37 research outputs found

    The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

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    The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

    Interrater Reliability of NI-RADS on Posttreatment PET/Contrast-enhanced CT Scans in Head and Neck Squamous Cell Carcinoma

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    Purpose: To evaluate the interrater reliability among radiologists examining posttreatment head and neck squamous cell carcinoma (HNSCC) fluorodeoxyglucose PET/contrast-enhanced CT (CECT) scans using Neck Imaging Reporting and Data System (NI-RADS). Materials and Methods: In this retrospective study, images in 80 patients with HNSCC who underwent posttreatment surveillance PET/CECT and immediate prior comparison CECT or PET/CECT (from June 2014 to July 2016) were uploaded to the American College of Radiology's cloud-based website, Cortex. Eight radiologists from seven institutions with variable NI-RADS experience independently evaluated each case and assigned an appropriate prose description and NI-RADS category for the primary site and the neck site. Five of these individuals were experienced readers (> 5 years of experience), and three were novices (< 5 years of experience). In total, 640 lexicon-based and NI-RADS categories were assigned to lesions among the 80 included patients by the eight radiologists. Light generalization of Cohen κ for interrater reliability was performed. Results: Of the 80 included patients (mean age, 63 years ± 10 [standard deviation]), there were 58 men (73%); 60 patients had stage IV HNSCC (75%), and the most common tumor location was oropharynx (n = 32; 40%). Light κ for lexicon was 0.30 (95% CI: 0.23, 0.36) at the primary site and 0.31 (95% CI: 0.24, 0.37) at the neck site. Light κ for NI-RADS category was 0.55 (95% CI: 0.46, 0.63) at the primary site and 0.60 (95% CI: 0.48, 0.69) at the neck site. Percent agreement between lexicon and correlative NI-RADS category was 84.4% (540 of 640) at the primary site and 92.6% (593 of 640) at the neck site. There was no significant difference in interobserver agreement among the experienced versus novice raters. Conclusion: Moderate agreement was achieved among eight radiologists using NI-RADS at posttreatment HNSCC surveillance imaging

    A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

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    Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research

    Tick-, mosquito-, and rodent-borne parasite sampling designs for the National Ecological Observatory Network

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    Parasites and pathogens are increasingly recognized as significant drivers of ecological and evolutionary change in natural ecosystems. Concurrently, transmission of infectious agents among human, livestock, and wildlife populations represents a growing threat to veterinary and human health. In light of these trends and the scarcity of long-term time series data on infection rates among vectors and reservoirs, the National Ecological Observatory Network (NEON) will collect measurements and samples of a suite of tick-, mosquito-, and rodent-borne parasites through a continental-scale surveillance program. Here, we describe the sampling designs for these efforts, highlighting sampling priorities, field and analytical methods, and the data as well as archived samples to be made available to the research community. Insights generated by this sampling will advance current understanding of and ability to predict changes in infection and disease dynamics in novel, interdisciplinary, and collaborative ways. (Résumé d'auteur

    Tick-, Mosquito-, and Rodent-Borne Parasite Sampling Designs for the National Ecological Observatory Network [Special Feature: NEON Design]

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    Parasites and pathogens are increasingly recognized as significant drivers of ecological and evolutionary change in natural ecosystems. Concurrently, transmission of infectious agents among human, livestock, and wildlife populations represents a growing threat to veterinary and human health. In light of these trends and the scarcity of long-term time series data on infection rates among vectors and reservoirs, the National Ecological Observatory Network (NEON) will collect measurements and samples of a suite of tick-, mosquito-, and rodent-borne parasites through a continental-scale surveillance program. Here, we describe the sampling designs for these efforts, highlighting sampling priorities, field and analytical methods, and the data as well as archived samples to be made available to the research community. Insights generated by this sampling will advance current understanding of and ability to predict changes in infection and disease dynamics in novel, interdisciplinary, and collaborative ways

    Self-help interventions for depressive disorders and depressive symptoms: a systematic review

