Predicting moment-to-moment attentional state

Conscious but not thinking-Mind-blanks during visuomotor tracking: An fMRI study of endogenous attention lapses

Attention lapses (ALs) are complete lapses of responsiveness in which performance is briefly but completely disrupted and during which, as opposed to microsleeps, the eyes remain open. Although the phenomenon of ALs has been investigated by behavioural and physiological means, the underlying cause of an AL has largely remained elusive. This study aimed to investigate the underlying physiological substrates of behaviourally identified endogenous ALs during a continuous visuomotor task, primarily to answer the question: Were the ALs during this task due to extreme mind-wandering or mind-blanks? The data from two studies were combined, resulting in data from 40 healthy non-sleep-deprived subjects (20M/20F; mean age 27.1 years, 20-45). Only 17 of the 40 subjects were used in the analysis due to a need for a minimum of two ALs per subject. Subjects performed a random 2-D continuous visuomotor tracking task for 50 and 20 min in Studies 1 and 2, respectively. Tracking performance, eye-video, and functional magnetic resonance imaging (fMRI) were recorded simultaneously. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorise lapses of responsiveness as microsleeps or ALs. Changes in neural activity during 85 ALs (17 subjects) relative to responsive tracking were estimated by whole-brain voxel-wise fMRI and by haemodynamic response (HR) analysis in regions of interest (ROIs) from seven key networks to reveal the neural signature of ALs. Changes in functional connectivity (FC) within and between the key ROIs were also estimated. Networks explored were the default mode network, dorsal attention network, frontoparietal network, sensorimotor network, salience network, visual network, and working memory network. Voxel-wise analysis revealed a significant increase in blood-oxygen-level-dependent activity in the overlapping dorsal anterior cingulate cortex and supplementary motor area region but no significant decreases in activity; the increased activity is considered to represent a recovery-of-responsiveness process following an AL. This increased activity was also seen in the HR of the corresponding ROI. Importantly, HR analysis revealed no trend of increased activity in the posterior cingulate of the default mode network, which has been repeatedly demonstrated to be a strong biomarker of mind-wandering. FC analysis showed decoupling of external attention, which supports the involuntary nature of ALs, in addition to the neural recovery processes. Other findings were a decrease in HR in the frontoparietal network before the onset of ALs, and a decrease in FC between default mode network and working memory network. These findings converge to our conclusion that the ALs observed during our task were involuntary mind-blanks. This is further supported behaviourally by the short duration of the ALs (mean 1.7 s), which is considered too brief to be instances of extreme mind-wandering. This is the first study to demonstrate that at least the majority of complete losses of responsiveness on a continuous visuomotor task are, if not due to microsleeps, due to involuntary mind-blanks.

Prefrontal GABA and glutamate-glutamine levels affect sustained attention

Attention levels fluctuate during the course of daily activities. However, factors underlying sustained attention are still unknown. We investigated mechanisms of sustained attention using psychological, neuroimaging, and neurochemical approaches. Participants were scanned with functional magnetic resonance imaging (fMRI) while performing gradual-onset, continuous performance tasks (gradCPTs). In gradCPTs, narrations or visual scenes gradually changed from one to the next. Participants pressed a button for frequent Go trials as quickly as possible and withheld responses to infrequent No-go trials. Performance was better for the visual gradCPT than for the auditory gradCPT, but the 2 were correlated. The dorsal attention network was activated during intermittent responses, regardless of sensory modality. Reaction-time variability of gradCPTs was correlated with signal changes (SCs) in the left fronto-parietal regions. We also used magnetic resonance spectroscopy (MRS) to measure levels of glutamate-glutamine (Glx) and γ-aminobutyric acid (GABA) in the left prefrontal cortex (PFC). Glx levels were associated with performance under undemanding situations, whereas GABA levels were related to performance under demanding situations. Combined fMRI-MRS results demonstrated that SCs of the left PFC were positively correlated with neurometabolite levels. These findings suggest that a neural balance between excitation and inhibition is involved in attentional fluctuations and brain dynamics.

