Bilateral focused ultrasound medial thalamotomies for trigeminal neuropathic pain: a randomized controlled study.

OBJECTIVE: Medial thalamotomy has been shown to benefit patients with neuropathic pain, but widespread adoption of this procedure has been limited by reporting of clinical outcomes in studies without a control group. This study aimed to minimize confounders associated with medial thalamotomy for treating chronic pain by using modern MRI-guided stereotactic lesioning and a rigorous clinical design. METHODS: This prospective, double-blinded, randomized controlled trial in 10 patients with trigeminal neuropathic pain used sham procedures as controls. Participants underwent assessments by a pain psychologist and pain management clinician, including use of the following measures: the Numeric Pain Rating Scale (NPRS); patient-reported outcome measures; and patient's impression of improvement at baseline, 1 day, 1 week, 1 month, and 3 months postprocedure. Patients in the treated group underwent bilateral focused ultrasound (FUS) medial thalamotomy targeting the central lateral nucleus. Patients in the control group underwent sham procedures with energy output disabled. The primary efficacy outcome measure was between-group differences in pain intensity (using the NPRS) at baseline and at 3 months postprocedure. Adverse events were measured for safety and included MRI analysis. Exploratory measures of connectivity and metabolism were analyzed using diffusion tensor imaging, functional MRI, and PET, respectively. RESULTS: There were no serious complications from the FUS procedures. MRI confirmed bilateral medial thalamic ablations. There was no significant improvement in pain intensity from baseline to 3 months, either for patients undergoing FUS medial thalamotomy or for sham controls; and the between-group change in NPRS score as the primary efficacy outcome measure was not significantly different. Patient-reported outcome assessments demonstrated improvement (i.e., a decrease) only in pain interference with enjoyment of life at 3 months. There was a perception of benefit at 1 week, but only for patients treated with FUS and not for the sham cohort. Advanced neuroimaging showed that these medial thalamic lesions altered structural connectivity with the postcentral gyrus and demonstrated a trend toward hypometabolism in the insula and amygdala. CONCLUSIONS: This randomized controlled trial of bilateral FUS medial thalamotomy did not reduce the intensity of trigeminal neuropathic pain, although it should be noted that the ability to estimate the magnitude of treatment effects is limited by the small cohort.

Pulsatile Basal Insulin Secretion Is Driven by Glycolytic Oscillations.

In fasted and fed states, blood insulin levels are oscillatory. While this phenomenon is well studied at high glucose levels, comparatively little is known about its origin under basal conditions. We propose a possible mechanism for basal insulin oscillations based on oscillations in glycolysis, demonstrated using an established mathematical model. At high glucose, this is superseded by a calcium-dependent mechanism.

Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis.

Recent theories of cortical function construe the brain as performing hierarchical Bayesian inference. According to these theories, the precision of prediction errors plays a key role in learning and decision-making, is controlled by dopamine and contributes to the pathogenesis of psychosis. To test these hypotheses, we studied learning with variable outcome-precision in healthy individuals after dopaminergic modulation with a placebo, a dopamine receptor agonist bromocriptine or a dopamine receptor antagonist sulpiride (dopamine study n = 59) and in patients with early psychosis (psychosis study n = 74: 20 participants with first-episode psychosis, 30 healthy controls and 24 participants with at-risk mental state attenuated psychotic symptoms). Behavioural computational modelling indicated that precision weighting of prediction errors benefits learning in health and is impaired in psychosis. FMRI revealed coding of unsigned prediction errors, which signal surprise, relative to their precision in superior frontal cortex (replicated across studies, combined n = 133), which was perturbed by dopaminergic modulation, impaired in psychosis and associated with task performance and schizotypy (schizotypy correlation in 86 healthy volunteers). In contrast to our previous work, we did not observe significant precision-weighting of signed prediction errors, which signal valence, in the midbrain and ventral striatum in the healthy controls (or patients) in the psychosis study. We conclude that healthy people, but not patients with first-episode psychosis, take into account the precision of the environment when updating beliefs. Precision weighting of cortical prediction error signals is a key mechanism through which dopamine modulates inference and contributes to the pathogenesis of psychosis.

Modeling areal measures of campsite impacts on the Appalachian National Scenic Trail to enhance ecological sustainability.

