Search

The study of the synchronization phenomenon in the functional brain networks of individuals with Attention Deficit Hyperactivity Disorder (ADHD) has always been of interest to researchers. ADHD is a prevalent psychiatric disorder among children, which in addition to other problems, makes it difficult to recognize facial emotions correctly. However, the synchronization stability, which indicates the ability of a network to remain in a synchronous state, is still unknown in ADHD. This study investigates the phase synchronization stability and robustness of functional brain networks in boys with ADHD while observing the facial emotions. The primary brain networks of 22 boys with ADHD and 22 healthy ones are constructed using their electroencephalogram signals by the Phase Locking Value (PLV) method. Then, significant subnetworks (P-Value < 0.05) are extracted from the primary brain network by applying the Network-Based Statistic (NBS) method. Three measures, including the second smallest eigenvalue, largest eigenvalue, and eigenratio of these eigenvalues, are calculated from all brain subnetworks as network robustness and synchronization stability criteria. The statistical tests indicate no significant differences between the second smallest eigenvalues in brain networks of two ADHD and healthy groups, representing the same robustness of brain networks topological features to perturbations in both groups. The largest eigenvalues and the eigenratios extracted from the functional brain networks of the ADHD group are significantly (P-Value < 0.05) lower than the healthy one, which shows an increased synchronizability in the brain networks of the ADHD group. This alteration in the phase synchronization stability may be associated with a deficit in the emotional processing of the brain network in the ADHD group. [ABSTRACT FROM AUTHOR]

• Holistic functional brain networks are modeled via the multi-head mechanism. • Both spatial and temporal patterns are modeled via the guided attention mechanism. • It has superior modeling ability and generalizability on different fMRI datasets. • The modeled brain networks can better predict individual cognitive measures. Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations. [Display omitted] [ABSTRACT FROM AUTHOR]

• Propose a novel Recurrent Wasserstein Generative Adversarial Net (RWGAN) for unsupervised modeling of volumetric fMRI data. • With LASSO regression, the RWGAN can effectively learn meaningful brain representation including both temporal features and spatial features (functional brain networks, FBNs) from fMRI. • The RWGAN performed better in feature extraction of fMRI based on small training datasets than other deep learning models. • The generator of RWGAN can yield high quality of fake data for potential application of fMRI augmentation. To understand brain cognition and disorders, modeling the mapping between mind and brain has been of great interest to the neuroscience community. The key is the brain representation, including functional brain networks (FBN) and their corresponding temporal features. Recently, it has been proven that deep learning models have superb representation power on functional magnetic resonance imaging (fMRI) over traditional machine learning methods. However, due to the lack of high-quality data and labels, deep learning models tend to suffer from overfitting in the training process. In this work, we applied a recurrent Wasserstein generative adversarial net (RWGAN) to learn brain representation from volumetric fMRI data. Generative adversarial net (GAN) is widely used in natural image generation and is able to capture the distribution of the input data, which enables the extraction of generalized features from fMRI and thus relieves the overfitting issue. The recurrent layers in RWGAN are designed to better model the local temporal features of the fMRI time series. The discriminator of RWGAN works as a deep feature extractor. With LASSO regression, the RWGAN model can decompose the fMRI data into temporal features and spatial features (FBNs). Furthermore, the generator of RWGAN can generate high-quality new data for fMRI augmentation. The experimental results on seven tasks from the HCP dataset showed that the RWGAN can learn meaningful and interpretable temporal features and FBNs, compared to HCP task designs and general linear model (GLM) derived networks. Besides, the results on different training datasets showed that the RWGAN performed better on small datasets than other deep learning models. Moreover, we used the generator of RWGAN to yield fake subjects. The result showed that the fake data can also be used to learn meaningful representation compared to those learned from real data. To our best knowledge, this work is among the earliest attempts of applying generative deep learning for modeling fMRI data. The proposed RWGAN offers a novel methodology for learning brain representation from fMRI, and it can generate high-quality fake data for the potential use of fMRI data augmentation. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is one common psychiatric illness that manifests in neurological and developmental disorders, which can last throughout a person's life and cause challenges in social interaction, communication, and behavior. Since the standard ASD diagnosis is highly based on the symptoms of the disease, it is difficult to make an early diagnosis to take the best cure opportunity. Compared to the standard methods, functional brain network (FBN) could reveal the statistical dependence among neural architectures in brains and provide potential biomarkers for the early neuro-disease diagnosis and treatment of some neurological disorders. However, there are few FBN estimation methods that take into account the noise during the data acquiring process, resulting in poor quality of FBN and thus poor diagnosis results. To address such issues, we provide a brand-new approach for estimating FBNs under a noise modeling framework. In particular, we introduce a noise term to model the representation errors and impose a regularizer to incorporate noise prior into FBNs estimation. More importantly, the proposed method can be formulated as conducting traditional FBN estimation based on transformed fMRI data, which means the traditional methods can be elegantly modified to support noise modeling. That is, we provide a plug-and-play noise module capable of being embedded into different methods and adjusted according to different noise priors. In the end, we conduct abundant experiments to identify ASD from normal controls (NCs) based on the constructed FBNs to illustrate the effectiveness and flexibility of the proposed method. Consequently, we achieved up to 13.04% classification accuracy improvement compared with the baseline methods., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ma, Peng and Gao.)

