Biology subjects

Kucyi, A.

Publications and source records attributed to Kucyi, A..

13 recordsLinked to original sources

bioRxiv2026

Electroencephalographic activity patterns distinguish interdigitated default and frontoparietal networks

The brain is comprised of distributed functional networks that operate on diverse time scales. Evidence from precision fMRI shows that canonical networks, including default (DN), frontoparietal control (FPCN), and dorsal attention (dATN), each fractionate into multiple finely interdigitated networks. However, as fMRI is blind to fast processes underlying neural communication, there remains a need to neurophysiologically characterize individual-level network fractionation. To address this need, we conducted a simultaneous EEG-fMRI study, including multiple sessions per individual, resting-state and task conditions, and carbon wire loop sensors for optimized artifact correction. Using precision network mapping, we discovered dissociable electrophysiological correlates of blood-oxygen-level-dependent activity for two networks within the boundaries of the canonical DN. One (DNa) related more positively with alpha and high-beta power, while another (DNb) related more negatively with theta and low-beta power. Univariate and machine learning analyses further revealed electrophysiological dissociations between networks within the canonical FPCN (FPCNa versus FPCNb) but not dATN. The network-specific electrophysiological activity patterns were reliable across sessions, only modestly variable between resting-state and demanding task conditions, and observable for neighboring nodes of different networks across multiple cortical zones. Our findings provide insight into rhythmic neural processes that fine-grained networks may engage to support divergent cognitive operations.

neuroscience↗

bioRxiv2026

Neuroimaging of intimate partner violence can underrepresent the most affected survivors

Neuroimaging is becoming an increasingly prominent tool to understand intimate partner violence (IPV) and partner-inflicted brain injury (BI). Yet, by nature of IPV and partner-inflicted BI, those most affected may be the least likely to be eligible, participate, and produce usable data in neuroimaging studies. A critical question therefore remains of who is represented in neuroimaging research on IPV and related BI. Here, we examined representativeness at three major stages of neuroimaging sample construction using screening records from 352 women and a subsequent cohort of 207 enrolled women with lifetime exposure to physical IPV. At the eligibility stage, three in five women eligible for behavioral research were deemed ineligible for magnetic resonance imaging (MRI) at initial screening. At the participation stage, the 61 women who underwent resting-state functional MRI (fMRI) had more education, were more likely to be employed, and reported lower psychiatric and neurobehavioral symptom burden than enrolled women who did not undergo neuroimaging. Finally, at the quality-control stage following fMRI, motion-based exclusion disproportionately removed women who had sustained the most partner-inflicted BIs. Our findings empirically reveal that eligibility, participation, and quality control each bias the demographic, clinical, abuse, and BI burden represented in an fMRI sample, and therefore have key implications for generalizability. Measuring and reporting representation bias can help identify whose experiences are underrepresented in the evidence, and accordingly reduce the risk that IPV neuroimaging research understates the very harms it seeks to characterize.

neuroscience↗

bioRxiv2026

Real-time fMRI-triggered experience-sampling: a proof-of-concept study

Much of a typical individuals mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional Magnetic Resonance Imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience-sampling (rt-fMRI-ES). We analyzed blood-oxygenation-level-dependent signals in real time during a wakeful resting state (n=60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external-attention time-locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation compared to other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendationhttps://osf.io/sd4hu (Date of in-principle acceptance: 07/24/2024; under temporary private embargo)

neuroscience↗

bioRxiv2026

A multi-session simultaneous EEG-fMRI dataset with repeated experience sampling

The integration of electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) can be used to characterize temporal and spatial components of neural activity during unfolding mental experience. Here we introduce a multi-session simultaneous EEG-fMRI dataset with measures of continuous behavior and spontaneous mental experience. Data components, organized in Brain Imaging Dataset Structure (BIDS) format, include fMRI, EEG with carbon wire loop sensors for artifact removal, continuous performance task responses, experience sampling ratings, and mental health surveys, from 24 healthy adults. Tasks included the gradual-onset continuous performance task and resting state with intermittent experience sampling of 13 unique thought dimensions (36 repetitions, including 468 total ratings, per participant). The same protocol was completed on two different days, yielding approximately 1.33 hours of simultaneous EEG-fMRI data per individual. The dataset may be used to explore the behavioral and experiential relevance of brain activity during the wakeful resting state. The dataset also provides a means to study the reliability of relationships between fMRI and EEG features across sessions within individuals.

neuroscience↗

bioRxiv2025

Limited generalizability of dynamic fMRI correlates of adolescent rumination

Rumination, or perseverative negative self-referential thinking, is a hallmark of depression. In adults, a dynamic resting-state fMRI model of trait rumination was recently identified through predictive modelling. In adolescents, a development period during which rumination and depression increase, the neurobiological correlates of ruminative thinking are less clear. In the current preregistered study, we examine dynamic connectivity correlates of self-reported rumination in the largest sample of adolescents to date (n = 443, containing clinical and non-clinical individuals). Notably, the adult model failed to generalize to our sample. In addition, linear models trained on default-mode network (DMN) connectivity, as well as whole-brain connectome models, failed to generalize to held-out data. In an exploratory random forest analysis, we found significant prediction performance of a model where increased variability between DMN-cerebellum, DMN-dorsal attention network, and DMN-DMN connections was nominally associated with higher rumination. However, the model did not generalize to an external sample with lower rumination scores and a distinct scanner protocol. Our findings illustrate the difficulty of characterizing the neurodevelopment of risk factors for depression.

