Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box.
Ontology highlight
ABSTRACT:
SUBMITTER: Iturria-Medina Y
PROVIDER: S-EPMC8140107 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:While both federal agencies and professional associations emphasize the importance of neuroscience outreach, this goal seldom reaches the undergraduate neuroscience classroom. However, incorporating outreach into undergraduate neuroscience classes is an efficient means to reach not only future scientists, but also the future practitioners (K-12 teachers, social service workers, etc.) with whom neuroscientists hope to communicate. It also provides a vehicle for faculty members to engage in outreach activities that are typically un- or under-rewarded in faculty reviews. In this article, a Neuroscience Community Outreach Project (NCOP) is described. The project has been used in three offerings of a Cognitive Neuroscience course at a small liberal arts college, shared and applied at a large state university, and presented at a regional Society for Neuroscience meeting as an example of outreach opportunities for faculty. The NCOP assignment is a student-driven, modular activity that can be easily incorporated into existing neuroscience course frameworks. The assignment builds on student interests and connections in the community, providing a way for faculty at institutions without formal outreach programs to incorporate neuroscience outreach into the classroom and connect students to online resources. Several sample student projects are described across three broad domains (K-12 outreach, presentations to social service organizations, and media / popular press presentations). The article ends with a set of suggestions addressing common faculty concerns about incorporating community outreach into the undergraduate neuroscience classroom.
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Project description:We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder.
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Project description:Lethal lung developmental disorders are a rare but important group of pediatric diffuse lung diseases presenting with neonatal respiratory failure. On the basis of histopathological appearance at lung biopsy or autopsy, they have been termed: alveolar capillary dysplasia with misalignment of the pulmonary veins, acinar dysplasia, congenital alveolar dysplasia, and other unspecified primary pulmonary hypoplasias. However, the histopathological continuum in these lethal developmental disorders has made accurate diagnosis challenging, which has implications for recurrence risk. Over the past decade, genetic studies in infants with alveolar capillary dysplasia with misalignment of the pulmonary veins have revealed the causative role of the dosage-sensitive FOXF1 gene and its noncoding regulatory variants in the distant lung-specific enhancer at chromosome 16q24.1. In contrast, the molecular bases of acinar dysplasia and congenital alveolar dysplasia have remained poorly understood. Most recently, disruption of the TBX4-FGF10-FGFR2 epithelial-mesenchymal signaling pathway has been reported in patients with these lethal pulmonary dysplasias. Application of next-generation sequencing techniques, including exome sequencing and whole-genome sequencing, has demonstrated their complex compound inheritance. These data indicate that noncoding regulatory elements play a critical role in lung development in humans. We propose that for more precise lethal lung developmental disorder diagnosis, a diagnostic pathway including whole-genome sequencing should be implemented.
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Project description:The neuroimaging literature on attention-deficit/hyperactivity disorder (ADHD) is growing rapidly. Here, we provide a critical overview of neuroimaging studies published recently, highlighting perspectives that may be of relevance for clinicians. After a comprehensive search of PubMed, Ovid, Web of Science, and EMBASE, we located 41 pertinent papers published between January 2011 and April 2012, comprising both structural and functional neuroimaging studies. This literature is increasingly contributing to the notion that the pathophysiology of ADHD reflects abnormal interplay among large-scale brain circuits. Moreover, recent studies have begun to reveal the mechanisms of action of pharmacological treatment. Finally, imaging studies with a developmental perspective are revealing the brain correlates of ADHD over the lifespan, complementing clinical observations on the phenotypic continuity and discontinuity of the disorder. However, despite the increasing potential to eventually inform clinical practice, current imaging studies do not have validated applications in day-to-day clinical practice. Although novel analytical techniques are likely to accelerate the pace of translational applications, at the present we advise caution regarding inappropriate commercial misuse of imaging techniques in ADHD.
