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Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Direct electrical stimulation, the transient 'lesional' method probing brain function, has been utilized in identifying the language cortex and preserving language function during epilepsy and neuro-oncological surgeries for about a century. However, comparison of functional maps of the language cortex across languages/continents based on cortical stimulation remains unclear. We conducted a retrospective multicentre study including four cohorts of direct electrical stimulation mapping from four centres across three continents, where three indigenous languages (English, French and Mandarin) are spoken. All subjects performed the two most common language tasks: number counting and picture naming during stimulation. All language sites were recorded and normalized to the same brain template. Next, Spearman's correlation analysis was performed to explore the consistency of the distributions of the language cortex across centres, a kernel density estimation to localize the peak coordinates, and a hierarchical cluster analysis was performed to detect the crucial epicenters. A total of 598 subjects with 917 speech arrest sites (complete interruption of ongoing counting) and 423 anomia sites (inability to name or misnaming) were included. Different centres presented highly consistent distribution patterns for speech arrest (Spearman's coefficient r ranged from 0.60 to 0.85, all pair-wise correlations P < 0.05), and similar patterns for anomia (Spearman's coefficient r ranged from 0.37 to 0.80). The combinational speech arrest map was divided into four clusters: cluster 1 mainly located in the ventral precentral gyrus and pars opercularis, which contained the peak of speech arrest in the ventral precentral gyrus; cluster 2 in the ventral and dorsal precentral gyrus; cluster 3 in the supplementary motor area; cluster 4 in the posterior superior temporal gyrus and supramarginal gyrus. The anomia map revealed two clusters: one was in the posterior part of the superior and middle temporal gyri, which peaked at the posterior superior temporal gyrus; and the other within the inferior frontal gyrus, peaked at the pars triangularis. This study constitutes the largest series to date of language maps generated from direct electrical stimulation mapping. The consistency of data provides evidence for common language networks across languages, in the context of both speech and naming circuit. Our results not only clinically offer an atlas for language mapping and protection, but also scientifically provide better insight into the functional organization of language networks., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Chronic cerebral ischemia leads to vascular cognitive impairment (VCI) that exacerbates along with ischemia time and eventually develops into dementia. Recent advances in molecular neuroimaging contribute to understand its pathological characteristics. We previously traced the anisotropic diffusion of water molecules suggests that chronic cerebral ischemia leads to irreversible progressive damage to white matter integrity. However, the abnormalities of gray matter activity following chronic cerebral ischemia remains not entirely understood. In this study, in vivo hydrogen proton magnetic resonance spectroscopy ( 1 H-MRS) was applied to longitudinally track the neurochemical metabolic disorder of gray matter associated with working memory, and optogenetics modulation of neurochemical metabolism was performed for targeted treatment of VCI. The results showed that the concentration of N-acetylaspartate (NAA) in the right hippocampus, left hippocampus, right medial prefrontal cortex (mPFC) and mediodorsal thalamus was decreased as early as 7 days after chronic cerebral ischemia, subsequently gamma-aminobutyric acid (GABA) declined whereas myo-inositol (mI) and glutamate (Glu) increased at 14 days, as well as choline (Cho) lost at 28 days, concurrently the change of Glu and GABA in the mPFC and hippocampus was ischemia time-dependent manner within 1 month. Behaviorally, working memory and object recognition memory were impaired at 14 days, 28 days that significantly correlated with neurochemical metabolic disorders. Interestingly, using optogenetics modulation of PV neurons in the mPFC, the metabolic abnormalities of NAA and GABA in working memory neural circuit could be repaired after chronic cerebral ischemia, together with behavior improvements. These findings suggested that as early as 1∼4 weeks after chronic cerebral ischemia, the metabolism of NAA, Glu, mI and Cho was synchronously impaired in neural circuit of hippocampus-mediodorsal thalamus-mPFC, and the loss of GABA delayed in the hippocampus, and optogenetics modulation of parvalbumin (PV) neurons in the mPFC can improve the neurochemical metabolism of working memory neural circuit and enhance working memory., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Consciousness is supported by integrated brain activity across widespread functionally segregated networks. The functional magnetic resonance imaging-derived global brain signal is a candidate marker for a conscious state, and thus the authors hypothesized that unconsciousness would be accompanied by a loss of global temporal coordination, with specific patterns of decoupling between local regions and global activity differentiating among various unconscious states., Methods: Functional magnetic resonance imaging global signals were studied in physiologic, pharmacologic, and pathologic states of unconsciousness in human natural sleep (n = 9), propofol anesthesia (humans, n = 14; male rats, n = 12), and neuropathological patients (n = 21). The global signal amplitude as well as the correlation between global signal and signals of local voxels were quantified. The former reflects the net strength of global temporal coordination, and the latter yields global signal topography., Results: A profound reduction of global signal amplitude was seen consistently across the various unconscious states: wakefulness (median [1st, 3rd quartile], 0.46 [0.21, 0.50]) versus non-rapid eye movement stage 3 of sleep (0.30 [0.24, 0.32]; P = 0.035), wakefulness (0.36 [0.31, 0.42]) versus general anesthesia (0.25 [0.21, 0.28]; P = 0.001), healthy controls (0.30 [0.27, 0.37]) versus unresponsive wakefulness syndrome (0.22 [0.15, 0.24]; P < 0.001), and low dose (0.07 [0.06, 0.08]) versus high dose of propofol (0.04 [0.03, 0.05]; P = 0.028) in rats. Furthermore, non-rapid eye movement stage 3 of sleep was characterized by a decoupling of sensory and attention networks from the global network. General anesthesia and unresponsive wakefulness syndrome were characterized by a dissociation of the majority of functional networks from the global network. This decoupling, however, was dominated by distinct neuroanatomic foci (e.g., precuneus and anterior cingulate cortices)., Conclusions: The global temporal coordination of various modules across the brain may distinguish the coarse-grained state of consciousness versus unconsciousness, while the relationship between the global and local signals may define the particular qualities of a particular unconscious state.