neuroscience↗

bioRxiv2025

Neural sensitivity to the heartbeat is modulated by spontaneous fluctuations in subjective arousal during wakeful rest

Spontaneous thoughts, occupying much of ones awake time in daily life, are often colored by emotional qualities. While spontaneous thoughts have been associated with various neural correlates, the relationship between subjective qualities of ongoing experiences and the brains sensitivity to bodily signals (i.e., interoception) remains largely unexplored. Given the well-established role of interoception in emotion, clarifying this relationship may elucidate how processes relevant to mental health, such as arousal and anxiety, are regulated. We used EEG and ECG to measure the heartbeat evoked potential (HEP), an index of interoceptive processing, while 51 adult participants (34 male, 20 female) visually fixated on a cross image and let their minds wander freely. At pseudo-random intervals, participants reported their momentary level of arousal. This measure of affective arousal was highly variable within and between individuals but was statistically unrelated to several markers of physiological arousal, including heart rate, heart rate variability, time on task, and EEG alpha power at posterior electrodes. A cluster-based permutation analysis revealed that the HEP amplitude was increased during low relative to high affective arousal in a set of frontal electrodes during the 340 - 356 millisecond window after heartbeat onset. This HEP effect was more pronounced in individuals who reported high, relative to low, levels of trait anxiety. Together, our results offer novel evidence that at varying levels of trait anxiety, the brain differentially modulates sensitivity to bodily signals in coordination with the momentary, spontaneous experience of affective arousal--a mechanism that may operate independently of physiological arousal. Significance StatementOur findings highlight the relationships between spontaneous fluctuations in affective arousal, brain-body interactions, and anxiety, offering new insights into how interoception fluctuates with changes in internal states. By showing that interoceptive processing is heightened during lower affective arousal and that this effect is amplified in individuals with higher trait anxiety, our study suggests the brain adaptively downregulates interoceptive sensitivity in response to fluctuating internal states. These results have implications for understanding how spontaneous thoughts shape interoception and emotion, particularly in clinical contexts where dysregulated interoception is linked to anxiety and mood disorders. More broadly, our work underscores the need to distinguish between different forms of arousal, advancing understanding of the taxonomy and ways of measuring arousal.

neuroscience↗

bioRxiv2024

Connectome predictive modeling of trait mindfulness

IntroductionTrait mindfulness refers to ones disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. MethodsTo address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. ResultsIn the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. ConclusionsWe present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

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bioRxiv2024

Dynamic functional connectivity correlates of trait mindfulness in early adolescence

BackgroundTrait mindfulness, the tendency to attend to present-moment experiences without judgement, is negatively correlated with adolescent anxiety and depression. Understanding the neural mechanisms underlying trait mindfulness may inform the neural basis of psychiatric disorders. However, few studies have identified brain connectivity states that correlate with trait mindfulness in adolescence, nor have they assessed the reliability of such states. MethodsTo address this gap in knowledge, we rigorously assessed the reliability of brain states across 2 functional magnetic resonance imaging (fMRI) scan from 106 adolescents aged 12 to 15 (50% female). We performed both static and dynamic functional connectivity analyses and evaluated the test-retest reliability of how much time adolescents spent in each state. For the reliable states, we assessed associations with self-reported trait mindfulness. ResultsHigher trait mindfulness correlated with lower anxiety and depression symptoms. Static functional connectivity (ICCs from 0.31-0.53) was unrelated to trait mindfulness. Among the dynamic brains states we identified, most were unreliable within individuals across scans. However, one state, an hyperconnected state of elevated positive connectivity between networks, showed good reliability (ICC=0.65). We found that the amount of time that adolescents spent in this hyperconnected state positively correlated with trait mindfulness. ConclusionsBy applying dynamic functional connectivity analysis on over 100 resting-state fMRI scans, we identified a highly reliable brain state that correlated with trait mindfulness. The brain state may reflect a state of mindfulness, or awareness and arousal more generally, which may be more pronounced in those who are higher in trait mindfulness.

neuroscience↗

bioRxiv2024

Individual variability in neural representations of mind-wandering

Mind-wandering is a frequent, daily mental activity, experienced in unique ways in each person. Yet neuroimaging evidence relating mind-wandering to brain activity, for example in the default mode network (DMN), has relied on population-rather than individual-based inferences due to limited within-individual sampling. Here, three densely-sampled individuals each reported hundreds of mind-wandering episodes while undergoing multi-session functional magnetic resonance imaging. We found reliable associations between mind-wandering and DMN activation when estimating brain networks within individuals using precision functional mapping. However, the timing of spontaneous DMN activity relative to subjective reports, and the networks beyond DMN that were activated and deactivated during mind-wandering, were distinct across individuals. Connectome-based predictive modeling further revealed idiosyncratic, whole-brain functional connectivity patterns that consistently predicted mind-wandering within individuals but did not fully generalize across individuals. Predictive models of mind-wandering and attention that were derived from larger-scale neuroimaging datasets largely failed when applied to densely-sampled individuals, further highlighting the need for personalized models. Our work offers novel evidence for both conserved and variable neural representations of self-reported mind-wandering in different individuals. The previously-unrecognized inter-individual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.

