Probabilistic Threshold Research Articles

Different diseases, different needs: Patient preferences for gene therapy in lysosomal storage disorders, a probabilistic threshold technique survey

BackgroundGene therapy is currently in development for several monogenetic diseases including lysosomal storage disorders. Limited evidence is available on patient preferences for gene therapy in this population. In this study, we compare gene therapy-related risk tolerance between people affected by three lysosomal storage diseases currently faced with different therapeutic options and prognoses.MethodsA survey including the probabilistic threshold technique was developed in which respondents were asked to choose between gene therapy and the current standard of care. The attributes included to establish participants’ risk tolerance were previously identified in focus groups of affected people or their representatives, namely: risk of mild side effects, severe side effects, the need for additional medication, and the likelihood of long-term effectiveness. The survey was distributed among people receiving outpatient care for type 1 Gaucher disease (good prognosis with current treatment options), Fabry disease (varying prognosis with current treatment options, XY-genotype on average more severely affected than XX), and parents representing people with severe forms of mucopolysaccharidosis type III A/B (poor prognosis, no disease-specific therapy available).ResultsA total of 85 surveys were completed (15 Gaucher disease respondents, 62 Fabry disease respondents (17 self-identifying male), eight parents of ten people with mucopolysaccharidosis type III). Disease groups with higher disease severity trended towards higher risk tolerance: Gaucher disease respondents were most cautious and predominantly preferred the current standard of care as opposed to MPS III representatives who were more risk tolerant. Respondents with Fabry disease were most heterogeneous in their risk tolerance, with male participants being more risk tolerant than female participants. Long-term effectiveness was the attribute in which respondents tolerated the least risk.ConclusionsPeople affected by a lysosomal storage disease associated with a poorer prognosis and less effective current treatment options trended towards more risk tolerance when choosing between gene therapy and the current standard of care. This study shows the importance of involvement of patient preferences before and during the development process of new treatment modalities such as gene therapy for rare diseases, to ensure that innovative therapies align with the wishes and needs of people affected by these diseases.

Automating thresholds in probabilistic linkage

ObjectiveThe Office for National Statistics aim is to create a platform containing a variety of datasets interlinked via data spines. The resource burden linking this many datasets is high, and so research to maximise automation is a key aim of the platform’s methodology. Probabilistic linkage is a standard linkage technique that utilises clerical resolution – a very costly process – to find acceptance threshold parameters. Here, we present research into automating threshold assignment. Every admin dataset is different, causing the probabilistically matched pairs scores to change, a set threshold cannot be used. Instead, a data-informed threshold must be set. ApproachA gold standard linked dataset was linked through Fellegi-Sunter, stopping at the point of finding the probabilistic threshold. To find the threshold, graphs plotting the number of records at each rounded score were created and reviewed, specifically looking at the spread of gold standard links, deterministic links, unique and non-unique probabilistic links. This review suggested a method of threshold assignment that provides multiple quality levels: a high-precision threshold, and a high-recall threshold. This presentation will showcase these graphs and explain how the two thresholds are calculated. The 2021 Census is one dataset linked within the gold standard linked dataset. The 2021 Census has high quality and relatively clean data, but is likely not representative of administrative data needed in the data platform. Therefore, next steps are testing this method on other datasets with a variety of quality, characteristics, and types.

Mitigating Label Bias in Machine Learning: Fairness through Confident Learning

Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and cause classifiers to inherit these biases. In this paper, we demonstrate that despite only having access to the biased labels, it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning. In the context of confident learning, low self-confidence usually indicates potential label errors; however, this is not always the case. Instances, particularly those from underrepresented groups, might exhibit low confidence scores for reasons other than labeling errors. To address this limitation, our approach employs truncation of the confidence score and extends the confidence interval of the probabilistic threshold. Additionally, we incorporate with co-teaching paradigm for providing a more robust and reliable selection of fair instances and effectively mitigating the adverse effects of biased labels. Through extensive experimentation and evaluation of various datasets, we demonstrate the efficacy of our approach in promoting fairness and reducing the impact of label bias in machine learning models.

