The association cortex supports cognitive functions enabling flexible behavior. Here, we explored the organization of human association cortex by mathematically formalizing the notion that a ...behavioral task engages multiple cognitive components, which are in turn supported by multiple overlapping brain regions. Application of the model to a large data set of neuroimaging experiments (N = 10 449) identified complex zones of frontal and parietal regions that ranged from being highly specialized to highly flexible. The network organization of the specialized and flexible regions was explored with an independent resting-state fMRI data set (N = 1000). Cortical regions specialized for the same components were strongly coupled, suggesting that components function as partially isolated networks. Functionally flexible regions participated in multiple components to different degrees. This heterogeneous selectivity was predicted by the connectivity between flexible and specialized regions. Functionally flexible regions might support binding or integrating specialized brain networks that, in turn, contribute to the ability to execute multiple and varied tasks.
The posterior lateral prefrontal cortex-specifically, the inferior frontal junction (IFJ)-is thought to exert a key role in the control of attention. However, the precise nature of that role remains ...elusive. During the voluntary deployment and maintenance of visuospatial attention, the IFJ is typically coactivated with a core dorsal network consisting of the frontal eye field and superior parietal cortex. During stimulus-driven attention, IFJ instead couples with a ventrolateral network, suggesting that IFJ plays a role in attention distinct from the dorsal network. Because IFJ rapidly switches activation patterns to accommodate conditions of goal-directed and stimulus-driven attention (Asplund CL, Todd JJ, Snyder AP, Marois R. Nat Neurosci 13: 507-512, 2010), we hypothesized that IFJ's primary role is to dynamically reconfigure attention rather than to maintain attention under steady-state conditions. This hypothesis predicts that in a goal-directed visuospatial cuing paradigm IFJ would transiently deploy attention toward the cued location, whereas the dorsal attention network would maintain attentional weights during the delay between cue and target presentation. Here we tested this hypothesis with functional magnetic resonance imaging while subjects were engaged in a Posner cuing task with variable cue-target delays. Both IFJ and dorsal network regions were involved in transient processes, but sustained activity was far more evident in the dorsal network than in IFJ. These results support the account that IFJ primarily acts to shift attention whereas the dorsal network is the main locus for the maintenance of stable attentional states. NEW & NOTEWORTHY Goal-directed visuospatial attention is controlled by a dorsal fronto-parietal network and lateral prefrontal cortex. However, the relative roles of these regions in goal-directed attention are unknown. Here we present evidence for their dissociable roles in the transient reconfiguration and sustained maintenance of attentional settings: while maintenance of attentional settings is confined to the dorsal network, the configuration of these settings at the beginning of an attentional episode is a function of lateral prefrontal cortex.
How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state ...functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.
Attention is the process that selects which sensory information is preferentially processed and ultimately reaches our awareness. Attention, however, is not a unitary process; it can be captured by ...unexpected or salient events (stimulus driven) or it can be deployed under voluntary control (goal directed), and these two forms of attention are implemented by largely distinct ventral and dorsal parieto-frontal networks. For coherent behavior and awareness to emerge, stimulus-driven and goal-directed behavior must ultimately interact. We found that the ventral, but not dorsal, network can account for stimulus-driven attentional limits to conscious perception, and that stimulus-driven and goal-directed attention converge in the lateral prefrontal component of that network. Although these results do not rule out dorsal network involvement in awareness when goal-directed task demands are present, they point to a general role for the lateral prefrontal cortex in the control of attention and awareness.
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Unified attentional bottleneck in the human brain Tombu, Michael N; Asplund, Christopher L; Dux, Paul E ...
Proceedings of the National Academy of Sciences - PNAS,
08/2011, Volume:
108, Issue:
33
Journal Article
Peer reviewed
Open access
Human information processing is characterized by bottlenecks that constrain throughput. These bottlenecks limit both what we can perceive and what we can act on in multitask settings. Although ...perceptual and response limitations are often attributed to independent information processing bottlenecks, it has recently been suggested that a common attentional limitation may be responsible for both. To date, however, evidence supporting the existence of such a "unified" bottleneck has been mixed. Here, we tested the unified bottleneck hypothesis using time-resolved fMRI. Experiment 1 isolated brain regions involved in the response selection bottleneck that limits speeded dual-task performance. These same brain regions were not only engaged by a perceptual encoding task in Experiment 2, their activity also tracked delays to a speeded decision-making task caused by concurrent perceptual encoding (Experiment 3). We conclude that a unified attentional bottleneck, including the inferior frontal junction, superior medial frontal cortex, and bilateral insula, temporally limits operations as diverse as perceptual encoding and decision-making.
