Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
, 3 (4), 317-38

Brain Connectivity and Visual Attention

Affiliations
Review

Brain Connectivity and Visual Attention

Emily L Parks et al. Brain Connect.

Abstract

Emerging hypotheses suggest that efficient cognitive functioning requires the integration of separate, but interconnected cortical networks in the brain. Although task-related measures of brain activity suggest that a frontoparietal network is associated with the control of attention, little is known regarding how components within this distributed network act together or with other networks to achieve various attentional functions. This review considers both functional and structural studies of brain connectivity, as complemented by behavioral and task-related neuroimaging data. These studies show converging results: The frontal and parietal cortical regions are active together, over time, and identifiable frontoparietal networks are active in relation to specific task demands. However, the spontaneous, low-frequency fluctuations of brain activity that occur in the resting state, without specific task demands, also exhibit patterns of connectivity that closely resemble the task-related, frontoparietal attention networks. Both task-related and resting-state networks exhibit consistent relations to behavioral measures of attention. Further, anatomical structure, particularly white matter pathways as defined by diffusion tensor imaging, places constraints on intrinsic functional connectivity. Lastly, connectivity analyses applied to investigate cognitive differences across individuals in both healthy and diseased states suggest that disconnection of attentional networks is linked to deficits in cognitive functioning, and in extreme cases, to disorders of attention. Thus, comprehensive theories of visual attention and their clinical translation depend on the continued integration of behavioral, task-related neuroimaging, and brain connectivity measures.

Figures

FIG. 1.
FIG. 1.
The architecture of the Guided Search Model of human visual search performance. Attention is guided to the most highly activated combination of features. Attentional biasing may reflect bottom-up sources related to the salience of local contrasts within the display, and/or top-down sources related to the observer's goals and expectations. Modified from Wolfe (1994) and reproduced with permission from Kramer and Madden (2008).
FIG. 2.
FIG. 2.
Behavioral performance varies with the direction of Granger causal influences between the dorsal attention network (DAN) and ventral attention network (VAN). Linear fits are shown, where R is the Spearman correlation coefficient and p is the significance level. Stronger Granger causal influences from the DAN to the VAN were positively correlated with improved task performance (red), while stronger Granger causal influences from the VAN to the DAN were negatively correlated with task performance (blue). Reproduced with permission from Wen et al. (2012).
FIG. 3.
FIG. 3.
Resting-state functional connectivity data reveals two widely distributed, anticorrelated brain networks. Positive nodes are significantly correlated with seed regions within the frontoparietal attention network (task-positive seeds) and significantly anticorrelated with seed regions in the default mode network (DMN) that routinely deactivate during attention-demanding cognitive tasks (task-negative seeds). Negative nodes are significantly correlated with task-negative seed regions and significantly anticorrelated with task-positive seed regions. (Left) Lateral and medial views of left hemisphere. (Center) Dorsal view. (Right) Lateral and medial views of right hemisphere. Reproduced with permission from Fox et al. (2005).
FIG. 4.
FIG. 4.
Diffusion tensor imaging (DTI)-based structural connectivity overlaps functional connectivity in the default mode network (DMN). (A) Intrinsic functional connectivity in the DMN in a group of six participants. The sagittal view depicts the posterior cingulate cortex/retrosplenial cortex (PCC/RSC) and medial prefrontal cortex (mPFC) clusters. Prominent bilateral medial temporal lobe (MTL) clusters are visible in the coronal image. (B) DTI fiber tractography in a single subject demonstrates the cingulum bundle (blue tracts) connecting the PCC/RSC to the mPFC. (C) Schematic representation of the structural and functional connections between these three nodes of the DMN. Modified from Greicius et al. (2009) and reproduced with permission from Damoiseaux and Greicius (2009).
FIG. 5.
FIG. 5.
The development of two proposed adult control networks involves both the segregation and integration of the brain regions that comprise them. Graphs formed from putative task-control regions in children and adults. Regions of interest (ROI) locations are drawn to correspond to topographic brain locations. Right-sided ROIs are displayed on the right and anterior ROIs at the top of each graph. Resting-state functional connectivity revealed a significant deviation between children and adults in two previously described control networks (Dosenbach et al., 2007). (A) The top 75 connections in children revealed that the two control networks were connected by a bridge connection: anterior prefrontal cortex–dorsolateral prefrontal cortex (aPFC-dlPFC). The dorsal ACC/msFC region was incorporated into the frontoparietal network. Children lacked connections from the dlPFC to intraparietal sulcus (IPS) and inferior parietal lobule (IPL). (B) In adults, resting-state functional connectivity revealed two separate control networks. Modified with permission from Fair et al. (2007).
FIG. 6.
FIG. 6.
Analysis of 913 healthy participants reveals both age-related increases and decreases in resting-state functional connectivity. Normal aging is associated with pronounced decreases (A, left panel) in long-range functional connectivity density that map into the default mode network (DMN) and dorsal frontoparietal attention networks (B, light blue pattern) and with increases (A, right panel) that map into somatosensory and cerebellar networks (B, orange pattern). Reproduced with permission from Tomasi and Volkow (2012).

Similar articles

See all similar articles

Cited by 27 articles

See all "Cited by" articles

Publication types

MeSH terms

LinkOut - more resources

Feedback