Defect detection and localisation using guided wave images from array data processed by nonlinear autoregressive exogenous model and Gamma statistical operator

Guided wave structural health monitoring (GWSHM) systems, using the delay-and-sum imaging algorithm, are an efficient solution to detect and localise defects in industrial structures. However, the image artifacts caused by either imperfect detection or sensor lay-out limitations make it difficult to identify and locate defects accurately. In order to enhance the performance of defect detection and localisation in GWSHM systems, this paper proposes a three-step procedure for post-processing guided wave signals prior to image formation. In the first step, the signals are processed using the nonlinear autoregressive exogenous model to suppress noise from benign features. The second step calculates the probability of defect presence based on the rescaled Gamma cumulative distribution function. This probabilistic threshold is then determined from the quantile mapping. Finally, a guide wave image is formed using the delay-and-sum imaging algorithm. The experimental validation was performed to inspect a 6 mm-diameter through-thickness circular hole on an aluminium plate and the defects were further scaled as simulated datasets to test its detectability under various amplitudes. In the second procedure step, the detection and localisation performance of the proposed procedure was compared with that of using the signal difference coefficient and the Rayleigh maximum likelihood estimator. It is shown that the proposed procedure can enhance the contrast between damaged and undamaged regions, providing more reliable and accurate guided wave images.

Metaheuristic-Assisted Contextual Post-Filtering Method for Event Recommendation System

In today’s world, the web is a prominent communication channel. However, the variety of strategies available on event-based social networks (EBSNs) also makes it difficult for users to choose the events that are most relevant to their interests. In EBSNs, searching for events that better fit a user’s preferences are necessary, complex, and time consuming due to a large number of events available. Toward this end, a community-contributed data event recommender framework assists consumers in filtering daunting information and providing appropriate feedback, making EBSNs more appealing to them. A novel customized event recommendation system that uses the “multi-criteria decision-making (MCDM) approach” to rank the events is introduced in this research work. The calculation of categorical, geographical, temporal, and social factors is carried out in the proposed model, and the recommendation list is ordered using a contextual post-filtering system that includes Weight and Filter. To align the recommendation list, a new probabilistic weight model is added. To be more constructive, this model incorporates metaheuristic reasoning, which will fine-tune the probabilistic threshold value using a new hybrid algorithm. The proposed hybrid model is referred to as Beetle Swarm Hybridized Elephant Herding Algorithm (BSH-EHA), which combines the algorithms like Elephant Herding Optimization (EHO) and Beetle Swarm Optimization (BSO) Algorithm. Finally, the top recommendations will be given to the users.

From the modeling of diadromous species’ marine distributions to the characterization of their current and future marine habitats

IntroductionMany diadromous fish species are currently facing the threat of generalized population extirpations across their range, and conservation efforts implemented primarily within the continental domain may not be sufficient to ensure their viability. In consequence, better understanding the use of the marine environment by these species turned out to be critical.Material and methodWe developed marine species distribution models for two species, Allis shad (Alosa alosa) and the European flounder (Platichthys flesus), and proposed a way to interpret the habitat suitability from a more functional perspective. Our approach defined marine habitats as highly suitable environments that remained with a high potential through the species life stage duration at sea. First, a species-specific probabilistic threshold was applied to select for highly suitable environments each year of the simulations. Then, annual maps of high suitability were combined into recurrence maps showing areas were the environmental conditions remained the most suitable across the life at sea of the given species, these areas being called ‘marine habitats’. We applied this approach to study the current and future marine habitats of Allis shad and European flounder under climate change scenarios.ResultsThe main current marine habitats of these species were coastal, primarily in the Bay of Biscay for Allis shad and in the English Channel-North Sea for European flounder. These habitats are expected to experience minimal changes by mid-century, and the species may even benefit from new habitats at higher latitudes. However, the European flounder is likely to face greater challenges in the central part of its range by the end of the century, as potential spawning grounds may be threatened.DiscussionThis study provided a way to rethink ‘traditional’ suitability maps by integrating basic knowledge on the diadromous species life at sea. Our findings were part of a larger effort to link continental and marine habitats in improving management of diadromous species.

Maximum Acceptable Risk Estimation Based on a Discrete Choice Experiment and a Probabilistic Threshold Technique.

