cadmium (Cd) continuous exposure and a visual and molecular approach was adopted. Fluorescent vital dye DASPEI was used to assess hair cell regeneration and the gene expression of claudin b (cldnb) and phoenix (pho), was analyzed. Metallotionein-2 (mt2) gene expression was used as standard molecular marker of metal toxicity and confirmed the higher toxicity of Cd compared to Zn. In addition, Cd caused a delay in hair cell regeneration compared to Zn. Molecular analysis showed cldnb gene expression increased in relation to the metal concentrations u