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Background: Neurodegenerative diseases, such as Parkinson's disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures., Methods: Twenty-eight individuals with Parkinson's disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person's body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms., Results: The support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively., Conclusion: These results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic., (© 2024. The Author(s).)

Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research., (© 2024 International League Against Epilepsy.)

Purpose: The purpose of this study was to collect preliminary data to assess whether participation in adaptive video gaming using a pneumatic sip-and-puff video game controller may provide respiratory or health benefits for individuals with cervical-level spinal cord injuries., Methods: A survey was anonymously distributed to potential participants and consisted of four sections: (1) General Information, (2) Gaming Habits, (3) Respiratory Quality of Life, and (4) Impact of Adaptive Video Gaming on Respiratory Health., Results: The study included 124 individuals with cervical-level spinal cord injuries. Participants had primarily positive self-rated health and good respiratory quality of life. Nearly half of the participants (47.6%) Agreed or Strongly Agreed that their breathing control has improved after using their sip-and-puff gaming controller and 45.2% Agreed or Strongly Agreed that their respiratory health has improved. Individuals who Agreed or Strongly Agreed that adaptive video gaming has improved their breathing control also reported a significantly higher level of exertion while gaming compared to those who did not Agree or Strongly Agree ( p  = 0.00029)., Conclusions: It is possible that there are respiratory benefits of using sip-and-puff video game controllers for individuals with cervical spinal cord injuries. The benefits reported by users were found to be dependent on their level of exertion while playing video games. Further exploration in this area is needed due to the positive benefits reported by participants.

Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinics. Specifically, estimated walking velocity was similar to annotated 10-m walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pretrained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but after training a prosthetic-specific joint detector video-based gait analysis also works on these individuals. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code for the gait transformer and the trained weights are available at https://github.com/peabody124/GaitTransformer ., (© 2024. The Author(s).)

Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing more sensitive tools for research. There are numerous steps between obtaining videos to extracting accurate biomechanical results and limited research to guide many critical design decisions in these pipelines. In this work, we analyze several of these steps including the algorithm used to detect keypoints and the keypoint set, the approach to reconstructing trajectories for biomechanical inverse kinematics and optimizing the IK process. Several features we find important are: 1) using a recent algorithm trained on many datasets that produces a dense set of biomechanically-motivated keypoints, 2) using an implicit representation to reconstruct smooth, anatomically constrained marker trajectories for IK, 3) iteratively optimizing the biomechanical model to match the dense markers, 4) appropriate regularization of the IK process. Our pipeline makes it easy to obtain accurate biomechanical estimates of movement in a rehabilitation hospital.

Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis. This could enable much more frequent and quantitative characterization of gait impairments, allowing better monitoring of outcomes and responses to interventions. However, the impact of different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy has not been thoroughly evaluated. We tested these algorithmic choices on data acquired from a multicamera system from a heterogeneous sample of 53 individuals in a rehabilitation hospital. We found that using a top-down keypoint detector and reconstructing trajectories with an implicit function enabled accurate, smooth, and anatomically plausible trajectories, with a noise in the step width estimates compared to a GaitRite walkway of only 9mm.

This paper presents a fully wireless microelectrode array (MEA) system-on-chip (SoC) with 65,536 electrodes for non-penetrative cortical recording and stimulation, featuring a total sensing area of 6.8mm×7.4mm with a 26.5μm×29μm electrode pitch. Sensing, data telemetry, and powering are monolithically integrated on a single chip, which is made mechanically flexible to conform to the surface of the brain by substrate removal to a total thickness of 25μm allowing it to be contained entirely in the subdural space under the skull.

We diagnosed 66 peripheral nerve injuries in 34 patients who survived severe coronavirus disease 2019 (COVID-19). We combine this new data with published case series re-analyzed here (117 nerve injuries; 58 patients) to provide a comprehensive accounting of lesion sites. The most common are ulnar (25.1%), common fibular (15.8%), sciatic (13.1%), median (9.8%), brachial plexus (8.7%) and radial (8.2%) nerves at sites known to be vulnerable to mechanical loading. Protection of peripheral nerves should be prioritized in the care of COVID-19 patients. To this end, we report proof of concept data of the feasibility for a wearable, wireless pressure sensor to provide real time monitoring in the intensive care unit setting., (© 2022. The Author(s).)

