Biology subjects

Roberts, J.

Publications and source records attributed to Roberts, J..

46 records · Page 3Linked to original sources

bioRxiv2019

Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

The fusion of simultaneously recorded EEG and fMRI data is of great value to neuroscience research due to the complementary properties of the individual modalities. Traditionally, techniques such as PCA and ICA, which rely on strong strong non-physiological assumptions such as orthogonality and statistical independence, have been used for this purpose. Recently, tensor decomposition techniques such as parallel factor analysis have gained more popularity in neuroimaging applications as they are able to inherently contain the multidimensionality of neuroimaging data and achieve uniqueness in decomposition without imposing strong assumptions. Previously, the coupled matrix-tensor decomposition (CMTD) has been applied for the fusion of the EEG and fMRI. Only recently the coupled tensor-tensor decomposition (CTTD) has been proposed. Here for the first time, we propose the use of CTTD of a 4th order EEG tensor (space, time, frequency, and participant) and 3rd order fMRI tensor (space, time, participant), coupled partially in time and participant domains, for the extraction of the task related features in both modalities. We used both the sensor-level and source-level EEG for the coupling. The phase shifted paradigm signals were incorporated as the temporal initializers of the CTTD to extract the task related features. The validation of the approach is demonstrated on simultaneous EEG-fMRI recordings from six participants performing an N-Back memory task. The EEG and fMRI tensors were coupled in 9 components out of which 7 components had a high correlation (more than 0.85) with the task. The result of the fusion recapitulates the well-known attention network as being positively, and the default mode network working negatively time-locked to the memory task.

bioinformatics↗

bioRxiv2019

Evidence for rapid phenotypic and behavioral change in a recently established cavefish population

Substantial morphological and behavioral shifts often accompany rapid environmental change, yet, little is known about the early stages of cave colonization. Relative to surface streams, caves are extreme environments with perpetual darkness and low nutrient availability. The Mexican tetra (Astyanax mexicanus), has repeatedly colonized caves throughout Mexico, suggesting an ability to adapt to these conditions. Here, we survey for phenotypic and behavioral differences between a surface population and a cave population of A. mexicanus that has recently colonized Honey Creek Cave, Comal County, Texas, likely within the last century. We found that fish from Honey Creek Cave and fish from Honey Creek surface populations differ significantly in morphological traits including length, coloration, body condition, eye size, and dorsal fin placement. Cavefish also exhibit an increased number of superficial neuromasts relative to surface fish. Behaviorally, cavefish consume fewer worms when trials are performed in both lighted and darkened conditions. Cavefish are more aggressive than surface fish and exhibit fewer behaviors associated with stress. Further in contrast to surface fish, cavefish prefer the edges to the center of an arena and are qualitatively more likely to investigate a novel object placed in the tank. While cavefish and surface fish were wild-caught and developmental environment likely play a role in shaping these differences, our work demonstrates morphological and behavioral shifts for Texas cavefish and offers an exciting opportunity for future work to explore the genetic and environmental contributions to early cave colonization.

animal behavior and cognition↗

bioRxiv2019

ADHD symptoms map onto noise-driven structure-function decoupling between hub and peripheral brain regions

Adults with childhood-onset attention-deficit hyperactivity disorder (ADHD) show altered whole-brain connectivity. However, the relationship between structural and functional brain abnormalities, the implications for the development of life-long debilitating symptoms, and the underlying mechanisms remain uncharted. We recruited a unique sample of 80 medication-naive adults with a clinical diagnosis of childhood-onset ADHD without psychiatric comorbidities, and 123 age-, sex-, and intelligence-matched healthy controls. Structural and functional connectivity matrices were derived from diffusion spectrum imaging and multi-echo resting-state functional MRI data. Hub, feeder, and local connections were defined using diffusion data. Individual-level measures of structural connectivity and structure-function coupling were used to contrast groups and link behavior to brain abnormalities. Computational modeling was used to test possible neural mechanisms underpinning observed group differences in the structure-function coupling. Structural connectivity did not significantly differ between groups but, relative to controls, ADHD showed a reduction in structure-function coupling in feeder connections linking hubs with peripheral regions. This abnormality involved connections linking fronto-parietal control systems with sensory networks. Crucially, lower structure-function coupling was associated with higher ADHD symptoms. Results from our computational model further suggest that the observed structure-function decoupling in ADHD is driven by heterogeneity in neural noise variability across brain regions. By highlighting a neural cause of a clinically meaningful breakdown in the structure-function relationship, our work provides novel information on the nature of chronic ADHD. The current results encourage future work assessing the genetic and neurobiological underpinnings of neural noise in ADHD, particularly in brain regions encompassed by fronto-parietal systems.