Adolescence is the peak period for the onset of generalized anxiety disorder (GAD). Brain networks of cognitive and affective control in adolescents are not well developed when their exposure to external stimuli suddenly increases.Reasonable parental monitoring is especially important during this period.To examine the role of parental monitoring in the development of functional brain networks of GAD, we conducted a cross-validation-based predictive study based on the functional brain networks of 192 participants. We found that a set of functional brain networks, especially the default mode network and its connectivity with the frontoparietal network, could predict the ages of adolescents, which was replicated in three independent samples.Importantly, the difference between predicted age and chronological age significantly mediated the relationship between parental monitoring and anxiety levels. These findings suggest that inadequate parental monitoring plays a crucial role in the delayed development of specific brain networks associated with GAD in adolescents. Our work highlights the important role of parental monitoring in adolescent development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Functional brain networks (FBNs) are gaining increasing attention in computational neuroscience due to their ability to reveal dynamic interdependencies between brain regions. The dynamics of such networks during cognitive activity between stimulus and response using multi-channel electroencephalogram (EEG), recorded from 16 healthy human participants are explored in this research. Successive EEG segments of 500ms duration starting from the onset of cognitive stimulation have been used to analyze and understand the cognitive dynamics. The approach employs a combination of signal processing techniques, nonlinear statistical measures and graph-theoretical analysis. The efficacy of this approach in detecting and tracking cognitive load induced changes in EEG data is clearly demonstrated using graph metrics. It is revealed that most cognitive activity occurs within approximately 500ms of the stimulus presentation in addition to temporal variability in the FBNs. It is shown that mutual information (MI), a nonlinear measure, produces good correlations between the EEG channels thus enabling the construction of FBNs which are sensitive to cognitive load induced changes in EEG. Analyses of the dynamics of FBNs and the visualization approach reveal hard to detect subtle changes in cognitive function and hence may lead to a better understanding of cognitive processing in the brain. The techniques exploited have the potential to detect human cognitive dysfunction (impairments). [ABSTRACT FROM AUTHOR]

Electrophysiological oscillatory coherence between brain regions has been proposed to facilitate functional long-range connectivity within neurocognitive networks. This notion is supported by intracortical recordings of coherence in singled-out corticocortical connections in the primate cortex. However, the manner in which this operational principle manifests in the task-sensitive connectivity that supports human naturalistic performance remains undercharacterized. Here, we demonstrate task-sensitive reconfiguration of global patterns of coherent connectivity in association with a set of easier and more demanding naturalistic tasks, ranging from picture comparison to speech comprehension and object manipulation. Based on whole-cortex neuromagnetic recording in healthy behaving individuals, the task-sensitive component of long-range corticocortical coherence was mapped at spectrally narrow-band oscillatory frequencies between 6 and 20 Hz (theta to alpha and low-beta bands). This data-driven cortical mapping unveiled markedly distinct and topologically task-relevant spatiospectral connectivity patterns for the different tasks. The results demonstrate semistable oscillatory states relevant for neurocognitive processing. The present findings decisively link human behavior to corticocortical coherence at oscillatory frequencies that are widely thought to convey long-range, feedback-type neural interaction in cortical functional networks. Hum Brain Mapp 36:2455-2469, 2015. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Introduction: With the increasing demand for human-machine collaboration systems, more and more attention has been paid to the influence of human factors on the performance and security of the entire system. Especially in high-risk, high-precision, and difficult special tasks (such as space station maintenance tasks, anti-terrorist EOD tasks, surgical robot teleoperation tasks, etc.), there are higher requirements for the operator's perception and cognitive level. However, as the human brain is a complex and open giant system, the perception ability and cognitive level of the human are dynamically variable, so that it will seriously affect the performance and security of the whole system., Methods: The method proposed in this paper innovatively explained this phenomenon from two dimensions of brain space and time and attributed the dynamic changes of perception, cognitive level, and operational skills to the mental state diversity and the brain neuroplasticity. In terms of the mental state diversity, the mental states evoked paradigm and the functional brain network analysis method during work were proposed. In terms of neuroplasticity, the cognitive training intervention paradigm and the functional brain network analysis method were proposed. Twenty-six subjects participated in the mental state evoked experiment and the cognitive training intervention experiment., Results: The results showed that (1) the mental state of the subjects during work had the characteristics of dynamic change, and due to the influence of stimulus conditions and task patterns, the mental state showed diversity. There were significant differences between functional brain networks in different mental states, the information processing efficiency and the mechanism of brain area response had changed significantly. (2) The small-world attributes of the functional brain network of the subjects before and after the cognitive training experiment were significantly different. The brain had adjusted the distribution of information flow and resources, reducing costs and increasing efficiency as a whole. It was demonstrated that the global topology of the cortical connectivity network was reconfigured and neuroplasticity was altered through cognitive training intervention., Discussion: In summary, this paper revealed that mental state and neuroplasticity could change the information processing efficiency and the response mechanism of brain area, thus causing the change of perception, cognitive level and operational skills, which provided a theoretical basis for studying the relationship between neural information processing and behavior., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Zhang, Zhu, Lu, Wang and Zhang.)