Campsite impacts in protected natural areas are most effectively minimized by a containment strategy that focuses use on a limited number of sustainable campsites that spatially concentrate camping activities. This research employs spatial autoregressive (SAR) modeling to evaluate the relative influence of use-related, environmental, and managerial factors on two salient measures of campsite impact. Relational analyses examined numerous field-collected and GIS-derived indicators, including several new indicators calculated using high-resolution Light Detection and Ranging (LiDAR) topographic data to evaluate the influence of terrain characteristics on the dependent variables. Chosen variables in the best SAR models explained 35% and 30% of the variation in campsite size and area of vegetation loss on campsites. Results identified three key indicators that managers can manipulate to enhance the sustainability of campsites: campsite type, and terrain characteristics relating to landform slope and topographic roughness. Results support indirect management methods that rely on the location, design, construction, and maintenance of campsites, instead of direct regulations that restrict visitation or visitor freedoms. As visitation pressures continue to increase, this knowledge can be applied to select and promote the use of more ecologically sustainable campsites.

Influence of an InGaN superlattice pre-layer on the performance of semi-polar (11-22) green LEDs grown on silicon.

It is well-known that it is crucial to insert either a single InGaN underlayer or an InGaN superlattice (SLS) structure (both with low InN content) as a pre-layer prior to the growth of InGaN/GaN multiple quantum wells (MQWs) served as an active region for a light-emitting diode (LED). So far, this growth scheme has achieved a great success in the growth of III-nitride LEDs on c-plane substrates, but has not yet been applied in the growth of any other orientated III-nitride LEDs. In this paper, we have applied this growth scheme in the growth of semi-polar (11-22) green LEDs, and have investigated the impact of the SLS pre-layer on the optical performance of semi-polar (11-22) green LEDs grown on patterned (113) silicon substrates. Our results demonstrate that the semi-polar LEDs with the SLS pre-layer exhibit an improvement in both internal quantum efficiency and light output, which is similar to their c-plane counterparts. However, the performance improvement is not so significant as in the c-plane case. This is because the SLS pre-layer also introduces extra misfit dislocations for the semi-polar, but not the c-plane case, which act as non-radiative recombination centres.

Research Note: Molecular and pathologic characterization of avian adenovirus isolated from the oviducts of laying hens in eastern Japan.

Cases of poor egg production were investigated in 2 layer farms from Ibaraki Prefecture in eastern Japan. To identify any microbial agents that may have caused the problem, necropsy, bacterial isolation, histopathology, and virus detection were performed. Members of the avian adenoviruses was detected by PCR in oviduct samples from both farms; chicken anemia virus coinfection was also confirmed in one of the farms. Avian adenovirus was isolated from the oviducts of the affected chickens on each farm. Inoculation into chick embryos showed tropism for the chorio-allantoic membrane. Stunting and hemorrhaging was observed in all infected embryos, as well as death in a few. Inoculation of 1-day-old specific pathogen-free chicks, and 400-day-old commercial hens, did not result in any significant findings. The isolated viruses were analyzed by sequencing of the hexon gene and were confirmed as fowl adenovirus type-c serotype-4 (FAdV-4). The 2 virus strains were found to be 99.29% similar to each other. One of the strains, Japan/Ibaraki/Y-H6/2016, was 99.15% similar to the KR5 strain. The other, Japan/Ibaraki/M-HB2/2016, was 99.57% similar to the KR5 strain. Fiber-2 gene analysis confirmed the identity as FAdV-4 that is closely related to nonpathogenic strains. Although nonpathogenic to chicks and laying hens, this infection can possibly cause economic damage. Perhaps the bigger concern is the effect on infected breeder operations. Because the virus is fatal to 9.09% of infected embryos, this could translate to a considerable loss in chick production owing to embryonic death. This is the first report of detection and isolation of FAdV-4 from the chicken oviduct; however, further studies are needed to elucidate its impact on both layer and breeder flocks. Indeed, FAdV-4 has negative effects on the avian reproductive tract as well.

The Expanded Role of Roof-Rats (Rattus rattus) in Salmonella spp. Contamination of a Commercial Layer Farm in East Japan.

