Characterizing Attention with Predictive Network Models

Abstract

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction.

Keywords: attention; connectome; fMRI; functional connectivity; predictive models; sustained attention.

Figure 1. Connectome-based predictive modeling

The connectome-based predictive modeling (CPM) approach identifies functional connectivity networks that are related to behavior, and measures strength in these networks within previously unseen individuals to make predictions about their behavior [–67]. First, every participant’s whole-brain connectivity pattern is calculated by correlating the fMRI activity timecourses of every pair of regions, or nodes, in a brain atlas. Next, behaviorally relevant connections are identified by correlating every connection in the brain with behavior across subjects. Connections that are most strongly related to behavior in the positive and negative directions are retained for model building. A linear model relates each individual’s positive network strength (i.e., the sum of the connections in their positive network) and negative network strength (i.e., the sum of the connections in their negative network) to their behavioral score. The model is then applied to a novel individual’s connectivity data to generate a behavioral prediction. Predictive power is assessed by correlating predicted and observed behavioral scores across the group.

Figure 2. Canonical attention networks and networks used to build the sustained attention CPM

Nodes of canonical networks were defined using MNI coordinates or Talairach coordinates converted to MNI space from representative articles. For each x, y, z coordinate, the closest node of the Shen et al. (2013) atlas [69] was identified using the knnsearch function in Matlab. Nodes and fully connected networks were then visualized using BioImage Suite [105]. As such, these figures are meant to be illustrative summaries rather than quantitative analyses of well-studied canonical networks. Nodes of the alerting, orienting, and executive control network were defined using Tables 3–5 in Ref. [17]. Six left-hemisphere nodes of the dorsal and ventral attention networks [12] were defined using Table 5 in Ref. [106]; symmetrical right-hemisphere nodes were also included for visualization. Nodes of the task-positive and task-negative networks were defined using Table 1 in Ref. [31]. The high-attention network (orange) and low-attention network (turquoise) were defined in Ref. [65]. Nodes of these networks are sized according to their total number of connections.

Figure 3. A suite of predictive models to predict behavior

One benefit of using fMRI data to predict an individual’s behavior and clinical symptoms is that multiple predictive models can be applied to a single fMRI dataset. For example, the sustained attention CPM can be applied to predict sustained attention [65]. At the same time, separate models may be applied to predict fluid intelligence [67], and, hypothetically, a number of behaviors, traits, and symptoms, such as working memory, emotion regulation, personality traits, and symptoms of autism.