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    <p>Abstract</p> <p>Background</p> <p>Research suggests that depressive disorders exist on a continuum, with subthreshold symptoms causing considerable population burden and increasing individual risk of developing major depressive disorder. An alternative strategy to professional treatment of subthreshold depression is population promotion of effective self-help interventions that can be easily applied by an individual without professional guidance. The evidence for self-help interventions for depressive symptoms is reviewed in the present work, with the aim of identifying promising interventions that could inform future health promotion campaigns or stimulate further research.</p> <p>Methods</p> <p>A literature search for randomised controlled trials investigating self-help interventions for depressive disorders or depressive symptoms was performed using PubMed, PsycINFO and the Cochrane Database of Systematic Reviews. Reference lists and citations of included studies were also checked. Studies were grouped into those involving participants with depressive disorders or a high level of depressive symptoms, or non-clinically depressed participants not selected for depression. A number of exclusion criteria were applied, including trials with small sample sizes and where the intervention was adjunctive to antidepressants or psychotherapy.</p> <p>Results</p> <p>The majority of interventions searched had no relevant evidence to review. Of the 38 interventions reviewed, the ones with the best evidence of efficacy in depressive disorders were S-adenosylmethionine, St John's wort, bibliotherapy, computerised interventions, distraction, relaxation training, exercise, pleasant activities, sleep deprivation, and light therapy. A number of other interventions showed promise but had received less research attention. Research in non-clinical samples indicated immediate beneficial effects on depressed mood for distraction, exercise, humour, music, negative air ionisation, and singing; while potential for helpful longer-term effects was found for autogenic training, light therapy, omega 3 fatty acids, pets, and prayer. Many of the trials were poor quality and may not generalise to self-help without professional guidance.</p> <p>Conclusion</p> <p>A number of self-help interventions have promising evidence for reducing subthreshold depressive symptoms. Other forms of evidence such as expert consensus may be more appropriate for interventions that are not feasible to evaluate in randomised controlled trials. There needs to be evaluation of whether promotion to the public of effective self-help strategies for subthreshold depressive symptoms could delay or prevent onset of depressive illness, reduce functional impairment, and prevent progression to other undesirable outcomes such as harmful use of substances.</p

    Distinct abilities associated with matching same identity faces vs. discriminating different faces: Evidence from individual differences in prosopagnosics and controls

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    Previous face matching studies provide evidence that matching same identity faces (match trials) and discriminating different face identities (non-match trials) rely on distinct processes. For example, instructional studies geared towards improving face matching in applied settings have often found selective improvements in match or non-match trials only. Additionally, a small study found that developmental prosopagnosics (DPs) have specific deficits in making match but not non-match judgments. In the current study, we sought to replicate this finding in DPs and examine how individual differences across DPs and controls in match vs. non-match performance relate to featural vs. holistic processing abilities. 43 DPs and 27 controls matched face images shown from similar front views or with varied lighting or viewpoint. Participants also performed tasks measuring featural (eyes/mouth) and holistic processing (part-whole task). We found that DPs showed worse overall matching performance than controls and that their relative match vs. non-match deficit depended on image variation condition, indicating that DPs do not consistently show match- or non-match-specific deficits. When examining the association between holistic and featural processing abilities and match vs. non-match trials in the entire group of DPs and controls, we found a very clear dissociation: Match trials significantly correlated with eye processing ability (r=.48) but not holistic processing (r=.11), whereas non-match trials significantly correlated with holistic processing (r=.32) but not eye processing (r=.03). This suggests that matching same identity faces relies more on eye processing while discriminating different faces relies more on holistic processing

    Monte Carlo based dosimetry of extraoral photobiomodulation for prevention of oral mucositis

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    Abstract Photobiomodulation therapy (PBMT) is recommended for prevention and treatment of oral mucositis, a painful condition that occurs in cancer patients. Intraoral PBMT is limited to treating distal oral mucosa and oropharynx. Extraoral PBMT may provide a more efficient intervention. The goal of this study was to develop a clinically viable protocol for extraoral PBMT. Monte Carlo modeling was used to predict the distribution of 850 nm light for four treatment sites, using anatomical data obtained from MRI and optical properties from the literature. Simulated incident light power density was limited to 399 mW/cm2 to ensure treatment safety and to prevent tissue temperature increase. The results reveal that total tissue thickness determines fluence rate at the oral mucosa, whereas the thickness of individual tissue layers and melanin content are of minor importance. Due to anatomical differences, the fluence rate varied greatly among patients. Despite these variations, a universal protocol was established using a median treatment time methodology. The determined median treatment times required to deliver efficacious dose between 1 and 6 J/cm2 were within 15 min. The developed PBMT protocol can be further refined using the combination of pretreatment imaging and the Monte Carlo simulation approach implemented in this study

    Validation of a Monte Carlo Modelling Based Dosimetry of Extraoral Photobiomodulation

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    An in vivo validation study was performed to confirm the accuracy of extraoral photobiomodulation therapy (PBMT) dosimetry determined by modelling. The Monte Carlo technique was utilized to calculate the fluence rate and absorbed power of light delivered through multi-layered tissue. Optical properties used during Monte Carlo simulations were taken from the literature. Morphological data of four study volunteers were acquired using magnetic resonance imaging (MRI) scans. Light emitting diode (LED) coupled to a power meter were utilized to measure transmitted power through each volunteer’s cheek, in vivo. The transmitted power determined by Monte Carlo modelling was compared to the in vivo measurements to determine the accuracy of the simulations. Experimental and simulation results were in good agreement for all four subjects. The difference between the mean values of the measured transmission was within 12% from the respective transmission obtained using Monte Carlo simulations. The results of the study indicate that Monte Carlo modelling is a robust and reliable method for light dosimetry
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