Rodents serve as amplifiers of Salmonella infections in poultry flocks and can serve as a source of Salmonella contamination in the environment even after thorough cleaning and disinfection. This study aims to determine the dynamics of Salmonella occurrence in rodents and its relation to Salmonella contamination in the layer farm environment, including air dusts and eggs. From 2008 to 2017, roof rats (Rattus rattus), environmental swabs, air dusts, and eggs were collected from an intensive commercial layer farm in East Japan and were tested for Salmonella spp. using standard procedures. In roof rat samples, the Salmonella isolation rate was reached at 10% (95% confidence interval [CI] 8.1-21.9) in which Salmonella Corvallis, Salmonella Infantis, Salmonella Potsdam, and Salmonella Mbandaka were the frequent isolates from the cecal portion of the intestines. On the other hand, the prevalence rate of Salmonella in environmental swabs was at 5.1% (95% CI 2.2-7.4) while air dusts were at 0.9% (95% CI 0.2-1.8). It was observed that the prevalence of predominant Salmonella serotypes shifted over time; in roof rats, it was noted that Salmonella Potsdam gradually replaced Salmonella Infantis. In environmental swabs and eggs, Salmonella Corvallis and Salmonella Potsdam increased significantly while Salmonella Infantis became less frequent. In air dusts, Salmonella Corvallis was observed to decrease and Salmonella Potsdam became more common. Based on our findings, the role of roof rats in the epidemiology of Salmonella in layer farms was expanded from being a reservoir and an amplifier host into a shifting vessel of the most predominant serotypes.

Complex Economic Behavior Patterns Are Constructed from Finite, Genetically Controlled Modules of Behavior.

Complex ethological behaviors could be constructed from finite modules that are reproducible functional units of behavior. Here, we test this idea for foraging and develop methods to dissect rich behavior patterns in mice. We uncover discrete modules of foraging behavior reproducible across different strains and ages, as well as nonmodular behavioral sequences. Modules differ in terms of form, expression frequency, and expression timing and are expressed in a probabilistically determined order. Modules shape economic patterns of feeding, exposure, activity, and perseveration responses. The modular architecture of foraging changes developmentally, and different developmental, genetic, and parental effects are found to shape the expression of specific modules. Dissecting modules from complex patterns is powerful for phenotype analysis. We discover that both parental alleles of the imprinted Prader-Willi syndrome gene Magel2 are functional in mice but regulate different modules. Our study found that complex economic patterns are built from finite, genetically controlled modules.

Optical and polarization properties of nonpolar InGaN-based light-emitting diodes grown on micro-rod templates.

We have demonstrated non-polar a-plane InGaN multiple-quantum-well (MQW) light-emitting diodes (LEDs) on sapphire, achieved by overgrowing on a micro-rod template with substantially improved crystal quality. Photoluminescence measurements show one main emission peak at 418 nm along with another weak peak at 448 nm. Wavelength mapping measurements carried out by using a high spatial-resolution confocal PL system indicate that the two emissions origin from different areas associated with the underlying micro-rod patterns. Electroluminescence measurements exhibit a negligible blue-shift of 1.6 nm in the peak wavelength of the main emission when the driving current increases from 10 to 100 mA, indicating that the quantum confined Stark effect is effectively suppressed in in the nonpolar LED. A polarization ratio of 0.49 is obtained for the low-energy emission (~448 nm), while the main emission (~418 nm) shows a polarization ratio of 0.34. Furthermore, the polarization ratios are independent of injection current, while the energy separation between m-polarized and c-polarized lights increases with the injection current for both emissions.

Image-based analysis and long-term clinical outcomes of deep brain stimulation for Tourette syndrome: a multisite study.

BACKGROUND: Deep brain stimulation (DBS) can be an effective therapy for tics and comorbidities in select cases of severe, treatment-refractory Tourette syndrome (TS). Clinical responses remain variable across patients, which may be attributed to differences in the location of the neuroanatomical regions being stimulated. We evaluated active contact locations and regions of stimulation across a large cohort of patients with TS in an effort to guide future targeting. METHODS: We collected retrospective clinical data and imaging from 13 international sites on 123 patients. We assessed the effects of DBS over time in 110 patients who were implanted in the centromedial (CM) thalamus (n=51), globus pallidus internus (GPi) (n=47), nucleus accumbens/anterior limb of the internal capsule (n=4) or a combination of targets (n=8). Contact locations (n=70 patients) and volumes of tissue activated (n=63 patients) were coregistered to create probabilistic stimulation atlases. RESULTS: Tics and obsessive-compulsive behaviour (OCB) significantly improved over time (p<0.01), and there were no significant differences across brain targets (p>0.05). The median time was 13 months to reach a 40% improvement in tics, and there were no significant differences across targets (p=0.84), presence of OCB (p=0.09) or age at implantation (p=0.08). Active contacts were generally clustered near the target nuclei, with some variability that may reflect differences in targeting protocols, lead models and contact configurations. There were regions within and surrounding GPi and CM thalamus that improved tics for some patients but were ineffective for others. Regions within, superior or medial to GPi were associated with a greater improvement in OCB than regions inferior to GPi. CONCLUSION: The results collectively indicate that DBS may improve tics and OCB, the effects may develop over several months, and stimulation locations relative to structural anatomy alone may not predict response. This study was the first to visualise and evaluate the regions of stimulation across a large cohort of patients with TS to generate new hypotheses about potential targets for improving tics and comorbidities.

Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference.

A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance magnetic resonance imaging (scMRI) is a technique that maps brain regions with covarying gray matter densities across subjects. It provides a way to probe the anatomical structure underlying intrinsic connectivity networks (ICNs) through analysis of gray matter signal covariance. In this article, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender-, and IQ-matched controls. Specifically, we investigate topological differences in gray matter structure captured by structural correlation graphs derived from three ICNs strongly implicated in autism, namely the salience network, default mode network, and executive control network. By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism.

Kappa opioid receptors mediate yohimbine-induced increases in impulsivity in the 5-choice serial reaction time task.

Dynorphin (DYN), and its receptor, the kappa opioid receptor (KOR) are involved in drug seeking and relapse but the mechanisms are poorly understood. One hypothesis is that DYN/KOR activation promotes drug seeking through increased impulsivity, because many stimuli that induce DYN release increase impulsivity. Here, we systematically compare the effects of drugs that activate DYN/KOR on performance on the 5-choice serial reaction time task (5-CSRTT), a test of sustained attention and impulsivity. In Experiment 1, we determined the effects of U50,488 (0, 2.5, 5 mg/kg), yohimbine (0, 1.25, 2.5 mg/kg), and nicotine (0, 0.15, 0.3 mg/kg) on 5-CSRTT performance. In Experiment 2, we determined the effects of alcohol (0, 0.5, 1.0, 1.5 g/kg) on 5-CSRTT performance before and after voluntary, intermittent alcohol exposure. In Experiment 3, we determined the potential role of KOR in the pro-impulsive effects of yohimbine (1.25 mg/kg) and nicotine (0.3 mg/kg) by the prior administration of the KOR antagonist nor-BNI (10 mg/kg). Premature responding, the primary measure of impulsivity, was reduced by U50,488 and alcohol, but these drugs had a general suppressive effect. Yohimbine and nicotine increased premature responding. Yohimbine-, but not nicotine-induced increases in premature responding were blocked by nor-BNI, suggesting that impulsivity induced by yohimbine is KOR dependent. This may suggests a potential role for KOR-mediated increases in impulsivity in yohimbine-induced reinstatement.

Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale.

The identification of autistic individuals using resting state functional connectivity networks can provide an objective diagnostic method for autism spectrum disorder (ASD). The present state-of-the-art machine learning model using deep learning has a classification accuracy of 70.2% on the ABIDE (Autism Brain Imaging Data Exchange) data set. In this paper, we explore the utility of topological features in the classification of ASD versus typically developing control subjects. These topological features have been shown to provide a complementary source of discriminative information in applications such as 2D object classification and social network analysis. We evaluate the performance of three different representations of topological features - persistence diagrams, persistence images, and persistence landscapes - for autism classification using neural networks, support vector machines and random forests. We also propose a hybrid approach of augmenting topological features with functional correlations, which typically outperforms the models that use functional correlations alone. With this approach, even with a simple 3-layer neural network, we are able to achieve a classification accuracy of 69.2% on the ABIDE data set. However, our experiments also show that the improvement due to topological features is not always statistically significant. Therefore, we offer a cautionary tale to the practitioners regarding the limited discriminative power of topological features derived from fMRI data for the classification of autism.

Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance.

We propose a method to analyze the relationship between the shape of functional regions of the cortex and cognitive measures, such as reading ability and vocabulary knowledge. Functional regions on the cortical surface can vary not only in size and shape but also in topology and position relative to neighboring regions. Standard diffeomorphism-based shape analysis tools do not work well here because diffeomorphisms are unable to capture these topological differences, which include region splitting and merging across subjects. State-of-the-art cortical surface shape analyses compute derived regional properties (scalars), such as regional volume, cortical thickness, curvature, and gyrification index. However, these methods cannot compare the full extent of topological or shape differences in cortical regions. We propose icosahedral spatial pyramid matching (ISPM) of region borders computed on the surface of a sphere to capture this variation in regional topology, position, and shape. We then analyze how this variation corresponds to measures of cognitive performance. We compare our method to other approaches and find that it is indeed informative to consider aspects of shape beyond the standard approaches. Analysis is performed using a subset of 27 test/retest subjects from the Human Connectome Project in order to understand both the effectiveness and reproducibility of this method.

