Altered effective connectivity within the cingulo-frontal-parietal cognitive attention networks in chronic low back pain: a dynamic causal modeling study

MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation

Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has gradually become one of the hot subjects in the fields of neuroscience. In particular, the encoder-decoder based methods can effectively extract the connections in fMRI time series, which have achieved promising performance. However, these methods generally use Granger causality model, which may identify false directions due to the non-stationary characteristic of fMRI data. Additionally, fMRI datasets have limited sample sizes, which significantly constrains the development of these methods. In this paper, we propose a novel brain effective connectivity estimation method based on causal autoencoder with meta-knowledge transfer, called MetaCAE. The proposed approach employs a causal autoencoder (CAE) to extract causal dependencies from non-stationary fMRI time series, and leverages meta-knowledge transfer to improve the estimation accuracy on small-sample data. More specifically, MetaCAE first employs a temporal convolutional encoder to extract non-stationary temporal information from fMRI time series. Then it uses a structural equation model-based decoder to decode causal relationships between brain regions. Finally, it utilizes a model-agnostic meta-learning method to learn the meta-knowledge of the shared brain effective connectivity among different subjects, and transfers the meta-knowledge to the CAE to enhance its estimation ability on small-sample fMRI data. Comprehensive experiments on both simulated and real-world data demonstrate the efficacy of MetaCAE in estimating brain effective connectivity.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Functional Alterations of the Basal Ganglia Are Associated with Voluntary Activation of the Core Stabilizing Muscles in Patients with Chronic Low Back Pain: A Cross-Sectional Study

Purpose

Deficits in voluntary activation of the core stabilizing muscles are consistently observed in patients with chronic low back pain (CLBP); however, the underlying neural mechanism remains unclear. This cross-sectional study aimed at testing the hypothesis that the impaired voluntary activation of core stabilizing muscles is associated with structural and functional alterations in the basal ganglia, thalamus, and cortex in patients with CLBP.

Methods

We obtained structural and resting-state functional magnetic resonance imaging (rs-fMRI) data from 53 patients with CLBP and 67 healthy controls and estimated the alterations in grey matter volume (GMV) and functional and effective connectivity (EC) of regions with altered GMV via whole brain analysis. The voluntary activation of the multifidus (MF) and transversus abdominis (TrA) was evaluated by ultrasound imaging in these patients.

Results

Compared with the HCs, they displayed a significant decrease in GMV in the bilateral thalamus and caudate nucleus, a significant increase in GMV in the left middle frontal gyrus, and increased resting-state functional connectivity between the right caudate nucleus and the bilateral precuneus (voxel-level p < 0.005, Gaussian random field-corrected p < 0.05). The patients also showed increased EC from the right caudate nucleus to the bilateral precuneus, which was significantly correlated with voluntary activation of the bilateral MF and TrA (all p < 0.050).

Conclusions

Grey matter alterations may be confined to regions responsible for perception, motor control, and emotion regulation in patients with CLBP. The interrupted EC from the basal ganglia to the default mode network might be involved in the impairment of voluntary activation of the core stabilizing muscles.

Brain system segregation and pain catastrophizing in chronic pain progression

Pain processing involves emotional and cognitive factors that can modify pain perception. Increasing evidence suggests that pain catastrophizing (PC) is implicated, through pain-related self-thoughts, in the maladaptive plastic changes related to the maintenance of chronic pain (CP). Functional magnetic resonance imaging (fMRI) studies have shown an association between CP and two main networks: default mode (DMN) and dorsoattentional (DAN). Brain system segregation degree (SyS), an fMRI framework used to quantify the extent to which functional networks are segregated from each other, is associated with cognitive abilities in both healthy individuals and neurological patients. We hypothesized that individuals suffering from CP would show worst health-related status compared to healthy individuals and that, within CP individuals, longitudinal changes in pain experience (pain intensity and affective interference), could be predicted by SyS and PC subdomains (rumination, magnification, and helplessness). To assess the longitudinal progression of CP, two pain surveys were taken before and after an in-person assessment (physical evaluation and fMRI). We first compared the sociodemographic, health-related, and SyS data in the whole sample (no pain and pain groups). Secondly, we ran linear regression and a moderation model only in the pain group, to see the predictive and moderator values of PC and SyS in pain progression. From our sample of 347 individuals (mean age = 53.84, 55.2% women), 133 responded to having CP, and 214 denied having CP. When comparing groups, results showed significant differences in health-related questionnaires, but no differences in SyS. Within the pain group, helplessness (β = 0.325; p = 0.003), higher DMN (β = 0.193; p = 0.037), and lower DAN segregation (β = 0.215; p = 0.014) were strongly associated with a worsening in pain experience over time. Moreover, helplessness moderated the association between DMN segregation and pain experience progression (p = 0.003). Our findings indicate that the efficient functioning of these networks and catastrophizing could be used as predictors of pain progression, bringing new light to the influence of the interplay between psychological aspects and brain networks. Consequently, approaches focusing on these factors could minimize the impact on daily life activities.

