Biology subjects

Rosenberg, M. D.

Publications and source records attributed to Rosenberg, M. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Individual Differences in Sustained Attention Tune Semantic Abstraction in Neural Encoding of Narratives

Neural encoding modeling for narrative comprehension -- using LLM embeddings to predict the temporal dynamics of neural activation -- is widely used to evaluate how well the brain represents certain semantic information. This approach, however, typically collapses semantic representation into a scalar fidelity score aggregated across participants, overlooking potentially rich individual differences and qualitative differences in naturalistic neural representations. In the current study, we addressed this gap by investigating how neural semantic encoding varies across individuals with sustained attention, and whether this difference is qualitative in addition to quantitative. Using fMRI data from 49 participants listening to an audiobook, Le Petit Prince, in English, we trained encoding models with GPT-2 embeddings at varying layers and forecasting lengths. Encoding models trained on high-level embeddings (from the 8th layer of GPT-2) showed significant brain-LLM alignment in 73 of 122 cortical and subcortical ROIs, concentrated in the temporal cortex and the default mode network. Functional connectivity strength in a validated connectome-based predictive model of sustained attention correlated with encoding performance in 18 ROIs, most strongly in the insula and in control and attention networks, with no ROI showing significant opposite relationship. Decomposing semantic information by levels of abstraction showed that the neural signature of sustained attention was selectively associated with a greater proportion of high-level gist information encoded in the frontoparietal and attention networks, while the opposite relationship emerged for proportion of low-level information. Sustained attention scores further predicted stronger predictive coding of upcoming narrative content across multiple networks across the whole brain (control, default mode, salience/ventral attention, limbic, and somatomotor networks). These findings suggest that sustained attention does not uniformly amplify neural semantic encoding, but instead shifts neural representations toward more compressed, gist-level formats that support anticipation of upcoming content. Individual differences in brain-LLM alignment can be a signature of representational strategy rather than noise, with potential utility for cognitive phenotyping.

neuroscience↗

bioRxiv2026

Degraded neural representations foreshadow failures of sustained attention

Attention prioritizes relevant information from the environment, but does not do so consistently over time. Instead, we sometimes experience poor attentional states which are characterized by a higher degree of performance failures, or "lapses." The source of these lapses--whether they arise from attentional disengagement, failures of attentional selection, or simply failures of response control--is not clear. This study tests whether the strength of neural representations differs as a function of both attentional selection and modulation over time. We recorded electroencephalography (EEG) data while participants performed a sustained attention task requiring the selection of task-relevant items and withholding of responses to rare targets. Results show that relevant items receive prioritized processing in brain signals, as measured with EEG decoding. Yet, their neural representational fidelity weakens under poor sustained attentional states, foreshadowing upcoming behavioral failures. These findings support a neural basis of disengagement during poor attentional states, suggesting that lapses may arise from a decoupling of brain activity from task processing. Finally, pre-item representational strength predicted memory success for targets, suggesting that neural fidelity indexes an encoding-ready state. Results suggest that the quality of neural representations is affected by ongoing attention and plays a key role in processing and memory.

neuroscience↗

bioRxiv2026

Mnemonic Demand Reconfigures the Neural Architecture of Memory Encoding

Memory encoding must accommodate increasingly demanding loads, yet it remains unclear whether the underlying brain state is preserved or adaptively reconfigured as load increases. Here, we recorded EEG from 111 participants performing a visual memory task in which they encoded lists of real images ranging from 1 to 128 items, spanning low to high mnemonic demands, followed by a recognition test. We measured the similarity of set-size-specific interelectrode correlation patterns to a low-load reference. Similarity decreased monotonically as list length increased: patterns at set sizes below 4 remained close to the low-load configuration, whereas set sizes of 8 and above progressively shifted toward a distinct high-load configuration. This transition was captured by a single rotating eigenvector, revealing a low-dimensional trajectory from low- to high-load neural geometry. Smaller rotation angles from the low-load template predicted better memory performance at high loads. Together, these findings provide evidence for adaptive reconfiguration and reveal a structured, low-dimensional neural trajectory through which encoding architecture changes as mnemonic demands increase.