Personalized risk prediction of postoperative cognitive impairment - rationale for the EU-funded BioCog project.

Postoperative cognitive impairment is among the most common medical complications associated with surgical interventions - particularly in elderly patients. In our aging society, it is an urgent medical need to determine preoperative individual risk prediction to allow more accurate cost-benefit decisions prior to elective surgeries. So far, risk prediction is mainly based on clinical parameters. However, these parameters only give a rough estimate of the individual risk. At present, there are no molecular or neuroimaging biomarkers available to improve risk prediction and little is known about the etiology and pathophysiology of this clinical condition. In this short review, we summarize the current state of knowledge and briefly present the recently started BioCog project (Biomarker Development for Postoperative Cognitive Impairment in the Elderly), which is funded by the European Union. It is the goal of this research and development (R&D) project, which involves academic and industry partners throughout Europe, to deliver a multivariate algorithm based on clinical assessments as well as molecular and neuroimaging biomarkers to overcome the currently unsatisfying situation.

Riemannian Regression and Classification Models of Brain Networks Applied to Autism.

Functional connectivity from resting-state functional MRI (rsfMRI) is typically represented as a symmetric positive definite (SPD) matrix. Analysis methods that exploit the Riemannian geometry of SPD matrices appropriately adhere to the positive definite constraint, unlike Euclidean methods. Recently proposed approaches for rsfMRI analysis have achieved high accuracy on public datasets, but are computationally intensive and difficult to interpret. In this paper, we show that we can get comparable results using connectivity matrices under the log-Euclidean and affine-invariant Riemannian metrics with relatively simple and interpretable models. On ABIDE Preprocessed dataset, our methods classify autism versus control subjects with 71.1% accuracy. We also show that Riemannian methods beat baseline in regressing connectome features to subject autism severity scores.

Nonparametric Aggregation of Geodesic Trends for Longitudinal Data Analysis.

We propose a technique for analyzing longitudinal imaging data that models individual changes with diffeomorphic geodesic regression and aggregates these geodesics into a nonparametric group average trend. Our model is specifically tailored to the unbalanced and sparse characteristics of longitudinal imaging studies. That is, each individual has few data points measured over a short period of time, while the study population as a whole spans a wide age range. We use geodesic regression to estimate individual trends, which is an appropriate model for capturing shape changes over a short time window, as is typically found within an individual. Geodesics are also adept at handling the low sample sizes found within individuals, and can model the change between as few as two timepoints. However, geodesics are limited for modeling longer-term trends, where constant velocity may not be appropriate. Therefore, we develop a novel nonparametric regression to aggregate individual trends into an average group trend. We demonstrate the power of our method to capture non-geodesic group trends on hippocampal volume (real-valued data) and diffeomorphic registration of full 3D MRI from the longitudinal OASIS data.

Pdxdc1 modulates prepulse inhibition of acoustic startle in the mouse.

Current antipsychotic medications used to treat schizophrenia all target the dopamine D2 receptor. Although these drugs have serious side effects and limited efficacy, no novel molecular targets for schizophrenia treatment have been successfully translated into new medications. To identify novel potential treatment targets for schizophrenia, we searched for previously unknown molecular modulators of acoustic prepulse inhibition (PPI), a schizophrenia endophenotype, in the mouse. We examined six inbred mouse strains that have a range of PPI, and used microarrays to determine which mRNA levels correlated with PPI across these mouse strains. We examined several brain regions involved in PPI and schizophrenia: hippocampus, striatum, and brainstem, found a number of transcripts that showed good correlation with PPI level, and confirmed this with real-time quantitative PCR. We then selected one candidate gene for further study, Pdxdc1 (pyridoxal-dependent decarboxylase domain containing 1), because it is a putative enzyme that could metabolize catecholamine neurotransmitters, and thus might be a feasible target for new medications. We determined that Pdxdc1 mRNA and protein are both strongly expressed in the hippocampus and levels of Pdxdc1 are inversely correlated with PPI across the six mouse strains. Using shRNA packaged in a lentiviral vector, we suppressed Pdxdc1 protein levels in the hippocampus and increased PPI by 70%. Our results suggest that Pdxdc1 may regulate PPI and could be a good target for further investigation as a potential treatment for schizophrenia.

Multivariate characterization of white matter heterogeneity in autism spectrum disorder.

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders.