A Novel, Fast, Reliable, and Data-Driven Method for Simultaneous Single-Trial Mining and Amplitude—Latency Estimation Based on Proximity Graphs and Network Analysis

Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm

Hierarchy and Competition in CSCW applications: Model and case study

CSCW applications need to adapt themselves to the functional and organizational structures of people that use them. However they do not usually support division in groups with a certain hierarchical structure among them. In this paper, we propose and study a theoretical model of groupware appliations that reflects those hierarchical interactions. The proposed model is also intended to evaluate the effects in performance derived from competitive and collaborative relationships among the components of a hierarchy of groups. In order to demonstrate the above ideas, a groupware game, called Alymod, was designed and implemented using a modified version of a well-known CSCW Toolkit, namely Groupkit. Groupkit was modified in order to support group interactions in the same CSCW application. In Alymod, participants compete or collaborate within a hierarchical structure to achieve a common goal (completing gaps in a text, finishing numerical series, resolving University course examinations, etc.).Publicad

On the use of control surface excitation in flutter testing

peer reviewedFlutter testing is aimed at demonstrating that the aircraft flight envelope is flutter free. Response measurements from deliberate excitation of the structure are used to identify and track frequency and damping values against velocity. In this paper, the common approach of using a flight control surface to provide the excitation is examined using a mathematical model of a wing and control surface whose rotation is restrained by a simple actuator. In particular, it is shown that it is essential to use the demand signal to the actuator as a reference signal for data processing. Use of the actuator force (or strain) or control angle (or actuator displacement) as a reference signal is bad practice because these signals contain response information. It may also be dangerous in that the onset of flutter may not be seen in the test results

A comparison of blade tip timing data analysis methods

The experimental determination of the vibration characteristics of rotating engine blades is very important for fatigue failure considerations. One of the most promising techniques for measuring the frequency of blade vibrations is blade tip timing. In this paper, three vibration analysis methods were specifically formulated and applied to the tip timing problem for the first time, using data obtained from a simple mathematical blade tip timing simulation. The results from the methods were compared statistically in order to determine which of the techniques is more suitable. One of the methods, the global autoregressive instrumental variables approach, produced satisfactory results at realistic noise levels. However, all of the techniques produced biased results under certain circumstances

Paraneoplastic hypoglycaemia secondary to IGF-2 secretion from a metastatic gastrointestinal stromal tumour

We report the case of a 79-year-old male with previous history of non-Hodgkin's lymphoma in remission, who presented acutely to the Accident and Emergency department with recurrent episodes of hypoglycaemia. At the time of presentation, a random glucose was low at 1.4 mmol/l, which upon correction resolved his symptoms. In hindsight, the patient recalled having had similar episodes periodically over the past 2 months to which he did not give much notice. While hospitalized, he continued having episodes of symptomatic hypoglycaemia, requiring treatment with intravenous dextrose and per os steroids. Once stable, he was discharged on oral prednisolone and dietary advice. A computed tomography scan performed during inpatient stay showed multiple deposits in the abdomen. An ultrasound guided biopsy of one of the liver deposits was performed. Immunohistochemistry supported the diagnosis of a gastrointestinal stromal tumour (GIST) positive for CD34 and CD117. The diagnosis of non-islet cell tumour hypoglycaemia (NICTH) secondary to an IGF2 secreting GIST was confirmed with further biochemical investigations (IGF2=105.9 nmol/l; IGF2:IGF1 ratio 23, Upper Level of Normal (ULN) <10). Targeted cytoreductive treatment with Imatinib mesylate following assessment of the tumour's mutational status was successful in preventing hypoglycaemia over a 21-month follow-up observation period

Predictors of Impaired Glucose Regulation in Patients with Non-Alcoholic Fatty Liver Disease

Introduction. Many patients with non-alcoholic fatty liver disease (NAFLD) have impaired glucose regulation or type 2 diabetes mellitus (DM). We investigated characteristics of NAFLD patients associated with hyperglycemia. Methods. During a 2-hour oral glucose tolerance test (OGTT), serum glucose and insulin were measured in 152 NAFLD patients. Results. 48.7% of NAFLD patients had hyperglycemia. Age (odds ratio (OR) = 1.08, 95% confidence interval (CI): 1.03–1.13), body mass index (BMI) (OR = 1.12, 95% CI: 1.01–1.25), and lower high-density lipoprotein cholesterol (HDL-C) (OR = 0.95, 95% CI: 0.92–0.98) proved to be independent predictors of hyperglycemia. After OGTT, 30 min insulin was lower in hyperglycemic patients (74.2 ± 49.7 versus 94.5 ± 53.9 μIU/mL, P = 0.02), while 90 min insulin (170.1 ± 84.6 versus 122.9 ± 97.7 μU/mL, P = 0.01) and 120 min insulin (164.0 ± 101.2 versus 85.3 ± 61.9 μIU/mL, P < 0.01) were higher. Conclusions. NAFLD patients with higher BMI, lower HDL-C, or older age were more likely to have impaired glucose metabolism. An OGTT could be of value for early diagnosis of DM among this population

Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients. View Full-Text Keywords: neuropsychiatric systemic lupus erythematosus; rs-fMRI; graph theory; functional connectivity; surrogate data; machine learning; visuomotor ability; mental flexibilit

Morphometric analysis of structural MRI using schizophrenia meta-analytic priors distinguish patients from controls in two independent samples and in a sample of individuals with high polygenic risk

Schizophrenia (SCZ) is associated with structural brain changes, with considerable variation in the extent to which these cortical regions are influenced. We present a novel metric that summarises individual structural variation across the brain, while considering prior effect sizes, established via meta-analysis. We determine individual participant deviation from a within-sample-norm across structural MRI regions of interest (ROIs). For each participant, we weight the normalised deviation of each ROI by the effect size (Cohen’s d) of the difference between SCZ/control for the corresponding ROI from the SCZ Enhancing Neuroimaging Genomics through Meta-Analysis working group. We generate a morphometric risk score (MRS) representing the average of these weighted deviations. We investigate if SCZ-MRS is elevated in a SCZ case/control sample (N(CASE) = 50; N(CONTROL) = 125), a replication sample (N(CASE) = 23; N(CONTROL) = 20) and a sample of asymptomatic young adults with extreme SCZ polygenic risk (N(HIGH-SCZ-PRS) = 95; N(LOW-SCZ-PRS) = 94). SCZ cases had higher SCZ-MRS than healthy controls in both samples (Study 1: β = 0.62, P < 0.001; Study 2: β = 0.81, P = 0.018). The high liability SCZ-PRS group also had a higher SCZ-MRS (Study 3: β = 0.29, P = 0.044). Furthermore, the SCZ-MRS was uniquely associated with SCZ status, but not attention-deficit hyperactivity disorder (ADHD), whereas an ADHD-MRS was linked to ADHD status, but not SCZ. This approach provides a promising solution when considering individual heterogeneity in SCZ-related brain alterations by identifying individual’s patterns of structural brain-wide alterations