How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease.

Functional brain network, one of the main methods for brain functional studies, can provide the connectivity information among brain regions. In this research, EEG-based functional brain network is built and analyzed through a new wavelet limited penetrable visibility graph (WLPVG) approach. This approach first decompose EEG into δ, θ, α, β sub-bands, then extracting nonlinear features from single channel signal, in addition forming a functional brain network for each sub-band. Manual acupuncture (MA) as a stimulation to the human nerve system, may evoke varied modulating effects in brain activities. To investigating whether and how this happens, WLPVG approach is used to analyze the EEGs of 15 healthy subjects with MA at acupoint ST36 on the right leg. It is found that MA can influence the complexity of EEG sub-bands in different ways and lead the functional brain networks to obtain higher efficiency and stronger small-world property compared with pre-acupuncture control state. [ABSTRACT FROM AUTHOR]

Abstract: Mining dynamic and non-trivial patterns of interactions of functional brain networks has gained significance due to the recent advances in the field of computational neuroscience. Sophisticated data search capabilities, advanced signal processing techniques, statistical methods, complex network and graph mining algorithms to unfold and discover hidden patterns in the functional brain network supported with efficient visualization techniques are essential for making potential inferences of the results obtained. Visualization of change in activity during cognitive function is useful to discover and get insights into the hidden, novel and complex neuronal patterns and trends during the normal and cognitive load conditions from the graph/temporal representation of the functional brain network. This paper explores novel methods to detect and track the dynamics and complexity of the brain function. It also uses a new tool called Functional Brain Network Analysis and Visualization (FBNAV) tool to visualize the outcomes of various computational analyses to enable us to identify and study the changing neuronal patterns during various states of the brain activity using augmented/customisedTopoplots andHeadplots. The change detection algorithm tracks and visualizes the cognitive load induced changes across the scalp regions.These techniques may also be helpful to locate and identify patterns in certain abnormal mental states resulting due to some mental disorders such as stress. [Copyright &y& Elsevier]

The brain is a frustrated system that contains conflictual link arrangements named frustration. The frustration as a source of disorder prevents the system from settling into low-energy states and provides flexibility for brain network organization. In this research, we tried to identify the pattern of frustration formation in the brain at the levels of region, connection, canonical network, and hemisphere. We found that frustration formation has no uniform pattern. Some subcortical elements have an active role in frustration formation, despite low contributions from many cortical elements. Frustrating connections are mostly between-network connections, and triadic frustrations are mainly formed between three regions from three distinct canonical networks. We did not find any significant differences between brain hemispheres or any robust differences between the frustration formation patterns of various life-span stages. Our results may be interesting for those who study the organization of brain links and promising for those who want to manipulate brain networks., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2022 Massachusetts Institute of Technology.)

Covering neuroimaging evidence has demonstrated that epileptic symptoms are associated with the disrupted topological architecture of the brain network. Infantile spasms (IS) as an age-specific epileptic encephalopathy also showed abnormal structural or functional connectivity in specific brain regions or specific networks. However, little is known about the topological alterations of whole-brain functional networks in patients with IS. To fill this gap, we used the graph theoretical analysis to investigate the topological properties (whole-brain small-world property and modular interaction) in 17 patients with IS and 34 age- and gender-matched healthy controls. The functional networks in both groups showed efficient small-world architecture over the sparsity range from 0.05 to 0.4. While patients with IS showed abnormal global properties characterized by significantly decreased normalized clustering coefficient, normalized path length, small-worldness, local efficiency, and significantly increased global efficiency, implying a shift toward a randomized network. Modular analysis revealed decreased intra-modular connectivity within the default mode network (DMN) and fronto-parietal network but increased inter-modular connectivity between the cingulo-opercular network and occipital network. Moreover, the decreased intra-modular connectivity in DMN was significantly negatively correlated with seizure frequency. The inter-modular connectivity between the cingulo-opercular and occipital network also showed a significant correlation with epilepsy frequency. Together, the current study revealed the disrupted topological organization of the whole-brain functional network, which greatly advances our understanding of neuronal architecture in IS and may contribute to predict the prognosis of IS as disease biomarkers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Li, Yang and Huang.)

