Microsurgical resection is an effective method to treat brain arteriovenous malformations (BAVMs). Functional magnetic resonance imaging (fMRI) can evaluate the spatial relationship of nidus and eloquent. Diffuse BAVMs are related to poor outcomes postoperatively. The role of fMRI in evaluating outcomes in patients with different nidus types remains unclear. BAVM patients received microsurgical resection were included from a prospective, multicenter cohort study. All patients underwent fMRI evaluation preoperatively and were regularly followed up postoperatively. Diffuse BAVM is radiologically identified as nidus containing normal brain tissue interspersing between malformed vessels. Lesion-to-eloquent distance (LED) was calculated based on the relationship between nidus and eloquent. The primary outcome was 180-day unfavorable neurological status postoperatively. The risk of primary outcome was investigated within different BAVM nidus types. The LED\'s performance to predict poor outcome was evaluated using area under curve (AUC). 346 BAVM patients were included in this study. 93 (26.9%) patients were found to have a 180-day unfavorable outcome. Multivariate logistic analysis demonstrated LED (odd ratio [OR], 0.44; 0.34-0.57; P < 0.001) and mRS at admission (OR, 2.59; 1.90-3.54; P < 0.001) as factors of unfavorable outcome. Subgroup analysis showed LED and mRS at admission as factors of unfavorable outcome for patients with compact BAVMs (all P < 0.05), but not for patients with diffuse BAVMs. Subsequent analysis showed that LED performed poorly to predict the unfavorable outcome for patients with diffuse BAVMs, compared with patients with compact BAVMs (AUC as 0.69 vs. 0.86, P < 0.05). A larger cutoff value of LED to unfavorable outcome was found in patients with diffuse BAVMs (15 mm) compared with patients with compact BAVMs (4.7 mm). Usage of LED to evaluate postoperative outcome of patients with diffuse BAVMs differs from its use in patients with compact BAVMs. Specific assessment strategy considering BAVM nidus types could help improve patients\' outcome. MITASREAVM cohort (unique identifier: NCT02868008, https://clinicaltrials.gov/study/NCT02868008?term=NCT02868008&rank=1 ).

BACKGROUND: Brain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals.
METHODS: We introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany.
RESULTS: Our findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding.
CONCLUSIONS: These findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders.

Obesity is a modifiable risk factor for Alzheimer\'s disease (AD). However, its relation with tau pathology (i.e., aberrant tau protein behavior in tauopathies such as AD) has been inconclusive.
This study investigated the interaction between a high-fat diet (HFD) and tau pathology in adult male mice.
Transgenic mice overexpressing human P301S Tau (those with the pathology) and wild-type (WT) littermates were subjected to behavioral tests, functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and western blotting analysis to investigate the effects of prolonged HFD versus regular diet during adulthood.
HFD increased body weight in both WT and P301S mice but had minimal effect on blood glucose levels. The brain response to HFD was tau genotype-specific. WT mice exhibited decreased recognition memory and enhanced network connectivity in fMRI, while P301S mice exhibited white matter tract disorganization in DTI as the sole significant finding. The reduction of insulin receptor β, insulin downstream signaling, neuronal nuclear protein, CD68-positive phagocytic activity, and myelin basic protein level were confined to the cortex of WT mice. In contrast to P301S mice, WT mice showed significant changes in the tau protein and its phosphorylation levels along with increased soluble neurofilament light levels in the hippocampus.
HFD-induced brain dysfunction and pathological changes were blunted in mice with the pathology and more profound in healthy mice. Our findings highlight the need to consider this interaction between obesity and tau pathology when tailoring treatment strategies for AD and other tauopathies.

Cerebral small vessel disease (CSVD) is characterized by widespread functional changes in the brain, as evident from abnormal brain activations during cognitive tasks. However, the existing findings in this area are not yet conclusive. We systematically reviewed 25 studies reporting task-related fMRI in five cognitive domains in CSVD, namely executive function, working memory, processing speed, motor, and affective processing. The findings highlighted: (1) CSVD affects cognitive processes in a domain-specific manner; (2) Compensatory and regulatory effects were observed simultaneously in CSVD, which may reflect the interplay between the negative impact of brain lesion and the positive impact of cognitive reserve. Combined with behavioral and functional findings in CSVD, we proposed an integrated model to illustrate the relationship between altered activations and behavioral performance in different stages of CSVD: functional brain changes may precede and be more sensitive than behavioral impairments in the early pre-symptomatic stage; Meanwhile, compensatory and regulatory mechanisms often occur in the early stages of the disease, while dysfunction/decompensation and dysregulation often occur in the late stages. Overall, abnormal hyper-/hypo-activations are crucial for understanding the mechanisms of small vessel lesion-induced behavioral dysfunction, identifying potential neuromarker and developing interventions to mitigate the impact of CSVD on cognitive function.

