Benthic Microalgal Production at Stellwagen Bank, Massachusetts Bay, USA

Benthic microalgal chlorophyll a and production were measured at 3 sites at Stellwagen Bank, a cold temperate continental shelf habitat in Massachusetts Bay, USA, during August 1991. Benthic microalgal chlorophyll a averaged 39.8 Mg M-2, vs average integrated phytoplankton chlorophyll a of 25.9 mg m-2. Gross benthic microalgal production, measured by oxygen exchange in clear and opaque benthic chambers, averaged 20.9 mg C m-2 h-1. This production was supported by average daily light fluxes to the bottom that never exceeded 1 % of surface incident radiation and were as low as 4.7 μE M-2 s-1. These results indicate that benthic microalgal production can be spatially extensive and quantitatively important in continental shelf ecosystems

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Survival, density, and abundance of common bottlenose dolphins in Barataria Bay (USA) following the Deepwater Horizon oil spill

To assess potential impacts of the Deepwater Horizon oil spill in April 2010, we conducted boat-based photo-identification surveys for common bottlenose dolphins Tursiops truncatus in Barataria Bay, Louisiana, USA (~230 km2, located 167 km WNW of the spill center). Crews logged 838 h of survey effort along pre-defined routes on 10 occasions between late June 2010 and early May 2014. We applied a previously unpublished spatial version of the robust design capture-recapture model to estimate survival and density. This model used photo locations to estimate density in the absence of study area boundaries and to separate mortality from permanent emigration. To estimate abundance, we applied density estimates to saltwater (salinity > ~8 ppt) areas of the bay where telemetry data suggested that dolphins reside. Annual dolphin survival varied between 0.80 and 0.85 (95% CIs varied from 0.77 to 0.90) over 3 yr following the Deepwater Horizon spill. In 2 non-oiled bays (in Florida and North Carolina), historic survival averages approximately 0.95. From June to November 2010, abundance increased from 1300 (95% CI ± ~130) to 3100 (95% CI ± ~400), then declined and remained between ~1600 and ~2400 individuals until spring 2013. In fall 2013 and spring 2014, abundance increased again to approximately 3100 individuals. Dolphin abundance prior to the spill was unknown, but we hypothesize that some dolphins moved out of the sampled area, probably northward into marshes, prior to initiation of our surveys in late June 2010, and later immigrated back into the sampled area.Publisher PDFPeer reviewe

Cognitive phenotypes in late-onset epilepsy: results from the atherosclerosis risk in communities study

Introduction Cognitive phenotyping is a widely used approach to characterize the heterogeneity of deficits in patients with a range of neurological disorders but has only recently been applied to patients with epilepsy. In this study, we identify cognitive phenotypes in older adults with late-onset epilepsy (LOE) and examine their demographic, clinical, and vascular profiles. Further, we examine whether specific phenotypes pose an increased risk for progressive cognitive decline. Methods Participants were part of the Atherosclerosis Risk in Communities Study (ARIC), a prospective longitudinal community-based cohort study of 15,792 individuals initially enrolled in 1987–1989. LOE was identified from linked Centers for Medicare and Medicaid Services claims data. Ninety-one participants with LOE completed comprehensive testing either prior to or after seizure onset as part of a larger cohort in the ARIC Neurocognitive Study in either 2011–2013 or 2016–2017 (follow-up mean = 4.9 years). Cognitive phenotypes in individuals with LOE were derived by calculating test-level impairments for each participant (i.e., ≤1 SD below cognitively normal participants on measures of language, memory, and executive function/processing speed); and then assigning participants to phenotypes if they were impaired on at least two tests within a domain. The total number of impaired domains was used to determine the cognitive phenotypes (i.e., Minimal/No Impairment, Single Domain, or Multidomain). Results At our baseline (Visit 5), 36.3% met criteria for Minimal/No Impairment, 35% for Single Domain Impairment (with executive functioning/ processing speed impaired in 53.6%), and 28.7% for Multidomain Impairment. The Minimal/No Impairment group had higher education and occupational complexity. There were no differences in clinical or vascular risk factors across phenotypes. Of those participants with longitudinal data (Visit 6; n = 24), 62.5% declined (i.e., progressed to a more impaired phenotype) and 37.5% remained stable. Those who remained stable were more highly educated compared to those that declined. Discussion Our results demonstrate the presence of identifiable cognitive phenotypes in older adults with LOE. These results also highlight the high prevalence of cognitive impairments across domains, with deficits in executive function/processing speed the most common isolated impairment. We also demonstrate that higher education was associated with a Minimal/No Impairment phenotype and lower risk for cognitive decline over time

