Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?

Abstract

40Attention is a critical cognitive function, allowing humans to select, enhance, and sustain 41 focus on information of behavioral relevance. Attention contains dissociable neural and 42 psychological components. Nevertheless, some brain networks support multiple attentional 43 functions. Connectome-based Predictive Models (CPM), which associate individual differences 44 in task performance with functional connectivity patterns, provide a compelling example. A 45 sustained attention network model (saCPM) successfully predicted performance for selective 46 attention, inhibitory control, and reading recall tasks. Here we constructed a visual attentional 47 blink (VAB) model (vabCPM), comparing its performance predictions and network edges 48 associated with successful and unsuccessful behavior to the saCPM's. In the VAB, attention 49 devoted to a target often causes a subsequent item to be missed. Although frequently attributed to 50 attentional limitations, VAB deficits may attenuate when participants are distracted or deploy 51 attention diffusely. Participants (n=73; 24 males) underwent fMRI while performing the VAB 52 task and while resting. Outside the scanner, they completed other cognitive tasks over several 53 days. A vabCPM constructed from these data successfully predicted VAB performance. 54 Strikingly, the network edges that predicted better VAB performance (positive edges) predicted 55 worse selective and sustained attention performance, and vice versa. Predictions from the saCPM 56 mirrored these results, with the network's negative edges predicting better VAB performance. 57 Furthermore, the vabCPM's positive edges significantly overlapped with the saCPM's negative 58 edges, and vice versa. We conclude that these partially overlapping networks each have general 59 attentional functions. They may indicate an individual's propensity to diffusely deploy attention, 60 predicting better performance for some tasks and worse for others. 61 62 Significance statement 63A longstanding question in psychology and neuroscience is whether we have general 64 capacities or domain-specific ones. For such general capacities, what is the common function? 65Here we addressed these questions using the attentional blink (AB) task and neuroimaging. 66Individuals searched for two items in a stream of distracting items; the second item was often 67 missed when it closely followed the first. How often the second item was missed varied across 68 individuals, which was reflected in attention networks. Curiously, the networks' pattern of 69 function that was good for the AB was bad for other tasks, and vice versa. We propose that these 70 networks may represent not a general attentional ability, but rather the tendency to attend in a 71 less focused manner. 72

Intelligence and creativity share a common cognitive and neural basis.

“…Here, we provide a novel extension of this recent study (and the CPM approach) by identifying functional connectivity networks that predict both lower-order/specific and higher-order/general intelligence facets using fMRI data acquired during the divergent thinking task. This allowed us to quantify the degree of overlap between intelligence and creativity in the brain by identifying functional brain connections that predict both constructs (cf., Lake et al, 2019;Rosenberg et al, 2018;Wu et al, 2020).…”

Masked features of task states found in individual brain networks

“…These correlated signals recapitulate functional systems seen in task activations, and are seen even in spontaneous activity at rest 6 . Relative to rest, FC is altered subtly but significantly across diverse task states [7][8][9] and researchers have been able to use machine learning on FC signals to classify task states with good accuracy 7,[10][11][12][13][14][15][16] , even during self-driven tasks 15,17 . These findings of significant and predictable changes in FC during complex tasks improve our understanding of how goals alter the ways that brain regions interact.…”

A brain-based universal measure of attention: predicting task-general and task-specific attention performance and their underlying neural mechanisms from task and resting state fMRI

“…The frontal and parietal cortices are well-known to control attention (Corbetta and Shulman, 2002; Kanwisher and Wojciulik, 2000), but most studies have not compared their engagement across multiple attention tasks. In addition, only recently have studies begun to predict individual attentional behaviors from brain scans (Kucyi et al, 2017; Rosenberg et al, 2016a, 2017; Wu et al, 2020).…”