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bioRxiv2021

Resting State Functional Connectivity Predicts Future Changes in Sedentary Behavior

Information about a persons available energy resources is integrated in daily behavioral choices that weigh motor costs against expected rewards. It has been posited that humans have an innate attraction towards effort minimization and that executive control is required to overcome this prepotent disposition. With sedentary behaviors increasing at the cost of millions of dollars spent in health care and productivity losses due to physical inactivity-related deaths, understanding the predictors of sedentary behaviors will improve future intervention development and precision medicine approaches. In 64 healthy older adults participating in a 6-month aerobic exercise intervention, we use neuroimaging (resting state functional connectivity), baseline measures of executive function and accelerometer measures of time spent sedentary to predict future changes in objectively measured time spent sedentary in daily life. Using cross-validation and bootstrap resampling, our results demonstrate that functional connectivity between 1) the anterior cingulate cortex and the supplementary motor area and 2) the right anterior insula and the left temporoparietal/temporooccipital junction, predict changes in time spent sedentary, whereas baseline cognitive, behavioral and demographic measures do not. Previous research has shown activation in and between the anterior cingulate and supplementary motor area as well as in the right anterior insula during effort avoidance and tasks that integrate motor costs and reward benefits in effort-based decision making. Our results add important knowledge toward understanding mechanistic associations underlying complex sedentary behaviors.

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bioRxiv2021

Generalizable prediction of stimulus-independent, task-unrelated thought from functional brain networks

Neural substrates of "mind wandering" have been widely reported, yet experiments have varied in their contexts and their definitions of this psychological phenomenon, limiting generalizability. We aimed to develop and test the generalizability, specificity, and clinical relevance of a functional brain network-based marker for a well-defined feature of mind wandering--stimulus-independent, task-unrelated thought (SITUT). Combining functional MRI (fMRI) with online experience sampling in healthy adults, we defined a connectome-wide model of inter-regional coupling--dominated by default-frontoparietal control subnetwork interactions--that predicted trial-by-trial SITUT fluctuations within novel individuals. Model predictions generalized in an independent sample of attention-deficit/hyperactivity disorder (ADHD) adults. In three additional resting-state fMRI studies (total n=1,115), including healthy and ADHD populations, we demonstrated further prediction of SITUT (at modest effect sizes) defined using multiple trait-level and in-scanner measures. Our findings suggest that SITUT is represented within a common pattern of brain network interactions across time scales, populations, and contexts.

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bioRxiv2020

Two dominant brain states reflect optimal and suboptimal attention

Attention is not constant but fluctuates from moment to moment. Previous studies dichotomized these fluctuations into optimal and suboptimal states based on behavioral performance and investigated the difference in brain activity between these states. Although these studies implicitly assume there are two states, this assumption is not guaranteed. Here, we reversed the logic of these previous studies and identified unique states of brain activity during a sustained attention task. We demonstrate a systematic relationship between dynamic brain activity patterns (brain states) and behavioral underpinnings of sustained attention by explaining behavior from two dominantly observed brain states. In four independent datasets, a brain state characterized by default mode network activity was behaviorally optimal and a brain state characterized by dorsal attention network activity was suboptimal. Thus, our study provides compelling evidence for behaviorally optimal and suboptimal attentional states from the sole viewpoint of brain activity. We further demonstrated how these brain states were impacted by motivation, mind wandering, and attention-deficit hyperactivity disorder. Within-subject level modulators (motivation and mind wandering) impacted the optimality of behavior in the suboptimal brain state. In contrast, between-subject level differences (ADHD vs healthy controls) impacted the optimal brain state character, namely its frequency.

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bioRxiv2018

Anticorrelated inter-network electrophysiological activity varies dynamically with attentional performance and behavioral states

The default mode network (DMN) is thought to exhibit infraslow anticorrelated activity with dorsal attention (DAN) and salience (SN) networks across various behavioral states. To investigate the dynamics of activity across these networks on a finer timescale, we used human intracranial electroencephalography with simultaneous recordings within core nodes of the three networks. During attentional task performance, the three sites showed dissociable profiles of high-frequency broadband activity. Anticorrelated infraslow fluctuations of this activity were found across networks during task performance but also intermittently emerged during rest and sleep in concert with the expression of task-like network-level topographic patterns. Critically, on a finer timescale, DAN and SN activations preceded DMN deactivations by hundreds of milliseconds. Moreover, greater lagged, but not zero-lag, anticorrelation between DAN and DMN activity was associated with better attentional performance. These findings have implications for interpreting antagonistic network relationships and confirm the behavioral importance of time-lagged inter-network interactions.

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