Dynamic neural reconstructions of attended object location and features using EEG

Attention allows us to select relevant and ignore irrelevant information from our complex environments. What happens when attention shifts from one item to another? To answer this question, it is critical to have tools that accurately recover neural representations of both feature and location information with high temporal resolution. In the present study, we used human electroencephalography (EEG) and machine learning to explore how neural representations of object features and locations update across dynamic shifts of attention. We demonstrate that EEG can be used to create simultaneous time courses of neural representations of attended features (time point-by-time point inverted encoding model reconstructions) and attended location (time point-by-time point decoding) during both stable periods and across dynamic shifts of attention. Each trial presented two oriented gratings that flickered at the same frequency but had different orientations; participants were cued to attend one of them and on half of trials received a shift cue midtrial. We trained models on a stable period from Hold attention trials and then reconstructed/decoded the attended orientation/location at each time point on Shift attention trials. Our results showed that both feature reconstruction and location decoding dynamically track the shift of attention and that there may be time points during the shifting of attention when 1) feature and location representations become uncoupled and 2) both the previously attended and currently attended orientations are represented with roughly equal strength. The results offer insight into our understanding of attentional shifts, and the noninvasive techniques developed in the present study lend themselves well to a wide variety of future applications.NEW & NOTEWORTHY We used human EEG and machine learning to reconstruct neural response profiles during dynamic shifts of attention. Specifically, we demonstrated that we could simultaneously read out both location and feature information from an attended item in a multistimulus display. Moreover, we examined how that readout evolves over time during the dynamic process of attentional shifts. These results provide insight into our understanding of attention, and this technique carries substantial potential for versatile extensions and applications.

A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.

What came before: Assimilation effects in the categorization of time intervals

Assimilation is the process by which one judgment tends to approach some aspect of another stimulus or judgment. This effect has been known for over half a century in various domains such as the judgment of weight or sound intensity. However, the assimilation of judgments of durations have been relatively unexplored. In the current article, we present the results of five experiments in which participant s were required to judge the duration of a visual stimulus on each trial. In each experiment, we manipulated the pattern of durations they experienced in order to systematically separate the effects of the objective and subjective duration of stimuli on subsequent judgments. We found that duration judgments were primarily driven by prior judgments, with little, if any, effect of the prior objective stimulus duration. This is in contrast to the findings previously reported in regards to non-temporal judgments. We propose two mechanist explanations of this effect; a representational account in which judgments represent the speed of an underlying pacemaker, and an assimilation account in which judgment is based in prior experience. We further discuss results in terms of predictive coding, in which the previous rating is representative of a prior expectation, which is modified by current experience.

Attentional fluctuations and the temporal organization of memory

Event boundaries and temporal context shape the organization of episodic memories. We hypothesized that attentional fluctuations during encoding serve as "events" that affect temporal context representations and recall organization. Individuals encoded trial-unique objects during a modified sustained attention task. Memory was tested with free recall. Response time variability during the encoding tasks was used to characterize "in the zone" and "out of the zone" attentional states. We predicted that: 1) "in the zone", vs. "out of the zone", attentional states should be more conducive to maintaining temporal context representations that can cue temporally organized recall; and 2) temporally distant "in the zone" states may enable more recall "leaps" across intervening items. We replicated several important findings in the sustained attention and memory fields, including more online errors during "out of the zone" vs. "in the zone" attentional states and recall that was temporally structured. Yet, across four studies, we found no evidence for either of our main hypotheses. Recall was robustly temporally organized, and there was no difference in recall organization for items encoded "in the zone" vs. "out of the zone". We conclude that temporal context serves as a strong scaffold for episodic memory, one that can support organized recall even for items encoded during relatively poor attentional states. We also highlight the numerous challenges in striking a balance between sustained attention tasks (long blocks of a repetitive task) and memory recall tasks (short lists of unique items) and describe strategies for researchers interested in uniting these two fields.

Global Functional Connectivity at Rest Is Associated with Attention: An Arterial Spin Labeling Study

Neural markers of attention, including those frequently linked to the event-related potential P3 (P300) or P3b component, vary widely within and across participants. Understanding the neural mechanisms of attention that contribute to the P3 is crucial for better understanding attention-related brain disorders. All ten participants were scanned twice with a resting-state PCASL perfusion MRI and an ERP with a visual oddball task to measure brain resting-state functional connectivity (rsFC) and P3 parameters (P3 amplitudes and P3 latencies). Global rsFC (average rsFC across the entire brain) was associated with both P3 amplitudes (r = 0.57, p = 0.011) and P3 onset latencies (r = -0.56, p = 0.012). The observed P3 parameters were correlated with predicted P3 amplitude from the global rsFC (amplitude: r = +0.48, p = 0.037; latency: r = +0.40, p = 0.088) but not correlated with the rsFC over the most significant individual edge. P3 onset latency was primarily related to long-range connections between the prefrontal and parietal/limbic regions, while P3 amplitudes were related to connections between prefrontal and parietal/occipital, between sensorimotor and subcortical, and between limbic/subcortical and parietal/occipital regions. These results demonstrated the power of resting-state PCASL and P3 correlation with brain global functional connectivity.

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

Brain-phenotype models fail for individuals who defy sample stereotypes

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.

