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Gene size and fold change expression breakdown for CNS cell type-specific DE genes. Significant DE genes with CNS cell type-specific designations were broken down into 4 categories—(1) decreased expression, gene size 100 kb (light orange); (3) increased expression, gene size 100 kb (red). Within each category, DE genes are organized by their respective CNS cell type-specific distribution. The exact Log2 fold change and gene size (kb) values are listed for each DE gene. File format: Microsoft Excel spreadsheet. (XLS 66 kb)

This archive contains sample output files for the sample data accompanying the Princeton Handbook for Reproducible Neuroimaging. Outputs include the NIfTI images converted using HeuDiConv (v0.8.0) and organized according to the BIDS standard, quality control evaluation using MRIQC (v0.15.1), data preprocessed using fMRIPrep (v20.2.0), and other auxiliary files. All outputs were created according to the procedures outlined in the handbook, and are intended to serve as a didactic reference for use with the handbook. The sample data from which the outputs are derived were acquired (with informed consent) using the ReproIn naming convention on a Siemens Skyra 3T MRI scanner. The sample data include a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by Yeshurun et al., 2017 and is available as part of the Narratives collection, and the emotional faces task is similar to Chai et al., 2015. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.

This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available here and here. The package contains seven Comma-Separated Values (CSV) files and two PDF files: master_stage1_anonymized.csv: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded); master_stage2_snr1_anonymized.csv: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded); master_stage2_snr2_anonymized.csv: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded); submission_form_stage1.pdf: PDF export of the online form used in stage 1; submission_form_stage2.pdf: PDF export of the online form used in stage 2. For the manuscript, we analyzed these CSV files with scripts reported here. Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names: normalized rmse: Whole-brain root-mean-squared error relative to ground truth; rmse_detrend_tissue: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask; rmse_detrend_blood: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask; rmse_detrend_DGM: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate); DeviationFromLinearSlope: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0; CalcStreak: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth; DeviationFromCalcMoment: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model); Submission Identifier: Self-chosen unique identifier of the submission; Submission Identifier of the corresponding Stage 1 submission: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1; Changes with respect to Stage 1 submission: Self-reported information about modifications made to the algorithm for Stage 2; Number of submissions in Stage 2: The number of solutions that were submitted to Stage 2 with a similar algorithm; Sim1/Sim2: Filename of the submitted solutions for Stage 1; File name of the zip-file you are going to upload: Filename of the file uploaded to Stage 2; Full name of the algorithm: Self-reported full name of the algorithm used; Preferred Acronym: Self-reported acronym of the algorithm used; Algorithm-type: Self-reported type of algorithm used; Does your algorithm incorporate information derived from magnitude images?: Self-reported Yes/No; Regularization terms: Self-reported types of regularization terms involved; Did your algorithm use the provided frequency map or the four individual echo phase images?: Self-reported information about involved magnitude information; Publication-ready description of the reconstruction technique: Self-reported description of the algorithm; Publications that describe the algorithm: Self-reported literature reference; Algorithm publicly available?: Self-reported public availability of the algorithm; If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?: Self-reported willingness to share the algorithm code with the public; Specific information about this solution: Self-reported detailed information about the solution; Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge: Self reported agreement with publication of submitted solution; Ground truth was not explicitly or implicitly incorporated into your algorithm or solution: Self-reported confirmation that the ground truth was not incorporated in the solution.