• Logographic bilingualism and monolingualism varied in widespread functional connectivity. • Logographic bilingualism and monolingualism shared similar efficient topological structure of brain network. • The two languages within logographic bilingualism employed different brain networks for phonology. To explore the differences of functional brain networks in processing auditory phonological tasks between Cantonese-Mandarin bilinguals and Mandarin monolinguals. 31 Cantonese-Mandarin bilinguals and 31 Mandarin monolinguals performed auditory rhyming tasks under a functional magnetic resonance imaging (fMRI) scanning. Bilinguals performed two language tasks (Cantonese, CC; Mandarin, CM), while monolinguals performed only one task (Mandarin, MM). Graph theory and network-based statistic (NBS) analyses were used to reveal the differences of functional brain networks among CC, CM and MM. The functional brain networks of CC, CM and MM were all small-world, and there were no differences in network properties, but widespread differences in functional connectivity among distributed brain regions. The Cantonese-Mandarin bilingualism and Mandarin monolingualism shared similar efficient topological structure of brain network for auditory phonological processing, but varied in widespread functional connectivity. And the two languages within bilingualism employed different functional brain networks for auditory phonological processing. [ABSTRACT FROM AUTHOR]

• Phase lag index method avoids the volume conduction problem. • Deep learning combines the two steps of feature extraction and classification. • The link of convolutional neural network and functional brain network is effective. Driving in fatigue state will increase the occurrence probability of related traffic accidents and cause severe economic and societal problems. To tackle the issue, a deep learning approach is proposed for the automated recognition of driver fatigue using electroencephalography (EEG) signals obtained from real driving. The methodology here proposed consists of converting the multi-channel EEG recording into functional brain network (FBN) adjacency matrices based on phase lag index (PLI) and feeding them into various convolutional neural networks (CNN) as input. These CNN models with convolutional layer, rectifier linear activation unit (ReLU), pooling layer and fully connected layer are designed to extract hidden features from images representing FBN adjacency matrices and then to achieve the two-ways classification task. The experimental results indicate that the highest classification accuracy of 95.4 ± 2.0%, highest sensitivity of 93.9 ± 3.1%, highest precision of 95.5 ± 2.4%, highest F1 score of 94.7 ± 2.0% and highest value of area under the receiver operating curve (AUC-ROC = 0.9953) are achieved using Model 4 based on PLI adjacency matrices as input with the 10-fold cross validation strategy. Indeed, all the CNN models considered in this research achieved accuracy higher than 94.40%. It is hence concluded that the proposed CNN models have the ability to self-learn and pick up more distinguishable features from the input data without a separate feature extraction or feature selection procedure. The experimental results also confirmed the effectiveness of the combination of FBN and CNN for the recognition of driver fatigue. [ABSTRACT FROM AUTHOR]

• A quantitative evaluation and classification method of insomniac brain network is proposed. • Over-connectivity of insomniacs is found during resting state of daytime. • Proposed method is implemented in 20-second EEG. • Proposed method shows stability when connectivity estimated in different domains. Primary insomnia (PI) manifesting as insufficient and non-restorative sleep disturbs the function of central nervous system. Electroencephalogram (EEG), as a technique of recording the electrical signals of the brain, has demonstrated potential to access and quantify PI. However, most existing EEG indices rely on time–frequency analysis and separate channels, which limits its clinical application. In this study, we propose a novel quantitative evaluation method by introducing spatial information from resting-state brain networks of insomniacs to make rapid diagnosis implementable. To suppress false positive observations of coupling attributed to signal spread, the connections were binarized based on an adaptive threshold technology so that the statistical network characteristics were extracted automatically to form a comprehensive measurement index. The clinical experiments proved that the specificity of PI brain networks could be quantified objectively by the comprehensive index in the resting state. PI specificity showed consistency across the connectivity estimated in time (Pearson Correlation Coefficient, PCC), phase (Phase Lag Index, PLI) and frequency (Granger Causality, GC) domains. All the three kinds of connectivity revealed the significant difference between the PI patients and normal subjects (PCC: p = 0.0021, PLI: p = 0.0071, GC: p = 0.0142). The strong connectivity of PI consistent with clinical rating scale indicates the hyperarousal of PI brain. It is difficult to achieve normal inhibition, so it consumes more resources in the resting state. Further, bidirectional long short-term memory (Bi-LSTM) network was applied to classify the healthy status (normal or PI) automatically and achieved 85% accuracy and 90% sensitivity, which demonstrated its potential for clinical diagnosis. [ABSTRACT FROM AUTHOR]

• An new emotion EEG dataset, VREED, used virtual reality as mood induction procedure. • Provide the baseline performance of emotion classification on VREED. • Occipital and frontal regions played a more critical role in emotion processing. Emotion electroencephalography (EEG) datasets play a significant role in EEG-based emotion recognition research, providing a platform for comparisons of different emotion recognition methods. Most datasets used 2D images or videos as mood induction procedures (MIPs); however, considering the differences in EEG dynamics between 2D and 3D environments, experimental research based on 2D MIPs may have poor results being applied in a real 3D world. In this paper, we (1) developed a new emotion EEG dataset, virtual reality (VR) emotional EEG dataset (VREED), which used 3D VR videos as MIPs; and (2) Presented a baseline for the performance of negative/positive emotion classification in the new dataset. The best average accuracy of 73.77% ± 2.01% was obtained by using the combination of theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–49 Hz) relative power features. Additionally, we observed that occipital and frontal regions played a more critical role than other regions in emotion processing from Spearman correlation analysis and feature selection. This new VR emotion EEG dataset will be publicly available, and we encourage other researchers to evaluate their emotion classification methods on the VREED. [ABSTRACT FROM AUTHOR]

