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Objectives: To develop a deep learning (DL) model for differentiating between osteolytic osteosarcoma (OS) and giant cell tumor (GCT) on radiographs. Methods: Patients with osteolytic OS and GCT proven by postoperative pathology were retrospectively recruited from four centers (center A, training and internal testing; centers B, C, and D, external testing). Sixteen radiologists with different experiences in musculoskeletal imaging diagnosis were divided into three groups and participated with or without the DL model's assistance. DL model was generated using EfficientNet-B6 architecture, and the clinical model was trained using clinical variables. The performance of various models was compared using McNemar's test. Results: Three hundred thirty-three patients were included (mean age, 27 years ± 12 [SD]; 186 men). Compared to the clinical model, the DL model achieved a higher area under the curve (AUC) in both the internal (0.97 vs. 0.77, p = 0.008) and external test set (0.97 vs. 0.64, p < 0.001). In the total test set (including the internal and external test sets), the DL model achieved higher accuracy than the junior expert committee (93.1% vs. 72.4%; p < 0.001) and was comparable to the intermediate and senior expert committee (93.1% vs. 88.8%, p = 0.25; 87.1%, p = 0.35). With DL model assistance, the accuracy of the junior expert committee was improved from 72.4% to 91.4% (p = 0.051). Conclusion: The DL model accurately distinguished osteolytic OS and GCT with better performance than the junior radiologists, whose own diagnostic performances were significantly improved with the aid of the model, indicating the potential for the differential diagnosis of the two bone tumors on radiographs. Critical relevance statement: The deep learning model can accurately distinguish osteolytic osteosarcoma and giant cell tumor on radiographs, which may help radiologists improve the diagnostic accuracy of two types of tumors. Key points: • The DL model shows robust performance in distinguishing osteolytic osteosarcoma and giant cell tumor. • The diagnosis performance of the DL model is better than junior radiologists'. • The DL model shows potential for differentiating osteolytic osteosarcoma and giant cell tumor. [ABSTRACT FROM AUTHOR]

In conclusion, the finding that tumor genetics, pathology, and location are significantly correlated with language reorganization in brain tumor patients might contribute to the choice of the optimal surgical procedure and timing. Against this background, Pasquini et al. described a retrospective investigation of 405 patients with left-hemispheric tumors and 49 patients with right-hemispheric tumors as controls [[2]]. 8895248 2 Pasquini L, Yildirim O, Silveira P. Effect of tumor genetics, pathology, and location on fMRI of language reorganization in brain tumor patients. In terms of the effect of tumor location on language reorganization, significant associations exist between the frontal LI, Broca LI, and tumor involvement of Broca's area (BA). [Extracted from the article]

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Previous studies have indicated alterations of brain functional connectivity in PMS patients. However, little is known about the overall organization of brain network in PMS patients. Functional magnetic resonance imaging data deriving from 20 PMS patients and 21 healthy controls (HCs). Pearson correlation between mean time-series was used to estimate connectivity matrix between each paired regions of interest, and the connectivity matrix for each participant was then binarized. Graph theory analysis was applied to assess each participant's global and local topological properties of brain functional network. Correlation analysis was performed to evaluate relationships between the daily rating of severity of problems (DRSP) and abnormal network properties. PMS patients had lower small-worldness values than HCs. PMS-related alterations of nodal properties were mainly found in the posterior cingulate cortex, precuneus and angular gyrus. The PMS-related abnormal connectivity components were mainly associated with the thalamus, putamen and middle cingulate cortex. In the PMS group, the DRSP score were negatively correlated with the area under the curves of nodal local efficiency in the posterior cingulate cortex. Our study suggests that the graph-theory method may be one potential tool to detect disruptions of brain connections and may provide important evidence for understanding the PMS from the disrupted network organization perspective. [ABSTRACT FROM AUTHOR]

