Tislelizumab versus chemotherapy as second-line treatment for European and North American patients with advanced or metastatic esophageal squamous cell carcinoma: a subgroup analysis of the randomized phase III RATIONALE-302 study

BACKGROUND

The phase III RATIONALE-302 study evaluated tislelizumab, an anti-programmed cell death protein 1 antibody, as second-line (2L) treatment for advanced/metastatic esophageal squamous cell carcinoma (ESCC). This prespecified exploratory analysis investigated outcomes in patients from Europe and North America (Europe/North America subgroup).

PATIENTS AND METHODS

Patients with tumor progression during/after first-line systemic treatment were randomized 1 : 1 to open-label tislelizumab or investigator's choice of chemotherapy (paclitaxel, docetaxel, or irinotecan).

RESULTS

The Europe/North America subgroup comprised 108 patients (tislelizumab: n = 55; chemotherapy: n = 53). Overall survival (OS) was prolonged with tislelizumab versus chemotherapy (median: 11.2 versus 6.3 months), with a hazard ratio (HR) of 0.55 [95% confidence interval (CI) 0.35-0.87]; HR was similar irrespective of programmed death-ligand 1 score [≥10%: 0.47 (95% CI 0.18-1.21); <10%: 0.55 (95% CI 0.30-1.01)]. Median progression-free survival was 2.3 versus 2.7 months with tislelizumab versus chemotherapy [HR: 0.97 (95% CI 0.64-1.47)]. Overall response rate was greater with tislelizumab (20.0%) versus chemotherapy (11.3%), with more durable response (median duration of response: 5.1 versus 2.1 months). Tislelizumab had a favorable safety profile versus chemotherapy, with fewer patients experiencing ≥grade 3 treatment-related adverse events (13.0% versus 51.0%). Those on tislelizumab experienced less deterioration in health-related quality of life, physical functioning, and/or disease- and treatment-related symptoms (i.e. fatigue, pain, and eating problems) as compared to those on chemotherapy, per the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (QLQ-C30) and QLQ-OES18 scores.

CONCLUSIONS

As a 2L therapy for advanced/metastatic ESCC, tislelizumab improved OS and had a favorable safety profile as compared to chemotherapy in European/North American ESCC patients in the randomized phase III RATIONALE-302 study.

Machine Learning Differentiation of Autism Spectrum Sub-Classifications

PURPOSE

Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum.

METHODS

We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data.

RESULTS

The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum.

CONCLUSION

Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.

Tislelizumab versus chemotherapy as second-line treatment of advanced or metastatic esophageal squamous cell carcinoma (RATIONALE 302): impact on health-related quality of life

Background

RATIONALE 302 (NCT03430843) an open-label, phase III study of second-line treatment of advanced/metastatic esophageal squamous cell carcinoma (ESCC), reported that tislelizumab, relative to investigator-chosen chemotherapy (ICC), was associated with improvements in overall survival and a favorable safety profile. This study assessed the health-related quality of life (HRQoL) and ESCC-related symptoms of patients in RATIONALE 302.

Methods

Adults with advanced/metastatic ESCC whose disease progressed following prior systemic therapy were randomized 1 : 1 to receive either tislelizumab or ICC (paclitaxel, docetaxel, or irinotecan). HRQoL was measured using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 items (EORTC QLQ-C30), the EORTC Quality of Life Questionnaire Oesophageal Cancer Module 18 items (QLQ-OES18), and the EuroQoL Five-Dimensions Five-Levels (EQ-5D-5L) visual analogue scale. Mixed effect modeling for repeated measurements examined changes from baseline to weeks 12 and 18. The Kaplan–Meier method was used to examine time to deterioration.

Results

Overall, 512 patients were randomized to tislelizumab (n = 256) or ICC (n = 256). The tislelizumab arm maintained QLQ-C30 global health status/quality whereas the ICC arm worsened at week 12 {difference in least square (LS) mean change: 5.8 [95% confidence interval (CI): 2.0-9.5], P = 0.0028} and week 18 [difference in LS mean change: 8.1 (95% CI: 3.4-12.8), P = 0.0008]. Physical functioning (week 18) and fatigue (weeks 12 and 18) worsened less in the tislelizumab compared with the ICC arm. The tislelizumab arm improved in reflux symptoms, whereas the ICC worsened at week 12 [difference in LS mean change: −4.1 (95% CI: −7.6 to −0.6), P = 0.0229]. The visual analogue scale remained consistent in the tislelizumab arm whereas it worsened in the ICC arm. The hazard of time to deterioration was lower in tislelizumab patients compared with ICC for physical functioning and reflux.

Conclusions

HRQoL, including fatigue symptoms and physical functioning, was maintained in patients with advanced or metastatic ESCC receiving tislelizumab compared with ICC-treated patients. These results provide additional support for the benefits of tislelizumab in this patient population.