Developmental dyslexia (DD) is a neurological-based learning disorder that affects 5-17.5% of children. Handwriting difficulty is a prevailing symptom of dyslexia, but its neural mechanisms remain elusive. Using functional magnetic resonance imaging (fMRI), this study examined functional brain networks associated with handwriting in a copying task in Chinese children with DD ( n = 17) and age-matched children ( n = 36). We found that dyslexics showed reduced network connectivity between the sensory-motor network (SMN) and the visual network (VN), and between the default mode network (DMN) and the ventral attention network (VAN) during handwriting, but not during drawing geometric figures. Moreover, the connectivity strength of the networks showing group differences was correlated with handwriting speed, reading and working memory, suggesting that the handwriting deficit in DD is linked with disruption of a large-scale brain network supporting motoric, linguistic and executive control processes. Taken together, this study demonstrates the alternations of functional brain networks that underly the handwriting deficit in Chinese dyslexia, providing a new clue for the neural basis of DD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Li, Bi, Xu and Yang.)

Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 ( P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kwon, Kim, Son, Jang, Kim, Lee and Lee.)

The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging, we constructed a scale-free binary graph for each subject, using internodal time series correlation of regions of interest as a proximity measure. The resulting network could be embedded onto manifolds of various curvatures and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Using the 𝕊 1 /ℍ 2 model, we reduced the dimension of the network into two-dimensional hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the hyperbolic discs. Each individual disc revealed relevance with its anatomic counterpart and absence of center-spaced node. Using the hyperbolic distance on the 𝕊 1 /ℍ 2 model, we could detect the anomaly of network in autism spectrum disorder subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale., (© 2022 Massachusetts Institute of Technology.)

To study whether Tai Chi (TC) practice can improve the brain connectivity of the prefrontal lobe of college students, the positive psychological capital questionnaires and resting EEG signals were acquired from 50 college students including 25 TC practitioners and 25 demographically matched TC healthy controls. The results showed that the score of the positive psychological capital questionnaire of the TC group was significantly higher than that of the control group, and the node degree of the frontal lobe and temporal lobe of both groups was positively correlated with the score of the positive psychological capital questionnaire. In addition, the response time of the TC group under auditory stimulation was significantly shorter than that of the control group, and there was a significant positive correlation between response time and its characteristic path length, and a significant negative correlation with global efficiency. Meanwhile, during the selected range of sparsity, the difference in global network parameters between two groups is significant in the alpha band. Under all single sparsity, the clustering coefficient, global efficiency, and local efficiency of the TC group have a higher trend, while the characteristic path length tended to be shorter. In the analysis of the local characteristics of the resting brain functional network, it was found that the node degree of the frontal lobe and temporal lobe of the TC group was higher, and the difference was significant in some nodes. These results all point to the fact that TC practice has a certain impact on specific brain areas of the brain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Geng, Du, Si and Wang.)

The brain network is usually constructed by estimating the connectivity matrix and thresholding it at an arbitrary level. The problem with this standard method is that we do not have any generally accepted criteria for determining a proper threshold. Thus, we propose a novel multiscale framework that models all brain networks generated over every possible threshold. Our approach is based on persistent homology and its various representations such as the Rips filtration, barcodes, and dendrograms. This new persistent homological framework enables us to quantify various persistent topological features at different scales in a coherent manner. The barcode is used to quantify and visualize the evolutionary changes of topological features such as the Betti numbers over different scales. By incorporating additional geometric information to the barcode, we obtain a single linkage dendrogram that shows the overall evolution of the network. The difference between the two networks is then measured by the Gromov–Hausdorff distance over the dendrograms. As an illustration, we modeled and differentiated the FDG-PET based functional brain networks of 24 attention-deficit hyperactivity disorder children, 26 autism spectrum disorder children, and 11 pediatric control subjects. [ABSTRACT FROM PUBLISHER]

In this issue of Neuron , Grayson et al. (2016) report how inhibition of amygdala impacts amygdalocortical and corticocortical functional connectivity. Their study predicts changes in functional brain topology, induced by pharmacologic modulation of neuroanatomical circuits using designer receptors exclusively activated by designer drugs (DREADDs), through virtual lesioning of amygdala in structural brain networks. [ABSTRACT FROM AUTHOR]

