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Neuroimaging evidence suggests that the default mode network (DMN) exhibits antagonistic activity with dorsal attention (DAN) and salience (SN) networks. Here we use human intracranial electroencephalography to investigate the behavioral relevance of fine-grained dynamics within and between these networks. The three networks show dissociable profiles of task-evoked electrophysiological activity, best captured in the high-frequency broadband (HFB; 70–170 Hz) range. On the order of hundreds of milliseconds, HFB responses peak fastest in the DAN, at intermediate speed in the SN, and slowest in the DMN. Lapses of attention (behavioral errors) are marked by distinguishable patterns of both pre- and post-stimulus HFB activity within each network. Moreover, the magnitude of temporally lagged, negative HFB coupling between the DAN and DMN (but not SN and DMN) is associated with greater sustained attention performance and is reduced during wakeful rest. These findings underscore the behavioral relevance of temporally delayed coordination between antagonistic brain networks. Brain imaging studies suggest that specific, large-scale, cortical networks show antagonistic activity with one another. Here, the authors studied the dynamics of these networks using implanted electrodes in the human brain, revealing that the coordination of inter-network dynamics on fast time scales relates to fluctuations in attention.

The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, background accuracy and mIoU were 99.68%, 81.36%, 99.88% and 81.36% for all patients; 99.52%, 84.86%, 99.93% and 84.86% for grade I meningiomas; 99.57%, 80.11%, 99.92% and 80.12% for grade II meningiomas; and 99.75%, 78.40%, 99.99% and 78.40% for grade III meningiomas, respectively. For grade classification, the accuracy values of the training and test datasets were 99.93% and 81.52% for all patients; 99.98% and 98.51% for grade I meningiomas; 99.91% and 66.67% for grade II meningiomas; and 99.88% and 73.91% for grade III meningiomas, respectively. The automated detection, segmentation and grade classification of meningiomas based on deep learning were accurate and reliable and may improve the monitoring and treatment of this frequently occurring tumor entity. Furthermore, the method could function as a useful tool for preassessment and preselection for radiologists, offering auxiliary information for clinical decision making in presurgical evaluation.

We studied the temporal dynamics of activity within and across functional MRI (fMRI)–derived nodes of intrinsic resting-state networks of the human brain using intracranial electroencephalography (iEEG) and repeated single-pulse electrical stimulation (SPES) in neurosurgical subjects implanted with intracranial electrodes. We stimulated and recorded from 2,133 and 2,372 sites, respectively, in 29 subjects. We found that N1 and N2 segments of the evoked responses are associated with intra- and internetwork communications, respectively. In a separate cognitive experiment, evoked electrophysiological responses to visual target stimuli occurred with less temporal separation across pairs of electrodes that were located within the same fMRI-defined resting-state networks compared with those located across different resting-state networks. Our results suggest intranetwork prior to internetwork information processing at the subsecond timescale.

Objective: Deep brain stimulation (DBS) has shown promising outcomes as new therapeutic opportunities for patients with treatment-resistant depression (TRD) who do not respond adequately to several consecutive treatments. This study aims to systematically review and conduct a meta-analysis on the efficacy and safety of DBS for TRD. Method: The literature was comprehensively reviewed using Medline, Google scholar, Cochrane library, Embase, and World Health Organization International Clinical Trials Registry Platform until January 2019. The studied outcomes included response, remission, recurrence, and adverse events (AEs) rates, and were reported as the rate ratio (RR) or pooled estimate with a 95% confidence interval (95% CI). Heterogeneity was measured by an I-square test and a sensitive analysis. Results: A total of 17 studies involving 7 DBS targets were included. For efficacy, DBS treatment was statistically beneficial for TRD, and the response, remission, and recurrence rates were 56% (ranging from 43 to 69%), 35% (ranging from 27 to 44%), and 14% (ranging from 4 to 25%), respectively. However, only two randomized-controlled trials (RCTs) considered the invalidity of DBS ( RR = 1.45, 95% CI = 0.50–4.21). For safety, the AEs rate was 67% (ranging from 54 to 80%). The AEs were common and moderate, but the problems related to suicide and suicidal ideation should not be underestimated. Conclusion: These findings suggest that DBS for TRD is considered promising, which should be confirmed by well-designed and large sample studies. Future basic research and comprehensive clinical trials are needed to reach better understanding on the mechanisms of action and optimal targeted structure.

Background: Diabetic vascular complications are a major cause of poor prognosis in patients with diabetes mellitus (DM). Mitophagy activation is a potential therapeutic target for type 2 diabetes mellitus (T2DM), but the role of low-intensity pulsed ultrasound (LIPUS) in this context remains unclear. Methods: The biological effects of LIPUS on endothelial cells under high glucose conditions were systematically evaluated using high glucose-treated human umbilical vein endothelial cells (HUVECs) and aortic tissues from diabetic rats as models, in combination with bioinformatics analysis and standard molecular and cellular biology techniques. Histological staining was further used to assess the protective role of LIPUS in the aortas of diabetic rats. Results: Bioinformatics analysis predicted that high glucose induces mitochondrial dysfunction, suppresses autophagy in HUVECs, impairs endothelial cell function, and activates fibroblasts. In vitro results were in agreement with these predictions. LIPUS treatment significantly counteracted these effects, restoring migration (p < 0.001) and angiogenesis (p < 0.05), increasing proliferation (p < 0.001), and decreasing apoptosis (p < 0.05). Mechanistically, LIPUS enhanced mitophagy, and its therapeutic effects were markedly diminished upon addition of the autophagy inhibitor 3-Methyladenine (3-MA). In vivo, LIPUS attenuated aortic endothelial damage and reduced collagen deposition in diabetic rats (p < 0.01). Conclusions: LIPUS may ameliorate hyperglycemia-induced endothelial cell dysfunction by activating mitophagy, and it also attenuates pathological damage in the abdominal aorta of diabetic rats, thereby providing experimental evidence for its application in the treatment of diabetic macrovascular complications.

Background This study aimed to explore the predictive value of 18 F-fluorodeoxyglucose positron emission tomography ([ 18 F]FDG PET) metabolic activity in determining postoperative motor function outcomes in patients with Rolandic focal cortical dysplasia (FCD). Methods We conducted a retrospective analysis of 62 patients with FCD who underwent resective surgery in the Rolandic area. Of these, 15 patients underwent task-based functional magnetic resonance imaging (fMRI). Motor functional reorganization and its relationship with PET metabolism were analyzed. Patients were classified into deficit and non-deficit groups according to their postoperative motor function status. PET metabolism in the resected motor cortex was compared between the two groups, and its correlation with postoperative muscle strength was further evaluated in the deficit group. Results Among the 15 patients who underwent fMRI evaluation, the extent of motor reorganization was negatively correlated with PET metabolism ( P  = 0.0006). PET analysis revealed significantly higher PET metabolic T -values in the resected Rolandic cortex of the motor deficit group compared to the non-deficit group ( P  = 0.0004). Furthermore, within the deficit group, PET metabolic T -values were negatively correlated with postoperative muscle strength ( P  = 0.017). The prediction model for postoperative motor function, demonstrated strong performance in cross-validation, with an area under the curve (AUC) of 0.82, a sensitivity of 83%, and a specificity of 76%. Conclusions The findings underscore the critical role of preoperative [ 18 F]FDG-PET in predicting motor outcomes following Rolandic cortex resection in epilepsy patients, highlighting its value in optimizing preoperative assessment and surgical planning.

Low-frequency oscillations (LFOs, 28 Hz) in the subthalamic nucleus(STN) are known to reflect cognitive conflict. However, it is unclear if LFOs mediate communication and functional interactions among regions implicated in conflict processing, such as the motor cortex (M1), premotor cortex (PMC), and superior parietal lobule (SPL). To investigate the potential contribution of LFOs to cognitive conflict mediation, we recorded M1, PMC, and SPL activities by right subdural electrocorticography (ECoG) simultaneously with bilateral STN local field potentials (LFPs) by deep brain stimulation electrodes in 13 patients with Parkinson's disease who performed the arrow version of the Eriksen flanker task. Elevated cue-related LFO activity was observed across patients during task trials, with the earliest onset in PMC and SPL. At cue onset, LFO power exhibited a significantly greater increase or a trend of a greater increase in the PMC, M1, and STN, and less increase in the SPL during high-conflict (incongruent) trials than in low-conflict (congruent) trials. The local LFO power increases in PMC, SPL, and right STN were correlated with response time, supporting the notion that these structures are critical hubs for cognitive conflict processing. This power increase was accompanied by increased functional connectivity between the PMC and right STN, which was correlated with response time across subjects. Finally, ipsilateral PMC-STN Granger causality was enhanced during high-conflict trials, with direction from STN to PMC. Our study indicates that LFOs link the frontal and parietal cortex with STN during conflicts, and the ipsilateral PMC-STN connection is specifically involved in this cognitive conflict processing. [Display omitted]