Competing Interests: The authors declare no conflict of interest.

Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the smaller body of work ensuring the outputs are clinically meaningful and properly calibrated. Gait analysis, typically performed in a dedicated lab, produces precise measurements including kinematics and step timing. Using over 7000 monocular video from an instrumented gait analysis lab, we trained a neural network to map 3D joint trajectories and the height of individuals onto interpretable biomechanical outputs including gait cycle timing and sagittal plane joint kinematics and spatiotemporal trajectories. This task specific layer produces accurate estimates of the timing of foot contact and foot off events. After parsing the kinematic outputs into individual gait cycles, it also enables accurate cycle-by-cycle estimates of cadence, step time, double and single support time, walking speed and step length.

Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson's correlation with the reference system was moderate for swing times ( r = 0.4-0.66), but stronger for stance and double support time ( r = 0.93-0.95). Cadence mean error was -0.25 steps/min ± 3.9 steps/min ( r = 0.97), while walking speed mean error was -0.02 ± 0.11 m/s ( r = 0.92). These preliminary results suggest that single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke, even while using assistive devices in uncontrolled environments. Such development opens the door to applications for gait analysis both inside and outside of clinical settings, without the need of sophisticated equipment., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2022 by S. Karger AG, Basel.)

Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method's ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods' estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments., (© 2021. The Author(s).)

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding., (© 2021. The Author(s).)

We propose a new paradigm for dense functional imaging of brain activity to surmount the limitations of present methodologies. We term this approach "integrated neurophotonics"; it combines recent advances in microchip-based integrated photonic and electronic circuitry with those from optogenetics. This approach has the potential to enable lens-less functional imaging from within the brain itself to achieve dense, large-scale stimulation and recording of brain activity with cellular resolution at arbitrary depths. We perform a computational study of several prototype 3D architectures for implantable probe-array modules that are designed to provide fast and dense single-cell resolution (e.g., within a 1-mm 3 volume of mouse cortex comprising ∼100,000 neurons). We describe progress toward realizing integrated neurophotonic imaging modules, which can be produced en masse with current semiconductor foundry protocols for chip manufacturing. Implantation of multiple modules can cover extended brain regions., Competing Interests: Declaration of Interests Three patents owned by the California Institute of Technology have been pursued in connection with this work: (1) Roukes (2011). Brain-machine interface based on photonic neural probe arrays. U.S.P.T.O. Patent No. 10,638,933, Issued May 5, 2020, Priority Date: December 8, 2011. (2) Segev, E., Moreaux, L.C., Fowler, T.M. Faraon, A., Roukes, M.L. Implantable, highly collimated light-emitters for biological applications. U.S.P.T.O. Patent No. 10,471,273, Issue date: 12 November 2019; Priority date: 16 October 2015. (3) Roukes et al. (2016). One-photon integrated neurophotonic systems. U.S.P.T.O. Patent Application 20160150963, Filing date: November 5, 2014; Publication date: June 2, 2016., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Clones of excitatory neurons derived from a common progenitor have been proposed to serve as elementary information processing modules in the neocortex. To characterize the cell types and circuit diagram of clonally related excitatory neurons, we performed multi-cell patch clamp recordings and Patch-seq on neurons derived from Nestin -positive progenitors labeled by tamoxifen induction at embryonic day 10.5. The resulting clones are derived from two radial glia on average, span cortical layers 2-6, and are composed of a random sampling of transcriptomic cell types. We find an interaction between shared lineage and connection type: related neurons are more likely to be connected vertically across cortical layers, but not laterally within the same layer. These findings challenge the view that related neurons show uniformly increased connectivity and suggest that integration of vertical intra -clonal input with lateral inter -clonal input may represent a developmentally programmed connectivity motif supporting the emergence of functional circuits., Competing Interests: CC, FS, PF, DK, SM, FS, SP, ZT, PJ, LH, SL, RC, KT, RS, PB, XJ, AT No competing interests declared, (© 2020, Cadwell et al.)

Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We tested this hypothesis by simultaneously recording population activity from primate visual cortex during a visual categorization task in which trial-to-trial uncertainty about stimulus orientation was relevant for the decision. We decoded the likelihood function from the trial-to-trial population activity and found that it predicted decisions better than a point estimate of orientation. This remained true when we conditioned on the true orientation, suggesting that internal fluctuations in neural activity drive behaviorally meaningful variations in the likelihood function. Our results establish the role of population-encoded likelihood functions in mediating behavior and provide a neural underpinning for Bayesian models of perception.

Simultaneous tracking of muscle activity and joint rotation is of significant interest in rehabilitation, but gold-standard methods with optical motion tracking and wireless electromyography recording typically restricts this to the laboratory setting. There has been significant progress using wear-able inertial measurement units (IMUs) for motion tracking, but there are no systems that can easily be deployed to home and provide simultaneous electromyography. We addressed this gap by developing a flexible, wearable, Bluetooth-connected sensor that records both IMU and EMG activity. The sensor runs an efficient quaternion-based complementary filter that estimates the sensor orientation while correcting for estimate drift and constraining magnetometer estimates to only influence heading. The difference in two sensor orientations is used to estimate the joint angle, which can be further improved with joint axis estimation. We demonstrate successful tracking of joint angle and muscle activity in a home environment with just the sensors and a smartphone.

When the brain has determined the position of a moving object, because of anatomical and processing delays the object will have already moved to a new location. Given the statistical regularities present in natural motion, the brain may have acquired compensatory mechanisms to minimize the mismatch between the perceived and real positions of moving objects. A well-known visual illusion-the flash lag effect-points toward such a possibility. Although many psychophysical models have been suggested to explain this illusion, their predictions have not been tested at the neural level, particularly in a species of animal known to perceive the illusion. To this end, we recorded neural responses to flashed and moving bars from primary visual cortex (V1) of awake, fixating macaque monkeys. We found that the response latency to moving bars of varying speed, motion direction, and luminance was shorter than that to flashes, in a manner that is consistent with psychophysical results. At the level of V1, our results support the differential latency model positing that flashed and moving bars have different latencies. As we found a neural correlate of the illusion in passively fixating monkeys, our results also suggest that judging the instantaneous position of the moving bar at the time of flash-as required by the postdiction/motion-biasing model-may not be necessary for observing a neural correlate of the illusion. Our results also suggest that the brain may have evolved mechanisms to process moving stimuli faster and closer to real time compared with briefly appearing stationary stimuli. NEW & NOTEWORTHY We report several observations in awake macaque V1 that provide support for the differential latency model of the flash lag illusion. We find that the equal latency of flash and moving stimuli as assumed by motion integration/postdiction models does not hold in V1. We show that in macaque V1, motion processing latency depends on stimulus luminance, speed and motion direction in a manner consistent with several psychophysical properties of the flash lag illusion.

Two African grey parrots ( Psittacus erithacus) and one hyacinth macaw ( Anodorhynchus hyacinthinus) were examined because of varying clinical signs ranging from general lethargy to seizure-like episodes and regurgitation. Radiography and fluoroscopy in the 3 birds demonstrated variable degrees of gastric abnormalities, suggesting the presence of foreign material or stricture-like defects. Upper gastrointestinal rigid endoscopy by ingluviotomy revealed foreign bodies that were removed endoscopically. Minor postoperative complications were pulmonary congestion or mild aspiration and cardiac arrhythmia, both of which resolved, and no serious deleterious effects were associated with endoscopy in the short or long term. Endoscopy is recommended for examination and removal of foreign bodies from the upper gastrointestinal tract because it is less invasive and traumatic than traditional surgical approaches.

