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The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge., Comment: 69 pages, 6 figures, 1 table

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage is direct comparison with histological data as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging, but ultimately affect what questions can be answered using the data. This work represents 'Part 2' of a series of recommendations and considerations for preclinical dMRI, where we focus on best practices for dMRI of ex vivo tissue. We first describe the value that ex vivo imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We then give guidelines for ex vivo protocols, including tissue preparation, imaging sequences and data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available ex vivo dMRI datasets and dedicated software packages. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist, and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge., Comment: 59 pages, 12 figures, part of ongoing efforts on ISMRM Diffusion Study Group initiative 'Best Practices (Consensus) for diffusion MRI'. arXiv admin note: text overlap with arXiv:2209.12994

Background: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis.Method: Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts.Results: Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naive. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P

Lockdowns have been implemented worldwide to halt the development of the COVID-19 pandemic, owing to the virus's potential threat and infectious nature. The coronavirus outbreak has wreaked havoc on the world and poses a serious hazard due to the virus's infectious natureObjective:To examine the positive and negative impacts of COVID-19 on educators (250) teaching at various public and private higher educational institutions in Pakistan during the lockdownMethods:A Google form was used to conduct an online cross-sectional survey. The questionnaire included questions about the following topics: demographic information. demographic details, physical exercise, health consciousness, relationship with family members, social relationship, Information and technology (IT) skills, digital courses, research activities, work from home, time spent on social media, expenditures, and lockdown activities. The sample was collected from 1st January 2021 to 12th May 2021 and 250 responses were recorded from the educators working from homethrough the snowball sampling technique. The data were analyzed through SPSS version 25. Results: The findings revealed an overall reduction in expenditures, preference for healthy and homemade food, adequate intake of supplements, enhancement of professional grooming (E-courses and IT skills), quality time with family, improvement in social relationships with family, friends, and colleagues. Conversely, the study also brought to the light negative impact of COVID-19 lockdown, for instance, overindulgence in social media, sedentary lifestyle, hard time-saving money, inadequate training for online classes, increased workload, strict schedule, incessant fear of downsizing, and pay cuts.Conclusions:The study recommends that educatorsengage themselves in yoga, meditation, physical exercise, and eat a healthy diet. Furthermore, they should continue upskilling the professional development through E-courses and technology-based skills.