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 audiovisual continuous performance task 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 audiovisual continuous performance task. A pretrained connectome-based model of sustained attention predicted participants' performance on out-of-scanner LTM tasks, 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.

Resting-state functional connectivity (rsFC)-brain connectivity observed when people rest with no external tasks-predicts individual differences in behaviour. 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 behaviour? Here, to test this question, we used 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 behaviour.

Abstract 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.

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 fMRI 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. In addition, 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.

Sustained attention is an important neurobiological process. Difficulties with attention play a key role in neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD) and autism. Here, we identified functional connections consistently associated with sustained attention across datasets, participant populations, and fMRI scan types. We interrogated five transdiagnostic, previously published connectome-based models predicting attention and autistic phenotypes. All models were related to sustained attention, including in samples comprising participants with autism. As expected, we observed that models predicting attention phenotypes shared more similar features with each other than models predicting autism symptoms. Interestingly, we observed no statistically significant model similarities when considering factors such as age, functional run type, or diagnosis. This suggests that functional connectivity patterns predicting individual differences in behavior tend to be phenotype-specific, regardless of age or clinical diagnosis. Our results underscore the importance of searching for consistent markers of transdiagnostic sustained attention phenotypes in neurodevelopmental conditions.

Maintaining attention to a task is essential for accomplishing it. However, attentional state fluctuates moment to moment and task-irrelevant information may compete for processing. What are the consequences of attentional fluctuations for what we remember? Do fluctuations in sustained attention vary the spotlight of selective attention, prioritizing task-relevant at the expense of task-irrelevant information? Or, are increases in sustained attentional state akin to a floodlight, enhancing processing of all information, regardless of task-relevance? In an online sample of 215 adults, participants were presented simultaneous streams of images and sounds and instructed to make responses based on only one modality. Afterwards, recognition memory for both images and sounds was tested. Across individuals, we found no evidence of a tradeoff between memory for task-relevant and task-irrelevant items. Within individuals, successful memory for a task-relevant item predicted successful memory for its task-irrelevant pair. Thus, the spotlight metaphor of attention does not extend to the dynamics of sustained attention. Rather, fluctuations in attention are more akin to a floodlight, affecting processing of all task information, regardless of relevance.

AbstractSustained 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’visualsustained attention performance generalizes to predictauditorysustained 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.

BACKGROUND:Between-subjects studies suggest that psychostimulants can shift whole-brain functional connectivity (FC) toward patterns linked to heightened sustained attention. In this study, we examined how a single dose of methamphetamine (MA) (20 mg) changes sustained attention and associated network-level functional organization in healthy adults. METHODS:We conducted a within-subject study in which 76 healthy participants completed 2 functional magnetic resonance imaging (fMRI) scanning sessions after taking MA or placebo. We tested whether MA selectively affects behavioral and fMRI connectivity signatures of sustained attention and arousal. RESULTS:Under MA, participants showed improved sustained attention task performance as well as FC 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 FC patterns observed during the MA and placebo conditions, identifying connections overlapping with networks related to arousal. CONCLUSIONS:Together, these findings are consistent with previous work on other psychostimulants such as 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.

Sustained attention fluctuates over time, affecting task-related processing and memory. However, it is less clear how attentional state affects processing and memory when images are accompanied by irrelevant visual information. We first quantify behavioral signatures of attentional state in an online sample (N1=92) and demonstrate that images presented in high attentional states are better remembered. Next, we test how sustained attention influences memory in two online samples (N2=188, N3=185) when task-irrelevant images are present. We show that high attention leads to better memory for both task-relevant and task-irrelevant images. This suggests that sustained attentional state does selectively affect processing for task-relevant information, but rather affects processing broadly, regardless of task relevance. Finally, we show that other components of attention such as selective attention contribute to the mnemonic fate of stimuli. Our findings highlight the necessity of considering and characterizing attention’s unique components and their effects on cognition.

Heterogeneity in brain activity can give 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 (Finn et al., 2020) which proposes a theoretical framework for testing the validity of such assumptions. Using intersubject representational similarity analysis in two independent movie-watching functional MRI (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.

The ability to maintain attention varies across and within individuals, affecting subsequent memory. How consistent is sustained attention to and its impacts on memory for information from different perceptual modalities? Can sustained attention performance be improved with real-time task manipulations? To ask these questions, we first developed an auditory-visual continuous performance task (avCPT). Online participants (N=110) completed two sessions of the avCPT in which they saw trial-unique scene images paired with trial-unique sounds every 1200 ms (500 trials). In each session, participants were instructed to attend to either images or sounds and press a button in response to frequent-category stimuli (indoor or outdoor scenes; natural or manmade sounds) and withhold responses to the infrequent category (10%). Performance (A’) on auditory and visual avCPT sessions was positively related across participants (r=.49, p<.001), suggesting that sustained attention is consistent across perceptual modalities. Furthermore, attentional state influenced subsequent recognition memory for visual stimuli. Infrequent-category images to which participants correctly withheld response were better remembered than those to which they incorrectly responded (t(105)=2.98, p=.004) but this effect was not observed for sounds. Given this evidence of reliability within individuals, we next asked whether performance can be improved via personalized feedback based on pupil size, an index of attentional state. In a modified version of the task above, participants categorized indoor and outdoor scenes whose clarity (% visual noise) varied as a function of real-time changes in pupil diameter. Whereas increasing perceptual difficulty in a low attention state (smaller pupil diameter) would improve A’ if poor performance results from mindlessness, decreasing difficulty in a low attention state would improve A’ if poor performance results from overload. While consistency in auditory and visual avCPT performance illustrate the stability of sustained attention performance across modalities, impacts of real-time task manipulation may provide evidence of malleability.