Dynamic Transitions Between Brain States Predict Auditory Attentional Fluctuations

Achievement of task performance is required to maintain a constant level of attention. Attentional level fluctuates over the course of daily activities. However, brain dynamics leading to attentional fluctuation are still unknown. We investigated the underlying mechanisms of sustained attention using functional magnetic resonance imaging (fMRI). Participants were scanned with fMRI while performing an auditory, gradual-onset, continuous performance task (gradCPT). In this task, narrations gradually changed from one to the next. Participants pressed a button for frequent Go trials (i.e., male voices) as quickly as possible and withheld responses to infrequent No-go trials (i.e., female voices). Event-related analysis revealed that frontal and temporal areas, including the auditory cortex, were activated during successful and unsuccessful inhibition of predominant responses. Reaction-time (RT) variability throughout the auditory gradCPT was positively correlated with signal changes in regions of the dorsal attention network: superior frontal gyrus and superior parietal lobule. Energy landscape analysis showed that task-related activations could be clustered into different attractors: regions of the dorsal attention network and default mode network. The number of alternations between RT-stable and erratic periods increased with an increase in transitions between attractors in the brain. Therefore, we conclude that dynamic transitions between brain states are closely linked to auditory attentional fluctuations.

Shifting expectations: Lapses in spatial attention are driven by anticipatory attentional shifts

Attention is dynamic, constantly shifting between different locations - sometimes imperfectly. How do goal-driven expectations impact dynamic spatial attention? A previous study (Dowd & Golomb, Psychological Science, 30(3), 343-361, 2019) explored object-feature binding when covert attention needed to be either maintained at a single location or shifted from one location to another. In addition to revealing feature-binding errors during dynamic shifts of attention, this study unexpectedly found that participants sometimes made correlated errors on trials when they did not have to shift attention, mistakenly reporting the features and location of an object at a different location. The authors posited that these errors represent "spatial lapses" attention, which are perhaps driven by the implicit sampling of other locations in anticipation of having to shift attention. To investigate whether these spatial lapses are indeed anticipatory, we conducted a series of four experiments. We first replicated in Psychological Science, 30(3), the original finding of spatial lapses, and then showed that these spatial lapses were not observed in contexts where participants are not expecting to have to shift attention. We then tested contexts where the direction of attentional shifts was spatially predictable, and found that participants lapse preferentially to more likely shift locations. Finally, we found that spatial lapses do not seem to be driven by explicit knowledge of likely shift locations. Combined, these results suggest that spatial lapses of attention are induced by the implicit anticipation of making an attentional shift, providing further insight into the interplay between implicit expectations, dynamic spatial attention, and visual perception.

Neural signatures of attentional engagement during narratives and its consequences for event memory

As we comprehend narratives, our attentional engagement fluctuates over time. Despite theoretical conceptions of narrative engagement as emotion-laden attention, little empirical work has characterized the cognitive and neural processes that comprise subjective engagement in naturalistic contexts or its consequences for memory. Here, we relate fluctuations in narrative engagement to patterns of brain coactivation and test whether neural signatures of engagement predict subsequent memory. In behavioral studies, participants continuously rated how engaged they were as they watched a television episode or listened to a story. Self-reported engagement was synchronized across individuals and driven by the emotional content of the narratives. In functional MRI datasets collected as different individuals watched the same show or listened to the same story, engagement drove neural synchrony, such that default mode network activity was more synchronized across individuals during more engaging moments of the narratives. Furthermore, models based on time-varying functional brain connectivity predicted evolving states of engagement across participants and independent datasets. The functional connections that predicted engagement overlapped with a validated neuromarker of sustained attention and predicted recall of narrative events. Together, our findings characterize the neural signatures of attentional engagement in naturalistic contexts and elucidate relationships among narrative engagement, sustained attention, and event memory.

EEG Correlates of Sustained Attention Variability during Discrete Multi-finger Force Control Tasks

The neurophysiological characteristics of sustained attention states are unclear in discrete multi-finger force control tasks. In this article, we developed an immersive visuo-haptic task for conducting stimulus-response measurements. Visual cues were randomly provided to signify the required amplitude and tolerance of fingertip force. Participants were required to respond to the visual cues by pressing force transducers using their fingertips. Response time variation was taken as a behavioral measure of sustained attention states during the task. 50% low-variability trials were classified as the optimal state and the other high-variability trials were classified as the suboptimal state using z-scoring over time. A 64-channel electroencephalogram (EEG) acquisition system was used to collect brain activities during the tasks. The haptics-elicited potential amplitude at 20 ∼ 40 ms in latency and over the frontal-central region significantly decreased in the optimal state. Furthermore, the alpha-band power in the spectra of 8 ∼ 13 Hz was significantly suppressed in the frontal-central, right temporal, and parietal regions in the optimal state. Taken together, we have identified neuroelectrophysiological features that were associated with sustained attention during multi-finger force control tasks, which would be potentially used in the development of closed-loop attention detection and training systems exploiting haptic interaction.

Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback

Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance = 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction p=7.23e-4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.

Impaired Frontal Midline Theta During Periods of High Reaction Time Variability in Schizophrenia

BACKGROUND

Impairment in cognitive control is one of the most significant cognitive deficits in schizophrenia. Although it has generally been associated with altered engagement of lateral and medial prefrontal cortices, how attention fluctuations affect this engagement is still not known. In this context, we explored sustained (or proactive) and transient (or reactive) control engagement by investigating frontal theta-band oscillations during periods of low- and high-performance instability, assumed to represent intraindividual attentional fluctuations.

METHODS

A total of 25 patients with schizophrenia (16 males) and 25 healthy matched control subjects (18 males) performed a long-sustained Go/NoGo task coupled with electroencephalographic recording. Proactive control was explored through frontal lateral theta during trial-by-trial conflict (Go N-1/Go N+1), whereas reactive control was explored through frontal midline theta and the N2 component during current-trial conflict (Go/NoGo). Variance in the time course of reaction time (RT) was computed to identify periods of low and high RT variability in each subject.

RESULTS

Patients with schizophrenia exhibited no frontal lateral theta activity regardless of the RT variability periods, whereas in control subjects, this activity was preserved only during periods of low RT variability (less error prone). During these periods, patients exhibited preserved midline frontal theta activity and N2. However, during high RT variability periods (more error prone), the midline theta activity was impaired in patients but preserved in control subjects.

CONCLUSIONS

Our results reveal that the efficient engagement of reactive control in patients with schizophrenia and of proactive control in control subjects was state dependent. The findings highlight the importance of accounting for attentional fluctuations when investigating cognitive control impairment in schizophrenia.

Breaking down the cocktail party: Attentional modulation of cerebral audiovisual speech processing

Recent studies utilizing electrophysiological speech envelope reconstruction have sparked renewed interest in the cocktail party effect by showing that auditory neurons entrain to selectively attended speech. Yet, the neural networks of attention to speech in naturalistic audiovisual settings with multiple sound sources remain poorly understood. We collected functional brain imaging data while participants viewed audiovisual video clips of lifelike dialogues with concurrent distracting speech in the background. Dialogues were presented in a full-factorial design, comprising task (listen to the dialogues vs. ignore them), audiovisual quality and semantic predictability. We used univariate analyses in combination with multivariate pattern analysis (MVPA) to study modulations of brain activity related to attentive processing of audiovisual speech. We found attentive speech processing to cause distinct spatiotemporal modulation profiles in distributed cortical areas including sensory and frontal-control networks. Semantic coherence modulated attention-related activation patterns in the earliest stages of auditory cortical processing, suggesting that the auditory cortex is involved in high-level speech processing. Our results corroborate views that emphasize the dynamic nature of attention, with task-specificity and context as cornerstones of the underlying neuro-cognitive mechanisms.

Hippocampal seed connectome-based modeling predicts the feeling of stress

Although the feeling of stress is ubiquitous, the neural mechanisms underlying this affective experience remain unclear. Here, we investigate functional hippocampal connectivity throughout the brain during an acute stressor and use machine learning to demonstrate that these networks can specifically predict the subjective feeling of stress. During a stressor, hippocampal connectivity with a network including the hypothalamus (known to regulate physiological stress) predicts feeling more stressed, whereas connectivity with regions such as dorsolateral prefrontal cortex (associated with emotion regulation) predicts less stress. These networks do not predict a subjective state unrelated to stress, and a nonhippocampal network does not predict subjective stress. Hippocampal networks are consistent, specific to the construct of subjective stress, and broadly informative across measures of subjective stress. This approach provides opportunities for relating hypothesis-driven functional connectivity networks to clinically meaningful subjective states. Together, these results identify hippocampal networks that modulate the feeling of stress.

Functional connectivity predicts changes in attention observed across minutes, days, and months

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

There is no single functional atlas even for a single individual: Functional parcel definitions change with task

The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.