Detection of Epileptogenic Cortical Malformations with Surface-Based MRI Morphometry

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery

Subthreshold psychosis in 22q11.2 deletion syndrome: multisite naturalistic study

Nearly one-third of individuals with 22q11.2 deletion syndrome (22q11.2DS) develop a psychotic disorder during life, most of them by early adulthood. Importantly, a full-blown psychotic episode is usually preceded by subthreshold symptoms. In the current study, 760 participants (aged 6-55 years) with a confirmed hemizygous 22q11.2 microdeletion have been recruited through 10 medical sites worldwide, as part of an international research consortium. Of them, 692 were nonpsychotic and with complete measurement data. Subthreshold psychotic symptoms were assessed using the Structured Interview for Prodromal Syndromes (SIPS). Nearly one-third of participants met criteria for positive subthreshold psychotic symptoms (32.8%), less than 1% qualified for acute positive subthreshold symptoms, and almost a quarter met criteria for negative/disorganized subthreshold symptoms (21.7%). Adolescents and young adults (13-25 years) showed the highest rates of subthreshold psychotic symptoms. Additionally, higher rates of anxiety disorders and attention deficit/hyperactivity disorder (ADHD) were found among the study participants with subthreshold psychotic symptoms compared to those without. Full-scale IQ, verbal IQ, and global functioning (GAF) scores were negatively associated with participants' subthreshold psychotic symptoms. This study represents the most comprehensive analysis reported to date on subthreshold psychosis in 22q11.2DS. Novel findings include age-related changes in subthreshold psychotic symptoms and evidence that cognitive deficits are associated with subthreshold psychosis in this population. Future studies should longitudinally follow these symptoms to detect whether and how early identification and treatment of these manifestations can improve long-term outcomes in those that eventually develop a psychotic disorder

Enhanced Maternal Origin of the 22q11.2 Deletion in Velocardiofacial and DiGeorge Syndromes

Velocardiofacial and DiGeorge syndromes, also known as 22q11.2 deletion syndrome (22q11DS), are congenital-anomaly disorders caused by a de novo hemizygous 22q11.2 deletion mediated by meiotic nonallelic homologous recombination events between low-copy repeats, also known as segmental duplications. Although previous studies exist, each was of small size, and it remains to be determined whether there are parent-of-origin biases for the de novo 22q11.2 deletion. To address this question, we genotyped a total of 389 DNA samples from 22q11DS-affected families. A total of 219 (56%) individuals with 22q11DS had maternal origin and 170 (44%) had paternal origin of the de novo deletion, which represents a statistically significant bias for maternal origin (p = 0.0151). Combined with many smaller, previous studies, 465 (57%) individuals had maternal origin and 345 (43%) had paternal origin, amounting to a ratio of 1.35 or a 35% increase in maternal compared to paternal origin (p = 0.000028). Among 1,892 probands with the de novo 22q11.2 deletion, the average maternal age at time of conception was 29.5, and this is similar to data for the general population in individual countries. Of interest, the female recombination rate in the 22q11.2 region was about 1.6–1.7 times greater than that for males, suggesting that for this region in the genome, enhanced meiotic recombination rates, as well as other as-of-yet undefined 22q11.2-specific features, could be responsible for the observed excess in maternal origin

What we learn about bipolar disorder from large-scale neuroimaging:Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness