Preprocessing Techniques for Neuroimaging Modalities: An In-Depth Analysis

Neuroimage analysis and data processing from various neuro-imaging modalities have been a multidisciplinary research field for a long time. Numerous types of research have been carried out in the area for multiple applications of neuroimaging and intelligent techniques to make faster and more accurate results. Different modalities gather information for detecting, treating, and identifying various neurological disorders. Each modality generates different kinds of data, including images and signals. Applying artificial intelligence-based techniques for analysing the inputs from the neuroimaging modalities requires preprocessing. Preprocessing techniques are used to fine-tune the data for better results and the application of intelligent methods. Various techniques and pipelines/workflows (steps for preprocessing the data from the imaging modalities) have been developed and followed by multiple researchers for the preprocessing of neuroimaging data. The preprocessing steps include the steps followed in removing noisy data from the inputs, converting the data to a different format, and adding additional information to improve the performance of the algorithm on the data. In this chapter, we compare the various neuroimaging techniques, the type of data they generate and the preprocessing techniques that various researchers frequently use to process data to apply them in artificial intelligence-based algorithms for the classification, prediction, and prognosis of various neurological disorders

Leishmania blood parasite dynamics during and after treatment of visceral leishmaniasis in Eastern Africa: A pharmacokineticpharmacodynamic model

Background With the current treatment options for visceral leishmaniasis (VL), recrudescence of the parasite is seen in a proportion of patients. Understanding parasite dynamics is crucial to improving treatment efficacy and predicting patient relapse in cases of VL. This study aimed to characterize the kinetics of circulating Leishmania parasites in the blood, during and after different antileishmanial therapies, and to find predictors for clinical relapse of disease. Methods Data from three clinical trials, in which Eastern African VL patients received various antil-eishmanial regimens, were combined in this study. Leishmania kinetoplast DNA was quantified in whole blood with real-time quantitative PCR (qPCR) before, during, and up to six months after treatment. An integrated population pharmacokinetic-pharmacodynamic model was developed using non-linear mixed effects modelling. Results Parasite proliferation was best described by an exponential growth model, with an in vivo parasite doubling time of 7.8 days (RSE 12%). Parasite killing by fexinidazole, liposomal amphotericin B, sodium stibogluconate, and miltefosine was best described by linear models directly relating drug concentrations to the parasite elimination rate. After treatment, parasite growth was assumed to be suppressed by the host immune system, described by an Emax model driven by the time after treatment. No predictors for the high variability in onset and magnitude of the immune response could be identified. Model-based individual predictions of blood parasite load on Day 28 and Day 56 after start of treatment were predictive for clinical relapse of disease. Conclusion This semi-mechanistic pharmacokinetic-pharmacodynamic model adequately captured the blood parasite dynamics during and after treatment, and revealed that high blood parasite loads on Day 28 and Day 56 after start of treatment are an early indication for VL relapse, which could be a useful biomarker to assess treatment efficacy of a treatment regimen in a clinical trial setting. (grant agreement 305178); the World Health Organization-Special Programme for Research and Training in Tropical Diseases (WHO-TDR); the French Development Agency (AFD), France (grant number CZZ2062); UK aid, UK; the Federal Ministry of Education and Research (BMBF) through KfW, Germany; the Medicor Foundation; Médecins Sans Frontières International; the Swiss Agency for Development and Cooperation, Switzerland (grant number 81017718 and 642.33/2010/0779/02 NID); the Dutch Ministry of Foreign Affairs, the Netherlands (grant number PDP15CH21); the Spanish Agency for International Development Cooperation (AECID) and other private foundations and individuals. T.P.C.D. is personally supported by the ZonMw/Dutch Research Council (NWO) Veni grant (project no. 91617140) and the Swedish Research Council (VR 2022-01251). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Population pharmacokinetics of a combination of miltefosine and paromomycin in Eastern African children and adults with visceral leishmaniasis