Pace yourself: Neural activation and connectivity changes over time vary by task type and pacing

Performance deterioration over time, or time-on-task (TOT) effects, can be observed across a variety of tasks, but little attention has been paid to how TOT-related brain activity may differ based on task pacing and cognitive demands. Here, we employ a set of three closely related tasks to investigate the effect of these variables on fMRI activation and connectivity. When participants dictated the pace of their own responses, activation and network connectivity within the dorsal attention network (DAN) increased over short time scales (~2-3 min), a phenomenon that was not observed when participants had no control over their pace of work. Reaction time slowing was also the most pronounced in this self-paced task. In contrast, TOT-related changes in default-mode network (DMN) activity and connectivity, DAN-DMN anti-correlations, and pupil diameter did not differ based on pacing or task instructions. Over a longer (~10 min) time scale, task-positive activation and connectivity decreased in all paradigms, in agreement with older findings. These results highlight dynamic patterns of resource allocation that have not previously been observed in fMRI experiments, and speak to the idea that the brain may strategically allocate resources depending on the task at hand and the time scale of work.

Fluctuations of Attention and Working Memory

Attention and working memory are intricately related, yet there remain ambiguities in how to best characterize this relationship. In his review, Oberauer formalizes several dimensions for the relationship between attention and working memory, focusing especially on the supporting role of attention during working memory maintenance. In this commentary, we highlight how attention and working memory relate on a broader time scale via trial-to-trial fluctuations. Specifically, we briefly describe evidence and implications of these fluctuations of attention and working memory. A strong link has been shown behaviorally (e.g., interleaved sustained attention and working memory tasks) and neurally (e.g., pre-trial predictors of working memory success), yet fluctuations of attention and working memory are also distinct. Thus, we argue that attention and working memory fluctuate synchronously but not synonymously.

DC Shifts-fMRI: A Supplement to Event-Related fMRI

Event-related fMRI have been widely used in locating brain regions which respond to specific tasks. However, activities of brain regions which modulate or indirectly participate in the response to a specific task are not event-related. Event-related fMRI can't locate these regulatory regions, detrimental to the integrity of the result that event-related fMRI revealed. Direct-current EEG shifts (DC shifts) have been found linked to the inner brain activity, a fusion DC shifts-fMRI method may have the ability to reveal a more complete response of the brain. In this study, we used DC shifts-fMRI to verify that even when responding to a very simple task, (1) The response of the brain is more complicated than event-related fMRI generally revealed and (2) DC shifts-fMRI have the ability of revealing brain regions whose responses are not in event-related way. We used a classical and simple paradigm which is often used in auditory cortex tonotopic mapping. Data were recorded from 50 subjects (25 male, 25 female) who were presented with randomly presented pure tone sequences with six different frequencies (200, 400, 800, 1,600, 3,200, 6,400 Hz). Our traditional fMRI results are consistent with previous findings that the activations are concentrated on the auditory cortex. Our DC shifts-fMRI results showed that the cingulate-caudate-thalamus network which underpins sustained attention is positively activated while the dorsal attention network and the right middle frontal gyrus which underpin attention orientation are negatively activated. The regional-specific correlations between DC shifts and brain networks indicate the complexity of the response of the brain even to a simple task and that the DC shifts can effectively reflect these non-event-related inner brain activities.

Type 1 diabetes and working memory processing of emotional faces

Several executive functions decline with the development of type-1 diabetes (T1D), particularly working memory (WM). In adults, WM ensures efficient cognitive processing by focusing on task-relevant information while suppressing distractors. It has been well documented that WM can be influenced by emotional stimuli, which may facilitate the retention of information, interfere with uptake, or even affect its capacity. We evaluated the effect of T1D on visual WM processing using emotional faces as stimuli, in young patients with satisfactory clinical evolution, and matched controls without T1D. All subjects performed a 2-back task detecting facial identity using neutral, happy or fearful faces in a block design for fMRI. Behavioral performance was similar with the exception that patients responded significantly slower. Most importantly, between-group differences were found in patterns of brain activation. In comparison, more widespread brain activation -predominantly prefrontal- was found in the participants with T1D when processing neutral faces, while a decrease was observed when processing happy and fearful ones. Statistical contrasts demonstrated significantly-different activation patterns between groups when processing emotional faces, as controls exhibited greater activation in the cuneus, posterior cortex and parahippocampal gyrus, while the patients showed greater activation in the prefrontal structures. Results may reflect compensatory efforts made to minimize the deleterious effects of disease development on attention allocation processes and the operational efficiency of WM. The results suggest that emotional parameters should be periodically assessed in individuals with T1D in order to anticipate the emergence of attention and WM impairment.