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Global health status and HRQoL remained consistent in the tislelizumab arm whereas the ICC arm experienced worsening.

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Fatigue and physical functioning worsened in both arms; however, the worsening was greater in the ICC arm.

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The tislelizumab arm was at lower risk of reaching the threshold for worsening in physical functioning and reflux.

The effects of tislelizumab plus chemotherapy as first-line treatment on health-related quality of life of patients with advanced squamous non-small cell lung cancer: Results from a phase 3 randomized clinical trial

BACKGROUND

This study assessed the effects of adding tislelizumab to first-line standard-of- care chemotherapy on the health-related quality of life (HRQoL) of patients with advanced squamous non-small cell lung cancer (sq-NSCLC).

PATIENTS AND METHODS

Patients in this open-label, multicenter, phase 3 RATIONALE 307 trial were randomized to one of the three arms: tislelizumab plus carboplatin and paclitaxel (Arm A), tislelizumab plus carboplatin and nab-paclitaxel (Arm B), or paclitaxel plus carboplatin (Arm C). HRQoL was measured using the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 (QLQ-C30) and the EORTC Quality of Life Questionnaire Lung Cancer 13-item module (QLQ-LC13). Mean score change from baseline at Weeks 6 and 12 in the QLQ-C30's global health status/quality of life (GHS/QoL), fatigue, and physical functioning scores and QLQ-LC13 lung cancer specific subscales were examined. Time to deterioration was estimated for the GHS/QoL score.

RESULTS

A total of 355 sq-NSCLC patients received at least one dose of study drug and completed at least one HRQoL assessment. The GHS/QoL scores improved in Arms A and B relative to Arm C at Weeks 6 and 12. Arms A and B also experienced a reduction in most lung cancer-specific symptoms relative to Arm C. Time to deterioration of GHS/QoL was not reached by any of the three arms.

CONCLUSIONS

The addition of tislelizumab to platinum-based chemotherapy is associated with improvements in sq-NSCLC patients' HRQoL, especially in GHS/QoL and most importantly in lung cancer-specific symptoms including coughing, dyspnea, and hemoptysis.

Exploring the Ecology of Third Age Informal Language Learner Groups

This research explores the factors involved in the emergence of an independently organized Third Age informal language learner group in a community centre in Japan. The methodology applies PPCT (Process-Person-Context-Time) from Bronfenbrenner’s bioecological approach to provide a detailed perspective of the people, the environment and settings over time to show how these factors interact to construct an emergent learner group. The analysis looks at how and why this specific learner ecology emerges and ultimately, how it can benefit the Third Age and inform healthy ageing policy. The findings show that by engaging in second language learning, the participants find meaningful and active involvement in the group by creating a setting that welcomes self-expression, while balancing limiting and facilitating factors of resilience and reciprocal support, self-management, sage-ing, interest, agency, and responsibility. The result is the creation of a multilingual, multicultural, and multigenerational place of inclusion within the community. The study highlights the heterogeneity of the 3rd Age and illustrates the interplay of contexts outside of the learner group from micro to macro, individual and group resources, and the influence of the specific social time period. It also shows the social importance of creating opportunities for autonomous informal language learning settings in the community while highlighting the impact of Third Age agency.

A pooled multi-national validation study of a machine learning, high-sensitivity troponin-based multi-proteomic model to predict the presence of obstructive coronary artery disease

Background

Undetected obstructive coronary artery disease (oCAD) is a global health problem associated with significant morbidity and mortality. A need exists for an accurate and easily accessible diagnostic test for oCAD. Using machine learning, a multi-biomarker blood diagnostic test for oCAD based on high-sensitivity cardiac troponin-I (hs-cTnI) has been developed.

Purpose

To validate the performance of a previously developed, algorithmically weighted, multiple protein diagnostic panel to diagnose oCAD in a pooled multi-national cohort and to compare the diagnostic panel's performance to predict oCAD to hs-cTnI alone.

Methods

Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. hs-cTnI blood samples were assayed on the Siemens Atellica and Abbott Diagnostics ARCHITECT immunoassay platforms. Adiponectin and Kidney Injury Molecule-1 were measured with a multiplex assay on blood samples via the Luminex 100/200 xMAP platform. Individual data from a total of 924 patients with a mixture of acute and lesser acute presentations from three centers were pooled (Table 1). oCAD was defined as &gt;50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or &gt;70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel's performance to predict oCAD was also compared to hs-cTnI alone.

Results

The multiple protein panel had an area under the receiver-operating characteristic curve of 0.80 (95% CI, 0.77, 0.83, p&lt;0.001) for the presence of oCAD (Figure 1). At optimal cutoff, the score had 74% sensitivity, 72% specificity, and a positive predictive value of 81% for oCAD. The multiple biomarker panel had a diagnostic odds ratio of 7.48 (95% CI 5.55, 10.09, p&lt;0.001). In comparison, in patients without an acute MI, hs-cTnI alone had an area under the receiver-operating characteristic curve of 0.63 (95% CI, 0.60, 0.67, p&lt;0.001)) for oCAD (Figure 1).