Objectives: To improve visceral leishmaniasis (VL) treatment in Eastern Africa, 14-and 28-day combination regimens of paromomycin plus allometrically dosed miltefosine were evaluated. As the majority of patients affected by VL are children, adequate paediatric exposure to miltefosine and paromomycin is key to ensuring good treatment response. Methods: Pharmacokinetic data were collected in a multicentre randomized controlled trial in VL patients from Kenya, Sudan, Ethiopia and Uganda. Patients received paromomycin (20 mg/kg/day for 14 days) plus miltefosine (allometric dose for 14 or 28 days). Population pharmacokinetic models were developed. Adequacy of exposure and target attainment of paromomycin and miltefosine were evaluated in children and adults. Results: Data from 265 patients (59% ≤12 years) were available for this pharmacokinetic analysis. Paromomycin exposure was lower in paediatric patients compared with adults [median (IQR) end-of-Treatment AUC0-24h 187 (162-203) and 242 (217-328) μg·h/mL, respectively], but were both within the IQR of end-of-Treatment exposure in Kenyan and Sudanese adult patients from a previous study. Cumulative miltefosine end-of-Treatment exposure in paediatric patients and adults [AUCD0-28 517 (464-552) and 524 (456-567) μg·day/mL, respectively] and target attainment [time above the in vitro susceptibility value EC90 27 (25-28) and 30 (28-32) days, respectively] were comparable to previously observed values in adults. Conclusions: Paromomycin and miltefosine exposure in this new combination regimen corresponded to the desirable levels of exposure, supporting the implementation of the shortened 14 day combination regimen. Moreover, the lack of a clear exposure-response and exposure-Toxicity relationship indicated adequate exposure within the therapeutic range in the studied population, including paediatric patients

Beyond the call of duty: Why customers contribute to firm-hosted commercial online communities

Firm-hosted commercial online communities, in which customers interact to solve each other's service problems, represent a fascinating context to study the motivations of collective action in the form of knowledge contribution to the community. We extend a model of social capital based on Wasko and Faraj (2005) to incorporate and contrast the direct impact of commitment to both the online community and the host firm, as well as reciprocity, on quality and quantity of knowledge contribution. In addition, we examine the moderating influence of three individual attributes that are particularly relevant to the firm-hosted community context: perceived informational value, sportsmanship, and online interaction propensity. We empirically test our framework using self-reported and objective data from 203 members of a firm-hosted technical support community. In addition to several interesting moderating effects, we find that a customer's online interaction propensity, commitment to the community, and the informational value s/he perceives in the community are the strongest drivers of knowledge contribution

Moulded cast compared with K-wire fixation after manipulation of an acute dorsally displaced distal radius fracture : the DRAFFT 2 RCT

Background: Patients with a displaced fracture of the distal radius are frequently offered surgical fixation. Manipulation of the fracture and moulded plaster casting is an alternative treatment that avoids metal implants, but evidence of its effectiveness is lacking. Objective: To compare functional outcomes, quality-of-life outcomes, complications and resource use among patients with a dorsally displaced fracture of the distal radius treated with manipulation and surgical fixation with Kirschner wires (K-wires) and those treated with manipulation and moulded cast. Design: Pragmatic, superiority, multicentre, randomised controlled trial with a health economic evaluation. Setting: A total of 36 orthopaedic trauma centres in the UK NHS. Participants: Patients (aged ≥ 16 years) treated for an acute dorsally displaced fracture of the distal radius were potentially eligible. Patients were excluded if their injury had occurred > 2 weeks previously, if the fracture was open, if it extended > 3 cm from the radiocarpal joint or if it required open reduction, or if the participant was unable to complete questionnaires. DOI: 10.3310/RLCF6332 Health Technology Assessment 2022 Vol. 26 No. 11 Copyright © 2022 Costa et al. This work was produced by Costa et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This is an Open Access publication distributed under the terms of the Creative Commons Attribution CC BY 4.0 licence, which permits unrestricted use, distribution, reproduction and adaption in any medium and for any purpose provided that it is properly attributed. See: https://creativecommons.org/licenses/by/4.0/. For attribution the title, original author(s), the publication source – NIHR Journals Library, and the DOI of the publication must be cited. vii Interventions: Participants were randomly assigned in theatre (1 : 1) to receive a moulded cast (i.e. the cast group) or surgical fixation with K-wires (i.e. the K-wire group) after fracture manipulation. Main outcome measures: The primary outcome measure was the Patient-Rated Wrist Evaluation score at 12 months, analysed on an intention-to-treat basis. Health-related quality of life was recorded using the EuroQol-5 Dimensions, five-level version, and resource use was recorded from a health and personal social care perspective. Results: Between January 2017 and March 2019, 500 participants (mean age 60 years, 83% women) were randomly allocated to receive a moulded cast (n = 255) or surgical fixation with K-wire (n = 245) following a manipulation of their fracture. A total of 395 (80%) participants were included in the primary analysis at 12 months. There was no difference in the Patient-Rated Wrist Evaluation score at 1 year post randomisation [cast group: n = 200, mean score 21.2 (standard deviation 23.1); K-wire group: n = 195, mean score 20.7 (standard deviation 22.3); adjusted mean difference –0.34 (95% confidence interval –4.33 to 3.66); p = 0.87]. A total of 33 (13%) participants in the cast group required surgical fixation for loss of fracture position in the first 6 weeks, compared with one participant in the K-wire group (odds ratio 0.02, 95% confidence interval 0.001 to 0.10). The base-case cost-effectiveness analysis showed that manipulation and surgical fixation with K-wires had a higher mean cost than manipulation and a moulded cast, despite similar mean effectiveness. The use of K-wires is unlikely to be cost-effective, and sensitivity analyses found this result to be robust. Limitations: Because the interventions were identifiable, neither patients nor clinicians could be blind to their treatment. Conclusions: Surgical fixation with K-wires was not found to be superior to moulded casting following manipulation of a dorsally displaced fracture of the distal radius, as measured by Patient-Rated Wrist Evaluation score. However, one in eight participants treated in a moulded cast required surgery for loss of fracture reduction in the first 6 weeks. After a successful closed reduction, clinicians may consider a moulded cast as a safe and cost-effective alternative to surgical fixation with K-wires. Future work: Further research should focus on optimal techniques for immobilisation and manipulation of this type of fracture, including optimal analgesia, and for rehabilitation of the patient after immobilisation. Trial registration: This trial is registered as ISRCTN11980540 and UKCRN Portfolio 208830. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 11. See the NIHR Journals Library website for further project information