We aimed to empirically compare maximum acceptable risk results estimated using both a discrete choice experiment (DCE) and a probabilistic threshold technique (PTT). Members of the UK general public (n = 982) completed an online survey including a DCE and a PTT (in random order) measuring their preferences for preventative treatment for rheumatoid arthritis. For the DCE, a Bayesian D-efficient design consisting of four blocks of 15 choice tasks was constructed including six attributes with varying levels. The PTT used identical risk and benefit attributes. For the DCE, a panel mixed-logit model was conducted, both mean and individual estimates were used to calculate maximum acceptable risk. For the PTT, interval regression was used to calculate maximum acceptable risk. Perceived complexity of the choice tasks and preference heterogeneity were investigated for both methods. Maximum acceptable risk confidence intervals of both methods overlapped for serious infection and serious side effects but not for mild side effects (maximum acceptable risk was 32.7 percent-points lower in the PTT). Although, both DCE and PTT tasks overall were considered easy or very easy to understand and answer, significantly more respondents rated the DCE choice tasks as easier to understand compared with those who rated the PTT as easier (7-percentage point difference; p < 0.05). Maximum acceptable risk estimate confidence intervals based on a DCE and a PTT overlapped for two out of the three included risk attributes. More respondents rated the DCE as easier to understand. This may suggest that the DCE is better suited in studies estimating maximum acceptable risk for multiple risk attributes of differing severity, while the PTT may be better suited when measuring heterogeneity in maximum acceptable risk estimates or when investigating one or more serious adverse events.

Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent transaction, the detection of fraud is a challenging task to find an effective solution. In this study, autoencoder with probabilistic threshold shifting of XGBoost (AE-XGB) for credit card fraud detection is designed. Initially, AE-XGB employs autoencoder the prevalent dimensionality reduction technique to extract data features from latent space representation. Then the reconstructed lower dimensional features utilize eXtreame Gradient Boost (XGBoost), an ensemble boosting algorithm with probabilistic threshold to classify the data as fraudulent or legitimate. In addition to AE-XGB, other existing ensemble algorithms such as Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Random Forest, Categorical Boosting (CatBoost), LightGBM and XGBoost are compared with optimal and default threshold. To validate the methodology, we used IEEE-CIS fraud detection dataset for our experiment. Class imbalance and high dimensionality characteristics of dataset reduce the performance of model hence the data is preprocessed and trained. To evaluate the performance of the model, evaluation indicators such as precision, recall, f1-score, g-mean and Mathews Correlation Coefficient (MCC) are accomplished. The findings revealed that the performance of the proposed AE-XGB model is effective in handling imbalanced data and able to detect fraudulent transactions with 90.4% of recall and 90.5% of f1-score from incoming new transactions.

Social contagion induced by uncertain information.

Information and individual activities often spread globally through the network of social ties. While social contagion phenomena have been extensively studied within the framework of threshold models, it is common to make an assumption that may be violated in reality: each individual can observe the neighbors' states without error. Here, we analyze the dynamics of global cascades under uncertainty in an otherwise standard threshold model. Each individual uses statistical inference to estimate the probability distribution of the number of active neighbors when deciding whether to be active, which gives a probabilistic threshold rule. Unlike the deterministic threshold model, the spreading process is generally nonmonotonic, as the inferred distribution of neighbors' states may be updated as a new signal arrives. We find that social contagion may occur as a self-fulfilling event in that misperception may trigger a cascade in regions where cascades would never occur under certainty.

EnsMOD: A Software Program for Omics Sample Outlier Detection.

Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare biological states. Two recent publications describe robust algorithms for detecting transcriptomic sample outliers, but neither algorithm had been incorporated into a software tool for scientists. Here we describe Ensemble Methods for Outlier Detection (EnsMOD) which incorporates both algorithms. EnsMOD calculates how closely the quantitation variation follows a normal distribution, plots the density curves of each sample to visualize anomalies, performs hierarchical cluster analyses to calculate how closely the samples cluster with each other, and performs robust principal component analyses to statistically test if any sample is an outlier. The probabilistic threshold parameters can be easily adjusted to tighten or loosen the outlier detection stringency. EnsMOD can be used to analyze any omics dataset with normally distributed variance. Here it was used to analyze a simulated proteomics dataset, a multiomic (proteome and transcriptome) dataset, a single-cell proteomics dataset, and a phosphoproteomics dataset. EnsMOD successfully identified all of the simulated outliers, and subsequent removal of a detected outlier improved data quality for downstream statistical analyses.