Conclusions

In this multinational pooled cohort, a previously described novel machine learning, multiple biomarker panel provided high accuracy to diagnose patients for oCAD.

Funding Acknowledgement

Type of funding sources: Private company. Main funding source(s): Prevencio, Inc. Table 1. Pooled Variable DataFigure 1. ROC for HART CADhs and hs-cTnI

Patient acceptability and usability of a self-administered electronic patient-reported outcome assessment in HIV care: relationship with health behaviors and outcomes

We assessed acceptability/usability of tablet-based patient-reported outcome (PRO) assessments among patients in HIV care, and relationships with health outcomes using a modified Acceptability E-Scale (AES) within a self-administered PRO assessment. Using multivariable linear regression, we measured associations between patient characteristics and continuous combined AES score. Among 786 patients (median age=48; 91% male; 49% white; 17% Spanish-speaking) overall mean score was 26/30 points (SD: 4.4). Mean scores per dimension (max 5, 1=lowest acceptability, 5=highest): ease of use 4.7, understandability 4.7, time burden 4.3, overall satisfaction 4.3, helpfulness describing symptoms/behaviors 4.2, and enjoyability 3.8. Higher overall score was associated with race/ethnicity (+1.3 points/African-American patients (95%CI:0.3-2.3); +1.6 points/Latino patients (95%CI:0.9-2.3) compared to white patients). Patients completing PROs in Spanish scored +2.4 points on average (95%CI:1.6-3.3). Higher acceptability was associated with better quality of life (0.3 points (95%CI:0.2-0.5)) and adherence (0.4 points (95%CI:0.2-0.6)). Lower acceptability was associated with: higher depression symptoms (-0.9 points (95%CI:-1.4 to -0.4)); recent illicit opioid use (-2.0 points (95%CI:-3.9 to -0.2)); multiple recent sex partners (-0.8 points (95%CI:-1.5 to -0.1)). While patients endorsing depression symptoms, recent opioid use, condomless sex, or multiple sex partners found PROs less acceptable, overall, patients found the assessments highly acceptable and easy to use.

Diffusion tensor cardiovascular magnetic resonance detects altered myocardial microstructure in patients with acute st-elevation myocardial infarction

Background

Diffusion Tensor Cardiovascular Magnetic Resonance (DT-CMR) can quantify metrics of tissue integrity (mean diffusivity [MD] and fractional anisotropy [FA]) and changes in laminar microstructures (sheetlets), which reorientate from more wall-parallel in diastole (DIA) towards wall-perpendicular in systole (SYS) as the myocardium thickens, quantified by E2 angle [E2A]. Microstructural changes after STEMI may provide new insights into adverse LV remodelling and risk stratification.

Methods

In vivo DT-CMR was performed 3–5 days after PPCI for first presentation STEMI (N=19, mean age 57±9, 79% male). DT-CMR was acquired in 2 short-axes (SYS & DIA) using a STEAM-EPI sequence. 12 segment analysis of MD, FA, E2A and E2A mobility (ΔE2A = E2ASYS − E2ADIA) was performed. Infarct (INF) segments were defined as &gt;25% LGE, adjacent (ADJ, located contiguous to INF) and remote (REM, all other segments). Wilcoxon signed rank tests were used with threshold P&lt;0.017 (Bonferroni corrected).

Results

See Table.

MD in both SYS and DIA was significantly higher in INF and ADJ regions compared to REM. FA in both SYS and DIA was lower in the INF and ADJ compared to REM. E2ADIA was higher in INF, indicating a more wall-perpendicular orientation of sheetlets, compared to ADJ and REM zones. E2ASYS in INF was significantly reduced, indicating a more wall-parallel orientation of sheetlets, compared to ADJ and REM regions, resulting in significantly reduced sheetlet mobility (ΔE2A).

Conclusions

Microstructural changes can be detected after acute STEMI by in vivo DT-CMR. Zonal changes in MD and FA may suggest loss of barriers to water diffusion and altered cardiomyocyte organisation, respectively. We provide the first report of reduced sheetlet mobility after acute STEMI in INF. Ongoing work is evaluating the mechanisms and prognostic importance of altered sheetlet mobility after STEMI.