neuroscience↗

bioRxiv2019

NMR Methods for Quantitative Isotopomer Rates in Real-Time Metabolism of Cells

Tracer-based metabolism is becoming increasingly important to study metabolic mechanisms in cells. NMR offers several approaches to measure label incorporation in metabolites, including 13C and 1H-detected spectra. The latter are generally more sensitive but quantification depends on the proton carbon 1JCH coupling constant which varies significantly between different metabolites. It is therefore not possible to have one experiment optimised for all metabolites and quantification of 1H-edited spectra such as HSQCs requires precise knowledge of coupling constants. Increasing interest in tracer-based and metabolic flux analysis requires robust analyses with reasonably small acquisition times. Here we compare 13C-filtered and 13C-edited methods for quantification with a special focus towards application in real-time NMR of cancer cells under near-physiological conditions. We find an approach using a double-filter most suitable and sufficiently robust to reliably obtain 13C-incorporations from difference spectra. This is demonstrated for JJN3 multiple myeloma cells processing glucose over 24h.

biochemistry↗

bioRxiv2018

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

ObjectiveTo validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.\n\nSettingThis cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.\n\nParticipantsFor retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included.\n\nResultsAt severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P<001), SOFA (0.74; P<.001), and SIRS (0.62; P<.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nConclusionsThe MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.\n\nKEY POINTSO_ST_ABSQuestionC_ST_ABSIs a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?\n\nFindingsIn a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset. In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nMeaningThe accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes.\n\nSTRENGTHS AND LIMITATIONS OF THIS STUDYO_LIA retrospective study of machine learning severe sepsis prediction from a dataset with 510,497 patient encounters demonstrates high accuracy up to 48 hours prior to onset.\nC_LIO_LIA multicenter clinical study of real-world data using this machine learning algorithm for severe sepsis alerts achieved reductions of in-hospital mortality, length of stay, and 30-day readmissions.\nC_LIO_LIThe required presence of an ICD-9 code to classify a patient as severely septic in our retrospective analysis potentially limits our ability to accurately classify all patients.\nC_LIO_LIOnly adults in US hospitals were included in this study.\nC_LIO_LIFor the real-world section of the study, we cannot eliminate the possibility that implementation of a sepsis algorithm raised general awareness of sepsis within a hospital, which may lead to higher recognition of septic patients, independent of algorithm performance.\nC_LI

bioinformatics↗

bioRxiv2018

An Automated Behavioral Apparatus to Assess Distal Forelimb Function in Non-Human Primates

BackgroundPrimate distal forelimb behaviors are commonly assessed using reach-to-grasp tasks. While these tasks are widely recognized as sensitive assays for forelimb function, they often require experimenter input, lack precise temporal cues for physiological monitoring, and can be expensive.\n\nNew MethodUsing components developed by open-source electronics platforms, we have designed and tested a low-cost system to measure distal forelimb function in non-human primates. Our system is inexpensive; it is made primarily of acrylic and 3D printed plastic parts. Our control software, developed in MATLAB, was also used to control two cameras in order to capture and process video during behavior. The system was equipped with sensors, motors and microcontrollers to control the timing of the task and facilitate synchronization between behavior and neurophysiology with high temporal precision.\n\nResultsWe demonstrate that this system can be used to monitor motor recovery after stroke and investigate neurophysiological correlates of motor control.\n\nComparison with Existing MethodsCompared to a previous version of this task, our setup reduces experimenter input while providing unbiased delivery of behavioral cues and behavioral measurements with the temporal precision necessary for electrophysiological studies.\n\nConclusionsIn summary, our system will allow unbiased monitoring of forelimb function in both healthy and injured animals that is compatible with electrophysiological studies.