neuroscience↗

bioRxiv2026

Drinking Motives Synchronize Behavioral and Neural Craving Responses to Alcohol-drinking Videos

Alcohol drinking motives vary among individuals and shape experiences and beliefs about alcohol, influencing the processing of alcohol-related cues. In real-life settings, these cues are contextually rich, amplifying the role of such individualized drinking motives on cue processing. However, previous literature has primarily relied on images of alcohol, which lack contexts and differ significantly from real-life. Here, aiming to investigate real-life craving, we examined the role of alcohol drinking motives in craving in response to naturalistic alcohol-drinking videos. We asked fifty-three problematic alcohol users to speak about their reasons for drinking alcohol to capture unique alcohol drinking motives of each individual. Participants also underwent functional MRI while watching fifteen alcohol-drinking videos, and reported their subjective level of craving and self-relatedness for each video. Behavioral data analysis revealed that individuals with greater alcohol use severity tended to report greater cue-induced craving, but only when they reported that a video was related to themselves. Inter-subject representational similarity analysis showed that participants with similar alcohol drinking motives, reflected in shared drinking reasons and similar self-relatedness to the videos, exhibited synchronized craving-related neural responses during video-watching. Notably, these shared neural processes mediated the link between similar drinking motives and similar self-reported craving levels across participants. Together, our findings highlight the crucial role of alcohol drinking motives in shaping cue-induced alcohol craving, and provide deeper insights into craving in real-world contexts.

neuroscience↗

bioRxiv2026

A common connectome-based neural reference space across affective, autonomic, and wakefulness arousal

Arousal is often invoked to explain state-dependent variability in attention, emotion, memory, and decision-making. However, the term is used inconsistently, referring variously to affective experience, autonomic activation, and states of wakefulness. The extent to which different operationalizations of arousal reflect a shared neurobiological basis remains unclear, limiting efforts to unify findings across studies. We applied dynamic connectome predictive models to five fMRI datasets spanning naturalistic movie watching, story listening, wakeful rest, and sleep. We derived measures of affective, autonomic, and wakefulness arousal from subjective ratings, pupil dilation, and EEG, respectively. Models trained to predict arousal in one dataset generalized across datasets, indicating that dynamic connectivity captures shared arousal-related dynamics across measures and task contexts. The models also predicted manually scored sleep stages, suggesting that they capture arousal dynamics that extend to the graded fluctuations in arousal during sleep. Decoded arousal dynamics during movie-viewing predicted how well participants later recalled movie events, reproducing classic arousal-dependent memory enhancement effects. Model predictions were supported by overlapping functional connections, including a subset shared across all models. The largest proportion of shared connections was between the salience and somatomotor networks, suggesting that increased coordination between salience detection and action readiness may be a common feature across multiple operationalizations of arousal. Together, these results are consistent with a connectome-based neural reference space for arousal, in which different varieties share a core set of predictive connections. Our findings offer a quantitative framework for integrating arousal-related findings across tasks and modalities.

