Functional connectivity of the language area in migraine: a preliminary classification model.

BACKGROUND: Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment. METHODS: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI(rfMRI), and diffusion weighted scans were obtained from 31 patients with migraine, and 17 controls. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into diagnostic groups based on functional connectivity (FC) and derive networks and parcels contributing to the model. PageRank centrality analysis was also performed on the structural connectome to identify changes in hubness. RESULTS: Our model attained an area under the receiver operating characteristic curve (AUC-ROC) of 0.68, which rose to 0.86 following hyperparameter tuning. FC of the language network was most predictive of the model's classification, though patients with migraine also demonstrated differences in the accessory language, visual and medial temporal regions. Several analogous regions in the right hemisphere demonstrated changes in PageRank centrality, suggesting possible compensation. CONCLUSIONS: Although our small sample size demands caution, our preliminary findings demonstrate the utility of our method in providing a network-based perspective to diagnosis and treatment of migraine.

Individual differences in pain sensitivity in drug-naive patients with major depressive disorder: an fMRI study.

Patients with major depressive disorder (MDD) often report pain; however, the pain-related brain mechanism that contributes to MDD with pain remains largely unclear. In the current study, we aimed to observe the cortical responses by employing fMRI technique combined with thermal stimulation paradigm in 17 major depressive disorder patients with pain (MDDP), 19 major depressive disorder patients without pain (MDDNP), and 25 age- and gender-matched healthy control (HC) subjects. Participants completed the Hamilton Depression Rating Scale-17 (HAMD-17) and provided pain intensity ratings in response to noxious heat (51 °C) during task-fMRI scanning by visual analogue scale (VAS). In our results, there was no difference in pain intensity ratings during tonic heat stimulation between the HC group and MDDNP group (p > 0.05), while the MDDNP group had significantly higher HAMD scores compared with the HC group (p < 0.001). The MDDNP group had decreased brain activation in the postcentral gyrus (PCG) compared with the HC group, implying abnormal activation of the PCG may associate with the characterized depressive mood of painless MDD (p < 0.05). Additionally, there was no difference in HAMD scores between the MDDP group and MDDNP group (p > 0.05), while the MDDP group had significantly greater pain during tonic heat stimulation compared with the MDDNP group (p < 0.01). The MDDP group showed enhanced activation in the PCG compared with the MDDNP group (p < 0.05), which may relate to the abnormal regulation of pain in painful MDD. Our results suggested that higher PCG activation may play an important role in facilitating the occurrence of pain in depression.