Skipping Breakfast Affects the Early Steps of Cognitive Processing

Abstract. It has been generally accepted that skipping breakfast adversely affects cognition, mainly disturbing the attentional processes. However, the effects of short-term fasting upon brain functioning are still unclear. We aimed to evaluate the effect of skipping breakfast on cognitive processing by studying the electrical brain activity of young healthy individuals while performing several working memory tasks. Accordingly, the behavioral results and event-related brain potentials (ERPs) of 20 healthy university students (10 males) were obtained and compared through analysis of variances (ANOVAs), during the performance of three n-back working memory (WM) tasks in two morning sessions on both normal (after breakfast) and 12-hour fasting conditions. Significantly fewer correct responses were achieved during fasting, mainly affecting the higher WM load task. In addition, there were prolonged reaction times with increased task difficulty, regardless of breakfast intake. ERP showed a significant voltage decrement for N200 and P300 during fasting, while the amplitude of P200 notably increased. The results suggest skipping breakfast disturbs earlier cognitive processing steps, particularly attention allocation, early decoding in working memory, and stimulus evaluation, and this effect increases with task difficulty.

Detecting Mind Wandering: An Objective Method via Simultaneous Control of Respiration and Fingertip Pressure

Mind wandering happens when one train of thought, related to a current undertaking, is interrupted by unrelated thoughts. The detection and evaluation of mind wandering can greatly help in understanding the attention control mechanism during certain focal tasks. Subjective assessments such as random thought-probe and spontaneous self-report are the ways previous research has assessed mind wandering. Here we propose a task in which participants are asked to simultaneously control respiration and fingertip pressure. They are instructed to click a force sensor at the exact moment of inhalation and exhalation of their respiration. The temporal synchronization between the respiratory signals and the fingertip force pulses offers an objective index to detect mind wandering. Twelve participants engaged in the proposed task in which self-reports of mind wandering are compared with the proposed objective index. The results show that the participants reported significantly more mind-wandering episodes during the trials with a larger temporal synchronization than they did during those trials with a smaller temporal synchronization. The findings suggest that the temporal synchronization might be used as an objective marker of mind wandering in attention training and exploration of the attention control mechanism.

Mind-wandering, or the allocation of attentional resources, is sleep-driven across childhood

Mind-wandering or the spontaneous, uncontrolled changes in the allocation of attention resources (lapses) may cause variability in performance. In childhood, the relationship between the activation state of the brain, such as in attentional performance, and sleep has not been explored in detail. We investigated the role of sleep in attentional performance, and explored the most important parameters of their relationship. We objectively measured momentary lapses of attention of 522 children and correlated them with sleep schedules. In the subgroup of young children (age 7.1 ± 0.6 years; 60.8% girls), increasing age, long sleep duration and assessment closer to the previous night’s sleep period was associated with impaired performance speed and consistency. From pre-adolescence (age 9.4 ± 0.8 years; 50.5% girls) onwards somno-typologies may develop. As a result, in adolescence (age 13.4 ± 1.2 years; 51.3% girls) not only sleep duration but also sleep midpoint and sleep regularity influence the individual speed and stability of attention. Across development, regularity of sleep, individual sleep midpoint and bedtime become increasingly important for optimal performance throughout the day. Attentional performance and sleep shared almost half of their variance, and performance was sleep-driven across childhood. Future studies should focus on intra- and inter-individual differences in sleep-wake behavior to improve performance or decrease mind-wandering in youth by targeting sleep habits.

Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity

In cognitive neuroscience, focus is commonly placed on associating brain function with changes in objectively measured external stimuli or with actively generated cognitive processes. In everyday life, however, many forms of cognitive processes are initiated spontaneously, without an individual's active effort and without explicit manipulation of behavioral state. Recently, there has been increased emphasis, especially in functional neuroimaging research, on spontaneous correlated activity among spatially segregated brain regions (intrinsic functional connectivity) and, more specifically, on intraindividual fluctuations of such correlated activity on various time scales (time-varying functional connectivity). In this Perspective, we propose that certain subtypes of spontaneous cognitive processes are detectable in time-varying functional connectivity measurements. We define these subtypes of spontaneous cognitive processes and review evidence of their representations in time-varying functional connectivity from studies of attentional fluctuations, memory reactivation, and effects of baseline states on subsequent perception. Moreover, we describe how these studies are critical to validating the use of neuroimaging tools (e.g., fMRI) for assessing ongoing brain network dynamics. We conclude that continued investigation of the behavioral relevance of time-varying functional connectivity will be beneficial both in the development of comprehensive neural models of cognition, and in informing on best practices for studying brain network dynamics.