Ultrasonic multi-view data merging using the vector coherence factor

Over the years, ultrasonic imaging, both in the context of medical and industrial applications, has ushered in the development of many advanced techniques, some based on the use of the phase of the measured signal instead of its amplitude. These techniques use what is called coherence factors to enhance image quality. Leveraging these factors when calculating the image provides an effective solution to the low restitution of small scatterers, where specular reflectors in conventional ultrasonic imaging are strongly restituted, by weighting an amplitude-based beamformer output. In the field of nondestructive testing, this solution generally boils down to multiplying the well-known Total Focusing Method (TFM) image by a coherence factor map which is also obtained through delay-and-sum (DAS). It has also been demonstrated that coherence factor maps, especially in the case of the vector coherence factor (VCF), can be used as interpretable images themselves, which then results in an amplitude-free imaging process with many potential advantages. Additionally, the fusion of multi-view (or multi-mode) TFM images is still of particular interest since the burden of selecting a single or several views for an inspection scenario highly depends on the geometry and position of the defect that needs to be identified. Although sensitivity maps have been introduced to help in the selection of relevant views, no similar tool currently exists for views generated using directly coherence factor maps. In this study, two different merging approaches for multi-view VCF images are explored and evaluated: pixel-by-pixel summation and maximum pixel value selection. A probabilistic threshold, derived from the VCF statistical background, is also defined to enhance the resulting merged view contrast. The images obtained through the proposed methods were evaluated both qualitatively and quantitatively using the Contrast-to-Noise Ratio (CNR). Experiments were conducted on two sets of samples. The first one was a 20 mm thick steel plate with Flat Bottom Holes (FBH) machined on its edge mimicking reflectors used for calibration of a circumferential weld inspection apparatus. The second one was composed of two 19.05 mm (34′′) thick steel plates with EDM notches at several angles centered at the mid-wall or surface-breaking on the backwall. Since the experimental setup, consisting of a 60-element probe, with a center frequency of 7.5 MHz, standing on a 36°Rexolite wedge, involved exciting mostly shear waves, only views composed of the transversal wave paths were considered for merging. Results show that by applying either one of the proposed merging methods, all artificial defects are reconstructed with a satisfactory CNR (above 30 dB), irrespective of the shape or location of the defects. Overall, the pixel-by-pixel summation and the maximum pixel value methods gave similar results. Applying the proposed threshold on the merged view improved the CNR by up to 15.8 dB. Finally, these simple but generic and robust multi-view merging methods and their associated thresholds could be employed while using a simplified hardware setup, as demonstrated previously in the literature.

How to choose credible ensemble members for the sub-seasonal to seasonal prediction of precipitation?

The sub-seasonal to seasonal (S2S) prediction of precipitation is not only a hot topic but also a challenge. The traditional ensemble mean and ensemble probabilistic forecast methods cannot avoid the uncertainty of the initial value in the S2S prediction. Is there a more suitable ensemble postprocessing method for the S2S prediction? In this study, the hindcast data during the 1999–2010 summers from nine operational models in the international S2S prediction project has been evaluated. Based on the quantitative objective precipitation evaluation methods, such as the Equitable Threat Score and frequency bias methods, the climatological spatio-temporal distribution of the optimal probabilistic threshold on the S2S scale is proven to exist, and it can be used as the standard to judge how many ensemble members are credible. Then, different ensemble forecast strategies are adopted in different regions to construct a Deterministic Ensemble Forecast using an Optimal Probabilistic Threshold (DEFOPT) method for precipitation prediction. The hindcast data of eight S2S models outside the period 1999–2010 are used to verify the applicability of the DEFOPT method by using the historical optimal probabilistic threshold during 1999–2010. The results show that the DEFOPT outperforms the deterministic forecast from one initial value, the ensemble mean, and the deterministic ensemble forecast using a probabilistic threshold for the occurrence days of rainfall at the 1 mm and 5 mm thresholds (≥ 1 mm and ≥ 5 mm) over China during each pentad in most S2S models.

Suitability of Preference Methods Across the Medical Product Lifecycle: A Multicriteria Decision Analysis

ObjectivesThis study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC. MethodsApplying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC. All criteria were given a relative weight based on the ranking and rating in the survey after which an overall suitability score was calculated for each preference elicitation method per decision point. Sensitivity analyses were conducted to reflect uncertainty in the performance matrix. ResultsFifty-nine industry, 29 regulatory, and 5 health technology assessment representatives completed the surveys. Overall, “estimating trade-offs between treatment characteristics” and “estimating weights for treatment characteristics” were highly important criteria throughout all MPLC decision points, whereas other criteria were most important only for specific MPLC stages. Swing weighting and probabilistic threshold technique received significantly higher suitability scores across decision points than other methods. Sensitivity analyses showed substantial impact of uncertainty in the performance matrix. ConclusionAlthough discrete choice experiment is the most applied preference elicitation method, other methods should also be considered to address the needs of decision makers. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.

