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Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored., Methods: We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC., Results: Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity., Conclusions: ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.

Given the widespread use and relapse of methamphetamine (METH), it has caused serious public health burdens globally. However, the neurobiological basis of METH addiction remains poorly understood. Therefore, this study aimed to use magnetic resonance imaging (MRI) to investigate changes in brain networks and their connection to impulsivity and drug craving in abstinent individuals with METH use disorder (MUDs). A total of 110 MUDs and 55 age- and gender-matched healthy controls (HCs) underwent resting-state functional MRI and T1-weighted imaging scans, and completed impulsivity and cue-induced craving measurements. We applied independent component analysis to construct functional brain networks and multivariate analysis of covariance to investigate group differences in network connectivity. Mediation analyses were conducted to explore the relationships among brain-network functional connectivity (FC), impulsivity, and drug craving in the patients. MUDs showed increased connectivity in the salience network (SN) and decreased connectivity in the default mode network compared to HCs. Impulsivity was positively correlated with FC within the SN and played a completely mediating role between METH craving and FC within the SN in MUDs. These findings suggest alterations in functional brain networks underlying METH dependence, with SN potentially acting as a core neural substrate for impulse control disorders., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Studies have indicated that skilled soccer players possess superior decision-making abilities compared to their less-skilled counterparts. However, the underlying neural mechanism for this phenomenon remains incompletely understood. In our investigation, we explored distinctions in the topology of functional brain networks between skilled and less-skilled soccer players. Employing mediating analysis, we scrutinized the relationships among functional brain network parameters, training duration, and decision-making accuracy. Our findings revealed that skilled soccer players demonstrated significantly higher decision-making accuracy compared to their less-skilled counterparts. Skilled players also exhibited increased values in the cluster coefficient, characteristic path length and local efficiency but lower global efficiency. Moreover, we observed enhanced functional brain connectivity within the occipital and cingulo-opercular networks, as well as between the fronto-parietal and cingulo-opercular networks in skilled soccer players. Cluster coefficient and functional connectivity between fronto-parietal and cingulo-opercular networks had positive mediating effects on the association between training duration and sport decision-making accuracy. In conclusion, our study provides initial evidence for distinctions in functional brain network parameters between soccer players with varying skill levels and their relationship with sport decision-making accuracy., 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 © 2024 Elsevier B.V. All rights reserved.)

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application. To address these issues, we developed a VAE-GAN framework that combined a VAE (variational auto-encoder) with a GAN (generative adversarial net) for functional brain network identification and fMRI augmentation. As a generative model, the VAE-GAN models the distribution of fMRI so that it enables the extraction of more generalized features, and thus relieve the overfitting issue. The VAE-GAN is easier to train on fMRI than a standard GAN since it uses latent variables from VAE to generate fake data rather than relying on random noise that is used in a GAN, and it can generate higher quality of fake data than VAE since the discriminator can promote the training of the generator. In other words, the VAE-GAN inherits the advantages of VAE and GAN and avoids their limitations in modeling of fMRI data. Extensive experiments on task fMRI datasets from HCP have proved the effectiveness and superiority of the proposed VAE-GAN framework for identifying both temporal features and functional brain networks compared with existing models, and the quality of fake data is higher than those from VAE and GAN. The results on resting state fMRI of Attention Deficit Hyperactivity Disorder (ADHD)-200 dataset further demonstrated that the fake data generated by the VAE-GAN can help improve the performance of brain network modeling and ADHD classification., 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 Elsevier Ltd. All rights reserved.)

Background: Neonatal hypoxic-ischemic encephalopathy (HIE) is the main cause of neonatal death and disability worldwide. At present, there are few researches on the application of resting-state functional magnetic resonance imaging (rs-fMRI) to explore the brain development of HIE children. This study aimed to explore the changes of brain function in neonates with different degrees of HIE using rs-fMRI. Methods: From February 2018 to May 2020, 44 patients with HIE were recruited, including 21 mild patients and 23 moderate and severe patients. The recruited patients were scanned by conventional and functional magnetic resonance image, and the method of amplitude of low-frequency fluctuation and connecting edge analysis of brain network was used. Results: Compared with the mild group, the connections between the right supplementary motor area and the right precentral gyrus, the right lingual gyrus and the right hippocampus, the left calcarine cortex and the right amygdala, and the right pallidus and the right posterior cingulate cortex in the moderate and severe groups were reduced ( t values were 4.04, 4.04, 4.04, 4.07, all p  < 0.001, uncorrected). Conclusion: By analyzing the functional connection changes of brain network in infants with different degrees of HIE, the findings of the current study suggested that neonates with moderate to severe HIE lag behind those with mild HIE in emotional processing, sensory movement, cognitive function, and learning and memory. Chinese Clinical Trial Registry registration number: ChiCTR1800016409.

