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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]

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]

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]

Background: Premenstrual syndrome (PMS) is a prevalent gynecological disease and is significantly associated with abnormal neural activity. Acupuncture is an effective treatment on PMS in clinical practice. However, few studies have been performed to investigate whether acupuncture might modulate the abnormal neural activity in patients with PMS. Thereby, the aim of the study was to assess alterations of the brain activity induced by acupuncture stimulation in PMS patients. Methods: Twenty PMS patients were enrolled in this study. All patients received a 6-min resting-state functional magnetic resonance imaging (rs-fMRI) scan before and after electro-acupuncturing stimulation (EAS) at Sanyinjiao (SP6) acupoint in the late luteal phase of menstrual. Fractional amplitude of low-frequency fluctuation (fALFF) method was applied to examine the EAS-related brain changes in PMS patients. Results: Compared with pre-EAS at SP6, increased fALFF value in several brain regions induced by SP6, including brainstem, right thalamus, bilateral insula, right paracentral lobule, bilateral cerebellum, meanwhile, decreased fALFF in the left cuneus, right precuneus, left inferior temporal cortex. Conclusions: Our findings provide imaging evidence to support that SP6-related acupuncture stimulation may modulate the neural activity in patients with PMS. This study may partly interpret the neural mechanisms of acupuncture at SP6 which is used to treat PMS patients in clinical. Trial registration: The study was registered on http://www.chictr.org.cn. The Clinical Trial Registration Number is ChiCTR-OPC-15005918, registry in 29/01/2015. [ABSTRACT FROM AUTHOR]

Background: Acupuncture is an effective therapy for premenstrual syndrome (PMS). However, the mechanisms behind this method are still unclear. Our previous study found that aberrant amygdala resting-state functional networks were involved in PMS. Thereby, a deep investigation on the alterations of amygdala resting-state functional networks induced by acupuncture stimulation might contribute to a better understanding of the intricate mechanisms of acupuncture treatment on PMS. Methods: Twenty three PMS patients were recruited in this study. All patients received a 6-minute electro-acupuncture stimulation (EAS) at Sanyinjiao acupoint (SP6) and underwent two 6-minute resting-state fMRI scannings before and after EAS. With amygdala as the seed region, functional connectivity (FC) method was adopted to examine EAS-related modulation of intrinsic connectivity in PMS patients by comparing pre-EAS. Results: The results showed that EAS at SP6 induced increased FC between the left amygdala and brainstem, right hippocampus, and decreased FC between the left amygdala and left thalamus, bilateral supplementary motor area (SMA). Moreover, the results also showed that EAS at SP6 induced increased FC between the right amygdala and brainstem, right hippocampus, right orbitofrontal cortex, bilateral anterior cingulate cortex (ACC), and decreased FC between the right amygdala and right SMA. Conclusions: Based on the results of our previous study, our findings might improve our understanding of neural mechanisms behind acupuncture effects on PMS. [ABSTRACT FROM AUTHOR]