Amygdala is an important locus of dysfunction implicated in major depressive disorder(MDD). Aberrant amygdala networks(AN) had been reported in resting-state functional magnetic resonance imaging (rs-fMRI) study. The safety and efficacy of acupuncture treatment for MDD have been verified in previous clinical studies. This study is aimed to investigate whether acupuncture at GV20 could modulate the abnormal AN of patients with the first-episode, drug-naïve MDD by using rs-fMRI combined with functional connectivity (FC) method. Thirty MDD patient underwent 6-min rs-fMRI scans respectively before and after 20-min electro-acupuncture stimulate(EAS) at GV20. Twenty-nine healthy subjects underwent only a 6-min rs-fMRI scan. Based on the amygdala as the seed region, FC method was adopted to examine abnormal AN in patients by comparing with healthy subjects and to evaluate the influence of EAS on intrinsic connectivity within the AN in patients with MDD. Compared to healthy subjects, MDD patients had aberrant intrinsic AN which mainly showed increased FC between amygdala and hippocampus, precuneus, precentral gyrus and angular gyrus, as well as decreased FC between amygdala and orbital frontal cortex(OFC). Moreover, our results indicated that EAS at GV20 induced increased/decreased FC between amygdala and certain regions in MDD patients. In addition, the intrinsic amygdala FC within other certain brain regions in MDD patients were regulated by EAS at GV20. The abnormal AN of MDD patients could be modulated by EAS at GV20. Our findings may further provide the potential imaging evidence to support the modulatory mechanisms of acupuncture on MDD. [ABSTRACT FROM AUTHOR]

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Although the precise pathophysiology is not fully understood, it is increasingly believed that the central nervous system plays a vital role in the development of PMS. The aim of this study is to elucidate specific functional connectivity between the thalamus and cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 20 PMS patients and 21 healthy controls (HCs). Seed-based functional connectivity between the thalamus and six cortical regions of interest, including the prefrontal cortex (PFC), posterior parietal cortex, somatosensory cortex, motor cortex/supplementary motor area, temporal and occipital lobe, was adopted to identify specific thalamocortical connectivity in the two groups. Correlation analysis was then used to examine relationships between the neuroimaging findings and clinical symptoms. Activity in distinct cortical regions correlated with specific sub-regions of the thalamus in the two groups. Comparison between groups exhibited decreased prefrontal-thalamic connectivity and increased posterior parietal-thalamic connectivity in the PMS patients. Within the PMS group, the daily record of severity of problems (DRSP) score negatively correlated with the prefrontal-thalamic connectivity. Our findings may provide preliminary evidence for abnormal thalamocortical connectivity in PMS patients and may contribute to a better understanding of the pathophysiology of PMS. [ABSTRACT FROM AUTHOR]

Primary dysmenorrhea (PDM), a common gynecological disorder, is associated with structural and functional alterations in several subregions of the anterior cingulate cortex (ACC). However, systematic functional connectivity of the ACC subregions in PDM has not been clarified. In this study, we used resting-state functional magnetic resonance imaging (fMRI) data from forty-eight PDM patients and thirty-eight matched female healthy controls to investigate the functional connectivity of ACC subregions in PDM. Compared to healthy controls, PDM patients exhibited increased connectivity between the caudal ACC (cACC) and primary somatosensory cortex (SI), between the perigenual ACC (pACC) and caudate, and between the subgenual ACC (sACC) and medial prefrontal cortex (mPFC). PDM patients also showed decreased connectivity between the pACC and precuneus. In PDM group, the connectivity of the right pACC-right caudate positively correlated with disease duration, and the connectivity of the left pACC-left precuneus negatively correlated with disease severity. These present findings reveal that abnormal ACC connectivity may be implicated in the PDM-related disturbances in pain sensory, modulation, and affection. We hope that our study could enhance the understanding of the pathophysiology underlying PDM. [ABSTRACT FROM AUTHOR]

Objectives: To assess structural and functional changes of the amygdala due to premenstrual syndrome (PMS) using magnetic resonance imaging (MRI).Methods: Twenty PMS patients and 21 healthy control (HC) subjects underwent a 6-min resting-state fMRI scan during the luteal phase as well as scanning high-resolution T1-weighted images. Subcortical amygdala-related volume and functional connectivity (FC) were estimated between the two groups. Each subject completed a daily record of severity of problems (DRSP) to measure the severity of clinical symptoms.Results: Greater bilateral amygdalae volumes were found in PMS patients compared with HC subjects, and PMS patients had increased FC between the amygdala and certain regions of the frontal cortex (e.g. medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), right precentral gyrus), the right temporal pole and the insula, as well as decreased FC between the bilateral amygdalae and the right orbitofrontal cortex and right hippocampus. The strength of FC between the right amygdala and right precentral gyrus, left ACC and left mPFC were significantly and positively correlated with DRSP scores in PMS patients.Conclusions: Our findings may improve our understanding of the neural mechanisms involved in PMS.Key Points: • Functional and structural MRI used to explore amygdala in PMS patients. • Aberrant amygdala structural and functional connectivity were found in PMS patients. • Amygdala strength FC was positively correlated with individual clinical symptom scores. [ABSTRACT FROM AUTHOR]