Brain decoding is to predict the external stimulus information from the collected brain response activities, and visual information is one of the most important sources of external stimulus information. Decoding functional magnetic resonance imaging (fMRI) based on visual stimulation is helpful in understanding the working mechanism of the brain visual function regions. Traditional brain decoding algorithms cannot accurately extract stimuli features from fMRI. To address these shortcomings, this paper proposed a brain decoding algorithm based on a graph convolution network (GCN). Firstly, 11 regions of interest (ROI) were selected according to the human brain visual function regions, which can avoid the noise interference of the non-visual regions of the human brain; then, a deep three-dimensional convolution neural network was specially designed to extract the features of these 11 regions; next, the GCN was used to extract the functional correlation features between the different human brain visual regions. Furthermore, to avoid the problem of gradient disappearance when there were too many layers of graph convolutional neural network, the residual connections were adopted in our algorithm, which helped to integrate different levels of features in order to improve the accuracy of the proposed GCN. The proposed algorithm was tested on the public dataset, and the recognition accuracy reached 98.67%. Compared with the other state-of-the-art algorithms, the proposed algorithm performed the best.

Methamphetamine (MA) use affects the brain structure and function. However, no studies have investigated the relationship between changes in regional homogeneity (ReHo) and impulsivity in MA dependent individuals (MADs). The aim of this study was to investigate the changes of brain activity under resting state in MADs and their relationship to impulsivity using ReHo method. Functional magnetic resonance imaging (fMRI) was performed to collect data from 46 MADs and 44 healthy controls (HCs) under resting state. ReHo method was used to investigate the differences in average ReHo values between the two groups. The ReHo values abnormalities of the brain regions found in inter-group comparisons were extracted and correlated with impulsivity. Compared to the HCs, MADs showed significant increased ReHo values in the bilateral striatum, while the ReHo values of the bilateral precentral gyrus and the bilateral postcentral gyrus decreased significantly. The ReHo values of the left precentral gyrus were negatively correlated with the BIS-attention, BIS-motor, and BIS-nonplanning subscale scores, while the ReHo values of the postcentral gyrus were only negatively correlated with the BIS-motor subscale scores in MADs. The abnormal spontaneous brain activity in the resting state of MADs revealed in this study may further improve our understanding of the neuro-matrix of MADs impulse control dysfunction and may help us to explore the neuropathological mechanism of MADs related dysfunction and rehabilitation.

Integrating multimodal information into a unified perception is a fundamental human capacity. McGurk effect is a remarkable multisensory illusion that demonstrates a percept different from incongruent auditory and visual syllables. However, not all listeners perceive the McGurk illusion to the same degree. The neural basis for individual differences in modulation of multisensory integration and syllabic perception remains largely unclear. To probe the possible involvement of specific neural circuits in individual differences in multisensory speech perception, we first implemented a behavioral experiment to examine the McGurk susceptibility. Then, functional magnetic resonance imaging was performed in 63 participants to measure the brain activity in response to non-McGurk audiovisual syllables. We revealed significant individual variability in McGurk illusion perception. Moreover, we found significant differential activations of the auditory and visual regions and the left Superior temporal sulcus (STS), as well as multiple motor areas between strong and weak McGurk perceivers. Importantly, the individual engagement of the STS and motor areas could specifically predict the behavioral McGurk susceptibility, contrary to the sensory regions. These findings suggest that the distinct multimodal integration in STS as well as coordinated phonemic modulatory processes in motor circuits may serve as a neural substrate for interindividual differences in multisensory speech perception.