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): British Heart Foundation Clinical Research Training Fellowship

Human motor cortical beta bursts relate to movement planning and response errors

Motor cortical beta activity (13-30 Hz) is a hallmark signature of healthy and pathological movement, but its behavioural relevance remains unclear. Using high-precision magnetoencephalography (MEG), we show that during the classical event-related desynchronisation (ERD) and event-related synchronisation (ERS) periods, motor cortical beta activity in individual trials (n > 12,000) is dominated by high amplitude, transient, and infrequent bursts. Beta burst probability closely matched the trial-averaged beta amplitude in both the pre- and post-movement periods, but individual bursts were spatially more focal than the classical ERS peak. Furthermore, prior to movement (ERD period), beta burst timing was related to the degree of motor preparation, with later bursts resulting in delayed response times. Following movement (ERS period), the first beta burst was delayed by approximately 100 milliseconds when an incorrect response was made. Overall, beta burst timing was a stronger predictor of single trial behaviour than beta burst rate or single trial beta amplitude. This transient nature of motor cortical beta provides new constraints for theories of its role in information processing within and across cortical circuits, and its functional relevance for behaviour in both healthy and pathological movement.

Neural Competitive Queuing of Ordinal Structure Underlies Skilled Sequential Action

Fluent retrieval and execution of movement sequences is essential for daily activities, but the neural mechanisms underlying sequence planning remain elusive. Here participants learned finger press sequences with different orders and timings and reproduced them in a magneto-encephalography (MEG) scanner. We classified the MEG patterns for each press in the sequence and examined pattern dynamics during preparation and production. Our results demonstrate the “competitive queuing” (CQ) of upcoming action representations, extending previous computational and non-human primate recording studies to non-invasive measures in humans. In addition, we show that CQ reflects an ordinal template that generalizes across specific motor actions at each position. Finally, we demonstrate that CQ predicts participants’ production accuracy and originates from parahippocampal and cerebellar sources. These results suggest that the brain learns and controls multiple sequences by flexibly combining representations of specific actions and interval timing with high-level, parallel representations of sequence position.

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Non-invasive recordings in humans show “competitive queuing” of upcoming actions

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Queuing gradient mainly reflects a high-level template of sequence position

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Queuing gradient originates in ipsilateral parahippocampal and cerebellar areas

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Strength of queuing gradient predicts production accuracy

Quantifying the performance of MEG source reconstruction using resting state data

In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.

Inhibition of pyruvate dehydrogenase kinase improves pulmonary arterial hypertension in genetically susceptible patients

Pulmonary arterial hypertension (PAH) is a progressive vascular disease with a high mortality rate. It is characterized by an occlusive vascular remodeling due to a pro-proliferative and antiapoptotic environment in the wall of resistance pulmonary arteries (PAs). Proliferating cells exhibit a cancer-like metabolic switch where mitochondrial glucose oxidation is suppressed, whereas glycolysis is up-regulated as the major source of adenosine triphosphate production. This multifactorial mitochondrial suppression leads to inhibition of apoptosis and downstream signaling promoting proliferation. We report an increase in pyruvate dehydrogenase kinase (PDK), an inhibitor of the mitochondrial enzyme pyruvate dehydrogenase (PDH, the gatekeeping enzyme of glucose oxidation) in the PAs of human PAH compared to healthy lungs. Treatment of explanted human PAH lungs with the PDK inhibitor dichloroacetate (DCA) ex vivo activated PDH and increased mitochondrial respiration. In a 4-month, open-label study, DCA (3 to 6.25 mg/kg b.i.d.) administered to patients with idiopathic PAH (iPAH) already on approved iPAH therapies led to reduction in mean PA pressure and pulmonary vascular resistance and improvement in functional capacity, but with a range of individual responses. Lack of ex vivo and clinical response was associated with the presence of functional variants of SIRT3 and UCP2 that predict reduced protein function. Impaired function of these proteins causes PDK-independent mitochondrial suppression and pulmonary hypertension in mice. This first-in-human trial of a mitochondria-targeting drug in iPAH demonstrates that PDK is a druggable target and offers hemodynamic improvement in genetically susceptible patients, paving the way for novel precision medicine approaches in this disease.

A Fixed-point Scheme for the Numerical Construction of Magnetohydrostatic Atmospheres in Three Dimensions

Magnetohydrostatic models of the solar atmosphere are often based on idealized analytic solutions because the underlying equations are too difficult to solve in full generality. Numerical approaches, too, are often limited in scope and have tended to focus on the two-dimensional problem. In this article we develop a numerical method for solving the nonlinear magnetohydrostatic equations in three dimensions. Our method is a fixed-point iteration scheme that extends the method of Grad and Rubin (Proc. 2nd Int. Conf. on Peaceful Uses of Atomic Energy31, 190, 1958) to include a finite gravity force. We apply the method to a test case to demonstrate the method in general and our implementation in code in particular.

