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