neuroscience↗

bioRxiv2018

Transcription Factor Regulation of RNA polymerase’s Torsional Capacity

During transcription, RNA polymerase (RNAP) supercoils DNA as it forsward-translocates. Accumulation of this torsional stress in DNA can become a roadblock for an elongating RNAP and thus should be subject to regulation during transcription. Here, we investigate whether, and how, a transcription factor may regulate the torque generation capacity of RNAP and torque-induced RNAP stalling. Using a real-time assay based on an angular optical trap, we found that under a resisting torque, RNAP was highly prone to extensive backtracking. However, the presence of GreB, a transcription factor that facilitates the cleavage of the 3 end of the extruded RNA transcript, greatly suppressed backtracking and remarkably increased the torque that RNAP was able to generate by 65%, from 11.2 to 18.5 pN{middle dot}nm. Analysis of the real-time trajectories of RNAP position at a stall revealed the kinetic parameters of backtracking and GreB rescue. These results demonstrate that backtracking is the primary mechanism that limits transcription against DNA supercoiling and the transcription factor GreB effectively enhances the torsional capacity of RNAP. These findings broaden the potential impact of transcription factors on RNAP functionality.

biophysics↗

bioRxiv2018

First in-human PET study of 3 novel tau radiopharmaceuticals: RO6958948

Background[11C]RO-963, [11C]RO-643 and [18F]RO-948 (previously referred as [11C]RO6924963, [11C]RO6931643, and [18F]RO6958948, respectively) have been reported as promising PET tracers for tau imaging based on in vitro and preclinical PET data (1,2). Here we describe the first human evaluation of these novel radiotracers.\n\nMethodsAmyloid PET positive Alzheimers disease (AD) patients and young healthy subjects (YC) each received two different tau tracers. Dynamic 90 min scans were obtained after bolus injection of [11C]RO-963, [11C]RO-643 or [18F]RO-948. Arterial blood sampling was performed in 11 healthy controls (HC) and 11 AD. Regions were defined on MRI, and PET data were quantified by plasma reference graphical analysis (for VT) and target cerebellum ratio (SUVR60-90). SUVR images were also analyzed voxelwise. Five older healthy subjects (OC) each received two scans with [18F]RO-948 for evaluation of test-retest variability. Four AD subjects received a repeat [18F]RO-948 scan over about 1 year. Six additional HC (3M: 3F; 41-67y) each received one whole body dosimetry scan with [18F]RO-948.\n\nResultsIn YC, peak SUV values were observed in the temporal lobe with values of approximately 3.0 for [11C]RO-963, 1.5 for [11C]RO-643 and 3.5 for [18F]RO-948. Over all brain regions and subjects, the trend was that [18F]RO-948 had the highest peak SUV value, followed by [11C]RO-963, and then [11C]RO-643. Regional analysis of SUVR and VT for [11C]RO-643 and [18F]RO-948 clearly discriminated AD and HC groups. Compartmental modeling confirmed that [11C]RO-643 had lower brain entry than both [18F]RO-963 and [18F]RO-948, and [18F]RO-948 showed a better contrast between (predicted) areas of high vs low tau accumulation. Thus, our subsequent analysis focused on [18F]RO-948. Both voxelwise and region-based analysis of [18F]RO-948 binding in HC vs AD revealed multiple areas where AD and HC significantly differed. Of 22 high-binding regions, 13 showed significant group difference (following ANOVA, F=45, p<10-5). Voxelwise analysis also revealed a set of symmetrical clusters where AD>HC (threshold of p<0.001, cluster size k>50).\n\nConclusions[18F]RO-948 demonstrates superior characteristics to [11C]RO-643 and [18F]RO-963 for characterization of tau pathology in AD. Regional binding data and kinetic properties of RO-948 compare favorably with existing other tau PET tracers.

