Article numbers as a leading indicator of publication time

Aims: in health professions education (HPE), the use of statistics is commonly associated with somewhat larger samples, whereas smaller samples or single subjects (i.e., N = 1) are usually labelled as needing some kind of ‘qualitative’ approach. However, statistical methods can be very useful in small samples and for individual subjects as well, especially where we have time series of repeated measurements of the same outcome variable(s) of interest. The aim of this article is twofold: to demonstrate an example of a cross-correlation function for single subjects in a HPE context and to suggest a few settings in HPE where this cross-correlation function can be of use.Method: the example uses data from a recent Open Access publication on among others article numbers and publication time in a number of major HPE journals to examine the relation between the number of articles published and median publication time over time in the zero-cost Open-Source statistical program R version 4.0.5.Results: as to be expected, the number of articles published appears somewhat of a leading indicator of publication time: both number of articles in year ‘y’ and number of articles in year ‘y minus 1’ correlate > 0.6 with median publication time in year ‘y’, while correlations of other time differences (e.g., number of articles in year ‘y minus 2’ and median publication time in year ‘y’, or median publication time in year ‘y’ and number of articles in year ‘y plus 1’) are substantially smaller.Conclusion: in line with recent literature, this article demonstrates that the cross-correlation function can be used in the context of small samples and single subjects. While the example focusses on article numbers and publication times, it can equally be applied in for example studying relations between knowledge, skills and attitude in individuals, or relations between behaviors of individuals working in pairs or small groups.

A multi-dataset evaluation of frame censoring for task-based fMRI

Subject motion during fMRI can affect our ability to accurately measure signals of interest. In recent years, frame censoring—that is, statistically excluding motion-contaminated data within the general linear model using nuisance regressors—has appeared in several task-based fMRI studies as a mitigation strategy. However, there have been few systematic investigations quantifying its efficacy. In the present study, we compared the performance of frame censoring to several other common motion correction approaches for task-based fMRI using open data and reproducible workflows. We analyzed eight datasets available on OpenNeuro.org representing eleven distinct tasks in child, adolescent, and adult participants. Performance was quantified using maximum t-values in group analyses, and ROI-based mean activation and split-half reliability in single subjects. We compared frame censoring to the use of 6 and 24 canonical motion regressors, wavelet despiking, robust weighted least squares, and untrained ICA-based denoising. Thresholds used to identify censored frames were based on both motion estimates (FD) and image intensity changes (DVARS). Relative to standard motion regressors, we found consistent improvements for modest amounts of frame censoring (e.g., 1-2% data loss), although these gains were frequently comparable to what could be achieved using other techniques. Importantly, no single approach consistently outperformed the others across all datasets and tasks. These findings suggest that although frame censoring can improve results, the choice of a motion mitigation strategy depends on the dataset and the outcome metric of interest.

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.

Accurate Decoding of Imagined and Heard Melodies

Music perception requires the human brain to process a variety of acoustic and music-related properties. Recent research used encoding models to tease apart and study the various cortical contributors to music perception. To do so, such approaches study temporal response functions that summarise the neural activity over several minutes of data. Here we tested the possibility of assessing the neural processing of individual musical units (bars) with electroencephalography (EEG). We devised a decoding methodology based on a maximum correlation metric across EEG segments (maxCorr) and used it to decode melodies from EEG based on an experiment where professional musicians listened and imagined four Bach melodies multiple times. We demonstrate here that accurate decoding of melodies in single-subjects and at the level of individual musical units is possible, both from EEG signals recorded during listening and imagination. Furthermore, we find that greater decoding accuracies are measured for the maxCorr method than for an envelope reconstruction approach based on backward temporal response functions (bTRFenv). These results indicate that low-frequency neural signals encode information beyond note timing, especially with respect to low-frequency cortical signals below 1 Hz, which are shown to encode pitch-related information. Along with the theoretical implications of these results, we discuss the potential applications of this decoding methodology in the context of novel brain-computer interface solutions.

