Influence of Analysis Technique on Measurement of Diffusion Tensor Imaging Parameters

We compared results from various methods of analysis of diffusion tensor imaging (DTI) data from a single data set consisting of 10 healthy adolescents

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Resistivity and Induced Polarization Monitoring of Biogas combined with Microbial Ecology on a Brown field Site

The accumulation of biogenic greenhouse gases (methane, carbon dioxide) in organic sediments is an important factor in the redevelopment and risk management of many brownfield sites. Good practice with brownfield site characterization requires the identification of free-gas phases and pathways that allow its migration and release at the ground surface. Gas pockets trapped in the subsurface have contrasting properties with the surrounding porous media that favor their detection using geophysical methods. We have developed a case study in which pockets of gas were intercepted with multilevel monitoring wells, and their lateral continuity was monitored over time using resistivity. We have developed a novel interpretation procedure based on Archie’s law to evaluate changes in water and gas content with respect to a mean background medium. We have used induced polarization data to account for errors in applying Archie’s law due to the contribution of surface conductivity effects. Mosaics defined by changes in water saturation allowed the recognition of gas migration and groundwater infiltration routes and the association of gas and groundwater fluxes. The inference on flux patterns was analyzed by taking into account pressure measurements in trapped gas reservoirs and by metagenomic analysis of the microbiological content, which was retrieved from suspended sediments in groundwater sampled in multilevel monitoring wells. A conceptual model combining physical and microbiological subsurface processes suggested that biogas trapped at depth may have the ability to quickly travel to the surface. </jats:p

Influence of Analysis Technique on Measurement of Diffusion Tensor Imaging Parameters

PURPOSE: We compared results from various methods of analysis of diffusion tensor imaging (DTI) data from a single data set consisting of 10 healthy adolescents. METHODS: All subjects were imaged on a single 3T MRI system (single-shot echo-planar imaging (EPI) pulse sequence, b value 1000). We measured fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity and radial diffusivity values using 64 pixel rectangular regions of interest (ROIs) in the right-side, midline and left-side of the central portion of the splenium of the corpus callosum for fixed (i.e., at same sites in all subjects) and targeted (i.e., at sites of highest FA values) locations, We compared results with those obtained using 64 pixel oval ROIs and 100 pixel rectangular ROIs in same locations. Finally, we compared results from ROI-based methods and from tractography. All comparisons used the Wilcoxon signed rank test and the intraclass correlation of individual values. RESULTS: Compared to tractography, the average of mean ROI-based values was significantly higher for fixed FA (14%) and targeted FA (39%) values and significantly lower for ADC (16%) and radial diffusivity (38%) values. For solely ROI-based comparisons, significant differences were found in the following comparisons: 64 pixel ROI vs. 100 pixel ROI, oval ROI vs. rectangular ROI, targeted FA left of midline vs. mean targeted FA value, and targeted ROI right of midline vs. mean targeted FA value. CONCLUSION: Markedly different values were obtained when using either ROI-based or tractography-based techniques, or ROI analysis techniques that differ only relatively slightly

Incidence and preventability of adverse drug events among older persons in the ambulatory setting