Background: The lipid scrambling activity of protein extracts and purified scramblases is typically measured using a fluorescence-based assay. While the assay has yielded insight into the scramblase activity in crude membrane preparations, functional validation of candidate scramblases, stoichiometry of scramblase complexes as well as ATP-dependence of flippases, data analysis in its context has remained a task involving many manual steps., Results: With the extension package "flippant" to R, a free software environment for statistical computing and graphics, we introduce an integrated solution for the analysis and publication-grade graphical presentation of dithionite scramblase assays and demonstrate its utility in revisiting an originally manual analysis from the publication record, closely reproducing the reported results., Conclusions: "flippant" allows for quick, reproducible data analysis of scramblase activity assays and provides a platform for review, dissemination and extension of the strategies it employs.

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Dynamic range limitations are challenging to proteomics, particularly in clinical samples. Affinity proteomics partially overcomes this, yet suffers from dependence on reagent quality. SOMAscan, an aptamer-based platform for over 1000 proteins, avoids that issue using nucleic acid binders. Targets include low expressed proteins not easily accessible by other approaches. Here we report on the potential of SOMAscan for the study of differently sourced mesenchymal stem cells (MSC) in comparison to LC-MS/MS and RNA sequencing. While targeting fewer analytes, SOMAscan displays high precision and dynamic range coverage, allowing quantification of proteins not measured by the other platforms. Expression between cell types (ESC and MSC) was compared across techniques and uncovered the expected large differences. Sourcing was investigated by comparing subtypes: bone marrow-derived, standard in clinical studies, and ESC-derived MSC, thought to hold similar potential but devoid of inter-donor variability and proliferating faster in vitro. We confirmed subtype-equivalency, as well as vesicle and extracellular matrix related processes in MSC. In contrast, the proliferative nature of ESC was captured less by SOMAscan, where nuclear proteins are underrepresented. The complementary of SOMAscan allowed the comprehensive exploration of CD markers and signaling molecules, not readily accessible otherwise and offering unprecedented potential in subtype characterization., Significance: Mesenchymal stem cells (MSC) represent promising stem cell-derived therapeutics as indicated by their application in >500 clinical trials currently registered with the NIH. Tissue-derived MSC require invasive harvesting and imply donor-to-donor differences, to which embryonic stem cell (ESC)-derived MSC may provide an alternative and thus warrant thorough characterization. In continuation of our previous study where we compared in depth embryonic stem cells (ESC) and MSC from two sources (bone marrow and ESC-derived), we included the aptamer-based SOMAscan assay, complementing LC-MS/MS and RNA-seq data. Furthermore, SOMAscan, a targeted proteomics platform developed for analyzing clinical samples, has been benchmarked against established analytical platforms (LC-MS/MS and RNA-seq) using stem cell comparisons as a model., (Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: SomaLogic's SOMAscan™ assay platform allows the analysis of the relative abundance of over 1300 proteins directly from biological matrices such as blood plasma and serum. The data resulting from the assay is provided in a proprietary text-based format not easily imported into R., Results: readat is an R package for working with the SomaLogic ADAT file format. It provides functionality for importing, transforming and annotating data from these files. The package is free, open source, and available on Bioconductor and Bitbucket., Conclusions: readat integrates into both Bioconductor and traditional R workflows, rendering it easy to make use of ADAT files.

