Recovery of the default mode network after demanding neurofeedback training occurs in spatio-temporally segregated subnetworks

doi:10.1016/j.neuroimage.2012.08.061

NeuroImage

Volume 63, Issue 4, December 2012, Pages 1775-1781

https://doi.org/10.1016/j.neuroimage.2012.08.061 Get rights and content

Abstract

The default mode (DM) network is a major large-scale cerebral network that can be identified with functional magnetic resonance imaging (fMRI) during resting state. Most studies consider functional connectivity networks as stationary phenomena. Consequently, the transient behavior of the DM network and its subnetworks is still largely unexplored. Most functional connectivity fMRI studies assess the steady state of resting without any task. To specifically investigate the recovery of the DM network during the transition from activation to rest, we implemented a cognitively demanding real-time fMRI neurofeedback task that targeted down-regulation of the primary auditory cortex. Each of twelve healthy subjects performed 16 block-design fMRI runs (4 runs per day repeated on 4 days) resulting 192 runs in total. The analysis included data-driven independent component analysis (ICA) and high-resolution latency estimation between the four components that corresponded to subnetworks of the DM network. These different subnetworks reemerged after regulation with an average time lag or 3.3 s and a time lag of 4.4 s between the first and fourth components; i.e., the DM recovery first shifts from anterior to posterior, and then gradually focuses on the ventral part of the posterior cingulate cortex, which is known to be implicated in internally directed cognition. In addition, we found less reactivation in the early anterior subnetwork as regulation strength increased, but more reactivation with larger regulation for the late subnetwork that encompassed the ventral PCC. This finding confirms that the level of task engagement influences inversely the subsequent recovery of regions related to attention compared to those related to internally directed cognition.

Introduction

Deeper understanding of the brain's intrinsic functional organization is one of the main aims of functional magnetic resonance imaging (fMRI) at resting state (Biswal et al., 1995, Fox and Raichle, 2007). The spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal can be used to measure functional connectivity between different voxels or regions in the brain, and thus establish intrinsic large-scale functional networks. Such functional connectivity analysis gives insight about functional integration, and measuring it at resting state does not require a specific experimental setup or paradigm in contrast to conventional stimulus-related fMRI; this has a high potential for clinical applications (Broyd et al., 2009, Damoiseaux et al., 2008).

The “default-mode” (DM) network is one of the prototypical resting-state networks; i.e., it can be identified as the network that “deactivates” compared to the task baseline, or by using functional connectivity analysis (Damoiseaux et al., 2006, Fox and Raichle, 2007, Greicius et al., 2003, Laird et al., 2011). While it is known that the DM network restores during the transition from activation to rest, and that its deactivation is not the same for all tasks, little is known about the temporal properties of this recovery, in particular with respect to DM subnetworks.

Functional connectivity is defined statistical dependence between different voxels or regions and mostly measured by temporal correlation (Friston, 1994). Consequently, most studies assess the steady state of resting and thus assume stationarity. However, recent evidence suggests that functional connectivity can be modulated spontaneously (Raichle, 2010), by exogenous stimulation (Buchel et al., 1999) and by learning (Bassett et al., 2011, Lewis et al., 2009).

Here we assessed the recovery of the DM network following a demanding cognitive task. In particular, we used a real-time fMRI (rt-fMRI) neurofeedback paradigm (deCharms, 2007, Weiskopf et al., 2003) to let the subject down-regulate brain activity in the right primary auditory cortex. Previous work has shown that the suppression of the DM network during task is proportional to its level of cognitive demand or task difficulty, e.g. (Leech et al., 2011). In our case, we expect a strong de-activation of DM network during the task, followed by recovery in subsequent resting epochs. In addition, we use the regulation strength as measured in the auditory target region as a surrogate for task engagement. To obtain robust assessment of the recovery of the DM network, each subject performed 4 fMRI runs per day (session), which were repeated on 4 training days resulting in 16 sessions per subject, or 192 sessions in total. First, we made use of data-driven independent component analysis (ICA) to identify various temporally-coherent functional networks, and, in particular, four distinct DM subnetworks. For each of these components, we then investigated the time-lag differences and found statistically significant evidence for a specific sequence of how the DM subnetworks come back “online”. Finally, we found differences in correlation of the activity in these subnetworks and regulation strength, which brings new evidence to the neurophysiological DM components.

Section snippets

Subjects

Twelve healthy right-handed individuals participated for monetary compensation after giving informed consent approved by the local Ethics Committee. None suffered from current or prior neurological or psychiatric impairments or claustrophobia. Audition was normal. Mean age of participants was 28.37 y (range 24–33 y).

Task procedure

The rt-fMRI neurofeedback experimental was similar to a previous study (Haller et al., 2010). Each subject had 4 sessions performed in 4 different days, while a session was designed

Results

Using post-hoc GLM analysis, we showed that rt-fMRI leads to a successful down-regulation of brain activity levels in the target region in the right primary auditory cortex (Fig. 1a). Our second level analysis confirms a significant improvement in down-regulation as a regulation improves (Fig. 1b). Then, using ICA, we were able to identify four DM subnetworks (Fig. 2 and Table 1). The time-courses of these ICs were then submitted to the latency analysis (Fig. 3a), which resulted into the

Discussion

Our results reveals how the DM network recovers after a cognitively demanding rt-fMRI task, following a specific spatio-temporal sequence of its subnetworks; i.e., activity first proceeds from anterior to posterior, and then refocuses from the dorsal toward the ventral part of the PCC, which is one of the cortical hubs of the brain (Hagmann et al., 2008). We will first briefly revisit the concept of the DM network. Then, we discuss the partitioning of the DM network and its dynamic recovery in

Conclusion

In summary, we demonstrated that DM network recovery following a demanding rt-fMRI task occurs in four different subnetworks with different time latencies. The subnetworks take up functionally different parts of the DM network, and two of them are modulated by task intensity as measured by the regulation strength in the auditory region that was targeted by the real-time fMRI neurofeedback task. One limitation of our study is that causality between the subnetworks cannot be established. In that

Disclosure

No conflicts of interest.

Acknowledgments

This work has been supported in part by the Swiss National Science Foundation (under the grants 320030_127079/1 and PP00P2-123438) and in part by the Center for Biomedical Imaging (CIBM) of the EPFL and the Universities and Hospitals of Lausanne and Geneva. We thank all participants. We would like to thank Dr. Elena Allen and Prof. Tom Eichele for their invaluable help with the GIFT analysis.

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Recovery of the default mode network after demanding neurofeedback training occurs in spatio-temporally segregated subnetworks

Abstract

Introduction

Section snippets

Subjects

Task procedure

Results

Discussion

Conclusion

Disclosure

Acknowledgments

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Neurosci. Biobehav. Rev.

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Neurobiol. Aging

Trends Cogn. Sci.

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Trends Cogn. Sci.

NeuroImage

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NeuroImage

NeuroImage

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Proc. Natl. Acad. Sci. U. S. A.

Functional connectivity in the motor cortex of resting human brain using echo-planar MRI

Magn. Reson. Med.

An improved method for acquiring cerebrovascular reactivity maps

Magn. Reson. Med.

The predictive value of changes in effective connectivity for human learning

Science

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Ann. N. Y. Acad. Sci.

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Proc. Natl. Acad. Sci. U. S. A.

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