Top-down attention switches coupling between low-level and high-level areas of human visual cortex

Al-Aidroos, Naseem; Said, Christopher P.; Turk‐Browne, Nicholas B.

doi:10.1073/pnas.1202095109

Cited by 172 publications

(236 citation statements)

References 35 publications

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“…Stimulus-driven attention in the model might reflect enhanced communication between early and late visual cortical areas. This idea is supported by studies that demonstrated greater interarea correlations of activity between visual areas with attention (71)(72)(73)). An alternative, but not mutually exclusive, hypothesis is that attentional modulation is mediated by neural signals from frontoparietal cortices (74,75).…”

Section: Discussionmentioning

confidence: 72%

Attention model of binocular rivalry

Rankin

Rinzel

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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When the corresponding retinal locations in the two eyes are presented with incompatible images, a stable percept gives way to perceptual alternations in which the two images compete for perceptual dominance. As perceptual experience evolves dynamically under constant external inputs, binocular rivalry has been used for studying intrinsic cortical computations and for understanding how the brain regulates competing inputs. Converging behavioral and EEG results have shown that binocular rivalry and attention are intertwined: binocular rivalry ceases when attention is diverted away from the rivalry stimuli. In addition, the competing image in one eye suppresses the target in the other eye through a pattern of gain changes similar to those induced by attention. These results require a revision of the current computational theories of binocular rivalry, in which the role of attention is ignored. Here, we provide a computational model of binocular rivalry. In the model, competition between two images in rivalry is driven by both attentional modulation and mutual inhibition, which have distinct selectivity (feature vs. eye of origin) and dynamics (relatively slow vs. relatively fast). The proposed model explains a wide range of phenomena reported in rivalry, including the three hallmarks: (i) binocular rivalry requires attention; (ii) various perceptual states emerge when the two images are swapped between the eyes multiple times per second; (iii) the dominance duration as a function of input strength follows Levelt's propositions. With a bifurcation analysis, we identified the parameter space in which the model's behavior was consistent with experimental results.B inocular rivalry is a visual phenomenon in which perception alternates between incompatible monocular images presented to the two eyes. During binocular rivalry, perceptual experience evolves dynamically while the external inputs are held constant. Binocular rivalry thereby provides an opportunity to gain insights about the intrinsic cortical computations underlying visual perception (1, 2).In conventional models of binocular rivalry, the competition between two percepts has been characterized as mutual inhibition between two populations of neurons selective for each of the two stimuli (3-11). Notwithstanding the differences in their details, these models consider the neural processing underlying binocular rivalry to be an automatic process. These models predict, therefore, that the dynamics of binocular rivalry are influenced mainly by bottom-up sensory inputs.Converging experimental evidence has shown, however, that binocular rivalry also depends on attention (for a review, see ref.12). First, EEG has been used to measure a neural correlate of the perceptual alternations during binocular rivalry when observers pay attention to the rival stimuli (13). However, this rivalry-induced modulation of the EEG signal is largely or entirely eliminated when attention is diverted away from the stimuli (14). Second, behavioral experiments comparing the perceptual cons...

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Section: Discussionmentioning

confidence: 72%

Attention model of binocular rivalry

Rankin

Rinzel

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…To examine intrinsic functional connectivity, we analyzed activity correlated with the aIFO seeds across a functional run after removing the effect of the task events. This type of approach has been used by others (Al-Aidroos et al, 2012) and shown to result in patterns of functional connectivity that closely resemble those calculated from "true" resting state scans (Grady et al, 2014;Grigg and Grady, 2010a).The functional connectivity pattern accounting for the most covariance in the data is shown in Figure 6a (p < 0.001). The spatial pattern of brain regions was similar to the set of regions with increased activity for errors ( Figure 2a) and to the common task-related functional connectivity pattern ( Figure 5a); indeed, there was considerable overlap among these three spatial patterns (Figure 7).…”

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confidence: 74%

Section: Intrinsic Functional Connectivitymentioning

confidence: 96%

Age differences in brain activity related to unsuccessful declarative memory retrieval