Abiotic stress, including salinity, drought and cold, severely affect diverse aspects of plant development and production. Rice is an important crop that does not acclimate to cold; therefore, it is relatively sensitive to low temperature stress. Dehydration-responsive element-binding protein 1s (DREB1s)/C-repeat binding factors (CBFs) are well known for their function in cold tolerance, but the transcriptional regulation of CBFs remains elusive, especially in rice. Here, we performed a yeast one-hybrid assay using the promoter of CBF1, a cold-induced gene, to isolate transcriptional regulators of CBF1. Among the seven candidates identified, an indeterminate domain (IDD) protein named ROC1 (a regulator of CBF1) was further analyzed. The ROC1 transcript was induced by exogenously-treated auxin, while it was not altered by cold or ABA stimuli. ROC1-GFP was localized at the nucleus, and ROC1 showed trans-activation activity in yeast. The electrophoretic mobility shift assay (EMSA) and ChIP analyses revealed that ROC1 directly bound to the promoter of CBF1. Furthermore, ROC1 mutants exhibited chilling-sensitive symptoms and inhibited cold-mediated induction of CBF1 and CBF3, indicating that ROC1 is a positive regulator of cold stress responses. Taken together, this study identified the CBF1 regulator, and the results are important for rice plant adaptation to chilling stress.

Approximately 90% of Parkinson’s patients (PD) suffer from dysarthria. However, there is currently a lack of research on acoustic measurements and speech impairment patterns among Mandarin-speaking individuals with PD. This study aims to assess the diagnosis and disease monitoring possibility in Mandarin-speaking PD patients through the recommended speech paradigm for non-tonal languages, and to explore the anatomical and functional substrates. We examined total of 160 native Mandarin-speaking Chinese participants consisting of 80 PD patients, 40 healthy controls (HC), and 40 MRI controls. We screened the optimal acoustic metric combination for PD diagnosis. Finally, we used the objective metrics to predict the patient’s motor status using the Naïve Bayes model and analyzed the correlations between cortical thickness, subcortical volumes, functional connectivity, and network properties. Comprehensive acoustic screening based on prosodic, articulation, and phonation abnormalities allows differentiation between HC and PD with an area under the curve of 0.931. Patients with slowed reading exhibited atrophy of the fusiform gyrus (FDR p  = 0.010, R  = 0.391), reduced functional connectivity between the fusiform gyrus and motor cortex, and increased nodal local efficiency (NLE) and nodal efficiency (NE) in bilateral pallidum. Patients with prolonged pauses demonstrated atrophy in the left hippocampus, along with decreased NLE and NE. The acoustic assessment in Mandarin proves effective in diagnosis and disease monitoring for Mandarin-speaking PD patients, generalizing standardized acoustic guidelines beyond non-tonal languages. The speech impairment in Mandarin-speaking PD patients not only involves motor aspects of speech but also encompasses the cognitive processes underlying language generation.

During presurgical evaluation for focal epilepsy patients, the evidence supporting the use of high frequency oscillations (HFOs) for delineating the epileptogenic zone (EZ) increased over the past decade. This study aims to develop and validate an integrated automatic detection, classification and imaging pipeline of HFOs with stereoelectroencephalography (SEEG) to narrow the gap between HFOs quantitative analysis and clinical application. The proposed pipeline includes stages of channel inclusion, candidate HFOs detection and automatic labeling with four trained convolutional neural network (CNN) classifiers and HFOs sorting based on occurrence rate and imaging. We first evaluated the initial detector using an open simulated dataset. After that, we validated our full algorithm in a 20-patient cohort against three assumptions based on previous studies. Classified HFOs results were compared with seizure onset zone (SOZ) channels for their concordance. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were calculated representing the prediction ability of the labeled HFOs outputs for SOZ. The initial detector demonstrated satisfactory performance on the simulated dataset. The four CNN classifiers converged quickly during training, and the accuracies on the validation dataset were above 95%. The localization value of HFOs was significantly improved by HFOs classification. The AUC values of the 20 testing patients increased after HFO classification, indicating a satisfactory prediction value of the proposed algorithm for EZ identification. Our detector can provide robust HFOs analysis results revealing EZ at the individual level, which may ultimately push forward the transitioning of HFOs analysis into a meaningful part of the presurgical evaluation and surgical planning.