Biology subjects

Lenartowicz, A.

Publications and source records attributed to Lenartowicz, A..

5 recordsLinked to original sources

bioRxiv2026

Alpha-range neural oscillations are associated with multiple dynamic brain networks

We asked whether alpha range (8-12 Hz) EEG neural oscillations, a robust indicator of cortical excitability, are associated with dynamic BOLD brain networks. Two independent EEG-fMRI resting-state datasets (discovery: n = 94 and replication: n = 33) show that low- and high-alpha periods are associated with dynamic BOLD coactivation patterns (dCAPS) involving multiple dynamic networks rather than a single static network. Unique BOLD dCAPs consisting of visual cortex activation along with sensorimotor and attention networks in addition to the posterior thalamus, were mainly associated with periods of low-alpha power. However, overlapping BOLD dCAPs consisting of default, control, and attention networks were associated with periods of both low- and high-alpha power. The results suggest low-alpha dCAPS contain unique temporal-dependent configurations related to visual, sensorimotor, and attention networks while both low- and high-alpha dCAPs consist of overlapping temporal-dependent configurations involving CON, DMN, and attention networks involved in internal and external cognition. These findings reconcile discrepancies across static connectivity EEG-fMRI findings by showing that distinct transient BOLD configurations are associated with similar patterns of alpha EEG modulation in visual cortex, providing a dynamic framework for the gating function of alpha oscillations.

neuroscience↗

bioRxiv2026

Effects of Instructional Context on Neural Features of Attention during Learning Activities in Children with and without ADHD

Attention is foundational to learning, yet the extent to which features of the instructional environment differentially shape attentional engagement is not well understood. Here we leveraged mobile EEG and video-coded behavioral observation to examine attentional engagement in 6 to 10 years old children with and without a diagnosis of ADHD across instructional conditions varying in delivery modality (video watching, online, in-person) and management of learning (teacher-led versus student-led). EEG measures, including alpha-band (8-12Hz) oscillations, spectral slope and offset, and behavioral measures of active engagement (AE%), passive engagement (PE%), fidgeting, off-task behavior, were examined as a function of instructional context. Attentional engagement, as indicated by higher AE%, lower alpha power, flatter spectral slope and lower offset, was greatest in student-led learning, followed by teacher-led in person learning, synchronous online learning, and asynchronous learning, respectively. Child by instructional context interactions revealed that patterns of attentional engagement were largely consistent regardless of diagnosis, with group effects observed for motor behaviors (PE%, fidgeting) but not for measures of visual attention. Similarly, age showed only main effects, whereby older children showed higher passive engagement, lower fidgeting and off-task behavior, flatter spectral slope, and lower offset. The results underscore: (i) the importance of considering environmental context, such as instructional modality and management of learning, when examining mechanisms of attention and individual differences therein, and (ii) the value of recording multiple behavioral and neural measures, as not all indices commonly assumed to reflect attention capture the same underlying processes.

neuroscience↗

bioRxiv2026

Spectral and non-spectral EEG measures in the prediction of working memory task performance and psychopathology

Working memory (WM) supports the temporary maintenance of goal-relevant information and is disrupted across many neuropsychiatric disorders. We examined whether scalp electroencephalography (EEG) data features beyond spectral power, including waveform shape, broadband spectral structure, and signal complexity, provide complementary information for predicting cognitive and clinical outcomes. EEG was recorded from 200 adults spanning a broad range of neuropsychiatric symptom severity while they completed three WM task paradigms: Sternberg spatial WM (SWM), delayed face recognition (DFR), and dot pattern expectancy (DPX). Separate machine learning models were trained on EEG features from the encoding, delay, and probe phase of each task to predict participants task accuracy, reaction time (RT) variability, WM capacity, and psychopathology scores (Brief Psychiatric Rating Scale). A split-half analytic framework was used, with cross-validated model development in an exploratory dataset (N=100) and evaluation of statistically significant models in a held-out validation dataset (N=100). In the exploratory dataset, SWM task data best predicted WM capacity, DPX task data predicted RT variability, and DFR task data predicted psychopathology, suggesting that these three WM paradigms engage distinct neural processes relevant to different outcomes. No models reliably predicted task accuracy. Models incorporating features beyond spectral power generally outperformed power-only models, and task-derived features outperformed resting-state-derived features. However, only those models predicting WM capacity and RT variability generalized to the validation dataset; models predicting psychopathology did not. These findings demonstrate functional heterogeneity across WM paradigms, show that complementary EEG features enhance predictive modeling, and highlight the importance of rigorous validation for identifying robust brain-behavior relationships.

