Attention Deficits after Traumatic Brain Injury

Traumatic brain injury (TBI) frequently produces impairments of attention in humans. These can result in a failure to maintain consistent goal-directed behavior. A predominantly right-lateralized frontoparietal network is often engaged during attentionally demanding tasks. However, lapses of attention have also been
associated with increases in activation within the default mode network (DMN).

Here, we study TBI patients with sustained attention impairment, defined on the basis of the consistency of their behavioral performance over time.Weshow that sustained attention impairments in patients are associated with an increase inDMNactivation, particularly within the precuneus and posterior cingulate cortex. Furthermore, the interaction of the precuneus with the rest of theDMNat the start of the task, i.e., its functional connectivity, predicts which patients go on to show impairments of attention. Importantly, this predictive information is present before any behavioral evidence of sustained attention impairment, and the relationship is also found in a subgroup of patients without focal brain damage.

TBI often results in diffuse axonal injury, which produces cognitive impairment by disconnecting nodes in distributed brain networks. Using diffusion tensor imaging, we demonstrate that structural disconnection within the DMN also correlates with the level of sustained attention. These results show that abnormalities inDMNfunction are a sensitive marker of impairments of attention and suggest that changes in connectivity within theDMNare central to the development of attentional impairment after TBI.


Functional connectivity analysis. For the functional connectivity analyses,
we used an independent component analysis (ICA)-based approach
(using multivariate exploratory linear decomposition into independent
components) (Beckmann and Smith, 2004) in combination with a “dual
regression technique” (Filippini et al., 2009; Zuo et al., 2010; Leech et al.,
2011). This involved an initial ICA decomposition of the data, followed
by further functional connectivity analysis of the output of the ICA. This
approach has a number of advantages over the use of seed voxel-based
approaches and has been used to probe behavioral and pathology-related
differences in multiple populations (Damoiseaux et al., 2008; Filippini et
al., 2009).

Temporal concatenation ICA was first used to define 25 reference
network maps common to task performance in patients and controls
(Beckmann et al., 2005). We then selected a single reference component
that captured the previously described DMN, and a set of anticorrelated
brain regions described previously as being part of both an executive
control network (including the dorsolateral prefrontal cortex) and a salience
network (including the anterior cingulate and anterior insular
cortices) (Seeley et al., 2007). We refer to this network as the executive
control/salience network (EC/SN) (see Fig. 4A). To dissociate the respective
effect of DMN and EC/SN functional connectivity, this component
was split into its positive (DMN) and negative (EC/SN) parts.
These two subcomponents were further analyzed separately.
Preprocessed functional data from each individual were split into three equal parts, allowing the analysis of the initial and last thirds of the CRT separately.

Subsequent functional connectivity analysis was performed using the
dual regression approach (Filippini et al., 2009). This approach allows
the derivation of the individual independent component (IC) time
courses and spatial maps corresponding to the DMN and EC/SN.

Dual regression proceeds in three steps. In step 1, all unthresholded
group maps from the ICA output are linearly regressed against the first
and the last third of the preprocessed functional data from each individual
(spatial regression). This produces subject-specific time courses of
signal fluctuation corresponding to each group-level IC. In step 2, the
time courses are variance normalized and then linearly regressed against
the corresponding fMRI data (temporal regression), converting each
time series into subject-specific spatial maps of the corresponding component
(in this case the EC/SN and DMN). This method reliably produces
subject-specific approximations to the unthresholded spatial ICs
in the group ICA output (Zuo et al., 2010). In step 3, these individuallevel
dual-regression components were subsequently used to evaluate
individual functional connectivity. We extracted functional connectivity
from ROIs within the DMN and EC/SN separately for T1 and T3. Three
Figure 1. Overview of the methods used to analyze behavioral and brain changes during the performance of the CRT.

13444 • J. Neurosci., September 21, 2011 • 31(38):13442–13451 Bonnelle et al. • Sustained Attention Deficits and the Default Mode Network
ROIs were defined using the peaks of functional connectivity local maxima
from the DMN and EC/SN components. They corresponded to
the two major DMN nodes (MNI coordinates, precuneus, 6, 62, 28;
vmPFC, 2, 54, 8) and a region of the EC/SN where patients with
sustained attention impairment showed increased activation over time
[anterior cingulate cortex (ACC), 2, 22, 40]. The measures derived
from this analysis index the strength of functional connectivity from each
region to the rest of its network during T1 and T3 separately. For instance,
the measure plotted on Figure 4B represents the functional connectivity
of the precuneus to the rest of the DMN during T1, controlling
for age. The average gray matter densities of the DMN and EC/SN were
extracted from individual gray matter density maps used previously as
confound regressors in the FEAT analysis. To control for individual variability
in gray matter density, these measures were regressed-out of the
functional connectivity analyses.