CONTEXT: Adverse drug events, especially those that may be preventable, are among the most serious concerns about medication use in older persons cared for in the ambulatory clinical setting. OBJECTIVE: To assess the incidence and preventability of adverse drug events among older persons in the ambulatory clinical setting. DESIGN, SETTING, AND PATIENTS: Cohort study of all Medicare enrollees (30 397 person-years of observation) cared for by a multispecialty group practice during a 12-month study period (July 1, 1999, through June 30, 2000), in which possible drug-related incidents occurring in the ambulatory clinical setting were detected using multiple methods, including reports from health care providers; review of hospital discharge summaries; review of emergency department notes; computer-generated signals; automated free-text review of electronic clinic notes; and review of administrative incident reports concerning medication errors. MAIN OUTCOME MEASURES: Number of adverse drug events, severity of the events (classified as significant, serious, life-threatening, or fatal), and whether the events were preventable. RESULTS: There were 1523 identified adverse drug events, of which 27.6% (421) were considered preventable. The overall rate of adverse drug events was 50.1 per 1000 person-years, with a rate of 13.8 preventable adverse drug events per 1000 person-years. Of the adverse drug events, 578 (38.0%) were categorized as serious, life-threatening, or fatal; 244 (42.2%) of these more severe events were deemed preventable compared with 177 (18.7%) of the 945 significant adverse drug events. Errors associated with preventable adverse drug events occurred most often at the stages of prescribing (n = 246, 58.4%) and monitoring (n = 256, 60.8%), and errors involving patient adherence (n = 89, 21.1%) also were common. Cardiovascular medications (24.5%), followed by diuretics (22.1%), nonopioid analgesics (15.4%), hypoglycemics (10.9%), and anticoagulants (10.2%) were the most common medication categories associated with preventable adverse drug events. Electrolyte/renal (26.6%), gastrointestinal tract (21.1%), hemorrhagic (15.9%), metabolic/endocrine (13.8%), and neuropsychiatric (8.6%) events were the most common types of preventable adverse drug events. CONCLUSIONS: Adverse drug events are common and often preventable among older persons in the ambulatory clinical setting. More serious adverse drug events are more likely to be preventable. Prevention strategies should target the prescribing and monitoring stages of pharmaceutical care. Interventions focused on improving patient adherence with prescribed regimens and monitoring of prescribed medications also may be beneficial

Dynamic publication model for neurophysiology databases.

We have implemented a pair of database projects, one serving cortical electrophysiology and the other invertebrate neurones and recordings. The design for each combines aspects of two proven schemes for information interchange. The journal article metaphor determined the type, scope, organization and quantity of data to comprise each submission. Sequence databases encouraged intuitive tools for data viewing, capture, and direct submission by authors. Neurophysiology required transcending these models with new datatypes. Time-series, histogram and bivariate datatypes, including illustration-like wrappers, were selected by their utility to the community of investigators. As interpretation of neurophysiological recordings depends on context supplied by metadata attributes, searches are via visual interfaces to sets of controlled-vocabulary metadata trees. Neurones, for example, can be specified by metadata describing functional and anatomical characteristics. Permanence is advanced by data model and data formats largely independent of contemporary technology or implementation, including Java and the XML standard. All user tools, including dynamic data viewers that serve as a virtual oscilloscope, are Java-based, free, multiplatform, and distributed by our application servers to any contemporary networked computer. Copyright is retained by submitters; viewer displays are dynamic and do not violate copyright of related journal figures. Panels of neurophysiologists view and test schemas and tools, enhancing community support

Risk factors for adverse drug events among older adults in the ambulatory setting.

OBJECTIVES: To gather information on patient-level factors associated with risk of adverse drug events (ADEs) that may allow focus of prevention efforts on patients at high risk. DESIGN: Nested case-control study. SETTING: Large multispecialty group practice in New England. PARTICIPANTS: All Medicare enrollees cared for by a multispecialty group practice during 1 year (N=30,397 person-years from July 1, 1999, through June 30, 2000). For each patient with an ADE, a control was randomly selected. MEASUREMENTS: Data were abstracted from medical records on age, sex, comorbidities, and medication use at the time of the event. RESULTS: ADEs were identified in 1,299 older adults. Independent risk factors included being female and aged 80 and older. There were dose-response associations with the Charlson Comorbidity Index and number of scheduled medications. Patients taking anticoagulants, antidepressants, antibiotics, cardiovascular drugs, diuretics, hormones, and corticosteroids were at increased risk. In the analysis of preventable ADEs, the dose-response relationship with comorbidity and number of medications remained. Patients taking nonopioid analgesics (predominantly nonsteroidal antiinflammatory drugs and acetaminophen), anticoagulants, diuretics, and anti-seizure medications were at increased risk. CONCLUSION: Prevention efforts to reduce ADEs should be targeted toward older adults with multiple medical conditions or taking multiple medications, nonopioid analgesics, anticoagulants, diuretics, and antiseizure medications