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., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Variability quenching is a widespread neural phenomenon in which trial-to-trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability-quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting-state and stimulus-induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial-to-trial mean, TTM) and variability (TTV) of auditory stimulus-induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting-state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus-induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting-state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic-induced unconsciousness., (© 2018 Wiley Periodicals, Inc.)

Which temporal features that can characterize different brain states (i.e., consciousness or unconsciousness) is a fundamental question in the neuroscience of consciousness. Using resting-state functional magnetic resonance imaging (rs-fMRI), we investigated the spatial patterns of two temporal features: the long-range temporal correlations (LRTCs), measured by power-law exponent (PLE), and temporal variability, measured by standard deviation (SD) during wakefulness and anesthetic-induced unconsciousness. We found that both PLE and SD showed global reductions across the whole brain during anesthetic state comparing to wakefulness. Importantly, the relationship between PLE and SD was altered in anesthetic state, in terms of a spatial "decoupling." This decoupling was mainly driven by a spatial pattern alteration of the PLE, rather than the SD, in the anesthetic state. Our results suggest differential physiological grounds of PLE and SD and highlight the functional importance of the topographical organization of LRTCs in maintaining an optimal spatiotemporal configuration of the neural dynamics during normal level of consciousness. The central role of the spatial distribution of LRTCs, reflecting temporo-spatial nestedness, may support the recently introduced temporo-spatial theory of consciousness (TTC)., (© 2018 Wiley Periodicals, Inc.)

As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

The aim of this study is to investigate and compare how 12-weeks of Tai Chi Chuan and Baduanjin exercise can modulate brain structure and memory function in older adults. Magnetic resonance imaging and memory function measurements (Wechsler Memory Scale-Chinese revised, WMS-CR) were applied at both the beginning and end of the study. Results showed that both Tai Chi Chuan and Baduanjin could significantly increase grey matter volume (GMV) in the insula, medial temporal lobe, and putamen after 12-weeks of exercise. No significant differences were observed in GMV between the Tai Chi Chuan and Baduanjin groups. We also found that compared to healthy controls, Tai Chi Chuan and Baduanjin significantly improved visual reproduction subscores on the WMS-CR. Baduanjin also improved mental control, recognition, touch, and comprehension memory subscores of the WMS-CR compared to the control group. Memory quotient and visual reproduction subscores were both associated with GMV increases in the putamen and hippocampus. Our results demonstrate the potential of Tai Chi Chuan and Baduanjin exercise for the prevention of memory deficits in older adults.

For surgical operation in the functional area in the brain, it's commonly demanded to resect the lesion to the maximal extent on the basis of preserve the normal neural function, thus the precise localization of functional area is extremely important. As for the advantages of being widely available, easy to grasp and non-invasive, the functional MRI (fMRI) has come into wide use, while the application of language fMRI is still in the initial stage. It's important to choose appropriate fMRI task according to the individual condition of the subject, the commonly-adopted tasks include verb generation, picture naming, word recognition, word generation, etc. However, the effectiveness of using fMRI to localize language area is not totally satisfactory, adopting multiple task is an effective approach to improve the sensitivity of this technique. The application of resting state fMRI in the localization of language area and the further research of the role of fMRI in localizing the Chinese language area are the important future directions.

Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which "couple" the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Chinese processing has been suggested involving distinct brain areas from English. However, current functional localization studies on Chinese speech processing use mostly "indirect" techniques such as functional magnetic resonance imaging and electroencephalography, lacking direct evidence by means of electrocortical recording. In this study, awake craniotomies in 66 Chinese-speaking glioma patients provide a unique opportunity to directly map eloquent language areas. Intraoperative electrocortical stimulation was conducted and the positive sites for speech arrest, anomia, and alexia were identified separately. With help of stereotaxic neuronavigation system and computational modeling, all positive sites elicited by stimulation were integrated and a series of two- and three-dimension Chinese language probability maps were built. We performed statistical comparisons between the Chinese maps and previously derived English maps. While most Chinese speech arrest areas located at typical language production sites (i.e., 50% positive sites in ventral precentral gyrus, 28% in pars opercularis and pars triangularis), which also serve English production, an additional brain area, the left middle frontal gyrus (Brodmann's areas 6/9), was found to be unique in Chinese production (P < 0.05). Moreover, Chinese speakers' inferior ventral precentral gyrus (Brodmann's area 6) was used more than that in English speakers. Our finding suggests that Chinese involves more perisylvian region (extending to left middle frontal gyrus) than English. This is the first time that direct evidence supports cross-cultural neurolinguistics differences in human beings. The Chinese language atlas will also helpful in brain surgery planning for Chinese-speakers., (Copyright © 2015 Wiley Periodicals, Inc.)

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., (Copyright © 2014 Wiley Periodicals, Inc.)

Purpose: This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model., Methods: The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables., Results: To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics-based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points., Conclusions: The proposed method can effectively correct brain deformation induced by tumor resection.

Direct electrical stimulation, the transient 'lesional' method probing brain function, has been utilized in identifying the language cortex and preserving language function during epilepsy and neuro-oncological surgeries for about a century. However, comparison of functional maps of the language cortex across languages/continents based on cortical stimulation remains unclear. We conducted a retrospective multicentre study including four cohorts of direct electrical stimulation mapping from four centres across three continents, where three indigenous languages (English, French and Mandarin) are spoken. All subjects performed the two most common language tasks: number counting and picture naming during stimulation. All language sites were recorded and normalized to the same brain template. Next, Spearman's correlation analysis was performed to explore the consistency of the distributions of the language cortex across centres, a kernel density estimation to localize the peak coordinates, and a hierarchical cluster analysis was performed to detect the crucial epicenters. A total of 598 subjects with 917 speech arrest sites (complete interruption of ongoing counting) and 423 anomia sites (inability to name or misnaming) were included. Different centres presented highly consistent distribution patterns for speech arrest (Spearman's coefficient r ranged from 0.60 to 0.85, all pair-wise correlations P < 0.05), and similar patterns for anomia (Spearman's coefficient r ranged from 0.37 to 0.80). The combinational speech arrest map was divided into four clusters: cluster 1 mainly located in the ventral precentral gyrus and pars opercularis, which contained the peak of speech arrest in the ventral precentral gyrus; cluster 2 in the ventral and dorsal precentral gyrus; cluster 3 in the supplementary motor area; cluster 4 in the posterior superior temporal gyrus and supramarginal gyrus. The anomia map revealed two clusters: one was in the posterior part of the superior and middle temporal gyri, which peaked at the posterior superior temporal gyrus; and the other within the inferior frontal gyrus, peaked at the pars triangularis. This study constitutes the largest series to date of language maps generated from direct electrical stimulation mapping. The consistency of data provides evidence for common language networks across languages, in the context of both speech and naming circuit. Our results not only clinically offer an atlas for language mapping and protection, but also scientifically provide better insight into the functional organization of language networks. [ABSTRACT FROM AUTHOR]

The O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase 1 (IDH1) mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which has limited their wider clinical implementation. Accurate presurgical prediction of their statuses based on preoperative multimodal neuroimaging is of great clinical value for a better treatment plan. Currently, the available data set associated with this study has several challenges, such as small sample size and complex, nonlinear (image) feature-to-(molecular) label relationship. To address these issues, we propose a novel multi-label nonlinear matrix completion (MNMC) model to jointly predict both MGMT and IDH1 statuses in a multi-task framework. Specifically, we first employ a nonlinear random Fourier feature mapping to improve the linear separability of the data, and then use transductive multi-task feature selection (performed in a nonlinearly transformed feature space) to refine the imputed soft labels, thus alleviating the overfitting problem caused by small sample size. We further design an optimization algorithm with a guaranteed convergence ability based on a block prox-linear method to solve the proposed MNMC model. Finally, by using a single-center, multimodal brain imaging and molecular pathology data set of HGG, we derive brain functional and structural connectomics features to jointly predict MGMT and IDH1 statuses. Results demonstrate that our proposed method outperforms the previously widely used single- and multi-task machine learning methods. This paper also shows the promise of utilizing brain connectomics for HGG prognosis in a non-invasive manner. [ABSTRACT FROM AUTHOR]