Non-linear Parameter Estimates from Non-stationary MEG Data

We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Neuromagnetic effects of pico-Tesla stimulation

We used a double-blind experimental design to look for an effect of pico-Tesla magnetic stimulation in healthy subjects. Pico-Tesla stimulation is thought to increase the dominant frequency of 2–7 Hz oscillations in the human brain. We used magnetoencephalography to measure resting state brain activity. Each subject had two separate recording sessions consisting of three runs in between which they were given real or sham pT stimulation. We then tried to predict the real and sham stimulation sessions based on changes in the mean peak frequency in the 2–7 Hz band. Our predictions for these individual runs were 8 out of 14 at chance level (p = 0.39). After unblinding, we found no significant effect (p = 0.11) of an increase in the frequency range (2–7 Hz) across the subject group. Finally, we performed a Bayesian model comparison between the effect size predicted from previous clinical studies and a null model. Even though this study had a sensitivity advantage of at least one order of magnitude over previous work, we found the null model to be significantly (2000 times) more likely.

The Frontal Control of Stopping

Stopping is a critical aspect of brain function. Like other voluntary actions, it is defined by its context as much as by its execution. Its neural substrate must therefore reflect both. Here, we distinguish those elements of the underlying brain circuit that preferentially reflect contextual aspects of stopping from those related to its execution. Contextual complexity of stopping was modulated using a novel "Stop/Change-signal" task, which also allowed us to parameterize the duration of the stopping process. Human magnetoencephalographic activity and behavioral responses were simultaneously recorded. Whereas theta/alpha frequency activity in the right inferior frontal gyrus was most closely associated with the duration of the stopping process, earlier gamma frequency activity in the pre-supplementary motor area was unique in showing contextual modulation. These results differentiate the roles of 2 key frontal regions involved in stopping, a crucial aspect of behavioral control.

Temporal structure in associative retrieval

Electrophysiological data disclose rich dynamics in patterns of neural activity evoked by sensory objects. Retrieving objects from memory reinstates components of this activity. In humans, the temporal structure of this retrieved activity remains largely unexplored, and here we address this gap using the spatiotemporal precision of magnetoencephalography (MEG). In a sensory preconditioning paradigm, 'indirect' objects were paired with 'direct' objects to form associative links, and the latter were then paired with rewards. Using multivariate analysis methods we examined the short-time evolution of neural representations of indirect objects retrieved during reward-learning about direct objects. We found two components of the evoked representation of the indirect stimulus, 200 ms apart. The strength of retrieval of one, but not the other, representational component correlated with generalization of reward learning from direct to indirect stimuli. We suggest the temporal structure within retrieved neural representations may be key to their function.

Optimising beamformer regions of interest analysis

Beamforming is a spatial filtering based source reconstruction method for EEG and MEG that allows the estimation of neuronal activity at a particular location within the brain. The computation of the location specific filter depends solely on an estimate of the data covariance matrix and on the forward model. Increasing the number of M/EEG sensors, increases the quantity of data required for accurate covariance matrix estimation. Often however we have a prior hypothesis about the site of, or the signal of interest. Here we show how this prior specification, in combination with optimal estimations of data dimensionality, can give enhanced beamformer performance for relatively short data segments. Specifically we show how temporal (Bayesian Principal Component Analysis) and spatial (lead field projection) methods can be combined to produce improvements in source estimation over and above employing the approaches individually.

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This paper concerns optimising beamformer analysis for anatomical ROIs.

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Channel reduction is performed using an ROI projection and Bayesian PCA.

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This improves covariance matrix estimation for a given data length.

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The proposed approach results in improvements in source estimation.

Does function fit structure? A ground truth for non-invasive neuroimaging

There are now a number of non-invasive methods to image human brain function in-vivo. However, the accuracy of these images remains unknown and can currently only be estimated through the use of invasive recordings to generate a functional ground truth. Neuronal activity follows grey matter structure and accurate estimates of neuronal activity will have stronger support from accurate generative models of anatomy. Here we introduce a general framework that, for the first time, enables the spatial distortion of a functional brain image to be estimated empirically. We use a spherical harmonic decomposition to modulate each cortical hemisphere from its original form towards progressively simpler structures, ending in an ellipsoid. Functional estimates that are not supported by the simpler cortical structures have less inherent spatial distortion. This method allows us to compare directly between magnetoencephalography (MEG) source reconstructions based upon different assumption sets without recourse to functional ground truth.

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We use spherical harmonics to create generative cortical surface models.

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Accurate functional estimates will be best supported by veridical cortical models.

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The method provides spatial confidence bounds for non-invasive functional images.