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An influential theory suggests that integrated objects, rather than individual features, are the fundamental units that limit our capacity to temporarily store visual information (S. J. Luck & E. K. ...Vogel, 1997). Using a paradigm that independently estimates the number and precision of items stored in working memory (W. Zhang & S. J. Luck, 2008), here we show that the storage of features is not cost-free. The precision and number of objects held in working memory was estimated when observers had to remember either the color, the orientation, or both the color and orientation of simple objects. We found that while the quantity of stored objects was largely unaffected by increasing the number of features, the precision of these representations dramatically decreased. Moreover, this selective deterioration in object precision depended on the multiple features being contained within the same objects. Such fidelity costs were even observed with change detection paradigms when those paradigms placed demands on the precision of the stored visual representations. Taken together, these findings not only demonstrate that the maintenance of integrated features is costly; they also suggest that objects and features affect visual working memory capacity differently.
Legal decision-making in criminal contexts includes two essential functions performed by impartial “third parties:” assessing responsibility and determining an appropriate punishment. To explore the ...neural underpinnings of these processes, we scanned subjects with fMRI while they determined the appropriate punishment for crimes that varied in perpetrator responsibility and crime severity. Activity within regions linked to affective processing (amygdala, medial prefrontal and posterior cingulate cortex) predicted punishment magnitude for a range of criminal scenarios. By contrast, activity in right dorsolateral prefrontal cortex distinguished between scenarios on the basis of criminal responsibility, suggesting that it plays a key role in third-party punishment. The same prefrontal region has previously been shown to be involved in punishing unfair economic behavior in two-party interactions, raising the possibility that the cognitive processes supporting third-party legal decision-making and second-party economic norm enforcement may be supported by a common neural mechanism in human prefrontal cortex.
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•Haufe-transformed weights are much more reliable than original regression weights when computing feature importance.•Feature importance reliability is strongly positively correlated with prediction ...accuracy across phenotypes.•We show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error.•We discuss how our theoretical results relate with the reliability of imaging features and behavioral measures.
There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.
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Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological ...components. Nevertheless, some brain networks support multiple attentional functions. In this study, we used the visual attentional blink (VAB) as a test of the functional generalizability of the brain’s attentional networks. In a VAB task, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to limitations in attentional capacity or selection, VAB deficits attenuate when participants are distracted or deploy attention diffusely. The VAB is also behaviorally and theoretically dissociable from other attention tasks. Here we used Connectome-based Predictive Models (CPMs), which associate individual differences in task performance with functional connectivity patterns, to test their ability to predict performance for multiple attentional tasks. We constructed visual attentional blink (VAB) CPMs, and then used them and a sustained attention network model (saCPM; Rosenberg et al., 2016a) to predict performance. The latter model had been previously shown to successfully predict performance across tasks involving selective attention, inhibitory control, and even reading recall. Participants (n = 73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from VAB task data (behavior and fMRI) successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse performance for selective and sustained attention tasks, and vice versa. Predictions from applying the saCPM to the data mirrored these results, with the network’s negative edges predicting better VAB performance. The vabCPM’s positive edges partially yet significantly overlapped with the saCPM’s negative edges, and vice versa. Many positive edges from the vabCPM involved the default mode network, whereas many negative edges involved the salience/ventral attention network. We conclude that the vabCPM and saCPM networks reflect general attentional functions that influence performance on many tasks. The networks may indicate an individual’s propensity to deploy attention in a more diffuse or a more focused manner.
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When humans attempt to perform two tasks at once, execution of the first task usually leads to postponement of the second one. This task delay is thought to result from a bottleneck occurring at a ...central, amodal stage of information processing that precludes two response selection or decision-making operations from being concurrently executed. Using time-resolved functional magnetic resonance imaging (fMRI), here we present a neural basis for such dual-task limitations, e.g. the inability of the posterior lateral prefrontal cortex, and possibly the superior medial frontal cortex, to process two decision-making operations at once. These results suggest that a neural network of frontal lobe areas acts as a central bottleneck of information processing that severely limits our ability to multitask.
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