neuroscience↗

bioRxiv2026

Neural dynamics of updating social impressions during movie watching

Updating impressions of others is essential to navigating social life. As we get to know an individual, we update our impressions of them in accordance with new information. How the brain dynamically revises impressions in real time under naturalistic conditions remains unclear. Here, we address this question using functional magnetic resonance imaging (fMRI) and natural language analysis in a naturalistic social cognition paradigm. Across 10 runs, participants viewed a character-driven TV episode, reported moments of insight, and described their impressions of the characters. Results reveal that impressions progressively evolved over time. Individuals with more similar existing impressions exhibit greater neural synchrony during movie-watching, which predicts convergence in post-movie impressions. Neural synchrony in the right superior temporal sulcus (STS) mediates the influence of initial similarity on later alignment. Insight moments accompany neural pattern shifts widespread across cortical regions, including the temporoparietal junction (TPJ), dorsomedial prefrontal cortex (dmPFC) and STS, and the magnitude of the shifts tracks the degree of impression updating. Specifically, distinct forms of insight selectively update complementary components of impressions: character insight updates person-centered representations, whereas non-character insight shapes social-event structure. Together, these findings show that people update their impressions of others through dynamic shifts in distributed brain activity patterns at moments of insight, providing a novel and ecologically grounded neural account of social cognition. Significance statementPeople do not simply form first impressions and keep them. As we learn new things about others, our judgments change--sometimes gradually, sometimes in a flash of insight. By tracking brain activity while people watched a TV show and described how their impressions of the characters evolved, we found that people who saw a character in more similar ways also show more similar brain responses when watching them onscreen, which in turn leads to convergence in subsequent impressions. Such updating is closely tied to "aha" moments, when a person is suddenly understood in a new way. These moments were marked by rapid shifts in the brain representational patterns, and larger shifts predicted greater changes in impressions. These findings offer a more naturalistic account of how the brain revises our understanding of other people in everyday life.

neuroscience↗

bioRxiv2026

ReMind: A Retrospective Self-Report Paradigm for Studying Mind-Wandering Onset During Reading

Mind-wandering (MW) is a frequent and pervasive phenomenon, yet it is commonly assessed using self-reports or probe-based methods that offer limited temporal precision regarding its onset. In this study, we introduce a novel paradigm, ReMind, that estimates the onset and duration of MW episodes during natural reading by combining retrospective self-reports with eye-tracking. Participants indicated the words where they believed their mind started and stopped wandering, and these reports were aligned with gaze timestamps to estimate MW onset. Using data from 44 participants, we examined whether knowledge of MW onset improves the detection of MW from eye-tracking signals. To evaluate relevance for both self-report and thought-probe paradigms, we additionally simulated thought probes by randomly sampling time points during reading. Logistic regression classifiers trained on eye-tracking features extracted from time windows anchored to MW onset achieved AUROC scores of 0.659 and 0.621 under the self-report and simulated thought-probe paradigms, respectively, using leave-one-subject-out cross-validation. In both cases, onset-aligned windows outperformed classifiers trained using arbitrary MW windows. Sliding-window analyses further revealed systematic temporal changes around MW onset, with classification performance peaking at approximately 3 seconds after onset. Feature-level analyses showed reduced fixation rate and fixation dispersion, along with increased pupil size following MW onset. Together, these findings characterize the temporal progression from on-task reading to MW. Overall, ReMind provides a useful framework for studying the temporal dynamics of MW during naturalistic reading.

bioengineering↗

bioRxiv2026

Distinct Control States Underlie Voluntary Task Switching: Evidence for Capacity-Dependent Control Modes

People often switch tasks when attention wanes or an alternative task becomes more appealing. Such choices may reflect different control modes that may vary with working memory (WM) capacity. This study tested whether momentary attentional lapses prospectively predict voluntary task switching and whether this relationship depends on WM capacity. Participants performed a continuous performance task involving face and scene images, with blocks in which they either freely chose the next task or followed an externally imposed sequence. The results suggested a capacity-dependent pattern in which individuals with lower WM capacity were more likely to switch following lapse-prone blocks, whereas higher-capacity individuals tended to switch from relatively well-focused states. Eye-tracking revealed greater bias toward the competing irrelevant category before switches in lower-capacity individuals, accompanied by early conflict-related pupil dilation. Externally imposed task sequencing selectively reduced lapses in the lower-capacity group without affecting higher-capacity performance, suggesting that external structure can scaffold weaker internal goal maintenance. These findings suggest that the relationship between lapses and voluntary switching varies with WM capacity rather than being uniform across individuals. This pattern is consistent with a goal-competition account in which lapses reflect shifts in the balance between competing task goals, and voluntary switches may be preceded by different control states.

neuroscience↗

bioRxiv2025

A unique neural signature of long-term memory encoding from EEG inter-electrode correlation