2015

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Although memory recall is known to be reduced with normal aging, little is known about the patterns of brain activity that accompany these recall failures. By assessing faulty memory, we can identify the brain regions engaged during retrieval attempts in the absence of successful memory and determine the impact of aging on this functional activity. We used functional magnetic resonance imaging to examine age differences in brain activity associated with memory failure in three memory retrieval tasks: autobiographical (AM), episodic (EM) and semantic (SM). Compared to successful memory retrieval, both age groups showed more activity when they failed to recall a memory in regions consistent with the salience network (SLN), a brain network also associated with non-memory errors. Both groups also showed strong functional coupling among SLN regions during incorrect trials and in intrinsic patterns of functional connectivity. In comparison to young adults, older adults demonstrated (1) less activity within the SLN during unsuccessful AM trials; (2) weaker intrinsic functional connectivity between SLN nodes and dorsolateral prefrontal cortex; and (3) less differentiation of SLN functional connectivity during incorrect trials across memory conditions. These results suggest that the SLN is engaged during recall failures, as it is for non-memory errors, which may be because errors in general have particular salience for adapting behavior. In older adults, the dedifferentiation of functional connectivity within the SLN across memory conditions and the reduction of functional coupling between it and prefrontal cortex may indicate poorer internetwork communication and less flexible use of cognitive control processes, either while retrieval is attempted or when monitoring takes place after retrieval has failed. Keywords fMRI; aging; episodic memory; salience network; frontal lobe IntroductionA number of studies have provided extensive evidence of the neural circuitry that supports memory retrieval (e.g., Cabeza and Nyberg, 2000;Spaniol et al., 2009 CIHR Author Manuscript CIHR Author Manuscript CIHR Author Manuscript2008), as well as evidence of how aging influences this circuitry (e.g., Cabeza et al., 2005;Grady, 2012;Rajah and D'Esposito, 2005). However, most research has investigated how the brain supports successful memory performance (Addis and McAndrews, 2006;Brewer et al., 1998;Morcom et al., 2003;Wagner et al., 1998), and limited attention has been paid to brain networks involved in faulty memory retrieval, most notably faulty long-term memory recall. A number of processes may be engaged during recall, including goal setting, memory search, accessing (hopefully correct) stored memories, and evaluating retrieval success (Henson et al., 1999;Moscovitch, 1992;Rugg et al., 2002). The assessment of faulty memory retrieval can be used to identify brain regions whose activity reflects processes engaged when retrieval fails. Such processes can include cognitive control processes involved regardless of retrieval succes...

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“…Therefore, both residual and resting-state spontaneous correlations largely reflect the intrinsic organization of PFC. Although task-evoked changes in largescale spontaneous correlations are relatively small, they are statistically reliable (12,35) and influence behavior (27,36). Moreover, context-dependent information processing is also supported by dynamic changes in large-scale task-evoked correlations (37)(38)(39).…”

Section: Discussionmentioning

confidence: 99%

“…S2). For each selected voxel, we then regressed out a model of task variables to produce an estimate of spontaneous activity and computed all pairwise time series correlations (12,27). We next used multidimensional scaling (MDS) to reduce the dimensionality of the spontaneous correlation structure and visualize its relationship to context preferences (Fig.…”

Section: Significancementioning

confidence: 99%

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Waskom

Wagner

2017

Proc. Natl. Acad. Sci. U.S.A.

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Human prefrontal cortex supports goal-directed behavior by representing abstract information about task context. The organizational basis of these context representations, and of representations underlying other higher-order processes, is unknown. Here, we use multivariate decoding and analyses of spontaneous correlations to show that context representations are distributed across subnetworks within prefrontal cortex. Examining targeted prefrontal regions, we found that pairs of voxels with similar context preferences exhibited spontaneous correlations that were approximately twice as large as those between pairs with opposite context preferences. This subnetwork organization was stable across task-engaged and resting states, suggesting that abstract context representations are constrained by an intrinsic functional architecture. These results reveal a principle of fine-scaled functional organization in association cortex.fMRI | resting-state | rule | cognitive control | functional organization T he cerebral cortex exhibits functional organization at multiple spatial scales. At a coarse scale, the cortex is parcellated into functional areas (1, 2) that coordinate as networks through longrange connections (3-5). These areas represent and compute information with functional circuitry that is organized more finely. Some fine-scaled, intrinsic principles have been described in detail, particularly for sensory cortex (6, 7). In contrast, the subregional organization of prefrontal cortex (PFC), which gives rise to higherorder processes, including attention, decision-making, and goaldirected action (8, 9), remains largely uncharacterized. Whether PFC representations are encoded within an equipotential system or constrained by an intrinsic functional architecture is currently unknown.Sensory cortex can be mapped by parametrically varying stimulus attributes and measuring changes in the neural response, but complex and dynamic response properties limit the effectiveness of this approach for mapping PFC and other association regions. An alternate strategy is to leverage spontaneous variability in neural activity. Neural responses to repeated presentations of sensory stimuli, or in the absence of stimulation and explicit task demands, exhibit variability that is attributed to ongoing spontaneous activity (10-13). Traditional analyses consider spontaneous activity to be noise, but there is increasing evidence that shared spontaneous variability is a signature of functional organization (14). Analyses of spontaneous correlations have been used to identify multiple large-scale functional networks (4,5,15) and boundaries between functional areas (2, 16-18) in human and nonhuman primate association cortex. The spontaneous correlation structure also mirrors established fine-scaled principles of functional organization in visual cortex, such as preferences for retinotopic position (19) and stimulus orientation (20). We therefore leveraged spontaneous activity to examine the finescaled functional organization of human PFC.We focus...

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Top-down attention switches coupling between low-level and high-level areas of human visual cortex

Cited by 172 publications

References 35 publications

Attention model of binocular rivalry

Attention model of binocular rivalry

Age differences in brain activity related to unsuccessful declarative memory retrieval

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

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