Anhedonia, the reduced ability to experience pleasure in response to otherwise rewarding stimuli, is a core symptom of major depressive disorder (MDD). Although the posterior ventromedial prefrontal cortex (pVMPFC) and its functional connections have been consistently implicated in MDD, their roles in anhedonia remain poorly understood. Furthermore, it is unknown whether anhedonia is primarily associated with intrinsic ‘resting-state' pVMPFC functional connectivity or an inability to modulate connectivity in a context-specific manner. To address these gaps, a pVMPFC region of interest was first identified using activation likelihood estimation meta-analysis. pVMPFC connectivity was then examined in relation to anhedonia and general distress symptoms of depression, using both resting-state and task-based functional magnetic resonance imaging involving pleasant music, in current MDD and healthy control groups. In MDD, pVMPFC connectivity was negatively correlated with anhedonia but not general distress during music listening in key reward- and emotion-processing regions, including nucleus accumbens, ventral tegmental area/substantia nigra, orbitofrontal cortex and insula, as well as fronto-temporal regions involved in tracking complex sound sequences, including middle temporal gyrus and inferior frontal gyrus. No such dissociations were observed in the healthy controls, and resting-state pVMPFC connectivity did not dissociate anhedonia from general distress in either group. Our findings demonstrate that anhedonia in MDD is associated with context-specific deficits in pVMPFC connectivity with the mesolimbic reward system when encountering pleasurable stimuli, rather than a static deficit in intrinsic resting-state connectivity. Critically, identification of functional circuits associated with anhedonia better characterizes MDD heterogeneity and may help track of one of its core symptoms.

BackgroundTemporomandibular disorders (TMD) are characterized by pain and impaired masticatory functions. The Integrated Pain Adaptation Model (IPAM) predicts that alterations in motor activity may be associated with increased pain in some individuals. The IPAM highlights the diversity of patients' responses to orofacial pain and suggests that such diversity is related to the sensorimotor network of the brain. It remains unclear whether the pattern of brain activation reflects the diversity of patients' responses underlying the association between mastication and orofacial pain.ObjectiveThis meta‐analysis aims to compare the spatial pattern of brain activation, as the primary outcome of neuroimaging studies, between studies of mastication (i.e. Study 1: mastication of healthy adults) and studies of orofacial pain (i.e. Study 2: muscle pain in healthy adults and Study 3: noxious stimulation of the masticatory system in TMD patients).MethodsNeuroimaging meta‐analyses were conducted for two groups of studies: (a) mastication of healthy adults (Study 1, 10 studies) and (b) orofacial pain (7 studies), including muscle pain in healthy adults (Study 2) and noxious stimulation of the masticatory system in TMD patients (Study 3). Consistent loci of brain activation were synthesized using Activation Likelihood Estimation (ALE) with an initial cluster‐forming threshold (p < .05) and a threshold of cluster size (p < .05, familywise error‐corrected).ResultsThe orofacial pain studies have shown consistent activation in pain‐related regions, including the anterior cingulate cortex and the anterior insula (AIns). A conjunctional analysis of mastication and orofacial pain studies showed joint activation at the left AIns, the left primary motor cortex and the right primary somatosensory cortex.ConclusionThe meta‐analytical evidence suggests that the AIns, as a key region in pain, interoception and salience processing, contributes to the pain‐mastication association. These findings reveal an additional neural mechanism of the diversity of patients' responses underlying the association between mastication and orofacial pain.

Purpose/Objective(s): Brain metastasis (BM) is common in patients with breast cancer. Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM). The present study was designed to develop and evaluate a prognostic model to facilitate decisionmaking for patients with newly diagnosed brain metastasis from breast cancer. Materials/Methods: Based on the clinical data of BCBM patients treated in our hospital from 2002 to 2014, a nomogram was developed to predict survival using proportional hazards regression analysis. The model was validated internally by bootstrapping, and the concordance index (c-index) was calculated. We used a calibration curve and c-index to evaluate discriminatory and predictive ability, in order to compare the nomogram with widely used models, including recursive partitioning analysis (RPA), graded prognostic assessment (GPA), and breast-graded prognostic assessment (Breast-GPA). Results: A total of 411 BCBM patients were included in the development of this predictive model. Median overall survival was 14.1 months. Statistically significant predictors for patient survival included biologic subtype, Karnofsky performance score (KPS), leptomeningeal metastasis, extracranial metastasis, the number of brain metastases, and disease-free survival (DFS). A nomogram for predicting 1-and 2-year overall survival was constructed, which exhibited good accuracy in predicting overall survival with a concordance index of 0.735. This model outperformed RPA, GPA and Breast-GPA, based on the comparisons of the c-indexes. Conclusion: The nomogram constructed based on a multiple factor analysis could more accurately predict the individual survival probability of patients with BCBM, compared with existing models.

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