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Project description:Over the past few decades, several radiotracers have been developed for neuroimaging applications, especially in PET. Because of their low steric hindrance, PET radionuclides can be used to label molecules that are small enough to cross the blood brain barrier, without modifying their biological properties. As the use of 11C is limited by its short physical half-life (20 min), there has been an increasing focus on developing tracers labeled with 18F for clinical use. The first such tracers allowed cerebral blood flow and glucose metabolism to be measured, and the development of molecular imaging has since enabled to focus more closely on specific targets such as receptors, neurotransmitter transporters, and other proteins. Hence, PET and SPECT biomarkers have become indispensable for innovative clinical research. Currently, the treatment options for a number of pathologies, notably neurodegenerative diseases, remain only supportive and symptomatic. Treatments that slow down or reverse disease progression are therefore the subject of numerous studies, in which molecular imaging is proving to be a powerful tool. PET and SPECT biomarkers already make it possible to diagnose several neurological diseases in vivo and at preclinical stages, yielding topographic, and quantitative data about the target. As a result, they can be used for assessing patients' eligibility for new treatments, or for treatment follow-up. The aim of the present review was to map major innovative radiotracers used in neuroscience, and explain their contribution to clinical research. We categorized them according to their target: dopaminergic, cholinergic or serotoninergic systems, β-amyloid plaques, tau protein, neuroinflammation, glutamate or GABA receptors, or α-synuclein. Most neurological disorders, and indeed mental disorders, involve the dysfunction of one or more of these targets. Combinations of molecular imaging biomarkers can afford us a better understanding of the mechanisms underlying disease development over time, and contribute to early detection/screening, diagnosis, therapy delivery/monitoring, and treatment follow-up in both research and clinical settings.
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Project description:BackgroundRetrospective research requires longitudinal data, and repositories derived from electronic health records (EHR) can be sources of such data. With Health Information Technology for Economic and Clinical Health (HITECH) Act meaningful use provisions, many institutions are expected to adopt EHRs, but may be left with large amounts of financial and historical clinical data, which can differ significantly from data obtained from newer systems, due to lack or inconsistent use of controlled medical terminologies (CMT) in older systems. We examined different approaches for semantic enrichment of financial data with CMT, and integration of clinical data from disparate historical and current sources for research.MethodsSnapshots of financial data from 1999, 2004 and 2009 were mapped automatically to the current inpatient pharmacy catalog, and enriched with RxNorm. Administrative metadata from financial and dispensing systems, RxNorm and two commercial pharmacy vocabularies were used to integrate data from current and historical inpatient pharmacy modules, and the outpatient EHR. Data integration approaches were compared using percentages of automated matches, and effects on cohort size of a retrospective study.ResultsDuring 1999-2009, 71.52%-90.08% of items in use from the financial catalog were enriched using RxNorm; 64.95%-70.37% of items in use from the historical inpatient system were integrated using RxNorm, 85.96%-91.67% using a commercial vocabulary, 87.19%-94.23% using financial metadata, and 77.20%-94.68% using dispensing metadata. During 1999-2009, 48.01%-30.72% of items in use from the outpatient catalog were integrated using RxNorm, and 79.27%-48.60% using a commercial vocabulary. In a cohort of 16304 inpatients obtained from clinical systems, 4172 (25.58%) were found exclusively through integration of historical clinical data, while 15978 (98%) could be identified using semantically enriched financial data.ConclusionsData integration using metadata from financial/dispensing systems and pharmacy vocabularies were comparable. Given the current state of EHR adoption, semantic enrichment of financial data and integration of historical clinical data would allow the repurposing of these data for research. With the push for HITECH meaningful use, institutions that are transitioning to newer EHRs will be able to use their older financial and clinical data for research using these methods.
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Project description:In advancing precision psychiatry, we focus on what imaging technology and computational approaches offer for the future of diagnostic subtyping and personalized tailoring of interventions for sleep impairment in mood and anxiety disorders. Current diagnostic criteria for mood and anxiety tend to lump different forms of sleep disturbance together. Parsing the biological features of sleep impairment and brain circuit dysfunction is one approach to identifying subtypes within these disorders that are mechanistically coherent and offer targets for intervention. We focus on two large-scale neural circuits implicated in sleep impairment and in mood and anxiety disorders: the default mode network and negative affective network. Through a synthesis of existing knowledge about these networks, we pose a testable framework for understanding how hyper- versus hypo-engagement of these networks may underlie distinct features of mood and sleep impairment. Within this framework we consider whether poor sleep quality may have an explanatory role in previously observed associations between network dysfunction and mood symptoms. We expand this framework to future directions including the potential for connecting circuit-defined subtypes to more distal features derived from digital phenotyping and wearable technologies, and how new discovery may be advanced through machine learning approaches.
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