• The combination of deep learning model and functional brain network is effective. • Using deep learning approach, there is no need for hand-crafted feature steps. • Pre-trained deep learning model achieves better results on a smaller training set. Driving in an unfavorable driving state (UDS) is a safety issue and also an important cause of frequent traffic accidents. Finding corresponding countermeasures to decrease traffic accident rates and improve public road safety is critical but challenging. For the purpose of drawing on deep learning (DL) or transfer learning approach to automatically find features in images without hand-crafted feature extraction steps to re-mine the new synchronization and interaction mode between brain regions. In this study, the acquired multi-channel EEG records while performing driving experiments are innovatively converted into a two-dimensional phase coherence matrix (PC image), which is used as an input to a deep learning network (convolutional neural network) to accomplish the combination of deep learning models and functional brain network methods for the detection of UDS. With the motivation of enhancing the classification performance, two approaches, namely the extract image features approach using pre-trained deep learning models (Alexnet and Resnet 18) combined with support vector machine (SVM) and the transfer learning method based on pre-trained networks after fine-tuning are evaluated, respectively. The experimental results indicate that a highest accuracy of 98.44%, highest sensitivity of 100%, highest specificity of 97.02% and highest precision of 96.80% are obtained for the detection of driving-related unfavorable states by using the extract image features approach based on Alexnet network with the 'relu4′ layer activation combined with SVM. Additionally, transfer learning method based on Resnet 18 network after fine-tuning with a learning rate of 1e-3 and adam solver achieves an average classification accuracy of 97.90% outperforming other transfer learning methods considered in this work. It can be hence concluded that the innovative combination method of functional brain network and deep learning is effective for unfavorable driving state detection. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we studied the brain functional networks corresponding to the traditional multiple-block acupuncture task paradigm. Due to the complexity and sustainability seen during acupuncture, we wanted to investigate whether or not the effects during acupuncture are changing according to the multiple-block paradigm. We introduced the data driven method of independent component analysis (ICA) to identify brain functional networks activated during the course of acupuncture and to isolate different networks likely related to different aspects of the acupuncture experience. The comparisons between different resting states disclosed the discrepancies between the pre- and post-needling effects in the brain. Furthermore, the distinction between needle stimulation and the resting state indicated that there existed different functional brain networks. These results also portray time variability during the course of acupuncture. [Copyright &y& Elsevier]

Identification of congenital sensorineural hearing loss (SNHL) and early intervention, especially by cochlear implantation (CI), are crucial for restoring hearing in patients. However, high accuracy diagnostics of SNHL and prognostic prediction of CI are lacking to date. To diagnose SNHL and predict the outcome of CI, we propose a method combining functional connections (FCs) measured by functional magnetic resonance imaging (fMRI) and machine learning. A total of 68 children with SNHL and 34 healthy controls (HC) of matched age and gender were recruited to construct classification models for SNHL and HC. A total of 52 children with SNHL that underwent CI were selected to establish a predictive model of the outcome measured by the category of auditory performance (CAP), and their resting-state fMRI images were acquired. After the dimensional reduction of FCs by kernel principal component analysis, three machine learning methods including the support vector machine, logistic regression, and k-nearest neighbor and their voting were used as the classifiers. A multiple logistic regression method was performed to predict the CAP of CI. The classification model of voting achieves an area under the curve of 0.84, which is higher than that of three single classifiers. The multiple logistic regression model predicts CAP after CI in SNHL with an average accuracy of 82.7%. These models may improve the identification of SNHL through fMRI images and prognosis prediction of CI in SNHL., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Song, Qi, Jin, Yang, Qian, Yin, Zhao and Yu.)

Working Memory (WM) is a short-term memory for processing and storing information. When investigating WM mechanisms using Electroencephalogram (EEG), its rhythmic synchronization properties inevitably become one of the focal features. To further leverage these features for better improve WM task performance, this paper uses a novel algorithm: Weight K-order propagation number (WKPN) to locate important brain nodes and their coupling characteristic in different frequency bands while subjects are proceeding French word retaining tasks, which is an intriguing but original experiment paradigm. Based on this approach, we investigated the node importance of PLV brain networks under different memory loads and found that the connectivity between frontal and parieto-occipital lobes in theta and beta frequency bands enhanced with increasing memory load. We used the node importance of the brain network as a feature vector of the SVM to classify different memory load states, and the highest classification accuracy of 95% is obtained in the beta band. Compared to the Weight degree centrality (WDC) and Weight Page Rank (WPR) algorithm, the SVM with the node importance of the brain network as the feature vector calculated by the WKPN algorithm has higher classification accuracy and shorter running time. It is concluded that the algorithm can effectively spot active central hubs so that researchers can later put more energy to study these areas where active hubs lie in such as placing Transcranial alternating current stimulation (tACS).

To explore the change of brain connectivity in schizophrenics (SCZ), the resting-state EEG source functional connections of SCZ and healthy control (HC) were investigated in this paper. Different band single-layer networks, multilayer networks, and improved multilayer networks were constructed and their topological attributes were extracted. The topological attributes of SCZ and HC were automatically distinguished using ensemble learning methods called Ensemble Learning based on Trees and Soft voting method, and the effectiveness of different network construction methods was compared based on the classification accuracy. The results showed that the classification accuracy was 89.38% for α band network, 82.5% for multilayer network, and 86.88% for improved multilayer network. Comparing patients with SCZ to those with Alzheimer's disease (AD), the classification accuracy of improved multilayer network was the highest, which was 88.12%. The power spectrum in the α band of SCZ was significantly lower than HC, whereas there was no significant difference between SCZ and AD. This indicated that the improved multilayer network can effectively distinguish SCZ and other groups not only when their power spectrum was significantly different. The results also suggested that the improved multilayer topological attributes were regarded as biological markers in the clinical diagnosis of patients with schizophrenia and even other mental disorders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhu, Zhu, Li, Yang, Zheng, Xu and Li.)

