The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a “temporal circuit” characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in consciousness.

DTI-based functional neuronavigation contributes to maximal safe resection of cerebral gliomas with PT involvement, thereby decreasing postoperative motor deficits for both HGGs and low-grade gliomas while increasing high-quality survival for HGGs.

Adult IDH wild-type lower-grade gliomas are prognostically heterogeneous and do not have uniformly poor prognosis. Clinical information and additional markers, including MYB, EGFR, TERTp, and H3F3A, should be examined to delineate discrete favorable and unfavorable prognostic groups.

High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image features from magnetic resonance images (MRI). However, feature engineering is usually time consuming, laborious and subjective. Most importantly, the engineered features cannot effectively encode other predictive but implicit information provided by multi-modal neuroimages. We propose a two-stage learning-based method to predict the overall survival (OS) time of high-grade gliomas patient. At the first stage, we adopt deep learning, a recently dominant technique of artificial intelligence, to automatically extract implicit and high-level features from multi-modal, multi-channel preoperative MRI such that the features are competent of predicting survival time. Specifically, we utilize not only contrast-enhanced T1 MRI, but also diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI), for computing multiple metric maps (including various diffusivity metric maps derived from DTI, and also the frequency-specific brain fluctuation amplitude maps and local functional connectivity anisotropy-related metric maps derived from rs-fMRI) from 68 high-grade glioma patients with different survival time. We propose a multi-channel architecture of 3D convolutional neural networks (CNNs) for deep learning upon those metric maps, from which high-level predictive features are extracted for each individual patch of these maps. At the second stage, those deeply learned features along with the pivotal limited demographic and tumor-related features (such as age, tumor size and histological type) are fed into a support vector machine (SVM) to generate the final prediction result (i.e., long or short overall survival time). The experimental results demonstrate that this multi-model, multi-channel deep survival prediction framework achieves an accuracy of 90.66%, outperforming all the competing methods. This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine.

The recenT paradigm for managing acromegaly includes microsurgery or endoscopic surgery (transsphenoidal surgery or craniotomy), pharmacological therapy (somatostatin analogue, growth hormonereceptor antagonist, dopamine receptor agonists) and radiosurgery which reverse excessive growth hormone (GH) secretion, and shrink or ablate tumors without compromising normal pituitary function. Maximal control of GH level, reduction of systemic complications and mortality, control of tumor growth, preservation of pituitary function, and extension of life expectancy are the aims of disease management. But these Effect of presurgical long-acting octreotide treatment in acromegaly patients with invasive pituitary macroadenomas: a prospective randomized study Abstract. Therapeutic effects of presurgical long-acting octreotide treatment on tumor shrinkage, and short-and long-term postoperative GH and IGF-1 levels of acromegaly patients with invasive pituitary macroadenomas were investigated prospectively in Huashan Hospital, Shanghai, China. Thirty-nine untreated acromegaly patients, all with invasive pituitary macroadenomas, were randomly divided into two groups: experimental group (n=19), and control group (n=20). Patients in the experimental group received a three-month course of long-acting octreotide treatment before transsphenoidal surgery; the control group underwent surgery directly. Tumor shrinkage after drug treatment and short-and long-term postoperative GH and IGF-1 levels were analyzed in the two groups. Long-acting octreotide treatment reduced tumor size from 7893 ± 6450 to 4794 ± 4682 mm 3 . Mean shrinkage rate was 37.4 ± 30.9%. GH and IGF-1 levels of the experimental group were lower than the control group at 3 months, 6 months after surgery, and after long-term follow-up. Remission rate (both GH and IGF-1 normal) of the experimental group was higher at 3 and 6 months follow-up, but exhibited no advantage in longterm follow-up. In the experimental group, the total resection rate was higher in patients whose Hardy-Knosp grading decreased to ≤ 2 than those whose Hardy-Knosp grading is still ≥ 3 after drug pretreatment. In conclusion, presurgical long-acting octreotide treatment effectively reduces tumor size and invasion, which helps enhance early remission rates of invasive macroadenomas by transsphenoidal surgery, but does not appear to improve the long-term cure rate.

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Recent studies at the cellular and regional levels have pointed out the multifaceted importance of neural synchronization and temporal variance of neural activity. For example, neural synchronization and temporal variance has been shown by us to be altered in patients in the vegetative state (VS). This finding nonetheless leaves open the question of whether these abnormalities are specific to VS or rather more generally related to the absence of consciousness. The aim of our study was to investigate the changes of inter- and intra-regional neural synchronization and temporal variance of resting state activity in anesthetic-induced unconsciousness state. Applying an intra-subject design, we compared resting state activity in functional magnetic resonance imaging (fMRI) between awake versus anesthetized states in the same subjects. Replicating previous studies, we observed reduced functional connectivity within the default mode network (DMN) and thalamocortical network in the anesthetized state. Importantly, intra-regional synchronization as measured by regional homogeneity (ReHo) and temporal variance as measured by standard deviation (SD) of the BOLD signal were significantly reduced in especially the cortical midline regions, while increased in the lateral cortical areas in the anesthetized state. We further found significant frequency-dependent effects of SD in the thalamus, which showed abnormally high SD in Slow-5 (0.01-0.027 Hz) in the anesthetized state. Our results show for the first time of altered temporal variance of resting state activity in anesthesia. Combined with our findings in the vegetative state, these findings suggest a close relationship between temporal variance, neural synchronization and consciousness.

The role of cerebellum and cerebro-cerebellar system in neural plasticity induced by cerebral gliomas involving language network has long been ignored. Moreover, whether or not the process of reorganization is different in glioma patients with different growth kinetics remains largely unknown. To address this issue, we utilized preoperative structural and resting-state functional MRI data of 78 patients with left cerebral gliomas involving language network areas, including 46 patients with low-grade glioma (LGG, WHO grade II), 32 with high-grade glioma (HGG, WHO grade III/IV), and 44 healthy controls. Spontaneous brain activity, resting-state functional connectivity and gray matter volume alterations of the cerebellum were examined. We found that both LGG and HGG patients exhibited bidirectional alteration of brain activity in language-related cerebellar areas. Brain activity in areas with increased alteration was significantly correlated with the language and MMSE scores. Structurally, LGG patients exhibited greater gray matter volume in regions with increased brain activity, suggesting a structure-function coupled alteration in cerebellum. Furthermore, we observed that cerebellar regions with decreased brain activity exhibited increased functional connectivity with contralesional cerebro-cerebellar system in LGG patients. Together, our findings provide empirical evidence for a vital role of cerebellum and cerebro-cerebellar circuit in neural plasticity following lesional damage to cerebral language network. Moreover, we highlight the possible different reorganizational mechanisms of brain functional connectivity underlying different levels of behavioral impairments in LGG and HGG patients.