neuroscience↗

bioRxiv2018

Market Smells: Olfactory Detection and Identification in the Built Environment

ObjectivesWe know little about human olfactory ability in natural settings because current knowledge derives from lab-based studies using non-representative samples of convenience. The primary objective was to use a validated lab tool, the five-item odor identification test, to assess variation in olfactory ability in different environments. MethodsUsing the five-item test, we conducted two repeated measures experiments that assessed participant ability to correctly identify an odor source in different odor environments. We also examined consistency in odor labelling due to documented potential bias from idiosyncrasies in odor terms. ResultsWe found no variation in olfactory ability due to environment, but this may be due to methodological biases. First, subjective bias results from idiosyncratic differences in participant labelling and researcher coding of answer correctness. Second, better ability to learn odors may provide an advantage to women. Third, reducing positive female learning bias by analyzing consistency in response (regardless of correct odor source identification) fails to assess functional olfactory ability. Functional olfactory ability (naming correct odor source) is significantly better in females, especially in food-rich odor environments. ConclusionsEnvironment was not a significant factor in olfactory ability in this study but that result may be confounded by methodological biases. We not recommend odor identification as a field tool. Functional olfactory ability exhibits a sex-based pattern but consistency in recognizing the same odor does not. Food-rich odors may enhance olfactory ability in females. We discuss evolutionary and ecological implications of superior female functional olfactory ability relative to food foraging activity.

ecology↗

bioRxiv2018

PET Brain imaging of α7-nAChR with ASEM Reproducibility, occupancy, receptor density, and changes in schizophrenia

The 7 nicotinic acetylcholine receptor (nAChR) increasingly has been implicated in normal brain physiology, as well as in neuropsychiatric disorders. The a7-nAChR primarily is located in cerebral cortex and sub-cortical regions, compared to the 4{beta}2 nAChR subtype that has a more subcortical distribution. The highly cortical distribution suggests a role of a7-nAChR in cognition. We expanded the first-in-human PET imaging of 7-nAChR with [18F]ASEM from five to 21 healthy non-smoking volunteers and added preliminary evidence of binding in six male patients with schizophrenia. Study aims included 1) confirmation of test-retest reproducibility of [18F]ASEM binding in normal volunteers, 2) demonstration of specificity of [18F]ASEM binding by competition with DMXB-A, an 7-nAChR partial agonist previously tested in clinical trials of patients with schizophrenia, 3) estimation of [18F]ASEM binding potentials and 7-nAChR density in vivo in humans, and 4) 7-nAChR binding in patients with schizophrenia compared to healthy volunteers.\n\nTest-retest PET confirmed reproducibility (>90%) (variability [≤] 7%) of [18F]ASEM volume of distribution (VO_SCPLOWTC_SCPLOW) estimates in healthy volunteers. Repeated sessions of PET in five healthy subjects included baseline and effect of inhibition after oral administration of 150 mg DMXB-A. From reduction of binding potentials, we estimated the dose-dependent occupancy of 7-nAChR by DMXB-A at 17-49% for plasma concentrations at 60-200 nM DMXB-A. In agreement with evidence post-mortem, 7-nAChR density (Bmax) averaged 0.67-0.82 nM and inhibitor affinity constant (KO_SCPLOWIC_SCPLOW) averaged 170-385 nM. Median VO_SCPLOWTC_SCPLOW in a feasibility study of six patients with schizophrenia was lower than in healthy volunteers in cingulate cortex, frontal cortex, and hippocampus. Mann-Whitney test identified cingulate cortex and hippocampus as regions with significantly lower median VO_SCPLOWTC_SCPLOW in patients than in healthy volunteers when a single outlier patient was excluded from analysis (P = 0.02, corrected for multiple comparisons).

neuroscience↗