Classic memory models proposed that the encoding process involved in visual working memory (VWM) controls the bandwidth of encoding in long-term memory (LTM). Behaviorally, VWM and LTM accuracies are reliably correlated at the behavioral level, raising the question of whether LTM encoding uniquely engages processes that are distinct from VWM encoding. To investigate this, we recorded EEG activity as participants completed recognition memory tasks with set sizes of 32 and 128, far beyond typical VWM capacity. Using interelectrode correlation (IC) analysis, we found that IC patterns reliably predicted individual differences in LTM encoding across both set sizes, indicating a robust, domain-general neural signature. Importantly, this predictive power remained even after controlling for VWM and attentional control performance, suggesting that the model captures variance specific to LTM encoding. Temporally, predictive signals emerged only after stimulus onset and persisted for 500-600 ms. Early and late encoding phases involved distinct network structures, reflecting dynamic neural processes underlying individual differences in LTM encoding. Together, our findings reveal a unique and temporally dynamic neural signature that supports individual differences in LTM encoding, independent of general cognitive abilities. Significance StatementHumans maintain visual information using two distinct memory systems: visual working memory, which is capacity-limited, and long-term memory, which has virtually unlimited capacity. Despite their differences, individuals with better VWM performance often show superior LTM performance, raising the question of whether unique LTM encoding abilities exist beyond those accounted for by VWM. Here, we leveraged EEG data to examine interelectrode correlation patterns during memory encoding. Our results show that these neural patterns uniquely predict individual differences in LTM encoding ability, even after controlling for VWM performance. Furthermore, we showed that the interelectrode correlation patterns continuously track the temporal dynamics of LTM encoding over time.

neuroscience↗

bioRxiv2025

Decoding Distraction From the Human Brain: A Unique Neural Signature Beyond Failures of Selective Attention and Control

Attentional lapses are a ubiquitous feature of cognition, yet their underlying causes remain poorly understood. Theories of sustained attention often point to failures of cognitive control in maintaining the task-set, while data-driven approaches suggest that lapses may instead reflect a breakdown in the selection of task-relevant information. This study aimed to characterize the neural mechanisms of sustained attention lapses and to test whether EEG-based signatures of lapse-prone states are distinct from signatures of failures of selective attention and task-set maintenance. Twenty adults completed a sustained attention go/no-go task while focusing on either numbers or letters, with EEG recorded simultaneously. Poor sustained attention was examined at two complementary levels: trial-level lapses, defined as no-go errors, and attentional states, derived from reaction-time variability and categorized as "in-the-zone" versus "out-of-the-zone". Across both levels, suboptimal sustained attention was associated with attenuated event-related potentials, most notably a reduced parietal P3 amplitude and weaker whole-scalp inter-electrode correlation. To isolate a unique EEG marker of lapse-prone state, a machine-learning classifier decoded attentional state from EEG activity. Cross-validated accuracy reached [~]80% and remained robust after controlling for reaction time. Finally, representational similarity analysis confirmed that this neural signature was dissociable from stimulus-side selection and task-set maintenance.

neuroscience↗

bioRxiv2025

Common brain network dynamics capture attention fluctuations in tasks and movies

Attentional states are highly dynamic and variable, fluctuating from moment to moment and showing stark differences across contexts. To what extent does functional brain reorganization capture variability in attentional states? In the present study we utilize a time-resolved measure of functional MRI connectivity to examine and compare the extent to which univariate activity and functional networks reflect second-to-second sustained attentional fluctuations. Sustained attention was measured objectively, using auditory and visual tasks, and subjectively while participants watched and listened to narratives. Results revealed that objective measures of sustained attention to images and sounds involved common patterns of neural activity and functional interactions. Additionally, networks related to sustained attentional performance during controlled tasks also predicted fluctuations in subjective attentional engagement while participants watched movies and listened to a podcast. Generalization between experimental and everyday task contexts highlights the robustness of time-resolved functional networks for capturing dynamic fluctuations in sustained attentional states.