Role of asymmetric methylarginine and connexin 43 in the regulation of pulmonary endothelial function

Abstract Circulating levels of asymmetric dimethylarginine (ADMA), a nitric oxide synthase inhibitor, are increased in patients with idiopathic pulmonary hypertension (IPAH). We hypothesized that ADMA abrogates gap junctional communication, required for the coordinated regulation of endothelial barrier function and angiogenesis, and so contributes to pulmonary endothelial dysfunction. The effects of ADMA on expression and function of gap junctional proteins were studied in human pulmonary artery endothelial cells; pulmonary endothelial microvascular cells from mice deficient in an enzyme metabolizing ADMA, dimethylarginine dimethylaminohydrolase I (DDAHI); and blood-derived endothelial-like cells from patients with IPAH. Exogenous and endogenous ADMA inhibited protein expression and membrane localization of connexin 43 (Cx43) in a nitric oxide/soluble guanosine monophosphate/c-jun-dependent manner in pulmonary endothelial cells, resulting in the inhibition of gap junctional communication, increased permeability, and decreased angiogenesis. The effects of ADMA were prevented by overexpression of DDAHI or Cx43 and by treatment with rotigaptide. Blood-derived endothelial-like cells from IPAH patients displayed a distinct disease-related phenotype compared to cells from healthy controls, characterized by reduced DDAHI expression, increased ADMA production, and abnormal angiogenesis. In summary, we show that ADMA induces pulmonary endothelial dysfunction via changes in expression and activity of Cx43. Cells from IPAH patients exhibit abnormal DDAHI/Cx43 signaling as well as differences in gap junctional communication, barrier function, and angiogenesis. Strategies that promote DDAHI/Cx43 signaling may have an endothelium-protective effect and be beneficial in pulmonary vascular disease.

The chronometry of risk processing in the human cortex

The neuroscience of human decision-making has focused on localizing brain activity correlating with decision variables and choice, most commonly using functional MRI (fMRI). Poor temporal resolution means these studies are agnostic in relation to how decisions unfold in time. Consequently, here we address the temporal evolution of neural activity related to encoding of risk using magnetoencephalography (MEG), and show modulations of electromagnetic power in posterior parietal and dorsomedial prefrontal cortex (DMPFC) which scale with both variance and skewness in a lottery, detectable within 500 ms following stimulus presentation. Electromagnetic responses in somatosensory cortex following this risk encoding predict subsequent choices. Furthermore, within anterior insula we observed early and late effects of subject-specific risk preferences, suggestive of a role in both risk assessment and risk anticipation during choice. The observation that cortical activity tracks specific and independent components of risk from early time-points in a decision-making task supports the hypothesis that specialized brain circuitry underpins risk perception.

Dynamic state allocation for MEG source reconstruction

Our understanding of the dynamics of neuronal activity in the human brain remains limited, due in part to a lack of adequate methods for reconstructing neuronal activity from noninvasive electrophysiological data. Here, we present a novel adaptive time-varying approach to source reconstruction that can be applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. The method is underpinned by a Hidden Markov Model (HMM), which infers the points in time when particular states re-occur in the sensor space data. HMM inference finds short-lived states on the scale of 100ms. Intriguingly, this is on the same timescale as EEG microstates. The resulting state time courses can be used to intelligently pool data over these distinct and short-lived periods in time. This is used to compute time-varying data covariance matrices for use in beamforming, resulting in a source reconstruction approach that can tune its spatial filtering properties to those required at different points in time. Proof of principle is demonstrated with simulated data, and we demonstrate improvements when the method is applied to MEG.

Population level inference for multivariate MEG analysis

Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are those features that maximise these differences. Here we propose a solution based on Canonical Variates Analysis (CVA) model scoring at the subject level and random effects Bayesian model selection at the group level. We apply this approach to beamformer reconstructed MEG data in source space. CVA estimates those multivariate patterns of activation that correlate most highly with the experimental design; the order of a CVA model is then determined by the number of significant canonical vectors. Random effects Bayesian model comparison then provides machinery for inferring the optimal order over the group of subjects. Absence of a multivariate dependence is indicated by the null model being the most likely. This approach can also be applied to CVA models with a fixed number of canonical vectors but supplied with different feature sets. We illustrate the method by identifying feature sets based on variable-dimension MEG power spectra in the primary visual cortex and fusiform gyrus that are maximally discriminative of data epochs before versus after visual stimulation.

High precision anatomy for MEG

Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1 mm. Estimates of relative co-registration error were < 1.5 mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6 month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5 mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.

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We introduce MEG coregistration scheme using 3D printed subject specific head casts.

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Using this scheme reduces relative/absolute coregistration errors to 1–2 mm levels.

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The ability to reposition between sessions results in high SNR data sets.

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At this level of coregistration error, we see a clear benefit for using individual cortical meshes.

Heterogeneity in lung (18)FDG uptake in pulmonary arterial hypertension: potential of dynamic (18)FDG positron emission tomography with kinetic analysis as a bridging biomarker for pulmonary vascular remodeling targeted treatments

BACKGROUND

Pulmonary arterial hypertension (PAH) is a disease of progressive vascular remodeling, characterized by dysregulated growth of pulmonary vascular cells and inflammation. A prevailing view is that abnormal cellular metabolism, notably aerobic glycolysis that increases glucose demand, underlies the pathogenesis of PAH. Increased lung glucose uptake has been reported in animal models. Few data exist from patients with PAH.

