More Like this

1. Age-related similarities and differences in cognitive and neural processing revealed by task-related microstate analysis

   https://doi.org/10.1016/j.neurobiolaging.2024.01.007  

   Denaro, Chandlyr M.; Reed, Catherine L.; Joshi, Jasmin; Petropoulos, Astrid; Thapar, Anjali; Hartley, Alan A. (April 2024, Neurobiology of Aging)

   We explored neural processing differences associated with aging across four cognitive functions. In addition to ERP analysis, we included task-related microstate analyses, which identified stable states of neural activity across the scalp over time, to explore whole-head neural activation differences. Younger and older adults (YA, OA) completed face perception (N170), word-pair judgment (N400), visual oddball (P3), and flanker (ERN) tasks. Age-related effects differed across tasks. Despite age-related delayed latencies, N170 ERP and microstate analyses indicated no age-related differences in amplitudes or microstates. However, age-related condition differences were found for P3 and N00 amplitudes and scalp topographies: smaller condition differences were found for in OAs as well as broader centroparietal scalp distributions. Age group comparisons for the ERN revealed similar focal frontocentral activation loci, but differential activation patterns. Our findings of differential age effects across tasks are most consistent with the STAC-r framework which proposes that age-related effects differ depending on the resources available and the kinds of processing and cognitive load required of various tasks. 

   more » « less
2. Comparing Simultaneous Scalp EEG Recordings from the OpenBCI Cyton and Brain Products BrainAmp

   https://doi.org/10.3390/s26041153  

   D’Amico, Alessandro; de_Sa, Virginia R (February 2026, Sensors)

   Portable, affordable electroencephalography (EEG) amplifiers could enable neuroscience-scale data collection. The open-source OpenBCI Cyton shows promise in this regard, but remains undervalidated for cognitive neuroscience ERP experiments. We simultaneously recorded eight scalp electrodes with both Cyton and gold-standard Brain Products BrainAmp amplifiers across P3b-, ERN-, and N400-eliciting tasks. Five healthy volunteers completed visual oddball (P3b), flankers (ERN), and word association (N400) tasks. We quantified within-subject signal similarity using Pearson r2, mean absolute error (MAE), mean arctangent absolute percentage error (MAAPE), and within-component window mean and standard deviation. Cyton signals showed r2 = 97–100%, MAE ≈ 1 µV, and MAAPE ≈ 20% with BrainAmp signals at ERP sites of interest. No significant differences emerged in mean amplitudes within ERP component windows across amplifiers, though standard deviations differed significantly. These results demonstrate that the Cyton records highly similar but not identical scalp EEG as research-grade equipment. This first multi-subject, concurrent scalp EEG validation across multiple ERP components validates the Cyton for cognitive neuroscience and supports broader adoption of affordable open-source tools. 

   more » « less
3. mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study

   https://doi.org/10.2196/25019  

   Wen, Hongyi; Sobolev, Michael; Vitale, Rachel; Kizer, James; Pollak, J P; Muench, Frederick; Estrin, Deborah (January 2021, JMIR Mental Health)

   null (Ed.)

   Background Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior. Objective The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application. Methods We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment (EMA) of state impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect). Results Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions. Conclusions The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed. Trial Registration ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653 

   more » « less
4. Procrastination and anxiety aspects of self-control predict individual differences in the mismatch negativity (MMN)

   https://doi.org/10.1016/j.ijpsycho.2025.113202  

   Denaro, Chandlyr M; Hartley, Alan A; Couperus, Jane W; Bukach, Cindy M; Reed, Catherine L (August 2025, International Journal of Psychophysiology)

   Self-control failures are often attributed to a lack of top-down behavioral regulation. Self-report measures of selfcontrol reflect higher-level cognitive appraisals of behavioral control and lower-level innate temperamental traits associated with reactive processes. However, few studies have examined the extent to which different aspects of self-control relate to reactive processes. Using a large sample (n = 246), we investigated whether individual differences in six self-control trait measures were predictive of low-level processing indexed by the mismatch negativity (MMN) elicited from a passive auditory oddball task. Larger MMNs are associated with greater prediction error when the input stimulus conflicts with expectation. Self-control traits were measured by effortful control, perfectionism, impulsiveness, procrastination, perceived stress, and anxiety scales. We assumed personality traits develop around innate differences in reaction to environmental changes, resulting in differential adult expressions of self-control reflected in the six traits. All but perfectionism and perceived stress correlated with MMN amplitudes: reater self-control associated with smaller MMNs; stronger negative traits associated with larger MMNs. When the correlated measures were entered into a backwards multiple regression on MMN amplitudes, procrastination and anxiety remained in the model as significant, relatively independent contributors. Procrastination may reflect a top-down modulation of underlying reactivity. Trait anxiety may reflect a basic temperament of greater reactivity to environmental change. Individuals with strong innate reactivity and weaker top-down processes may be hypervigilant to deviations from expectation, producing larger prediction errors and MMNs. Self-control failures may reflect reduced top-down control, when the balance in cortical activity favors reactive systems over the prefrontal cortex. 

   more » « less
5. A Recurrent Neural Network for Attenuating Non-cognitive Components of Pupil Dynamics

   https://doi.org/10.3389/fpsyg.2021.604522  

   Koorathota, Sharath; Thakoor, Kaveri; Hong, Linbi; Mao, Yaoli; Adelman, Patrick; Sajda, Paul (February 2021, Frontiers in Psychology)

   null (Ed.)

   There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal. 

   more » « less