Availability of Illegal Drugs During the COVID-19 Pandemic in Western Germany

Background: In response to the COVID-19-pandemic, a lockdown was established in the middle of March 2020 by the German Federal Government resulting in drastic reduction of private and professional traveling in and out of Germany with a reduction of social contacts in public areas.Research Questions: We seek evidence on whether the lockdown has led to a reduced availability of illegal drugs and whether subjects with substance-related problems tried to cope with possible drug availability issues by increasingly obtaining drugs via the internet, replacing their preferred illegal drug with novel psychoactive substances, including new synthetic opioids (NSO), and/or by seeking drug treatment.Methods: A questionnaire was anonymously filled in by subjects with substance-related disorders, typically attending low-threshold settings, drug consumption facilities, and inpatient detoxification wards from a range of locations in the Western part of Germany. Participants had to both identify their main drug of abuse and to answer questions regarding its availability, price, quality, and routes of acquisition.Results: Data were obtained from 362 participants. The most frequent main substances of abuse were cannabis (n = 109), heroin (n = 103), and cocaine (n = 75). A minority of participants reported decreased availability (8.4%), increased price (14.4%), or decreased quality (28.3%) of their main drug. About 81% reported no change in their drug consumption due to the COVID-19 pandemic and the lockdown. A shift to the use of novel psychoactive substances including NSO were reported only by single subjects. Only 1–2% of the participants obtained their main drug via the web.Discussion: Present findings may suggest that recent pandemic-related imposed restrictions may have not been able to substantially influence either acquisition or consumption of drugs within the context of polydrug users (including opiates) attending a range of addiction services in Germany.

Word Detection in Individual Subjects Is Difficult to Probe With Fast Periodic Visual Stimulation

Measuring cognition in single subjects presents unique challenges. On the other hand, individually sensitive measurements offer extraordinary opportunities, from informing theoretical models to enabling truly individualised clinical assessment. Here, we test the robustness of fast, periodic, and visual stimulation (FPVS), an emerging method proposed to elicit detectable responses to written words in the electroencephalogram (EEG) of individual subjects. The method is non-invasive, passive, and requires only a few minutes of testing, making it a potentially powerful tool to test comprehension in those who do not speak or who struggle with long testing procedures. In an initial study, Lochy et al. (2015) used FPVS to detect word processing in eight out of 10 fluent French readers. Here, we attempted to replicate their study in a new sample of 10 fluent English readers. Participants viewed rapid streams of pseudo-words with words embedded at regular intervals, while we recorded their EEG. Based on Lochy et al. (2015) we expected that words would elicit a steady-state response at the word-presentation frequency (2 Hz) over parieto-occipital electrode sites. However, across 40 datasets (10 participants, two conditions, and two regions of interest–ROIs), only four datasets met the criteria for a unique response to words. This corresponds to a 10% detection rate. We conclude that FPVS should be developed further before it can serve as an individually-sensitive measure of written word processing.

Relationship between Clinical, Demographic and Socio-economic Factors with Suicide Ideation; A cross-sectional Study

Background: Suicide has grown in global prevalence as a public health problem. We aimed to evaluate the association of socioeconomic factors, biochemical and hematologic tests, and suicide ideation. Methods: In this cross-sectional study, 8267 Iranian adults aged 35 – 65 years old were enrolled. The assessment of suicide ideation was made by the completion of Beck’s depression inventory (BDI) questionnaire; according to one specific item on the questionnaire: “have you ever decided to suicide in the past week?” Results: According to our results, 6.9 % of subjects had ideation of suicide. The results showed high levels of FBG, RBC, MCHC, and hs-CRP were associated with suicide ideation. Obese, single subjects, and current-smokers had a higher risk of suicide ideation. Conclusion: Increased physical activity, obesity, and smoking are associated with a high risk of suicide ideation; whilst, a high MCHC is related to low risk of suicide ideation in Iranian adults.

Recovery of neural dynamics criticality in personalized whole brain models of stroke

ABSTRACTThe critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single subjects using whole-brain models. Lesions engendered a sub-critical state that recovered over time in parallel with behavior. Notably, this improvement of criticality depended on the re-modeling of specific white matter connections. In summary, personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single subjects.

Word detection in individual subjects is difficult to probe with fast periodic visual stimulation

Measuring cognition in single subjects presents unique challenges. Yet individually sensitive measurements offer extraordinary opportunities, from informing theoretical models to enabling truly individualised clinical assessment. Here, we test the robustness of fast, periodic, visual stimulation (FPVS), an emerging method proposed to elicit detectable responses to written words in the electroencephalogram (EEG) of individual subjects. The method is non-invasive, passive, and requires only a few minutes of testing, making it a potentially powerful tool to test comprehension in those who do not speak or who struggle with long testing procedures. In an initial study, Lochy et al. (2015) used FPVS to detect word processing in 8 out of 10 fluent French readers. Here, we attempted to replicate their study in a new sample of ten fluent English readers. Participants viewed rapid streams of pseudo-words with words embedded at regular intervals, while we recorded their EEG. Based on Lochy et al., we expected that words would elicit a steady-state response at the word-presentation frequency (2 Hz) over parieto-occipital electrode sites. However, across 40 datasets (10 participants, two conditions, and two regions of interest - ROIs), only four datasets met the criteria for a unique response to words. This corresponds to a 10% detection rate. We conclude that FPVS should be developed further before it can serve as an individually-sensitive measure of written word processing.