The influence of nociceptive and neuropathic pain states on the processing of acute electrical nociceptive stimulation: A dynamic causal modeling study

Case report: The promising application of dynamic functional connectivity analysis on an individual with failed back surgery syndrome

Failed back surgery syndrome (FBSS), a chronic neuropathic pain condition, is a common indication for spinal cord stimulation (SCS). However, the mechanisms of SCS, especially its effects on supraspinal/brain functional connectivity, are still not fully understood. Resting state functional magnetic resonance imaging (rsfMRI) studies have shown characteristics in patients with chronic low back pain (cLBP). In this case study, we performed rsfMRI scanning (3.0 T) on an FBSS patient, who presented with chronic low back and leg pain following her previous lumbar microdiscectomy and had undergone permanent SCS. Appropriate MRI safety measures were undertaken to scan this subject. Seed-based functional connectivity (FC) was performed on the rsfMRI data acquired from the FBSS subject, and then compared to a group of 17 healthy controls. Seeds were identified by an atlas of resting state networks (RSNs), which is composed of 32 regions grouped into 8 networks. Sliding-window method and k-means clustering were used in dynamic FC analysis, which resulted in 4 brain states for each group. Our results demonstrated the safety and feasibility of 3T MRI scanning in a patient with implanted SCS system. Compared to the brain states of healthy controls, the FBSS subject presented very different FC patterns in less frequent brain states. The mean dwell time of brain states showed distinct distributions: the FBSS subject seemed to prefer a single state over the others. Although future studies with large sample sizes are needed to make statistical conclusions, our findings demonstrated the promising application of dynamic FC to provide more granularity with FC changes associated with different brain states in chronic pain.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Hyperconnectivity and High Temporal Variability of the Primary Somatosensory Cortex in Low-Back-Related Leg Pain: An fMRI Study of Static and Dynamic Functional Connectivity

Objective

To investigate the functional connectivity (FC) and its variability in the primary somatosensory cortex (S1) of patients with low-back-related leg pain (LBLP) in the context of the persistent stimuli of pain and numbness.

Patients and Methods

We performed functional magnetic resonance imaging on LBLP patients (n = 26) and healthy controls (HCs; n = 34) at rest. We quantified and compared static FC (sFC) using a seed-based analysis strategy, with 6 predefined bilateral paired spherical regions of interest (ROIs) in the S1 cortex. Then, we captured the dynamic FC using sliding window correlation of ROIs in both the LBLP patients and HCs. Furthermore, we performed a correlational analysis between altered static and dynamic FC and clinical measures in LBLP patients.

Results

Compared with controls, the LBLP patients had 1) significantly increased static FC between the left S1_back (the representation of the back in the S1) and right superior and middle frontal gyrus (SFG/MFG), between the left S1_chest and right SFG/MFG, between right S1_chest and right SFG/MFG, between the left S1_face and right MFG, and between the right S1_face and right inferior parietal lobule (P < 0.001, Gaussian random field theory correction); 2) increased dynamic FC only between the right S1_finger and the left precentral and postcentral gyrus and between the right S1_hand and the right precentral and postcentral gyrus (P < 0.01, Gaussian random field theory correction); and 3) a negative correlation between the Barthel index and the increased static FC between the left S1_face and right inferior parietal lobule (P = 0.048).

Conclusion

The present study demonstrated the hyperconnectivity of the S1 cortex to the default mode and executive control network in a spatial pattern and an increase in the tendency for signal variability in the internal network connections of the S1 cortex in patients with LBLP.