METHODS AND RESULTS

Dynamic positron emission tomography imaging with fluorine-18-labeled 2-fluoro-2-deoxyglucose ((18)FDG) ligand with kinetic analysis demonstrated increased mean lung parenchymal uptake in 20 patients with PAH, 18 with idiopathic PAH (IPAH) (FDG score: 3.27±1.22), and 2 patients with connective tissue disease (5.07 and 7.11) compared with controls (2.02±0.71; P<0.05). Further compartment analysis confirmed increased lung glucose metabolism in IPAH. Lung (18)FDG uptake and metabolism varied within the IPAH population and within the lungs of individual patients, consistent with the recognized heterogeneity of vascular pathology in this disease. The monocrotaline rat PAH model also showed increased lung (18)FDG uptake, which was reduced along with improvements in vascular pathology after treatment with dicholoroacetate and 2 tyrosine kinase inhibitors, imatinib and sunitinib. Hyperproliferative pulmonary vascular fibroblasts isolated from IPAH patients exhibited upregulated glycolytic gene expression, along with increased cellular (18)FDG uptake; both were reduced by dicholoroacetate and imatinib.

CONCLUSIONS

Some patients with IPAH exhibit increased lung (18)FDG uptake. (18)FDG positron emission tomography imaging is a tool to investigate the molecular pathology of PAH and its response to treatment.

Source reconstruction accuracy of MEG and EEG Bayesian inversion approaches

Electro- and magnetoencephalography allow for non-invasive investigation of human brain activation and corresponding networks with high temporal resolution. Still, no correct network detection is possible without reliable source localization. In this paper, we examine four different source localization schemes under a common Variational Bayesian framework. A Bayesian approach to the Minimum Norm Model (MNM), an Empirical Bayesian Beamformer (EBB) and two iterative Bayesian schemes (Automatic Relevance Determination (ARD) and Greedy Search (GS)) are quantitatively compared. While EBB and MNM each use a single empirical prior, ARD and GS employ a library of anatomical priors that define possible source configurations. The localization performance was investigated as a function of (i) the number of sources (one vs. two vs. three), (ii) the signal to noise ratio (SNR; 5 levels) and (iii) the temporal correlation of source time courses (for the cases of two or three sources). We also tested whether the use of additional bilateral priors specifying source covariance for ARD and GS algorithms improved performance. Our results show that MNM proves effective only with single source configurations. EBB shows a spatial accuracy of few millimeters with high SNRs and low correlation between sources. In contrast, ARD and GS are more robust to noise and less affected by temporal correlations between sources. However, the spatial accuracy of ARD and GS is generally limited to the order of one centimeter. We found that the use of correlated covariance priors made no difference to ARD/GS performance.

Dynamic causal modelling of lateral interactions in the visual cortex

This paper presents a dynamic causal model based upon neural field models of the Amari type. We consider the application of these models to non-invasive data, with a special focus on the mapping from source activity on the cortical surface to a single channel. We introduce a neural field model based upon the canonical microcircuit (CMC), in which neuronal populations are assigned to different cortical layers. We show that DCM can disambiguate between alternative (neural mass and field) models of cortical activity. However, unlike neural mass models, DCM with neural fields can address questions about neuronal microcircuitry and lateral interactions. This is because they are equipped with interlaminar connections and horizontal intra-laminar connections that are patchy in nature. These horizontal or lateral connections can be regarded as connecting macrocolumns with similar feature selectivity. Crucially, the spatial parameters governing horizontal connectivity determine the separation (width) of cortical macrocolumns. Thus we can estimate the width of macro columns, using non-invasive electromagnetic signals. We illustrate this estimation using dynamic causal models of steady-state or ongoing spectral activity measured using magnetoencephalography (MEG) in human visual cortex. Specifically, we revisit the hypothesis that the size of a macrocolumn is a key determinant of neuronal dynamics, particularly the peak gamma frequency. We are able to show a correlation, over subjects, between columnar size and peak gamma frequency — that fits comfortably with established correlations between peak gamma frequency and the size of visual cortex defined retinotopically. We also considered cortical excitability and assessed its relative influence on observed gamma activity. This example highlights the potential utility of dynamic causal modelling and neural fields in providing quantitative characterisations of spatially extended dynamics on the cortical surface — that are parameterised in terms of horizontal connections, implicit in the cortical micro-architecture and its synaptic parameters.

Ultrasmall superparamagnetic particles of iron oxide in patients with acute myocardial infarction: early clinical experience

BACKGROUND

Inflammation following acute myocardial infarction (MI) has detrimental effects on reperfusion, myocardial remodelling, and ventricular function. Magnetic resonance imaging using ultrasmall superparamagnetic particles of iron oxide can detect cellular inflammation in tissues, and we therefore explored their role in acute MI in humans.