Mesenchymal stem cells (MSC) are multipotent cells with great potential in therapy, reflected by more than 500 MSC-based clinical trials registered with the NIH. MSC are derived from multiple tissues but require invasive harvesting and imply donor-to-donor variability. Embryonic stem cell-derived MSC (ESC-MSC) may provide an alternative, but how similar they are to ex vivo MSC is unknown. Here we performed an in depth characterization of human ESC-MSC, comparing them to human bone marrow-derived MSC (BM-MSC) as well as human embryonic stem cells (hESC) by transcriptomics (RNA-seq) and quantitative proteomics (nanoLC-MS/MS using SILAC). Data integration highlighted and validated a central role of vesicle-mediated transport and exosomes in MSC biology and also demonstrated, through enrichment analysis, their versatility and broad application potential. Particular emphasis was placed on comparing profiles between ESC-MSC and BM-MSC and assessing their equivalency. Data presented here shows that differences between ESC-MSC and BM-MSC are similar in magnitude to those reported for MSC of different origin and the former may thus represent an alternative source for therapeutic applications. Finally, we report an unprecedented coverage of MSC CD markers, as well as membrane associated proteins which may benefit immunofluorescence-based applications and contribute to a refined molecular description of MSC.

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150-350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive 'excitatory' interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative 'inhibitory' interactions were less selective. Because of its superior performance, this 'sparse+latent' estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix.

Studies in several important areas of neuroscience, including analysis of single neurons as well as neural networks, continue to be limited by currently available experimental tools. By combining molecular probes of cellular function, such as voltage-sensitive or calcium-sensitive dyes, with advanced microscopy techniques such as multiphoton microscopy, experimental neurophysiologists have been able to partially reduce this limitation. These approaches usually provide the needed spatial resolution along with convenient optical sectioning capabilities for isolating regions of interest. However, they often fall short in providing the necessary temporal resolution, primarily due to their restrained laser scanning mechanisms. In this regard, we review a method of laser scanning for multiphoton microscopy that overcomes the temporal limitations of pervious approaches and allows for what is known as 3D Random Access Multiphoton (3D RAMP) microscopy, an imaging technique that supports full three dimensional recording of many sites of interest on physiologically relevant time scales.

Neural codes are believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher order correlations in natural scenes induced a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits.

Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1-2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations but can also be estimated and accounted for based on neuronal population activity., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: To determine a reference background urinary thallium level; to compare urinary thallium data from workers to this background level; to investigate factors affecting these levels and whether creatinine correction is appropriate., Methods: Urine samples from non-occupationally exposed people (n = 273, from 113 individuals) and workers (n = 896, from 447 individuals) were analysed for thallium by ICP-MS. A reference background level was calculated, defined as the 95th percentile value of a non-occupationally exposed population. Worker data were divided into two subsets: thallium workers (those who work directly with thallium or its compounds) and general workers; and compared to the background level. Bayesian linear mixed effects modelling was used to investigate factors affecting urinary thallium concentration and the efficacy of creatinine correction for the determination of urinary thallium., Results: The reference background urinary thallium level is 0.27 μmol/mol creatinine (creatinine-corrected) or 0.40 μg/l (uncorrected). Median values were 0.11 μmol/mol creatinine or 0.17 μg/l for non-occupationally exposed people, 0.12 μmol/mol creatinine or 0.20 μg/l for general workers and 0.19 μmol/mol creatinine or 0.41 μg/l for thallium workers. Variation was lower in creatinine-corrected models. Nine per cent of samples from general workers and 39 % of samples from thallium workers exceeded the creatinine-corrected background level. By 2010, 90 % of all workers had urinary thallium levels below the 95th percentile reference background level., Conclusions: Urinary thallium concentrations were higher in thallium workers than non-occupationally exposed people and general workers. Creatinine correction is appropriate.

Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.

Great progress has been made toward understanding the properties of single neurons, yet the principles underlying interactions between neurons remain poorly understood. Given that connectivity in the neocortex is locally dense through both horizontal and vertical connections, it is of particular importance to characterize the activity structure of local populations of neurons arranged in three dimensions. However, techniques for simultaneously measuring microcircuit activity are lacking. We developed an in vivo 3D high-speed, random-access two-photon microscope that is capable of simultaneous 3D motion tracking. This allows imaging from hundreds of neurons at several hundred Hz, while monitoring tissue movement. Given that motion will induce common artifacts across the population, accurate motion tracking is absolutely necessary for studying population activity with random-access based imaging methods. We demonstrate the potential of this imaging technique by measuring the correlation structure of large populations of nearby neurons in the mouse visual cortex, and find that the microcircuit correlation structure is stimulus-dependent. Three-dimensional random access multiphoton imaging with concurrent motion tracking provides a novel, powerful method to characterize the microcircuit activity in vivo.

Exposure to a blast wave has been proposed to cause mild traumatic brain injury (mTBI), with symptoms including altered cognition, memory, and behavior. This idea, however, remains controversial, and the mechanisms of blast-induced brain injury remain unknown. To begin to resolve these questions, we constructed a simple compressed air shock tube, placed rats inside the tube, and exposed them to a highly reproducible and controlled blast wave. Consistent with the generation of a mild injury, 2 weeks after exposure to the blast, we found that motor performance was unaffected, and a panel of common injury markers showed little or no significant changes in expression in the cortex, corpus callosum, or hippocampus. Similarly, we were unable to detect elevated spectrin breakdown products in brains collected from blast-exposed rats. Using an object recognition task, however, we found that rats exposed to a blast wave spent significantly less time exploring a novel object when compared with control rats. Intriguingly, we also observed a significant shortening of the axon initial segment (AIS) in both the cortex and hippocampus of blast-exposed rats, suggesting altered neuronal excitability after exposure to a blast. A computational model showed that shortening the AIS increased both threshold and the interspike interval of repetitively firing neurons. These results support the conclusion that exposure to a single blast wave can lead to mTBI with accompanying cognitive impairment and subcellular changes in the molecular organization of neurons.

Orientation tuning has been a classic model for understanding single-neuron computation in the neocortex. However, little is known about how orientation can be read out from the activity of neural populations, in particular in alert animals. Our study is a first step toward that goal. We recorded from up to 20 well isolated single neurons in the primary visual cortex of alert macaques simultaneously and applied a simple, neurally plausible decoder to read out the population code. We focus on two questions: First, what are the time course and the timescale at which orientation can be read out from the population response? Second, how complex does the decoding mechanism in a downstream neuron have to be to reliably discriminate between visual stimuli with different orientations? We show that the neural ensembles in primary visual cortex of awake macaques represent orientation in a way that facilitates a fast and simple readout mechanism: With an average latency of 30-80 ms, the population code can be read out instantaneously with a short integration time of only tens of milliseconds, and neither stimulus contrast nor correlations need to be taken into account to compute the optimal synaptic weight pattern. Our study shows that-similar to the case of single-neuron computation-the representation of orientation in the spike patterns of neural populations can serve as an exemplary case for understanding the computations performed by neural ensembles underlying visual processing during behavior.

Background: Biological monitoring (BM) aids exposure assessment but where this is based on incomplete collections of single urine voiding measurement of creatinine is often used to adjust analyte concentrations for the effects of fluid balance., Aims: To provide reference data on creatinine concentrations in urine samples from a population of UK workers., Methods: Urine samples sent to the Health and Safety Laboratory were analysed for creatinine by an automated kinetic Jaffe technique using alkaline picric acid and the results stored in a database. Statistical analysis of the data used linear mixed effects models on the natural log-transformed data., Results: Between 1996 and 2007, the laboratory analysed 49 506 urine samples from 20 433 UK adult workers. In the 42 817 samples where gender was known, 93% were from men and 7% were from women. The overall mean and median creatinine concentrations were both 12 mmol/l corresponding to 1.36 g/l. The mean (13 mmol/l) and median (12 mmol/l) creatinine concentrations for men were higher than those (9 and 10 mmol/l, respectively) for women., Conclusions: Gender differences in creatinine concentrations and the range of 0.3-3.0 g/l (2.653 and 26.53 mmol/l) traditionally used for confirming acceptability of urine samples mean that 2.5% of samples from male and 9% from female workers were flagged as 'low creatinine' and required a repeat sample. In addition, care should be taken interpreting any apparent gender differences in BM results to ensure that they are due to exposure and not an artefact of creatinine adjustment.