Modulating Reward Induces Differential Neurocognitive Approaches to Sustained Attention

Reward and motivation have powerful effects on cognition and brain activity, yet it remains unclear how they affect sustained cognitive performance. We have recently shown that a variety of motivators improve accuracy and reduce variability during sustained attention. In the current study, we investigate how neural activity in task-positive networks supports these sustained attention improvements. Participants performed the gradual-onset continuous performance task with alternating motivated (rewarded) and unmotivated (unrewarded) blocks. During motivated blocks, we observed increased sustained neural recruitment of task-positive regions, which interacted with fluctuations in task performance. Specifically, during motivated blocks, participants recruited these regions in preparation for upcoming targets, and this activation predicted accuracy. In contrast, during unmotivated blocks, no such advanced preparation was observed. Furthermore, during motivated blocks, participants had similar activation levels during both optimal (in-the-zone) and suboptimal (out-of-the-zone) epochs of performance. In contrast, during unmotivated blocks, task-positive regions were only engaged to a similar degree as motivated blocks during suboptimal (out-of-the-zone) periods. These data support a framework in which motivated individuals act as "cognitive investors," engaging task-positive resources proactively and consistently during sustaining attention. When unmotivated, however, the same individuals act as "cognitive misers," engaging maximal task-positive resources only during periods of struggle.

Decoupling of reaction time-related default mode network activity with cognitive demand

Reaction Time (RT) is associated with increased amplitude of the Blood Oxygen-Level Dependent (BOLD) response in task positive regions. Few studies have focused on whether opposing RT-related suppression of task activity also occurs. The current study used two Go/No-go tasks with different cognitive demands to examine regions that showed greater BOLD suppression for longer RT trials. These RT-related suppression effects occurred within the DMN and were task-specific, localizing to separate regions for the two tasks. In the task requiring working memory, RT-related de-coupling of the DMN occurred. This was reflected by opposing RT-BOLD effects for different DMN regions, as well as by reduced positive RT-related Psycho-Physiological Interaction (PPI) connectivity within the DMN and a lack of negative RT-related PPI connectivity between DMN and task positive regions. The results suggest that RT-related DMN suppression is task-specific. RT-related de-coupling of the DMN with more complex task demands may contribute to lapses of attention and performance decrements that occur during cognitively-demanding tasks.

Tracking behavioral and neural fluctuations during sustained attention: A robust replication and extension

Novel paradigms have allowed for more precise measurements of sustained attention ability and fluctuations in sustained attention over time, as well as the neural basis of fluctuations and lapses in performance. However, in recent years, concerns have arisen over the replicability of neuroimaging studies and psychology more broadly, particularly given the typically small sample sizes. One recently developed paradigm, the gradual-onset continuous performance task (gradCPT) has been validated behaviorally in large samples of participants. Yet neuroimaging studies investigating the neural basis of performance on this task have only been collected in small samples. The present study completed both a robust replication of the original neuroimaging findings and extended previous results from the gradCPT task using a large sample of 140 Veteran participants. Results replicate findings that fluctuations in attentional stability are tracked over time by BOLD activity in task positive (e.g., dorsal and ventral attention networks) and task negative (e.g., default network) regions. Extending prior results, we relate this coupling between attentional stability and on-going brain activity to overall sustained attention ability and demonstrate that this coupling strength, along with across-network coupling, could be used to predict individual differences in performance. Additionally, the results extend previous findings by demonstrating that temporal dynamics across the default and dorsal attention networks are associated with lapse-likelihood on subsequent trials. This study demonstrates the reliability of the gradCPT, and underscores the utility of this paradigm in understanding attentional fluctuations, as well as individual variation and deficits in sustained attention.

Default Network Activity Is Associated with Better Performance in a Vigilance Task

When attention has to be maintained over prolonged periods performance slowly fluctuates and errors can occur. It has been shown that lapses of attention are correlated with BOLD signals in frontal and parietal cortex. This raises the question how attentional fluctuations are linked to the fronto-parietal default network. Because the attentional state fluctuates slowly we expect that potential links between attentional fluctuations and brain activity should be observable on longer time scales and importantly also before the execution of the task. In the present study we used fMRI to identify brain activity that is correlated with vigilance, defined as fluctuations of reaction times (RT) during a sustained attention task. We found that brain activity in visual cortex, parietal lobe (PL), inferior and superior frontal gyrus, and supplementary motor area (SMA) was higher when the subject had a relatively long RT. In contrast to our expectations, activity in the default network (DN) was higher when subjects had a relatively short RT, that means when the performance was improved. This modulation in the DN was present already several seconds before the task execution, thus pointing to activity in the DN as a potential cause of performance increases in simple repetitive tasks.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention.

SIGNIFICANCE STATEMENT

Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention.

Similar patterns of neural activity predict memory function during encoding and retrieval

Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval.