neuroscience↗

bioRxiv2025

A neural network with episodic memory learns causal relationships between narrative events

Humans reflect on memories to make sense of ongoing events. Past work has shown that people retrieve causally related memories during comprehension, but the mechanisms underlying this process remain unclear. Here, we used a recurrent neural network augmented with a key-value episodic memory buffer and trained it to predict upcoming scenes while watching a television episode. At each time step, the model transformed the current scene into a value representing memory content and a key representing memory address, both stored as episodic memory. The model retrieved selective past values by applying self-attention over stored keys and integrated these memories with the current scene representation to generate predictions. The model retrieved memories similar to those retrieved by human participants watching the same episode during fMRI. Importantly, this similarity disappeared when causal relationships between events were controlled for. The model also represented causally related events with similar patterns, similar to how the human brain represents these events. These findings suggest that using two distinct memory representations allows the model to retrieve memories and organize events based on causal relationships, beyond semantic or perceptual similarities. Together, this work proposes a key-value episodic memory system as a candidate computational mechanism for how humans retrieve causally related memories to comprehend naturalistic events.

neuroscience↗

bioRxiv2025

Ongoing thoughts at rest reflect functional brain organization and behavior

Resting-state functional connectivity (rsFC)--brain connectivity observed when people rest with no external tasks--predicts individual differences in behavior. Yet, rest is not idle; it involves streams of thoughts. Are these ongoing thoughts reflected in FC and do they contribute to the relationship between rsFC and behavior? To test this question, we developed an annotated rest paradigm where participants rated and verbally described their thoughts after each rest period during functional MRI (N = 60). Our findings revealed rich and idiosyncratic thoughts across individuals. Similarity in thoughts was associated with more similar FC patterns within and across individuals. In addition, both thought ratings and topics could be decoded from FC. Furthermore, neuromarkers of these thoughts generalized to unseen individuals in the Human Connectome Project dataset (N = 908), where decoded thought patterns during rest predicted positive vs. negative trait-level individual differences. Together, our findings reveal that ongoing thoughts at rest are reflected in brain dynamics and these network patterns predict everyday cognition and experiences. Understanding subjective in-scanner experiences is thus crucial in characterizing the relationship between individual differences in functional brain organization and behavior.

neuroscience↗

bioRxiv2025

Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention

The brain is a complex dynamical system whose activity reflects changes in internal states, such as attention. While prior work has shown that large-scale brain activity reflects attention, the mechanism governing this association in a time-varying and task-dependent manner remains unknown. Here, we tested a hypothesis that the geometry of neural dynamics on the attractor landscape, or the movement along the "hills and valleys", reflects changes in attentional states over time and variations across controlled and naturalistic contexts. We fit a parametric dynamical systems model to fMRI data collected during rest, task performance, and naturalistic movie-watching. The model decomposes neural dynamics into components that are intrinsic versus extrinsically driven by stimuli. Model parameters were biologically meaningful, reflecting both cognitive states and individual differences. Model simulations revealed a set of attractors that mirrored functional brain networks, spanning the canonical gradient from sensorimotor to default mode network regions. The speed and direction of neural trajectories toward these attractors systematically varied across attentional states in a context-dependent manner. When participants were paying attention to effortful tasks, neural dynamics converged directly toward a task-relevant attractor, suggesting that it occupied a steeper region of the attractor landscape. In contrast, when participants were engaged in sitcom episodes, neural dynamics were in a flattened region of the landscape, directed away from the attractors. These findings demonstrate that while the positions of the attractors are largely determined by the cortical organization, the geometry of neural dynamics on the attractor landscape changes systematically across attentional states and situational contexts.

neuroscience↗

bioRxiv2025

Methamphetamine modulates functional connectivity signatures of sustained attention and arousal

