Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task.

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

WITHDRAWN: The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity.

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Beyond mind wandering: Performance variability and neural activity during off-task thought and other attention lapses.

To study the characteristics of attention lapses, a metronome response task and experience sampling were employed while recording fMRI data. Thought prompts queried several attention states (on-task, task-related interference, off-task, inattention). Off-task thoughts were probed on whether they arose in a spontaneous or constrained (i.e., directed) manner. Increased fMRI activation was observed in the default mode network during off-task thought and in subregions of the anterior cingulate cortex and inferior frontal gyrus during inattention. Activation also increased in the left hippocampus during constrained thoughts. Functional connectivity increased between the left superior temporal sulcus and right temporoparietal junction for constrained compared to spontaneous thoughts. Overall, behavioral results indicated a monotonic increase in performance variability from on-task to inattention. However, subtle but consistent differences were observed between self-reported attention state and performance. Results are discussed from perspectives of mind wandering frameworks, the function of brain networks, and the role of engagement in off-task thought.

Dissociating the Neural Correlates of Planning and Executing Tasks with Nested Task Sets.

Task processing (e.g., the preparation and execution of responses) and task representation (e.g., the activation and maintenance of stimulus-response and context information) are two facets of cognitive control supported by lateral frontal cortex (LFC). However, the mechanistic overlap (or distinction) between these two facets is unknown. We explored this by combining a complex task mapping with a precueing procedure. Participants made match/nonmatch judgments on pairs of stimuli during fMRI recording. Precues on each trial gave variable amounts of information to the participant in anticipation of the stimulus. Our results demonstrated that regions throughout LFC were more active at the stimulus (when responses could be executed) than at the cue (when they could only be prepared), indicating that they supported execution of the task agnostic to the specific task representation. A subset of regions in the left caudal LFC showed increased activity with more cue information at the cue and the reverse at the stimulus, suggesting their involvement in reducing uncertainty within the task representation. These results suggest that one component of task processing is preparing and executing the task according to the relevant representation, confined to left caudal LFC, whereas nonrepresentational functions that occur primarily during execution are supported by different regions throughout the rest of LFC. We further conducted an exploratory investigation of connectivity between the two groups of regions in this study and their potential relationship to the frontoparietal and cingulo-opercular networks. Regions with both patterns of activity appear to be part of the frontoparietal network.

Task structure boundaries affect response preparation.

Does cognitive control operate globally (across task sets) or locally (within a task set)? Recently, two of the current co-authors (Hazeltine and Schumacher 2016; Schumacher and Hazeltine 2016) proposed that humans represent tasks as task files: hierarchically structured, compartmentalized subsets of our current goals and motivations, task instructions, and relevant stimuli and responses that are selected during task performance according to associated contextual rules. Here, we hypothesize that these task representations bound the implementation of cognitive control at distinct levels of this hierarchical structure. To investigate how task structure influences the implementation of control processes, we conducted a pair of experiments that utilized a precuing procedure. To manipulate task structure, we gave participants mappings in which two stimulus sets were either mapped so that each set was separated by response hand or both sets were interleaved across hands. In Experiment 1, participants responded to sets of images distinguished by their semantic category; in Experiment 2, they responded to sets based on different perceptual features (viz., location or color). During each experiment, precues could give information about the stimulus category or response hand for the upcoming target. The results indicate that participants with separated mappings represented the task hierarchically, while those with interleaved mappings did not. This pattern was consistent across experiments, despite the differences in the way that each set of stimuli influenced representation of the low-level task features. These findings suggest that task structure can be represented hierarchically, and that this structure supports distinct cognitive control processes at different hierarchical levels.

Quasi-periodic patterns contribute to functional connectivity in the brain.

Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatiotemporal pattern, strength, and frequency of the QPPs between the two groups were compared and the contribution of QPPs to functional connectivity in the brain was measured. In task-performing individuals, the spatiotemporal pattern of the QPP changes, particularly in task-relevant regions, and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the strength of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings contribute to understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.

Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal.

Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain's intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual's global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

Working memory training improves visual short-term memory capacity.

Since antiquity, philosophers, theologians, and scientists have been interested in human memory. However, researchers today are still working to understand the capabilities, boundaries, and architecture. While the storage capabilities of long-term memory are seemingly unlimited (Bahrick, J Exp Psychol 113:1-2, 1984), working memory, or the ability to maintain and manipulate information held in memory, seems to have stringent capacity limits (e.g., Cowan, Behav Brain Sci 24:87-185, 2001). Individual differences, however, do exist and these differences can often predict performance on a wide variety of tasks (cf. Engle What is working-memory capacity? 297-314, 2001). Recently, researchers have promoted the enticing possibility that simple behavioral training can expand the limits of working memory which indeed may also lead to improvements on other cognitive processes as well (cf. Morrison and Chein, Psychol Bull Rev 18:46-60 2011). However, initial investigations across a wide variety of cognitive functions have produced mixed results regarding the transferability of training-related improvements. Across two experiments, the present research focuses on the benefit of working memory training on visual short-term memory capacity-a cognitive process that has received little attention in the training literature. Data reveal training-related improvement of global measures of visual short-term memory as well as of measures of the independent sub-processes that contribute to capacity (Awh et al., Psychol Sci 18(7):622-628, 2007). These results suggest that the ability to inhibit irrelevant information within and between trials is enhanced via n-back training allowing for selective improvement on untrained tasks. Additionally, we highlight a potential limitation of the standard adaptive training procedure and propose a modified design to ensure variability in the training environment.

Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention.

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.

Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually.

A better understanding of how behavioral performance emerges from interacting brain systems may come from analysis of functional networks using functional magnetic resonance imaging. Recent studies comparing such networks with human behavior have begun to identify these relationships, but few have used a time scale small enough to relate their findings to variation within a single individual's behavior. In the present experiment we examined the relationship between a psychomotor vigilance task and the interacting default mode and task positive networks. Two time-localized comparative metrics were calculated: difference between the two networks' signals at various time points around each instance of the stimulus (peristimulus times) and correlation within a 12.3-s window centered at each peristimulus time. Correlation between networks was also calculated within entire resting-state functional imaging runs from the same individuals. These metrics were compared with response speed on both an intraindividual and an interindividual basis. In most cases, a greater difference or more anticorrelation between networks was significantly related to faster performance. While interindividual analysis showed this result generally, using intraindividual analysis it was isolated to peristimulus times 4 to 8 s before the detected target. Within that peristimulus time span, the effect was stronger for individuals who tended to have faster response times. These results suggest that the relationship between functional networks and behavior can be better understood by using shorter time windows and also by considering both intraindividual and interindividual variability.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity.

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14~62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

Measuring task structure with transitional response times: Task representations are more than task sets.

The structure of task representations is widely studied with task-switching procedures in which the experimenter compares performance across predetermined categories of trial transitions (viz., switch costs). This approach has been productive, but relies on experimental assumptions about the relationships among stimulus-response mappings that define a set. Here, we develop a novel method of evaluating structure without relying on such assumptions. Participants responded to centrally presented stimuli and we computed the transitional response times (RTs; changes in RT as a function of specific response sequences) for each response combination. Conventional task-switch analyses revealed costs when the response switched from the left-side to the right or vice versa, but this switch cost was not affected by whether the stimuli belonged to a single category or to two distinct categories. In contrast, the transitional RT analysis provided fine-grained information about relationships among responses and how these relationships were affected by stimulus and response manipulations. Specifically, tasks containing a single stimulus category produced response chains in which neighboring responses had lower transitional RTs, while these chains were broken when two stimulus categories were used. We propose that the transitional RT approach offers a more detailed picture of the underlying task representation that reveals structure not detectable by conventional switch cost measures and does not require a priori assumptions about task organization.

Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans.

Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. In this study, however, we examine the spatiotemporal dynamics of the BOLD fluctuations to identify common patterns of propagation within a scan. A novel pattern finding algorithm was developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm was applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consisted of waves of high signal intensity, propagating in a lateral to medial direction across the cortex, replicating our previous findings (Majeed et al., 2009a). These waves were observed primarily in sensorimotor cortex, but also extended to visual and parietal association areas. A secondary pattern, confined to subcortical regions consisted of an initial increase and subsequent decrease in signal intensity in the caudate-putamen. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the "default-mode" (e.g., posterior cingulate and anterior medial prefrontal cortices) and the "task-positive" (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points was also observed. The pattern finding algorithm was shown to be reasonably insensitive to the variation in user-defined parameters, and the results were consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.

Investigating the modality specificity of response selection using a temporal flanker task.

The neurocognitive architecture for response selection is uncertain. Some theorists suggest that it is mediated by an amodal central mechanism, whereas others propose a set of independent control mechanisms. In a functional neuroimaging experiment, we investigated the nature of response selection by examining how its underlying brain mechanisms are affected by stimulus modality. To do this, we used a modified flanker task, in which the target and flanker (distractor) stimuli differed in time rather than space, making it accessible for both visual and auditory stimuli. As in the traditional flanker task, larger reaction times were observed for incongruent than congruent trials (i.e., a congruency effect) for both modalities. Congruency affected brain activation for both modalities in prefrontal cortex, parietal cortex, and the putamen. Modality-dependent activation was found in additional prefrontal and parietal regions for the visual modality and in left inferior prefrontal cortex for the auditory modality. Modality-dependent activity specifically related to response congruency was also found in sensory cortical regions. These data suggest that modality affects the brain regions throughout the cortex mediating response selection even for conceptually identical stimuli and tasks. They are consistent with the hypothesis that (at least partially) independent brain networks mediate response selection and that input modality may be a powerful factor for organizing neural activity to support task performance.

Implicit sequence learning is represented by stimulus-response rules.

