Biology subjects

Corriveau, A.

Publications and source records attributed to Corriveau, A..

10 recordsLinked to original sources

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

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↗

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

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

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

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

Functional brain networks predicting sustained attention are not specific to perceptual modality

Sustained attention is essential for daily life and can be directed to information from different perceptual modalities including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets (N1=29, N2=60, N3=17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality-general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.

neuroscience↗

bioRxiv2023

High performers demonstrate greater neural synchrony than low performers across behavioral domains

Heterogeneity in brain activity gives rise to heterogeneity in behavior, which in turn comprises our distinctive characteristics as individuals. Studying the path from brain to behavior, however, often requires making assumptions about how similarity in behavior scales with similarity in brain activity. Here, we expand upon recent work which proposes a theoretical framework for testing the validity of such assumptions. Using intersubject representational similarity analysis in two independent movie-watching fMRI datasets, we probe how brain-behavior relationships vary as a function of behavioral domain and participant sample. We find evidence that, in some cases, the neural similarity of two individuals is not correlated with behavioral similarity. Rather, individuals with higher behavioral scores are more similar to other high scorers whereas individuals with lower behavioral scores are dissimilar from everyone else. Ultimately, our findings motivate a more extensive investigation of both the structure of brain-behavior relationships and the tacit assumption that people who behave similarly will demonstrate shared patterns of brain activity.

neuroscience↗

bioRxiv2022

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior

Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely-sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

neuroscience↗

bioRxiv2019

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

In recent years, the use of a large number of object concepts and naturalistic object images has been growing enormously in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually-curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of uncontrolled naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.

neuroscience↗