A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample.

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data.

We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.

Factors Associated With Psychological Disturbances During the COVID-19 Pandemic: Multicountry Online Study.

BACKGROUND: Accumulating evidence suggests that the COVID-19 pandemic has negatively impacted the mental health of individuals. However, the susceptibility of individuals to be impacted by the pandemic is variable, suggesting potential influences of specific factors related to participants' demographics, attitudes, and practices. OBJECTIVE: We aimed to identify the factors associated with psychological symptoms related to the effects of the first wave of the pandemic in a multicountry cohort of internet users. METHODS: This study anonymously screened 13,332 internet users worldwide for acute psychological symptoms related to the COVID-19 pandemic from March 29 to April 14, 2020, during the first wave of the pandemic amidst strict lockdown conditions. A total of 12,817 responses were considered valid. Moreover, 1077 participants from Europe were screened a second time from May 15 to May 30, 2020, to ascertain the presence of psychological effects after the ease down of restrictions. RESULTS: Female gender, pre-existing psychiatric conditions, and prior exposure to trauma were identified as notable factors associated with increased psychological symptoms during the first wave of COVID-19 (P<.001). The same factors, in addition to being related to someone who died due to COVID-19 and using social media more than usual, were associated with persistence of psychological disturbances in the limited second assessment of European participants after the restrictions had relatively eased (P<.001). Optimism, ability to share concerns with family and friends like usual, positive prediction about COVID-19, and daily exercise were related to fewer psychological symptoms in both assessments (P<.001). CONCLUSIONS: This study highlights the significant impact of the COVID-19 pandemic at the worldwide level on the mental health of internet users and elucidates prominent associations with their demographics, history of psychiatric disease risk factors, household conditions, certain personality traits, and attitudes toward COVID-19.

Aging Effects and Test-Retest Reliability of Inhibitory Control for Saccadic Eye Movements.

Neuropsychological studies indicate that healthy aging is associated with a decline of inhibitory control of attentional and behavioral systems. A widely accepted measure of inhibitory control is the antisaccade task that requires both the inhibition of a reflexive saccadic response toward a visual target and the initiation of a voluntary eye movement in the opposite direction. To better understand the nature of age-related differences in inhibitory control, we evaluated antisaccade task performance in 78 younger (20-35 years) and 78 older (60-80 years) participants. In order to provide reliable estimates of inhibitory control for individual subjects, we investigated test-retest reliability of the reaction time, error rate, saccadic gain, and peak saccadic velocity and further estimated latent, not directly observable processed contributing to changes in the antisaccade task execution. The intraclass correlation coefficients (ICCs) for an older group of participants emerged as good to excellent for most of our antisaccade task measures. Furthermore, using Bayesian multivariate models, we inspected age-related differences in the performances of healthy younger and older participants. The older group demonstrated higher error rates, longer reaction times, significantly more inhibition failures, and late prosaccades as compared with young adults. The consequently lower ability of older adults to voluntarily inhibit saccadic responses has been interpreted as an indicator of age-related inhibitory control decline. Additionally, we performed a Bayesian model comparison of used computational models and concluded that the Stochastic Early Reaction, Inhibition and Late Action (SERIA) model explains our data better than PRO-Stop-Antisaccade (PROSA) that does not incorporate a late decision process.

Worsening of Preexisting Psychiatric Conditions During the COVID-19 Pandemic.

Objectives: To ascertain factors associated with worsening of psychiatric conditions during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This study anonymously examined 2,734 psychiatric patients worldwide for worsening of their preexisting psychiatric conditions during the COVID-19 pandemic. An independent clinical investigation of 318 psychiatric patients from United States was used for verification. Results: Valid responses mainly from 12 featured countries indicated self-reported worsening of psychiatric conditions in two-thirds of the patients assessed that was through their significantly higher scores on scales for general psychological disturbance, posttraumatic stress disorder, and depression. Female gender, feeling no control of the situation, reporting dissatisfaction with the response of the state during the COVID-19 pandemic, and reduced interaction with family and friends increased the worsening of preexisting psychiatric conditions, whereas optimism, ability to share concerns with family and friends, and using social media like usual were associated with less worsening. An independent clinical investigation from the United States confirmed worsening of psychiatric conditions during the COVID-19 pandemic based on identification of new symptoms that necessitated clinical interventions such as dose adjustment or starting new medications in more than half of the patients. Conclusions: More than half of the patients are experiencing worsening of their psychiatric conditions during the COVID-19 pandemic.

Time-delay model of perceptual decision making in cortical networks.

It is known that cortical networks operate on the edge of instability, in which oscillations can appear. However, the influence of this dynamic regime on performance in decision making, is not well understood. In this work, we propose a population model of decision making based on a winner-take-all mechanism. Using this model, we demonstrate that local slow inhibition within the competing neuronal populations can lead to Hopf bifurcation. At the edge of instability, the system exhibits ambiguity in the decision making, which can account for the perceptual switches observed in human experiments. We further validate this model with fMRI datasets from an experiment on semantic priming in perception of ambivalent (male versus female) faces. We demonstrate that the model can correctly predict the drop in the variance of the BOLD within the Superior Parietal Area and Inferior Parietal Area while watching ambiguous visual stimuli.