For nearly two decades, researchers have investigated spatial sequence learning in an attempt to identify what specifically is learned during sequential tasks (e.g., stimulus order, response order, etc.). Despite extensive research, controversy remains concerning the information-processing locus of this learning effect. There are three main theories concerning the nature of spatial sequence learning, corresponding to the perceptual, motor, or response selection (i.e., central mechanisms underlying the association between stimulus and response pairs) processes required for successful task performance. The present data investigate this controversy and support the theory that stimulus-response (S-R) rules are critical for sequence learning. The results from two experiments demonstrate that sequence learning is disrupted only when the S-R rules for the task are altered. When the S-R rules remain constant or involve only a minor transformation, significant sequence learning occurs. These data implicate spatial response selection as a likely mechanism mediating spatial sequential learning.

Task representation affects the boundaries of behavioral slowing following an error.

Researchers have recognized the role that task representation plays in our behavior for many years. However, the specific influence that the structure of one's task representation has on executive functioning has only recently been investigated. Prior research suggests that adjustments of cognitive control are affected by subtle manipulations of aspects of the stimulus-response pairs within and across task sets. This work has focused on examples of cognitive control such as response preparation, dual-task performance, and the congruency sequence effect. The current study investigates the effect of task representation on another example of control, post-error slowing. To determine if factors that influence how people represent a task affect how behavior is adjusted after an error, an adaptive attention-shifting task was developed with multiple task delimiting features. Participants were randomly assigned to a separate task set (two task sets) or an integrated task set (one task set) group. For the separate set group, the task sets switched after each trial. Results showed that only the integrated set group exhibited post-error slowing. This suggests that task representation influences the boundaries of cognitive control adjustments and has implications for our understanding of how control is organized when adjusting to errors in performance.

Parallel response selection disrupts sequence learning under dual-task conditions.

Some studies suggest that dual-task processing impairs sequence learning; others suggest it does not. The reason for this discrepancy remains obscure. It may have to do with the dual-task procedure often used. Many dual-task sequence learning studies pair the serial reaction time (SRT) task with a tone-counting secondary task. The tone-counting task, however, is not ideal for studying the cognitive processes involved in sequence learning. The present experiments sought to identify the nature of the interference responsible for disrupting sequence learning in dual-task situations using more tractable dual-task procedures. Experiments 1 and 2 showed that parallel-interfering central processing disrupts sequence learning. Experiment 3 used a novel combination of the SRT task as the secondary task in a psychological refractory period procedure. It showed that SRT task performance can be disrupted without disrupting sequence learning when that disruption involves a response-selection bottleneck rather than parallel response selection. Together, these results suggest that it is the overlap of central processes involved in successfully performing the 2 tasks concurrently that leads to learning deficits in dual-task sequence learning.

Prefrontal-posterior coupling mediates transitions between emotional states and influences executive functioning.

Emotions often result from fluctuating experiences with self-regulation unfolding over time. However, most research has been focused on neural responses to static, affective stimuli. We studied emotion transitions, which correspond to dynamic conditions of varying affective valence or intensities. Functional coupling of prefrontal and posterior cortex (EEG coherence) was recorded during exposure to stable versus changing emotion-eliciting images (static vs. dynamic conditions). Prefrontal-posterior coupling was decreased in the dynamic conditions compared to the static conditions. A decrease in prefrontal-posterior coupling implies less control of the prefrontal cortex over perceptual information, which may allow the brain to become more affected by emotional fluctuations. We also assessed the aftereffect of EEG coherence on executive functioning, utilizing the flanker task. Among individuals reporting higher chronic stress, executive functioning decreased after dynamic conditions. This decrease in executive functioning was mediated by the decrease in prefrontal-posterior coupling in the dynamic conditions. These findings suggest that the strength of prefrontal-posterior coupling is not only related to emotional transitions but also to executive functioning. The deterioration of executive functioning after dynamic emotional processing may reflect the additional cognitive effort required to process dynamic shifts in affective stimuli, and this relationship is exacerbated by chronic stress.

Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume.

Proactive interference occurs when previously relevant information interferes with retaining newer material. Overcoming proactive interference has been linked to the hippocampus and deemed critical for cognitive functioning. However, little is known about whether and how this ability can be improved or about the neural correlates of such improvement. Mindfulness training emphasizes focusing on the present moment and minimizing distraction from competing thoughts and memories. It improves working memory and increases hippocampal density. The current study examined whether mindfulness training reduces proactive interference in working memory and whether such improvements are associated with changes in hippocampal volume. 79 participants were randomized to a 4-week web-based mindfulness training program or a similarly structured creative writing active control program. The mindfulness group exhibited lower proactive interference error rates compared to the active control group following training. No group differences were found in hippocampal volume, yet proactive interference improvements following mindfulness training were significantly associated with volume increases in the left hippocampus. These results provide the first evidence to suggest that (1) mindfulness training can protect against proactive interference, and (2) that these benefits are related to hippocampal volumetric increases. Clinical implications regarding the application of mindfulness training in conditions characterized by impairments to working memory and reduced hippocampal volume such as aging, depression, PTSD, and childhood adversity are discussed.

Correction to: Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume.

The article Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume, written by Jonathan Greenberg, Victoria L. Romero, Seth Elkin-Frankston, Matthew A. Bezdek, Eric H. Schumacher, and Sara W. Lazar.