OP35 Suitability Of Preference Methods Across The Medical Product Lifecycle: A Multicriteria Decision Analysis

IntroductionTo understand the importance of the preference methods criteria to stakeholders at each decision point in the Medical Product Lifecycle (MPLC) and to determine the suitability of commonly applied preference methods (Discrete Choice Experiment [DCE], swing weighting [SW], probabilistic threshold technique [PTT], Best-Worst Scaling case 1 [BWS1], Best-Worst Scaling case 2 [BWS2]) for a given decision-point.MethodsNineteen preference methods criteria of an existing performance matrix were incorporated in an online survey of industry, regulatory, and health technology assessment (HTA) stakeholders. All methods criteria were given a relative weight based on the SW ranking and point allocation task in the survey. Based on this relative weight and the performance matrix values, an overall suitability score was calculated for each method per critical decision point along the MPLC. Several sensitivity analyses were conducted for which the performance matrix was adapted.ResultsIn total 59 industry, 29 regulatory, and 5 HTA representatives completed the survey. In general, ‘estimating trade-offs between characteristics’, and ‘estimating weights for treatment characteristics’ were important preference method criteria throughout all MPLC decision points, while other preference method criteria were most important only for specific MPLC stages. Both BWS1 and BWS2 seem equally suitable across decision points, DCEs seem most suitable during clinical development and regulatory launch, and SW and PTT seem most suitable throughout industry decision points. Sensitivity analysis showed substantial impact of slight changes in the performance matrix.ConclusionsWith rapid changes in preference research, performance matrices of preference methods should continue to be re-evaluated as more and more evidence accumulates. While DCE is the most applied preference elicitation method, other methods should also be considered to address the needs of MPLC stakeholders. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.

Acceptable risks of treatments to prevent rheumatoid arthritis among first-degree relatives: demographic and psychological predictors of risk tolerance

ObjectivesTo quantify tolerance to risks of preventive treatments among first-degree relatives (FDRs) of patients with rheumatoid arthritis (RA).MethodsPreventive treatments for RA are under investigation. In a preference survey, adult FDRs...

OP01 Convergent Validity Between Discrete Choice Experiment And Other Stated Preference Methods: A Multistudy Comparison

IntroductionTo assess convergent validity of stated preference methods in studies where they were used to elicit patient preferences for informing medical product decisions.MethodsIn four studies, two stated preference methods were used to elicit preferences of patients with neuromuscular diseases (NMD; n = 140, Discrete Choice Experiment [DCE] and Best-Worst Scaling [BWS] case 2), diabetes (n = 495, DCE and swing weighting [SW]), myocardial infarction (MI; n = 335, DCE and BWS case 1), and rheumatoid arthritis (RA; n = 982, DCE and probabilistic threshold technique [PTT]). In each study, results of the two methods were compared using a normalized preference measure for which confidence intervals (CIs) were estimated using nonparametric bootstrapping of 500 samples. Normalized preference measures comprised of mean relative attribute importance weights (NMD and diabetes studies), attribute uptake probability (MI study), or maximum acceptable risk (RA study).ResultsIn all four studies, attribute ranking showed similar patterns between DCE and other methods for the most important attributes. The same attribute had highest importance in three out of four studies. Significant differences were found in ranges of normalized preference measures of each study between DCE and the other methods: 4.1–43.4 versus 8.9–24.7 for DCE and BWS case 2 in NMD; 3.8–49.7 versus 11.9–16.8 for DCE and SW in diabetes; 2.0–85.5 versus 0.2–69.0 for DCE and BWS case 1 in MI; -3.5–49.2 versus 1.1–18.1 for DCE and PTT in RA.ConclusionsPreferences differed significantly between DCE and other preference methods implying limited convergent validity. The substantially larger ranges in normalized outcome measures in DCE compared to other methods, are likely due to differences in mechanics and bias related to the methods. Since none of the methods is considered the golden standard for measuring stated preferences as true preferences are unknown, further studies are necessary to compare stated preference methods, determine internal validity and data quality, and potentially measure external validity.