Cognitive dual-task cost depends on the complexity of the cognitive task, but not on age and disease

Introduction

Dual-tasking (DT) while walking is common in daily life and can affect both gait and cognitive performance depending on age, attention prioritization, task complexity and medical condition. The aim of the present study was to investigate the effects of DT on cognitive DT cost (DTC) (i) in a dataset including participants of different age groups, with different neurological disorders and chronic low-back pain (cLBP) (ii) at different levels of cognitive task complexity, and (iii) in the context of a setting relevant to daily life, such as combined straight walking and turning.

Materials and methods

Ninety-one participants including healthy younger and older participants and patients with Parkinson's disease, Multiple Sclerosis, Stroke and cLBP performed a simple reaction time (SRT) task and three numerical Stroop tasks under the conditions congruent (StC), neutral (StN) and incongruent (StI). The tasks were performed both standing (single task, ST) and walking (DT), and DTC was calculated. Mixed ANOVAs were used to determine the effect of group and task complexity on cognitive DTC.

Results

A longer response time in DT than in ST was observed during SRT. However, the response time was shorter in DT during StI. DTC decreased with increasing complexity of the cognitive task. There was no significant effect of age and group on cognitive DTC.

Conclusion

Our results suggest that regardless of age and disease group, simple cognitive tasks show the largest and most stable cognitive effects during DT. This may be relevant to the design of future observational studies, clinical trials and for clinical routine.

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.

Moxibustion for primary dysmenorrhea: A resting-state functional magnetic resonance imaging study exploring the alteration of functional connectivity strength and functional connectivity

Introduction

Primary dysmenorrhea (PDM) is a common gynecological disease and chronic pain disorder. Moxibustion, a form of traditional Chinese medicine therapy, has proven to be effective for PDM. However, the central mechanisms of PDM and moxibustion for PDM are still unclear. This study aims to explore the potential central mechanism of PDM and clarify the possible mechanism of moxibustion for relieving pain.

Materials and methods

A total of 23 PDM patients and 23 matched healthy controls (HCs) were enrolled. For PDM patients, resting-state functional magnetic resonance imaging (rs-fMRI) data were collected pre- and post-moxibustion treatment of 3 consecutive menstrual cycles, respectively. For HCs, rs-fMRI data were collected in the baseline. The resting-state functional connectivity strength (rs-FCS) analysis and the resting-state functional connectivity (rs-FC) analysis based on the region of interest (ROI) were combined to be conducted.

Results

Compared to HCs, PDM patients showed weaker rs-FCS in the left inferior frontal gyrus (IFG). After the moxibustion treatment, rs-FCS in the left IFG was increased with clinical improvement. Then, the left IFG was chosen as ROI, and the rs-FC analysis was conducted. It showed that the left IFG rs-FC in the bilateral anterior cingulate cortex (ACC)/middle cingulate cortex (MCC), the left posterior cingulate cortex (PCC)/precuneus (PCU), and the left parahippocampal gyrus (PHG) decreased after moxibustion treatment, most of which belong to the default mode network (DMN).

Conclusion

Our results highlight the role of the left IFG and the DMN in PDM. Specifically, the central mechanism of moxibustion for analgesia may be related to modulating the disorders of the reappraisal and processing of pain stimuli through influencing the cognition of pain.