Abstract Background Cardiovascular disease is the leading cause of death in patients with Duchenne muscular dystrophy (DMD)—a fatal X-linked genetic disorder. Late gadolinium enhancement (LGE) imaging is the current gold standard for detecting myocardial tissue remodeling, but it is often a late finding. Current research aims to investigate cardiovascular magnetic resonance (CMR) biomarkers, including native (pre-contrast) T1 and extracellular volume (ECV) to evaluate the early on-set of microstructural remodeling and to grade disease severity. To date, native T1 measurements in DMD have been reported predominantly at 1.5T. This study uses 3T CMR: (1) to characterize global and regional myocardial pre-contrast T1 differences between healthy controls and LGE + and LGE− boys with DMD; and (2) to report global and regional myocardial post-contrast T1 values and myocardial ECV estimates in boys with DMD, and (3) to identify left ventricular (LV) T1-mapping biomarkers capable of distinguishing between healthy controls and boys with DMD and detecting LGE status in DMD. Methods Boys with DMD (N = 28, 13.2 ± 3.1 years) and healthy age-matched boys (N = 20, 13.4 ± 3.1 years) were prospectively enrolled and underwent a 3T CMR exam including standard functional imaging and T1 mapping using a modified Look-Locker inversion recovery (MOLLI) sequence. Pre-contrast T1 mapping was performed on all boys, but contrast was administered only to boys with DMD for post-contrast T1 and ECV mapping. Global and segmental myocardial regions of interest were contoured on mid LV T1 and ECV maps. ROI measurements were compared for pre-contrast myocardial T1 between boys with DMD and healthy controls, and for post-contrast myocardial T1 and ECV between LGE + and LGE− boys with DMD using a Wilcoxon rank-sum test. Results are reported as median and interquartile range (IQR). p-Values

Additional file 12: Figure S10. The lack of negative radial displacement in the brain tissue can be attributed to the non-linear elastic response of the connective tissue in the PVS. a. The connective tissue in the PVS is possibly made up of extracellular matrix fibers (collagen) and fibroblasts. b. When arterioles dilate, the connective tissue is under compression (middle) and the fibers buckle (bend) rather than compress due to the low energy cost of bending. Therefore, there are very low elastic forces and our assumption that the forces in the PVS originate mainly from the fluid pressure is valid. c. When the arterioles constrict or return to their original size, the connective tissue is in tension and the fibers stretch, creating significantly larger elastic forces. In this case, our assumption that the forces in the PVS originate mainly from the fluid pressure does not hold and the fluid-structure interaction model cannot predict the behavior accurately.

Figure S2. Comparison of arterial, venous or overlap components proportional to enhancing tumor volume at 1.5 T and 3 T. The arterial component was significantly smaller in ‘other’ group when using 1.5 T (7.59%) as compared to 3 T (17.99%, p

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how descending neurons control an insect's movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and we associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a large collection of descending neurons, we found that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation. Movies of optogenetically activated split-Gal4 linesEach movie contains 1 second before and after optogenetic stimulation for all experimental (retinal +) and control (retinal -) flies for all stimulation trials.opto_movies.zip

Abstract Background Pathways to care are actions and strategies employed by individuals in order to get help for health-related distress and the related processes of care providers. On several systematic reviews regarding pathways to mental health care (PMHC), studies regarding South American countries were not present. This review synthesizes qualitative and quantitative research about PMHC in Brazil. Methods LILACS, MEDLINE and SCIELO databases were searched for papers regarding PMHC in Brazil. The results were organized in pathway stages, based on Goldberg and Huxley’s ‘model of Levels and Filters’ and on Kleinman’s framework of ‘Popular, Folk and Professional health sectors’. Analysis also considered the changes in national mental health policy over time. Results 25 papers were found, with data ranging from 1989 to 2013. Complex social networks were involved in the initial recognition of MH issues. The preferred points of first contact also varied with the nature and severity of problems. A high proportion of patients is treated in specialized services, including mild cases. There is limited capacity of primary care professionals to identify and treat MH problems, with some improvement from collaborative care in the more recent years. The model for crisis management and acute care remains unclear: scarce evidence was found over the different arrangements used, mostly stressing lack of integration between emergency, hospital and community services and fragile continuity of care. Conclusions The performance of primary care and the regulation of acute demands, especially crisis management, are the most critical aspects on PMHC. Although primary care performance seems to be improving, the balanced provision and integration between services for adequate acute and long-term care is yet to be achieved.