neuroscience↗

bioRxiv2026

Beyond Neural Noise: Critical Dynamics Predict Slower Reaction Times in Adults With and Without ADHD

Historically, neural variability observed during task was interpreted as "noise," assumed to obscure meaningful signal and thus something to be minimized both analytically by researchers and functionally by the brain. Changes to this signal-to-noise ratio have been proposed as a possible neural mechanism behind the increased reaction-time variability (RTV) in attention deficit hyperactivity disorder (ADHD). However, not all variability is the same - in some cases, variability can have some underlying "statistical structure" that can be beneficial to information processing. The challenge lies in distinguishing meaningful variability from random noise. The edge-of-synchrony critical point, which describes a system poised between synchronous and asynchronous regimes, could be a good theoretical framework to study these different types of neural variability. In this study, we investigate whether changes in criticality and oscillatory dynamics preceded slower behavioral responses during a bimodal continuous performance task in ADHD. We find evidence that, prior to slower responses, neural dynamics shift toward criticality in both ADHD and control groups, suggesting that increase variability in ADHD and during attention lapses are related to structured variability and not necessarily random noise. Notably, these findings run counter predictions based on the proposed model and previous literature on neural noise in this population, challenging predictions of edge-of-synchrony criticality as a unifying account of neural variability and behavioral performance. Furthermore, this effect did not emerge at the between-subject level, underscoring the limitations of relying on between-subject correlations to infer neural mechanisms. Impact StatementOur findings add new perspective to the hypothesis that links neural variability to reaction time variability in adults with and without ADHD. We found that neural dynamics shift towards criticality prior to slow reaction times in adults with and without ADHD, but in ADHD, dynamics lie closer to criticality regardless of response type, suggesting a different "attractor" state.

neuroscience↗

bioRxiv2022

Variability in cognitive task performance in early adolescence is associated with stronger between-network anticorrelation and future attention problems

BackgroundIntra-individual variability (IIV) during cognitive task performance is a key behavioral index of attention and consistent marker of ADHD. In adults, lower IIV has been associated with anticorrelation between the default mode network (DMN) and dorsal attention network (DAN) - thought to underlie effective allocation of attention. However, whether these behavioral and neural markers of attention are (i) associated with each other and (ii) can predict future attention-related deficits has not been examined in a developmental, population-based cohort. MethodsWe examined relationships at the baseline visit between IIV on three cognitive tasks, DMN-DAN anticorrelation, and parent-reported attention problems using the Adolescent Brain and Cognitive Development Study (n=11,878 participants, aged 9-10, female=47.8%). We also investigated whether behavioral and neural markers of attention at baseline predicted attention problems, 1, 2, and 3 years later. ResultsAt baseline, greater DMN-DAN anticorrelation was associated with lower IIV across all three cognitive tasks (unstandardized {beta}: 0.22-0.25). Older age at baseline was associated with stronger DMN-DAN anticorrelation and lower IIV ({beta}: -0.005--0.0004). Weaker DMN-DAN anticorrelation and IIV were cross-sectionally associated with attention problems ({beta}: 1.41-7.63). Longitudinally, lower IIV at baseline was associated with less severe attention problems, 1-3 years later, after accounting for baseline attention problems ({beta}: 0.288-0.77). ConclusionsThe results suggest that IIV in early adolescence is associated with worsening attention problems in a representative cohort of US youth. Attention deficits in early adolescence may be important for understanding and predicting future cognitive and clinical outcomes.

neuroscience↗