Objective To investigate the dynamic functional connectivity of thalamocortical networks in interictal migraine patients and whether clinical features are associated with abnormal connectivity. Methods We investigated dynamic functional network connectivity (dFNC) of the migraine brain in 89 interictal migraine patients and 70 healthy controls. We focused on the temporal properties of thalamocortical connectivity using sliding window cross-correlation, clustering state analysis, and graph-theory methods. Relationships between clinical symptoms and abnormal dFNC were evaluated using a multivariate linear regression model. Results Five dFNC brain states were identified to characterize and compare dynamic functional connectivity patterns. We demonstrated that migraineurs spent more time in a strongly interconnected between-network state, but they spent less time in a sparsely connected state. Interestingly, we found that abnormal posterior thalamus (pulvinar nucleus) dFNC with the visual cortex and the precuneus were significantly correlated with headache frequency of migraine. Further topologic measures revealed that migraineurs had significantly lower efficiency of information transfer in both global and local dFNC. Conclusion Our results demonstrated a transient pathologic state with atypical thalamocortical connectivity in migraineurs and extended current findings regarding abnormal thalamocortical networks and dysrhythmia in migraine.

Numerous studies have investigated the impacts of common types of chronic pain (CP) on patients’ cognitive function and observed that CP was associated with later dementia. More recently, there is a growing recognition that CP conditions frequently coexist at multiple body sites and may bring more burdens on patients’ overall health. However, whether and how multisite CP (MCP) contributes to an increased risk of dementia, compared to single-site CP (SCP) and pain-free (PF), is largely unclear. In the current study, utilizing the UK Biobank cohort, we first investigated dementia risk in individuals (n = 354,943) with different numbers of coexisting CP sites using Cox proportional hazards regression models. We then applied generalized additive models to investigate whether MCP leads to excessive deterioration of participants’ (n = 19,116) cognition and brain structure. We found that individuals with MCP were associated with significantly higher dementia risk, broader and faster cognitive impairment, and greater hippocampal atrophy than both PF individuals and those with SCP. Moreover, the detrimental effects of MCP on dementia risk and hippocampal volume aggravated along with the number of coexisting CP sites. Mediation analyses further revealed that the decline of fluid intelligence in MCP individuals was partially mediated by hippocampal atrophy. Our results suggested that cognitive decline and hippocampal atrophy interact biologically and may underlie the increased risk of dementia associated with MCP.

Thalamocortical dysrhythmia is a key pathology of chronic neuropathic pain, but few studies have investigated thalamocortical networks in chronic low back pain (cLBP) given its non-specific etiology and complexity. Using fMRI, we propose an analytical pipeline to identify abnormal thalamocortical network dynamics in cLBP patients and validate the findings in two independent cohorts. We first identify two reoccurring dynamic connectivity states and their associations with chronic and temporary pain. Further analyses show that cLBP patients have abnormal connectivity between the ventral lateral/posterolateral nucleus (VL/VPL) and postcentral gyrus (PoCG) and between the dorsal/ventral medial nucleus and insula in the less frequent connectivity state, and temporary pain exacerbation alters connectivity between the VL/VPL and PoCG and the default mode network in the more frequent connectivity state. These results extend current findings on thalamocortical dysfunction and dysrhythmia in chronic pain and demonstrate that cLBP pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics.

Objective To identify and validate an fMRI-based neural marker for migraine without aura (MwoA) and to examine its association with treatment response. Methods We conducted cross-sectional studies with resting-state fMRI data from 230 participants and machine learning analyses. In studies 1 through 3, we identified, cross-validated, independently validated, and cross-sectionally validated an fMRI-based neural marker for MwoA. In study 4, we assessed the relationship between the neural marker and treatment responses in migraineurs who received a 4-week real or sham acupuncture treatment, or were waitlisted, in a registered clinical trial. Results In study 1 (n = 116), we identified a neural marker with abnormal functional connectivity within the visual, default mode, sensorimotor, and frontal-parietal networks that could discriminate migraineurs from healthy controls (HCs) with 93% sensitivity and 89% specificity. In study 2 (n = 38), we investigated the generalizability of the marker by applying it to an independent cohort of migraineurs and HCs and achieved 84% sensitivity and specificity. In study 3 (n = 76), we verified the specificity of the marker with new datasets of migraineurs and patients with other chronic pain disorders (chronic low back pain and fibromyalgia) and demonstrated 78% sensitivity and 76% specificity for discriminating migraineurs from nonmigraineurs. In study 4 (n = 116), we found that the changes in the marker responses showed significant correlation with the changes in headache frequency in response to real acupuncture. Conclusion We identified an fMRI-based neural marker that captures distinct characteristics of MwoA and can link disease pattern changes to brain changes.

Previous coronavirus pandemics were associated elevated post-traumatic stress symptoms (PTSS), but the self-report and neurological basis of PTSS in patients who survived coronavirus disease 2019 (COVID-19) are largely unknown. We conducted a two-session study to record PTSS in the COVID-19 survivors discharged from hospitals for a short (i.e., about 3 months, Session 1) to a medium period (i.e., about 6 months, Session 2), as well as brain imaging data in Session 2. The control groups were non-COVID-19 locals. Session 1 was completed for 126 COVID-19 survivors and 126 controls. Session 2 was completed for 47 COVID-19 survivors and 43 controls. The total score of post-traumatic stress disorder (PTSD) checklist for DSM-5 (PCL-5) score was significantly higher in COVID-19 survivors compared with controls in both sessions. The PCL-5 score in COVID-19 survivors was positively correlated with the duration after discharge (r = 0.27, p = 0.003 for Session 1), and increased by 20% from Session 1 to Session 2 for the survivors who participated both sessions. The increase was positively correlated with individual’s test-retest duration (r = 0.46, p = 0.03). Brain structural volume and functional activity in bilateral hippocampus and amygdala were significantly larger in COVID-19 survivors compared with controls. However, the volumes of the left hippocampus and amygdala were negatively correlated with the PCL-5 score for the COVID-19 survivors. Our study suggests that COVID-19 survivors might face possible PTSS deteriorations, and highlights the importance of monitoring mental wellness of COVID-19 survivors.

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.