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.

Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities. Here, we test whether individuals who express more stable, typical, optimal, and distinctive patterns of functional connectivity perform better on cognitive tasks using data from three independent samples. We find that individuals with more stable task-based functional connectivity patterns perform better on attention and working memory tasks, even when controlling for behavioral performance stability. Additionally, we find initial evidence that individuals with more typical and optimal patterns of functional connectivity also perform better on these tasks. These results demonstrate that functional connectome stability within individuals and similarity across individuals predicts individual differences in cognition.

The recall and visualization of people and places from memory is an everyday occurrence, yet the neural mechanisms underpinning this phenomenon are not well understood. In particular, the temporal characteristics of the internal representations generated by active recall are unclear. Here, we used magnetoencephalography (MEG) and multivariate pattern analysis to measure the evolving neural representation of familiar places and people across the whole brain when human participants engage in active recall. To isolate self-generated imagined representations, we used a retro-cue paradigm in which participants were first presented with two possible labels before being cued to recall either the first or second item. We collected personalized labels for specific locations and people familiar to each participant. Importantly, no visual stimuli were presented during the recall period, and the retro-cue paradigm allowed the dissociation of responses associated with the labels from those corresponding to the self-generated representations. First, we found that following the retro-cue it took on average ∼1000 ms for distinct neural representations of freely recalled people or places to develop. Second, we found distinct representations of personally familiar concepts throughout the 4 s recall period. Finally, we found that these representations were highly stable and generalizable across time. These results suggest that self-generated visualizations and recall of familiar places and people are subserved by a stable neural mechanism that operates relatively slowly when under conscious control.

A great deal of recent empirical and theoretical work has examined whether it is possible to enhance cognitive functioning via behavioral (cognitive) training. While a growing body of research provides support for such a hypothesis, multiple critiques of the field have suggested that any positive findings in the field to date may be due to placebo effects, rather than reflecting "true" benefits of the training paradigms. Here, in a series of four experiments, we sought to purposefully induce placebo effects of this type in cognitive training-style setup. We did so in multiple outcome domains (fluid intelligence; spatial skills), employed multiple types of "training" paradigms (classic cognitive training using the N-back working memory task; the video game Tetris) and critically, combined explicit verbal instructions that participants in some groups "should" expect to improve their performance after completing their training with associative learning "evidence" that such improvements were occurring (via manipulated task designs). In no case, though, was a placebo effect observed. These results collectively provide evidence against the contention that placebo effects are a major driver of positive outcomes previously attributed to cognitive training interventions.

A detailed understanding of visual object representations in brain and behavior is fundamentally limited by the number of stimuli that can be presented in any one experiment. Ideally, the space of objects should be sampled in a representative manner, with (1) maximal breadth of the stimulus material and (2) minimal bias in the object categories. Such a dataset would allow the detailed study of object representations and provide a basis for testing and comparing computational models of vision and semantics. Towards this end, we recently developed the large-scale object image database THINGS of more than 26,000 images of 1,854 object concepts sampled representatively from the American English language (Hebart et al., 2019). Here we introduce THINGS-fMRI and THINGS-MEG, two large-scale brain imaging datasets using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). Over the course of 12 scanning sessions, 7 participants (fMRI: n = 3, MEG: n = 4) were presented with images from the THINGS database (fMRI: 8,740 images of 720 concepts, MEG: 22,448 images of 1,854 concepts) while they carried out an oddball detection task. To reduce noise, participants’ heads were stabilized and repositioned between sessions using custom head casts. To facilitate the use by other researchers, the data were converted to the Brain Imaging Data Structure format (BIDS; Gorgolewski et al., 2016) and preprocessed with fMRIPrep (Esteban et al., 2018). Estimates of the noise ceiling and general quality control demonstrate overall high data quality, with only small overall displacement between sessions. By carrying out a broad and representative multimodal sampling of object representations in humans, we hope this dataset to be of use for visual neuroscience and computational vision research alike.

Recall of familiar people or places elicits activation in distinct sets of cortical regions, including ventral temporal cortex, medial parietal cortex, and posterior parietal cortex. To investigate the temporal dynamics of visual recall from memory, we collected magnetoencephalography (MEG) data while participants (N=30) visualized highly familiar people or places requiring retrieval of long term internal representations. Conditions were personalized to each participant and they each provided the names of six personally familiar people (e.g. Aunt Sanika) and places (e.g. gym). In a retrocue paradigm, two names were presented sequentially on the screen (800 ms each with a 200 ms gap) followed by a blank screen (400 ms) and then the presentation of a retrocue (500 ms). This retrocue was either the number 1 or 2, indicating that the participant should visually recall either the first or the second item shown. Participants then visualized the cued item as vividly as possible for 4000 ms. Each condition was presented 32 times and cued 16 times over the course of 192 trials. Trials were broken into 8 runs in which each name was seen 4 times and cued for recall twice. Data was sampled across 272 channels at 1200 Hz with whole-brain coverage, then downsampled to 200 Hz. Principal component analysis was implemented to retain the components explaining 99% of the variance. Using both multivariate pair-wise classification and representational similarity analysis on the responses measured across sensors, we found significant decoding of people versus places starting around 900 ms after retrocue onset. This decoding persisted throughout the visual recall period. Further, we observed a tendency toward decoding individual people and places. These results demonstrate the ability to decode visual representations recalled from long term memory in the absence of any preceding visual stimulation.