Introduction: Some previous studies in patients with lung cancer have mainly focused on exploring the cognitive dysfunction and deficits of brain function associated with chemotherapy. However, little is known about functional brain alterations that might occur prior to chemotherapy. Therefore, this study aimed to evaluate brain functional changes in patients with nonchemotherapy before chemotherapy with non-small cell lung cancer (NSCLC)., Methods: Resting-state functional MRI data of 35 patients with NSCLC and 46 matched healthy controls (HCs) were acquired to construct functional brain networks. Graph theoretical analysis was then applied to investigate the differences of the network and nodal measures between groups. Finally, the receiver operating characteristic (ROC) curve analysis was performed to distinguish between NSCLC and HC., Results: Decreased nodal strength was found in the left inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part), inferior occipital gyrus, and right inferior frontal gyrus (triangular part) of patients with NSCLC while increased nodal strength was found in the right pallidum and thalamus. NSCLC also showed decreased nodal betweenness in the right superior occipital gyrus. Different hub regions distribution was found between groups, however, no hub regions showed group differences in the nodal measures. Furthermore, the ROC curve analysis showed good performance in distinguishing NSCLC from HC., Conclusion: These results suggested that topological abnormalities of pallido-thalamo-cortical circuit in functional brain network might be related to NSCLC prior to chemotherapy, which provided new insights concerning the pathophysiological mechanisms of NSCLC and could serve as promising biological markers for the identification of patients with NSCLC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Yin, Li, Li, Ni, Pan, Ma, Wu, Feng and Shen.)

Emotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC). In this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dynamics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotion-movement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory. We found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different. Much of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship. Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC. • Combine EEG and EMG to study the relationship between emotion and movement. • Movement associated with happy emotions can increase the complexity of the left hemisphere. • Movement associated with sad emotions can increase the complexity of the right hemisphere. • Increased grip strength leads to increased the CMC strength of EEG→EMG. • The network topology of movement under different emotions is significantly different. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (mTBI) is usually caused by a bump, blow, or jolt to the head or penetrating head injury, and carries the risk of inducing cognitive disorders. However, identifying the biomarkers for the diagnosis of mTBI is challenging as evident abnormalities in brain anatomy are rarely found in patients with mTBI. In this study, we tested whether the alteration of functional network dynamics could be used as potential biomarkers to better diagnose mTBI. We propose a sparse dictionary learning framework to delineate spontaneous fluctuation of functional connectivity into the subject-specific time-varying evolution of a set of overlapping group-level sparse connectivity components (SCCs) based on the resting-state functional magnetic resonance imaging (fMRI) data from 31 mTBI patients in the early acute phase (<3 days postinjury) and 31 healthy controls (HCs). The identified SCCs were consistently distributed in the cohort of subjects without significant inter-group differences in connectivity patterns. Nevertheless, subject-specific temporal expression of these SCCs could be used to discriminate patients with mTBI from HCs with a classification accuracy of 74.2% (specificity 64.5% and sensitivity 83.9%) using leave-one-out cross-validation. Taken together, our findings indicate neuroimaging biomarkers for mTBI individual diagnosis based on the temporal expression of SCCs underlying time-resolved functional connectivity., (© 2021 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

The accurate diagnosis of autism spectrum disorder (ASD), a common mental disease in children, has always been an important task in clinical practice. In recent years, the use of graph neural network (GNN) based on functional brain network (FBN) has shown powerful performance for disease diagnosis. The challenge to construct "ideal" FBN from resting-state fMRI data remained. Moreover, it remains unclear whether and to what extent the non-Euclidean structure of different FBNs affect the performance of GNN-based disease classification. In this paper, we proposed a new method named Pearson's correlation-based Spatial Constraints Representation (PSCR) to estimate the FBN structures that were transformed to brain graphs and then fed into a graph attention network (GAT) to diagnose ASD. Extensive experiments on comparing different FBN construction methods and classification frameworks were conducted on the ABIDE I dataset (n = 871). The results demonstrated the superiority of our PSCR method and the influence of different FBNs on the GNN-based classification results. The proposed PSCR and GAT framework achieved promising classification results for ASD (accuracy: 72.40%), which significantly outperformed competing methods. This will help facilitate patient-control separation, and provide a promising solution for future disease diagnosis based on the FBN and GNN framework., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The variability of the results obtained by the statistical analysis of functional human brain networks depend on multiple factors such as: the source of the fMRI data, the brain parcellations, the graph theory measures, and the threshold values applied to the functional connectivity matrices to obtain adjacency matrices of sparse graphs. Therefore, the brain network used for down-stream analysis is heavily dependent on the methods that are applied to the fMRI data to obtain and analyze such networks. In this paper we present the preliminary results of a multi-factorial assessment of the statistical analysis of functional human brain networks. The assessment was performed in the functional human brain networks obtained from the resting state fMRI data of ten imaging sites provided by the Autism Brain Imaging Data Exchange (ABIDE) preprocessed functional magnetic resonance database, with six different functional brain parcellations, six different graph theory measures, and three different threshold values applied to the corresponding connectivity matrices to obtain sparse graphs. The statistical analysis to detect differences between the networks representing autism and control subjects were performed with four different statistical methods, using the p-values to determine the levels of significance of the analysis. Our main results show a strong dependence of functional human brain networks statistical analysis on the brain parcellations, and on the graph theory measures. Our results further show that the results of these analysis are less dependent on the statistical tests methods and on the threshold values of the sparse graphs for all practical purposes. An additional result is that the levels of significance of the statistical tests obtained for data provided by individual sites were much higher than the global levels of significance obtained by averaging the results of all the sites, implying that the best results on the analysis of functional human brain networks are obtained when the source of the fMRI data is the same for all the data. Since reproducibility and reliability of functional brain network statistical analysis is strongly dependent on the graphs obtained from fMRI data; our expectation is that the novel results presented in this paper would further help researchers in this field to develop methods that are reliable and reproducible.