Measuring Five Dimensions of Religiosity Across Adolescence

This paper theorizes and tests a latent variable model of adolescent religiosity in which five dimensions of religiosity are interrelated: religious beliefs, religious exclusivity, external religiosity, private practice, and religious salience. Research often theorizes overlapping and independent influences of single items or dimensions of religiosity on outcomes such as adolescent sexual behavior, but rarely operationalizes the dimensions in a measurement model accounting for their associations with each other and across time. We use longitudinal structural equation modeling (SEM) with latent variables to analyze data from two waves of the National Study of Youth and Religion. We test our hypothesized measurement model as compared to four alternate measurement models and find that our proposed model maintains superior fit. We then discuss the associations between the five dimensions of religiosity we measure and how these change over time. Our findings suggest how future research might better operationalize multiple dimensions of religiosity in studies of the influence of religion in adolescence

Machine Learning Approaches for Efficient Analysis of Neuroimaging Techniques

Machine Learning has a significant role in each person’s daily life and plays a vital role in making life easier by contributing to various models where the machines learn and do the tasks better. Much research and development around machine learning algorithms and their applications are happening for classifying and clustering multiple types of data in several domains. Health care research also impacts machine learning in analysing different data for patients. Different types of image and Neuroimaging data analysis are the areas where a significant amount of research is happening with healthcare and machine learning. Neuroimaging data obtained from the imaging techniques like MRI, CT, fMRI, PET, and other techniques help doctors identify various disorders. Commonly studied diseases with the help of neuroimaging data include the disorders like Alzheimer’s, MCI, Parkinson’s Disease, and Autism. Machine learning algorithms are developed for the straightforward interpretation of neuroimaging data and identifying neurological disorders. Interpreting neuroimaging takes a lot of assumptions and risks by doctors; commonly used and developed Machine Learning models are CNN, SVM, ANN, and Deep CNN. The use of proper machine learning models can help doctors to validate their assumptions in critical conditions. The paper focuses on a survey of various approaches by researchers to bring out neuroimaging analysis models and identify effective models. The research also covers the multiple diseases and the best models available for detecting the disorders. This research aims to identify the challenges various researchers face while creating the models and the limitations of their models, and how machine learning algorithms could effectively analyse neuroimages

Applications of artificial intelligence to neurological disorders: Current technologies and open problems

Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinson’s disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each other’s field, leading to fruitful collaborations and effective solutions