Recent theoretical, neural, and clinical advances in sustained attention research

Models of attention often distinguish among attention subtypes, with classic models separating orienting, switching, and sustaining functions. Compared with other forms of attention, the neurophysiological basis of sustaining attention has received far less notice, yet it is known that momentary failures of sustained attention can have far-ranging negative effects in healthy individuals, and lasting sustained attention deficits are pervasive in clinical populations. In recent years, however, there has been increased interest in characterizing moment-to-moment fluctuations in sustained attention, in addition to the overall vigilance decrement, and understanding how these neurocognitive systems change over the life span and across various clinical populations. The use of novel neuroimaging paradigms and statistical approaches has allowed for better characterization of the neural networks supporting sustained attention and has highlighted dynamic interactions within and across multiple distributed networks that predict behavioral performance. These advances have also provided potential biomarkers to identify individuals with sustained attention deficits. These findings have led to new theoretical models explaining why sustaining focused attention is a challenge for individuals and form the basis for the next generation of sustained attention research, which seeks to accurately diagnose and develop theoretically driven treatments for sustained attention deficits that affect a variety of clinical populations.

Characterizing Attention with Predictive Network Models

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction.

Dynamic Brain Network Correlates of Spontaneous Fluctuations in Attention

Human attention is intrinsically dynamic, with focus continuously shifting between elements of the external world and internal, self-generated thoughts. Communication within and between large-scale brain networks also fluctuates spontaneously from moment to moment. However, the behavioral relevance of dynamic functional connectivity and possible link with attentional state shifts is unknown. We used a unique approach to examine whether brain network dynamics reflect spontaneous fluctuations in moment-to-moment behavioral variability, a sensitive marker of attentional state. Nineteen healthy adults were instructed to tap their finger every 600 ms while undergoing fMRI. This novel, but simple, approach allowed us to isolate moment-to-moment fluctuations in behavioral variability related to attention, independent of common confounds in cognitive tasks (e.g., stimulus changes, response inhibition). Spontaneously increasing tap variance ("out-of-the-zone" attention) was associated with increasing activation in dorsal-attention and salience network regions, whereas decreasing tap variance ("in-the-zone" attention) was marked by increasing activation of default mode network (DMN) regions. Independent of activation, tap variance representing out-of-the-zone attention was also time-locked to connectivity both within DMN and between DMN and salience network regions. These results provide novel mechanistic data on the understudied neural dynamics of everyday, moment-to-moment attentional fluctuations, elucidating the behavioral importance of spontaneous, transient coupling within and between attention-relevant networks.

Neural mechanisms of cue-approach training

Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training.

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Spontaneous Fluctuations in the Flexible Control of Covert Attention

Spontaneous fluctuations in cognitive flexibility are characterized by moment-to-moment changes in the efficacy of control over attentional shifts. We used fMRI to investigate the neural correlates in humans of spontaneous fluctuations in readiness to covertly shift attention between two peripheral rapid serial visual presentation streams. Target detection response time (RT) after a shift or hold of covert spatial attention served as a behavioral index of fluctuations in attentional flexibility. In particular, the cost associated with shifting attention compared with holding attention varied as a function of pretrial brain activity in key regions of the default mode network (DMN), but not the dorsal attention network. High pretrial activity within the DMN was associated with a greater increase in shift trial RT relative to hold trial RT, revealing that these areas are associated with a state of attentional stability. Conversely, high pretrial activity within bilateral anterior insula and the presupplementary motor area/supplementary motor area was associated with a greater decrease in shift trial RT relative to hold trial RT, reflecting increased flexibility. Our results importantly clarify the roles of the precuneus, medial prefrontal cortex, and lateral parietal cortex, indicating that reduced activity may not simply indicate greater task engagement, but also, specifically, a readiness to update the focus of attention. Investigation of the neural correlates of spontaneous changes in attentional flexibility may contribute to our understanding of disorders of cognitive control as well as healthy variability in the control of spatial attention.

SIGNIFICANCE STATEMENT

Individuals regularly experience fluctuations in preparatory cognitive control that affect performance in everyday life. For example, individuals are able to more quickly initiate a spatial shift of attention at some moments than at others. The current study revealed that pretrial brain activity in specific cortical regions predicted trial-by-trial changes in participants' abilities to flexibly shift the focus of attention. Intrinsically generated fluctuations in brain activity within several key default mode network regions, as well as within the anterior insula and presupplementary/supplementary motor areas, carried behavioral consequences for preparatory attentional control beyond lapses of attentional engagement. Our results are the first to link intrinsic variation in pretrial brain activity to moment-by-moment changes in preparatory attentional control over spatial selection.

A neuromarker of sustained attention from whole-brain functional connectivity

Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.