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Regonia, Takamura, Nakano, Ichikawa, Fermin, Okada, Okamoto, Yamawaki, Ikeda and Yoshimoto.)

• EO versus EC causes different EEG Characteristics during vibrotactile stimulation (VibroStim). • Only VibroStim with EC can improve topological organization of functional brain network. • VibroStim led to only alpha-ERD in the contralateral S1-M1 whether subjects keep EO or EC. • The integration of VibroStim into MI-BCI can improve MI-BCI's performance and be suitable for patients without eye control. Motor imaginary (MI)-based brain-computer interface (BCI) has shown great potential in neurorehabilitation. However, low reliability and concentration difficulty for some patients during most MI-BCIs involving visual tasks affect the performance of MI-BCI. The integration of vibrotactile stimulation (VibraoStim) with MI-based BCI can solve those issues. Therefore, the objective of this study is to explore the different effects between eyes-open (EO) and eyes-closed (EC) during VibroStim on the cerebral activity and network. We designed and conducted an experiment, in which VibroStim (75 Hz) was applied over the belly of the biceps muscle in the left arm for 15 healthy subjects during EC or EO. The resting-state and VibroStim brain activity with EC or EO were recorded using electroencephalography (EEG). The EEG analysis showed a significant increase in the delta and theta power at C4, and a significant decrease in the alpha power at C4 during EC VibroStim compared to the resting-state. No significant difference was identified in event-related desynchronization (ERD) following VibroStim during EC or EO. A significant increase in the global efficiency and a significant decrease in the characteristic path length during EC VibroStim were observed in the beta band, whereas no significant differences were found during EO VibroStim. These findings indicate, VibroStim feedback, which is suitable for patients with poor vision or those who have difficulty focusing on objects, may improve BCI decoding accuracy by increasing ERD lateralization whether subjects keep EO or EC. [ABSTRACT FROM AUTHOR]

• Watching 3D video caused higher global efficiency in brain networks compared to watching 2D video. • Regional efficiencies of brain networks differ between 2D and 3D video watching. • Classify brain states during the 2D and 3D video watching using functional connectivity features. To our knowledge, the present study is the first to use electroencephalography (EEG) to investigate the reorganizations of functional brain networks when watching 2D and 3D videos. We aimed to reveal the underlying neural mechanisms that may cause different visual experiences from a brain network perspective. The EEG activities of 40 healthy participants were recorded while watching 2D and 3D videos. By constructing multiband functional brain networks, we analyzed the network efficiencies from both macro- and micro-scales. We observed: 1) at the macro-scale, higher global efficiency in beta (16–32 Hz) and gamma (32–63 Hz) networks in the 3D group, and 2) at the micro-scale, higher occipital and parietal efficiencies in beta and gamma networks in the 3D group, and higher frontal efficiency in the alpha (8–16 Hz) network in the 2D group. Furthermore, using a small subset of functional connectivity features as inputs, a support vector machine classifier was used to classify the brain states induced by watching 2D and 3D videos. We achieved a satisfactory classification accuracy of 0.908 with an area of 0.96 under the receiver operating characteristic curve, using the top 18 features extracted from the beta band. Our findings are expected to uncover the underlying neural mechanisms related to different visual experiences during 2D and 3D video viewing from a brain network perspective. [ABSTRACT FROM AUTHOR]

• The proposed CNN denoise network estimates the noise distribution in EEG signal, which can process EEG signals with different signal to noise ratios. • The proposed functional brain network construction method dynamically constructs functional brain network to represent brain activity. • The proposed graph neural network model recognizes fatigue driving by combining functional brain network with EEG features. The performance of traditional fatigue driving recognition model is greatly reduced under noisy environment. In order to solve this problem, a fatigue driving recognition framework which could denoise the EEG signals based on deep convolutional neural network and dynamically constructed functional brain network was proposed. The framework constructed functional brain network based on singular value entropy and fractal dimension and used a graph neural network based on B-spline curve spatial convolution to classify the functional brain network. Recognition accuracy and recognition stability of the framework were analyzed. Experiment results show that our framework can achieve high accuracy in fatigue driving recognition. Furthermore, our framework can maintain the high accuracy of the recognition effect when the EEG channels are affected by noises. [ABSTRACT FROM AUTHOR]