Background: The brain's dynamic spontaneous neural activity and dynamic functional connectivity (dFC) are both important in supporting cognition, but how these two types of brain dynamics evolve and co-evolve in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remain unclear. The aim of the present study was to investigate recurrent and concurrent patterns of two types of dynamic brain states correlated with cognitive decline. Methods: The present study analyzed resting-state functional magnetic resonance imaging data from 62 SCD patients, 75 MCI patients, and 70 healthy controls (HCs). We used the sliding-window and clustering method to identify two types of recurrent brain states from both dFC and dynamic regional spontaneous activity, as measured by dynamic fractional amplitude of low-frequency fluctuations (dfALFF). Then, the occurrence frequency of a dFC or dfALFF state and the co-occurrence frequency of a pair of dFC and dfALFF states among all time points are extracted for each participant to describe their dynamics brain patterns. Results: We identified a few recurrent states of dfALFF and dFC and further ascertained the co-occurrent patterns of these two types of dynamic brain states (i.e., dfALFF and dFC states). Importantly, the occurrence frequency of a default-mode network (DMN)-dominated dFC state was significantly different between HCs and SCD patients, and the co-occurrence frequencies of a DMN-dominated dFC state and a DMN-dominated dfALFF state were also significantly different between SCD and MCI patients. These two dynamic features were both significantly positively correlated with Mini-Mental State Examination scores. Conclusion: Our findings revealed novel fMRI-based neural signatures of cognitive decline from recurrent and concurrent patterns of dfALFF and dFC, providing strong evidence supporting SCD as the transition phase between normal aging and MCI. This finding holds potential to differentiate SCD patients from HCs via both dFC and dfALFF as objective neuroimaging biomarkers, which may aid in the early diagnosis and intervention of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Premenstrual syndrome (PMS) is characterized by a series of emotional, physical and behavioral symptoms. Although PMS is related to dysfunctions of the central nervous system, the neuropathological mechanism of PMS still has not been clearly established. The aim of this study is to evaluate potential differences in both cortical thickness and subcortical volumes in PMS patients compared to healthy controls (HCs).Methods: Twenty PMS patients and twenty HCs underwent a structural magnetic resonance imaging scan and clinical assessment. Cortical thickness and subcortical volumes were computed using the FreeSurfer image analysis suite. Relationships between cortical thickness/subcortical volumes and the daily rating of severity of problems (DRSP) score were then measured in patients.Results: Compared to HCs, PMS patients exhibited reduced cortical thickness in the medial prefrontal cortex (MPFC), orbitofrontal cortex (OFC) and insula, and increased subcortical volumes of the amygdala, thalamus and pallidum. Furthermore, negative correlations were detected between the DRSP and cortical thickness in the anterior cingulate cortex and precuneus.Limitations: The study is limited by a small sample size and narrow age range of participants.Conclusions: Our findings indicate that the abnormal morphological changes are mainly implicated in emotional regulation and visceral perception in PMS patients. We hope that our study may contribute to a better understanding of PMS. [ABSTRACT FROM AUTHOR]

Background: Functional brain abnormalities have been noted in premenstrual syndrome (PMS). However, the brain structural alterations related to PMS remain unclear. This study aimed to identify possible abnormalities in gray matter (GM) volumes and structural covariance patterns among PMS patients.Methods: Structural magnetic resonance imaging data were obtained from 20 PMS patients and 20 healthy controls. Voxel-based morphometry (VBM) analysis was applied to examine GM volumes changes between the two groups. Receiver operating characteristic (ROC) curve was used to investigate the most reliable biomarker for distinguishing PMS patients from health controls based on the intergroup differences. Correlation analysis was then performed to assess relationships between the daily rating of severity of problems (DRSP) and abnormal brain regions. Finally, the regions identified from VBM analysis were served as seeds to characterize the whole-brain structural covariance patterns.Results: Compared with healthy controls, PMS patients showed increased GM volumes in the precuneus/posterior cingulate cortex (precuneus/PCC) and thalamus, and decreased GM volumes in the insula. The precuneus/PCC exhibited the highest classification power by ROC analysis and positively correlated with the DRSP. Moreover, different patterns of structural covariance in the two groups were mainly located in the dorsolateral prefrontal cortex, anterior cingulate cortex, angular gyrus and hippocampus.Limitations: This study is limited by a small sample and narrow age range of participants.Conclusions: Our findings may provide preliminary evidence for brain morphology alterations in PMS patients and contribute to a better understanding of the pathophysiology of PMS. [ABSTRACT FROM AUTHOR]

Background: Premenstrual syndrome (PMS) is becoming highly prevalent among female and is characterized by emotional, physical and behavior symptoms. Previous evidence suggested functional dysregulation of female brain was expected to be involved in the etiology of PMS. The aim of present study was to evaluate the alterations of spontaneous brain activity in PMS patients based on functional magnetic resonance imaging (fMRI).Methods: 20 PMS patients and 21 healthy controls underwent resting-state fMRI scanning during luteal phase. All participants were asked to complete a prospective daily record of severity of problems (DRSP) questionnaire.Results: Compared with healthy controls, the results showed that PMS patients had increased fALFF in bilateral precuneus, left hippocampus and left inferior temporal cortex, and decreased fALFF in bilateral anterior cingulate cortex (ACC) and cerebellum at luteal phase. Moreover, the DRSP scores of PMS patients were negatively correlated with the mean fALFF in ACC and positively correlated with the fALFF in precuneus.Limitations: (1) the study did not investigate whether or not abnormal brain activity differences between groups in mid-follicular phase, and within-group changes. between phases.(2) it was relatively limited sample size and the participants were young; (3) fALFF could not provide us with more holistic information of brain network;(4) the comparisons of PMS and premenstrual dysphoric disorder (PMDD) were not involved in the study.Conclusions: The present study shows abnormal spontaneous brain activity in PMS patients revealed by fALFF, which could provide neuroimaging evidence to further improve our understanding of the underlying neural mechanism of PMS. [ABSTRACT FROM AUTHOR]