Probabilistic delineation of subsurface connected pathways in alluvial aquifers under geological uncertainty

The present work aims to address the issue of the delineation of subsurface pathways carved in sedimentary aquifers from a stochastic perspective. A probabilistic approach to recognize preferential alluvial pathways is proposed relying only upon borehole data. These data can be employed to identify locations where the surficial aquifer is characterized by highly permeable (alluvial) hydrofacies. A Monte Carlo (MC) approach is set up to simulate stochastic realizations of local alluvial soil ratios in a bi-dimensional regularly gridded domain. Alluvial pathways are defined as the collection of cells whose simulated alluvial ratios are greater or equal to a first threshold, imposed on the simulated sediments ratio, and sharing a minimum occurrence probability, equal to a second probabilistic threshold. Then, probable highly permeable subsurface pathways throughout the domain for a real case-study application, within the province of Lecco (Lombardy, Northern Italy), are appraised. Subsurface alluvial pathways delineated upon geological data show channel-like patterns, like their surface counterparts. Subsurface discharge peaks in the central portion of these pathways, where permeable sediment probability is higher than elsewhere. Moreover, subsurface alluvial pathways features are consistent with available hydrogeological and piezometric information. The present approach has been also compared with a Multiple-Points Statistics approach, proving better at capturing the spatial patterns of connected subsurface pathways. Therefore, the present approach seems promising to be employed in the preliminarily understanding of large-scale groundwater circulation patterns, in particular for poorly monitored areas or serving as further analysis background.

Sensor fault detection and isolation via networked estimation: rank-deficient dynamical systems

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI.

Aversive anticipations modulate electrocortical correlates of decision-making and reward reversal learning, but not behavioral performance.

Predicting the consequences of one’s own decisions is crucial for organizing future behavior. However, when reward contingencies vary frequently, flexible adaptation of decisions is likely to depend on the situation. We examined the effects of an instructed threat context on choice behavior (i.e., reversal learning) and its electrocortical correlates. In a probabilistic decision-making task, 30 participants had to choose between two options that were either contingent on monetary gains or losses. Reward contingencies were reversed after reaching a probabilistic threshold. Decision-making and reversal learning were examined with two contextual background colors, which were instructed as signals for threat-of-shock or safety. Self-report data confirmed the threat context as more unpleasant, arousing, and threatening relative to safety condition. However, against our expectations, behavioral performance was comparable during the threat and safety conditions (i.e., errors-to-criterion, number of reversal, error rates, and choice times). Regarding electrocortical activity, feedback processing changed throughout the visual processing stream. The feedback-related negativity (FRN) reflected expectancy-driven processing (unexpected vs. congruent losses and gains), and the threat-selective P3 component revealed non-specific discrimination of gains vs. losses. Finally, the late positive potentials (LPP) showed strongly valence-specific processing (unexpected and congruent losses vs. gains). Thus, regardless of contextual threat, early and late cortical activity reflects an attentional shift from expectation- to outcome-based feedback processing. Findings are discussed in terms of reward, threat, and reversal-learning mechanisms with implications for emotion regulation and anxiety disorders.

Inconsistencies in atlas-based volumetric measures of the human nucleus basalis of Meynert: A need for high-resolution alternatives

The nucleus basalis of Meynert (nbM) is the major source of cortical acetylcholine (ACh) and has been related to cognitive processes and to neurological disorders. However, spatially delineating the human nbM in MRI studies remains challenging. Due to the absence of a functional localiser for the human nbM, studies to date have localised it using nearby neuroanatomical landmarks or using probabilistic atlases. To understand the feasibility of MRI of the nbM we set our four goals; our first goal was to review current human nbM region-of-interest (ROI) selection protocols used in MRI studies, which we found have reported highly variable nbM volume estimates. Our next goal was to quantify and discuss the limitations of existing atlas-based volumetry of nbM. We found that the identified ROI volume depends heavily on the atlas used and on the probabilistic threshold set. In addition, we found large disparities even for data/studies using the same atlas and threshold. To test whether spatial resolution contributes to volume variability, as our third goal, we developed a novel nbM mask based on the normalized BigBrain dataset. We found that as long as the spatial resolution of the target data was 1.3 mm isotropic or above, our novel nbM mask offered realistic and stable volume estimates. Finally, as our last goal we tried to discern nbM using publicly available and novel high resolution structural MRI ex vivo MRI datasets. We find that, using an optimised 9.4T quantitative T2⁎ ex vivo dataset, the nbM can be visualised using MRI. We conclude caution is needed when applying the current methods of mapping nbM, especially for high resolution MRI data. Direct imaging of the nbM appears feasible and would eliminate the problems we identify, although further development is required to allow such imaging using standard (f)MRI scanning.