Tracking cognitive fluctuations with multivoxel pattern time course (MVPTC) analysis

Abstract

The posterior parietal cortex, including the medial superior parietal lobule (mSPL), becomes transiently more active during acts of cognitive control in a wide range of domains, including shifts of spatial and nonspatial visual attention, shifts between working memory representations, and shifts between categorization rules. Furthermore, spatial patterns of activity within mSPL, identified using multivoxel pattern analysis (MVPA), reliably distinguish between different acts of control. Here we describe a novel multivoxel pattern-based analysis that uses fluctuations in cognitive state over time to reveal inter-regional functional connectivity. First, we used MVPA to model patterns of activity in mSPL associated with shifting or maintaining spatial attention. We then computed a multivoxel pattern time course (MVPTC) that reflects, moment-by-moment, the degree to which the pattern of activity in mSPL more closely matches an attention-shift pattern or a sustained-attention pattern. We then entered the MVPTC as a regressor in a univariate (i.e., voxelwise) general linear model (GLM) to identify voxels whose BOLD activity covaried with the MVPTC. This analysis revealed several regions, including the striatum of the basal ganglia and bilateral middle frontal gyrus, whose activity was significantly correlated with the MVPTC in mSPL. For comparison, we also conducted a conventional functional connectivity analysis, entering the mean BOLD time course in mSPL as a regressor in a univariate GLM. The latter analysis revealed correlations in extensive regions of the frontal lobes but not in any subcortical area. The MVPTC analysis provides greater sensitivity (e.g., revealing the striatal-mSPL connectivity) and greater specificity (i.e., revealing more-focal clusters) than a conventional functional connectivity analysis. We discuss the broad applicability of MVPTC analysis to a variety of neuroimaging contexts.

Introduction

Humans prioritize the processing of goal-relevant sensory information through voluntary shifts of selective attention. Several recent studies have reported that voluntary shifts of attention are associated with transient activity in the medial wall of the superior parietal lobule (mSPL; e.g., Kelley et al., 2008, Liu et al., 2003, Serences et al., 2004, Shomstein and Yantis, 2006, Shulman et al., 2009, Vandenberghe et al., 2001, Yantis et al., 2002). In contrast, sustained activity associated with maintaining attention is found in intraparietal sulcus and prefrontal cortex (e.g., Saygin and Sereno, 2008, Serences and Yantis, 2007, Silver et al., 2005). These findings suggest that mSPL plays a role in reconfiguring or shifting attention rather than in maintaining the current state of attention. Furthermore, this transient mSPL signal is also observed in non-spatial and non-perceptual acts of control, including shifts of categorization rule (Chiu & Yantis, 2009) and shifts of attention between working memory representations (Tamber-Rosenau, Esterman, Chiu, & Yantis, 2011). These studies thus implicate mSPL as a domain-independent hub for cognitive reconfiguration (Chiu and Yantis, 2009, Greenberg et al., 2010).

In addition to mSPL, other cortical and subcortical regions have been associated with cognitive reconfiguration. For example, dorsolateral prefrontal regions are often co-activated with parietal cortex, forming a dorsal frontoparietal attention control network for deployments of attention to goal-relevant sensory information (Corbetta & Shulman, 2002). Furthermore, many studies have demonstrated functional connectivity between dorsal parietal cortex and prefrontal cortex (e.g., Fox, Corbetta, Snyder, Vincent, & Raichle, 2006), and have shown that this connectivity affected behavior during spatial attention tasks (He et al., 2007, Thiebaut de Schotten et al., 2005). In addition to those cortical regions, the basal ganglia (BG) also have been implicated in shifts of spatial attention (e.g., Gitelman et al., 1999, Grande et al., 2006, Shulman et al., 2009), shifts of task set (e.g., Cools et al., 2006, Cools et al., 2004, Leber et al., 2008, Ravizza and Ivry, 2001), and updates in working memory (O’Reilly & Frank, 2006). However, functional connectivity has not been demonstrated between cortical control regions and subcortical structures (e.g., the BG) in humans during shifts of spatial attention. The current study was designed to examine the functional connectivity between mSPL and other cortical and subcortical regions using a novel multivariate technique.

One well-established approach to assessing functional connectivity first computes the time course of the mean blood oxygenation level dependent (BOLD) signal across all voxels within a seed region of interest (ROI). This time course is then entered as a univariate (voxelwise) general linear model (GLM) regressor in order to identify other voxels whose activity covaries with that of the seed region (e.g., Biswal et al., 1995, Friston et al., 1993). Another approach, beta series correlation (Rissman, Gazzaley, & D’Esposito, 2004), uses trial-by-trial beta coefficient values, instead of raw time series, within a seed region to explore correlations across the brain. More sophisticated approaches, such as psychophysiological interaction analysis (e.g., Diekhof et al., 2009, Duann et al., 2009) or dynamic causal modeling (e.g., Friston et al., 1997, Smith et al., 2006, Stephan et al., 2007), further include interaction terms among regressors to explore connectivity. Using the mean BOLD signal (or beta values) within an ROI to explore functional connectivity, however, assumes that all voxels within the ROI are all estimates of a single, common time series (thus justifying taking their average). Furthermore, this method relies on the presence of reliable activations (after correcting for multiple comparisons) in the seed regions of interest.

Here we develop an information-based functional connectivity method. In general, an information-based approach employs multivoxel pattern analysis (MVPA) to identify regions in which information expressed by spatiotemporal patterns of voxel activation reliably reflects distinct cognitive states (e.g., Chiu et al., 2011, Kamitani and Tong, 2005, Kriegeskorte et al., 2006, Norman et al., 2006, Polyn et al., 2005, Serences et al., 2009). Our novel information-based functional connectivity method exploits the multivoxel pattern of activity within a seed region by computing a continuous index of “pattern strength” as it evolves and fluctuates over time. This multivoxel pattern time course (MVPTC) then is used to identify other voxels whose activity covaries with the pattern strength within the seed region. This approach was motivated by recent studies (Esterman et al., 2009, Greenberg et al., 2010, Tamber-Rosenau et al., 2011) showing that spatial patterns of activity within mSPL reliably predicted which of several domains of cognitive control subjects were engaged in (e.g., spatial representations vs. rule representations; and visual vs. working memory representations) at each time point of a experimental run. Thus, multivoxel patterns of activity in mSPL provide a signature of cognitive state that is otherwise masked by averaging the BOLD signal across voxels within the ROI.

In the current study, subjects performed cue-evoked covert shifts of spatial attention in the task. We first identified a region of mSPL that exhibited transient increases in mean activity time-locked to shifts of attention. We then used MVPA (employing a linear support vector machine, or SVM) to model multivoxel activation patterns within mSPL associated with attention shifts and sustained attention, respectively. The resulting classifier was then applied to each time point in the entire mSPL time series, yielding a continuous information-based index (i.e., the classifier decision value) of the degree to which the mSPL pattern reflected shifting vs. sustained attention on a moment-by-moment basis. Finally, the MVPTC obtained from mSPL was entered as a regressor in a whole-brain voxelwise GLM to explore information-based functional connectivity between mSPL and the rest of the brain.