Purpose: We sought to evaluate the performance of a new "one-stop-shop" scan protocol combining brain computed tomography perfusion (CTP) and head-and-neck CT angiography (CTA) imaging for acute stroke patients using a 256-detector CT scanner.Method: From March to August 2020, 60 patients (30 men and 30 women) aged 22-88 years with suspected acute stroke were enrolled and randomly divided into 2 groups to undergo brain CTP and head-and-neck CTA with a 256-detector CT system. Group A used traditional scan protocol with a separate brain CTP and head-and-neck CT examination that included non-contrast-enhanced and contrast-enhanced acquisitions; group B used the new "one-stop-shop" scan protocol with head-and-neck CTA data inserted into brain CTP scans at the peak time (PT) of the arterial phase. The insertion point of the head-and-neck CTA data was determined by a test bolus. The examination time, contrast dose, radiation dose, and image quality were compared between the groups.Results: The total contrast dose was reduced by 40% in group B compared to group A (60 mL vs. 100 mL). The imaging time was 52.5 ± 2.6 s in group B and 74.9 ± 3.3 s in group A, showing a reduction of approximately 43% in group B. There was no significant difference in image quality both quantitatively and qualitatively between the groups (all P > 0.05). Group B had a slight reduction in dose length product (1139.0 ± 45.3 vs. 1211.6 ± 31.9 mGy·cm, P < 0.001).Conclusions: The proposed "one-stop-shop" scan protocol combining brain CTP and head-and-neck CTA on a 256-detector CT system can reduce imaging time and contrast dose, without affecting image quality or perfusion results, compared to the traditional protocol of separating the examinations. [ABSTRACT FROM AUTHOR]

[Display omitted] • The abnormal local and long-range FCD in MCI compared with HCs. • The reversed FCD was found in MCI after treatments of acupoint-related moxibustion. • After moxibustion treatment, the MoCA scores of MCI were improved. Mild cognitive impairment (MCI) is a general neurodegenerative disease. Moxibustion has been shown to have remarkable effect on cognitive improvement, however, less is known about the effect of moxibustion on MCI and its underlying neural mechanism. This study aimed to investigate the ameliorative brain network in MCI after treatments of acupoint-related moxibustion. Resting-state functional MRI were derived from 47 MCI patients and 30 healthy controls (HCs). Patients were randomized as Tiaoshen YiZhi (TSYZ, n = 27) and sham (SHAM, n = 20) acupoint moxibustion groups. Functional connectivity density (FCD) method and repeated-measures two-way analysis of variance (ANOVA) were performed to ascertain the interaction effects between groups (TSYZ and SHAM) and time (baseline and post-treatment). Abnormal FCD was examined between baseline and post-treatment in TSYZ and SHAM groups, respectively. Compared with HCs, MCI showed altered FCD in the middle frontal cortex (MFC), inferior frontal cortex, temporal pole, thalamus and middle cingulate cortex. After moxibustion treatment in MCI, 1) a significant time-by-groups interaction was observed in the medial prefrontal cortex (mPFC); 2) abnormal long-range FCD (lrFCD) in the mPFC and MFC were modulated in TSYZ group; 3) significantly improved clinical symptoms; 4) changed lrFCD in the MFC was significantly negatively correlated with the increased Montreal Cognitive Assessment scores in TSYZ group. These imaging findings suggest that treatments of acupoint-related moxibustion could improve lrFCD in certain regions related to self-related cognitive and decision making. Our study might promote understanding of MCI neural mechanisms and expand the clinical application of moxibustion in MCI. [ABSTRACT FROM AUTHOR]

• An new multi-task learning framework is devised for differential diagnosis of subjective cognitive decline and mild cognive impairment. • The proposed multi-task learning algorithm combines functional and structural brain information. • The proposed method can discover the most disease-related brain regions and their connectivity. • The extensive experiments demonstrate good classification performance against competing techniques. Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique. [Display omitted] [ABSTRACT FROM AUTHOR]