Theoretical consideration predicts that the alteration of local and shared information in the brain is a key element in the mechanism of anesthetic-induced unconsciousness. Ordinal pattern analysis, such as permutation entropy (PE) and symbolic mutual information (SMI), have been successful in quantifying local and shared information in neurophysiological data; however, they have been rarely applied to altered states of consciousness, especially to data obtained with functional magnetic resonance imaging (fMRI). PE and SMI analysis, together with the superb spatial resolution of fMRI recording, enables us to explore the local information of specific brain areas, the shared information between the areas, and the relationship between the two. Given the spatially divergent action of anesthetics on regional brain activity, we hypothesized that anesthesia would differentially influence entropy (PE) and shared information (SMI) across various brain areas, which may represent fundamental, mechanistic indicators of loss of consciousness. FMRI data were collected from 15 healthy participants during four states: wakefulness (W), light (conscious) sedation (L), deep (unconscious) sedation (D), and recovery (R). Sedation was produced by the common, clinically used anesthetic, propofol. Firstly, we found that that global PE decreased from W to D, and increased from D to R. The PE was differentially affected across the brain areas; specifically, the PE in the subcortical network was reduced more than in the cortical networks. Secondly, SMI was also differentially affected in different areas, as revealed by the reconfiguration of its spatial pattern (topographic structure). The topographic structures of SMI in the conscious states W, L, and R were distinctively different from that of the unconscious state D. Thirdly, PE and SMI were positively correlated in W, L, and R, whereas this correlation was disrupted in D. And lastly, PE changes occurred preferentially in highly connected hub regions. These findings advance our understanding of brain dynamics and information exchange, emphasizing the importance of topographic structure and the relationship of local and shared information in anesthetic-induced unconsciousness.

BACKGROUND: While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate methods for robotic arm control with noninvasive neural signal. Most of noninvasive control of robotic arm utilizes P300, steady state visually evoked potential, N2pc, and mental tasks differentiation. Even though these approaches demonstrated successful accuracy, they are limited in time efficiency and user intuition, and mostly require visual stimulation. Ultimately, velocity vector construction using electroencephalography activated by motion-related motor imagery can be considered as a substitution. In this study, a vision-aided brain-machine interface training system for robotic arm control is proposed and developed.
METHODS: The proposed system uses a Microsoft Kinect to detect and estimates the 3D positions of the possible target objects. The predicted velocity vector for robot arm input is compensated using the artificial potential to follow an intended one among the possible targets. Two participants with cervical spinal cord injury trained with the system to explore its possible effects.
RESULTS: In a situation with four possible targets, the proposed system significantly improved the distance error to the intended target compared to the unintended ones (p < 0.0001). Functional magnetic resonance imaging after five sessions of observation-based training with the developed system showed brain activation patterns with tendency of focusing to ipsilateral primary motor and sensory cortex, posterior parietal cortex, and contralateral cerebellum. However, shared control with blending parameter α less than 1 was not successful and success rate for touching an instructed target was less than the chance level (= 50%).
CONCLUSIONS: The pilot clinical study utilizing the training system suggested potential beneficial effects in characterizing the brain activation patterns.

Observing mouth movements has strikingly effects on the perception of speech. Any mismatch between sound and mouth movements will result in listeners perceiving illusory consonants (McGurk effect), whereas matching mouth movements assist with the correct recognition of speech sounds. Recent neuroimaging studies have yielded evidence that the motor areas are involved in speech processing, yet their contributions to multisensory illusion remain unclear. Using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) in an event-related design, we aimed to identify the functional roles of the motor network in the occurrence of multisensory illusion in female and male brains. fMRI showed bilateral activation of the inferior frontal gyrus (IFG) in audiovisually incongruent trials. Activity in the left IFG was negatively correlated with occurrence of the McGurk effect. The effective connectivity between the left IFG and the bilateral precentral gyri was stronger in incongruent than in congruent trials. The McGurk effect was reduced in incongruent trials by applying single-pulse TMS to motor cortex (M1) lip areas, indicating that TMS facilitates the left IFG-precentral motor network to reduce the McGurk effect. TMS of the M1 lip areas was effective in reducing the McGurk effect within the specific temporal range from 100 ms before to 200 ms after the auditory onset, and TMS of the M1 foot area did not influence the McGurk effect, suggesting topographical specificity. These results provide direct evidence that the motor network makes specific temporal and topographical contributions to the processing of multisensory integration of speech to avoid illusion.SIGNIFICANCE STATEMENT The human motor network, including the inferior frontal gyrus and primary motor cortex lip area, appears to be involved in speech perception, but the functional contribution to the McGurk effect is unknown. Functional magnetic resonance imaging revealed that activity in these areas of the motor network increased when the audiovisual stimuli were incongruent, and that the increased activity was negatively correlated with perception of the McGurk effect. Furthermore, applying transcranial magnetic stimulation to the motor areas reduced the McGurk effect. These two observations provide evidence that the motor network contributes to the avoidance of multisensory illusory perception.