Building on evidence that psychostimulants modulate whole-brain functional connectivity signatures of sustained attention, we examined how a single dose of methamphetamine (MA, 20 mg) changes network-level functional organization and sustained attention in healthy adults. Using a within-subject, placebo-controlled design, we tested whether MA selectively affects behavioral and fMRI connectivity signatures of sustained attention and arousal. Under MA, participants showed improved sustained attention task performance as well as functional connectivity signatures of higher sustained attention and arousal. These network changes emerged consistently across resting-state and task-based fMRI, indicating that MA influences attention- and arousal-related networks regardless of cognitive context. Furthermore, a support vector classifier distinguished functional connectivity patterns observed during the MA and placebo conditions, identifying connections overlapping with networks related to arousal. Together, these findings align with prior work on other psychostimulants like methylphenidate, showing that MA modulates sustained attention and related large-scale brain networks. By revealing how MA modulates attention-relevant brain connectivity patterns, our results highlight the utility of psychostimulants as causal tools for probing the robustness, generalizability, and interpretability of brain-based biomarkers of behavior.

neuroscience↗

bioRxiv2025

Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns

We make sense of everyday events by reasoning about their underlying causes. When we connect causal links between events separated in time, we often experience a sudden feeling of "aha!", or a moment of insight. What cognitive and neural processes underlie these moments of understanding? We hypothesized that narrative insight accompanies retrieving causally related past events in memory and updating the current event representation. To test this, we designed an fMRI study in which participants watched a TV episode that was cut into multiple events and presented in temporally scrambled orders. Participants pressed an "aha" button whenever they understood something new and verbally explained why they pressed in those moments at the end of each run. Supporting our prediction, more than 40% of insights included the retrieval of past events that were causally related to the current event. Neural patterns representing causally related past events were reinstated in cortical areas. This neural reinstatement drove sudden shifts in cortical representation patterns [~]2 s prior to aha button presses, reflecting an update in situational representation at moments of insight. Moreover, distributed areas in the brain represented causally related events with similar neural patterns, beyond their shared semantic or perceptual features. Together, the study suggests that we comprehend events by reinstating causally related past events via shared neural patterns, followed by updating neural patterns at moments of insight.

neuroscience↗

bioRxiv2025

Sustained attention is more closely related to long-term memory than to attentional control

Individuals differ in their ability to sustain attention. However, whether differences in sustained attention reflect differences in processes related to attentional control and working memory or long-term memory (LTM) remains underexplored. In Experiment 1, we conducted an online study (n = 136) measuring participants sustained attention, attention control and working memory, and LTM. We measured sustained attention with an audio-visual continuous performance task (avCPT) in which participants responded to images while inhibiting responses to infrequent targets; attention control and working memory with Flanker, change localization, and Simon tasks; and LTM with recognition and source memory tests. Factor analyses revealed that sustained attention formed a distinct factor from attention control and working memory and LTM. Individual differences in the sustained attention factor robustly predicted individual differences in LTM and, to a lesser extent, attention control and working memory. In Experiment 2, to test how neural signatures of sustained attention related to attention control and working memory and LTM, we analyzed fMRI functional connectivity patterns collected as 20 participants performed the avCPT. A pre-trained connectome-based model of sustained attention predicted participants performance on out-of-scanner LTM, but not attention control and working memory, tasks. Together these results suggest that individual differences in sustained attention, although correlated with attention control and working memory, are more closely related to LTM.

neuroscience↗

bioRxiv2024

Baby Open Brains: An Open-Source Repository of Infant Brain Segmentations

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations. Markers and expert reviewers manually segmented anatomical MRI data from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Anatomical images showed dramatic differences in myelination and intensities across the 1 to 9 month age range, emphasizing the need for densely sampled gold standard manual segmentations in these ages. The BOBs repository is publicly available through the Masonic Institute for the Developing Brain (MIDB) Open Data Initiative, which links S3 storage, Datalad for version control, and BrainBox for visualization. This repository represents an open-source paradigm, where new additions and changes can be added, enabling a community-driven resource that will improve over time and extend into new ages and protocols. These manual segmentations and the ongoing repository provide a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this repository provides a vitally needed foundation for early-life large-scale studies such as HBCD.

neuroscience↗