METHODS AND RESULTS

Sixteen patients with acute ST-segment elevation MI were recruited to undergo 3 sequential magnetic resonance scans within 5 days of admission at baseline, 24 and 48 hours following no infusion (controls; n=6) or intravenous infusion of ultrasmall superparamagnetic particles of iron oxide (n=10; 4 mg/kg). T2*-weighted multigradient-echo sequences were acquired and R2* values were calculated for specific regions of interest. In the control group, R2* values remained constant in all tissues across all scans with excellent repeatability (bias of -0.208 s(-1), coefficient of repeatability of 26.96 s(-1); intraclass coefficient 0.989). Consistent with uptake by the reticuloendothelial system, R2* value increased in the liver (84±49.5 to 319±70.0 s(-1); P<0.001) but was unchanged in skeletal muscle (54±8.4 to 67.0±9.5 s(-1); P>0.05) 24 hours after administration of ultrasmall superparamagnetic particles of iron oxide. In the myocardial infarct, R2* value increased from 41.0±12.0 s(-1) (baseline) to 155±45.0 s(-1) (P<0.001) and 124±35.0 s(-1) (P<0.05) at 24 and 48 hours, respectively. A similar but lower magnitude response was seen in the remote myocardium, where it increased from 39±3.2 s(-1) (baseline) to 80±14.9 s(-1) (P<0.001) and 67.0±15.7 s(-1) (P<0.05) at 24 and 48 hours, respectively.

CONCLUSIONS

Following acute MI, uptake of ultrasmall superparamagnetic particles of iron oxide occurs with the infarcted and remote myocardium. This technique holds major promise as a potential method for assessing cellular myocardial inflammation and left ventricular remodelling, which may have a range of applications in patients with MI and other inflammatory cardiac conditions.

Movement-related changes in local and long-range synchronization in Parkinson's disease revealed by simultaneous magnetoencephalography and intracranial recordings

Functional neurosurgery has afforded the opportunity to assess interactions between populations of neurons in the human cerebral cortex and basal ganglia in patients with Parkinson's disease (PD). Interactions occur over a wide range of frequencies, and the functional significance of those >30 Hz is particularly unclear. Do they improve movement, and, if so, in what way? We acquired simultaneously magnetoencephalography and direct recordings from the subthalamic nucleus (STN) in 17 PD patients. We examined the effect of synchronous and sequential finger movements and of the dopamine prodrug levodopa on induced power in the contralateral primary motor cortex (M1) and STN and on the coherence between the two structures. We observed discrete peaks in M1 and STN power at 60-90 Hz and at 300-400 Hz. All these power peaks increased with movement and levodopa treatment. Only STN activity at 60-90 Hz was coherent with activity in M1. Directionality analysis showed that STN gamma activity at 60-90 Hz tended to drive gamma activity in M1. The effects of levodopa on both local and distant synchronization at 60-90 Hz correlated with the degree of improvement in bradykinesia-rigidity as did local STN activity at 300-400 Hz. Despite this, there were no effects of movement type, nor interactions between movement type and levodopa in the STN, nor in the coherence between STN and M1. We conclude that synchronization at 60-90 Hz in the basal ganglia cortical network is prokinetic but likely through a modulatory effect rather than any involvement in explicit motor processing.

SU-E-I-53: Optimal KVp for Image Quality and Noise in Iodine Contrast in Head CTA

PURPOSE

For brain CT perfusion it is well established that 80 kVp is optimal. Although neuro-CT angiography is somewhat similar, emphasis is on the detection of aneurysms and related vascular pathologies throughout the brain. Thus it is necessary to visualize small and large blood vessels with contrast material, as well as form multi-planar views and 3D images, so image quality and noise in addition to contrast are important for thin slices. A study was initiated to determine the optimal kVp for neuro-CTA.

METHODS

A customized version of a commercial head phantom (CIRS 007TE-27 medium adult head CT dose phantom) was purchased to facilitate quantitative measurements with iodinated contrast material, contrast for white and gray matter, and to maintain the ability to perform dosimetry. The customization consisted of adding four 25 mm holes, 35 mm from the center arranged at 45 degree angles from the center, with solid rods equivalent with brain, white, and gray matter, as well as four fillable vials were included for study of contrast agents. Dosimetry measurements were carried out with standard pencil chamber and with 0.6 cc ionization chamber. For study of the optimal kVp for a head CTA, the vials were filled with four different concentrations of contrast, approximating low to medium concentrations that would be expected in such a study. The standard CTA protocol was followed, 64 × 0.625, pitch 0.53, rotation speed 0.5 second, and CTDIvol was kept constant for each kVp.

RESULTS

The best contrast was observed at 80 kVp; however, in order to achieve noise in CTA low enough to be clinically useful there may be issues with tube current capability for a clinical technique. Clinical investigation is underway.

CONCLUSIONS

The best balance of contrast and noise currently possible will be achieved at 100 kVp in a clinical scan.