Time estimation is fundamental for human survival. There have been increasing studies suggesting that distributed brain regions, such as the basal ganglia, cerebellum and the parietal cortex, may contribute to a dedicated neural mechanism of time estimation. However, evidence on the specific function of the subcortical and cortical brain regions and the interplay of them is scare. In this work, we explored how the subcortical and cortical networks function in time estimation during a time reproduction task using functional MRI (fMRI). Thirty healthy participants performed the time reproduction task in both auditory and visual modalities. Results showed that time estimation in visual and auditory modality recruited a subcortical-cortical brain network including the left caudate, left cerebellum, and right precuneus. Besides, the superior temporal gyrus (STG) was found essential in the difference between time estimation in visual and auditory modality. Using psychophysiological interaction (PPI) analysis, we observed an increase in the connection between left caudate and left precuneus using the left caudate as the seed region in temporal reproduction task than control task. This suggested that the left caudate is the key region connecting and transmitting information to other brain regions in the dedicated brain network of time estimation., (Copyright © 2023 IBRO. Published by Elsevier Ltd. All rights reserved.)

Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG). Firstly, we defined electrode node as brain area of vicinity of electrode to construct 32-node small-scale FBN. Pearson correlation coefficient (PCC) was used to construct correlation-based FBNs. Phase locking value (PLV) and phase synchronization index (PSI) were utilized to construct synchrony-based FBNs. Next, global properties and effects of emotion of different networks were compared. The difference of synchrony-based FBN concentrates in alpha band, and the number of differences is less than that of correlation-based FBN. Node properties of different small-scale FBNs have significant differences, offering a new basis for feature extraction of recognition regions in emotional FBNs. Later, we made partition of electrode nodes and 10 new brain areas were defined as regional nodes to construct 10-node large-scale FBN. Results show the impact of emotion on network clusters on the right forehead, and high valence enhances information processing efficiency of FBN by promoting connections in brain areas., (© 2022. International Federation for Medical and Biological Engineering.)

Background and Objective: 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., Methods: 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., Results: 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., Conclusions: 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

Objective: To explore the differences of functional brain networks in processing auditory phonological tasks between Cantonese-Mandarin bilinguals and Mandarin monolinguals., Methods: 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., Results: 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., Conclusions: 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., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: 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)., New Methods: 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., Results: 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., Comparison With Existing Methods: 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., Conclusions: Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual's tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain's functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Autism spectrum disorder (ASD) is a neurological and developmental disorder. Traditional diagnosis of ASD is typically performed through the observation of behaviors and interview of a patient. However, these diagnosis methods are time-consuming and can be misleading sometimes. Integrating machine learning algorithms with neuroimages, a diagnosis method, can possibly be established to detect ASD subjects from typical control subjects. In this study, we develop deep learning methods for diagnosis of ASD from functional brain networks constructed with brain functional magnetic resonance imaging (fMRI) data. The entire Autism Brain Imaging Data Exchange 1 (ABIDE 1) data set is utilized to investigate the performance of our proposed methods. First, we construct the brain networks from brain fMRI images and define the raw features based on such brain networks. Second, we employ an autoencoder (AE) to learn the advanced features from the raw features. Third, we train a deep neural network (DNN) with the advanced features, which achieves the classification accuracy of 76.2% and the receiving operating characteristic curve (AUC) of 79.7%. As a comparison, we also apply the same advanced features to train several traditional machine learning algorithms to benchmark the classification performance. Finally, we combine the DNN with the pretrained AE and train it with the raw features, which achieves the classification accuracy of 79.2% and the AUC of 82.4%. These results show that our proposed deep learning methods outperform the state-of-the-art methods.

An efficient network such as the human brain features a combination of global integration of information, driven by long-range connections, and local processing involving short-range connections. Whether these connections are equally damaged in multiple sclerosis is unknown, as is their relevance for cognitive impairment and brain function. Therefore, we cross-sectionally investigated the association between damage to short- and long-range connections with structural network efficiency, the functional connectome and cognition. From the Amsterdam multiple sclerosis cohort, 133 patients (age = 54.2 ± 9.6) with long-standing multiple sclerosis and 48 healthy controls (age = 50.8 ± 7.0) with neuropsychological testing and MRI were included. Structural connectivity was estimated from diffusion tensor images using probabilistic tractography (MRtrix 3.0) between pairs of brain regions. Structural connections were divided into short- (length < quartile 1) and long-range (length > quartile 3) connections, based on the mean distribution of tract lengths in healthy controls. To determine the severity of damage within these connections, (i) fractional anisotropy as a measure for integrity; (ii) total number of fibres; and (iii) percentage of tract affected by lesions were computed for each connecting tract and averaged for short- and long-range connections separately. To investigate the impact of damage in these connections for structural network efficiency, global efficiency was computed. Additionally, resting-state functional connectivity was computed between each pair of brain regions, after artefact removal with FMRIB's ICA-based X-noiseifier. The functional connectivity similarity index was computed by correlating individual functional connectivity matrices with an average healthy control connectivity matrix. Our results showed that the structural network had a reduced efficiency and integrity in multiple sclerosis relative to healthy controls (both P < 0.05). The long-range connections showed the largest reduction in fractional anisotropy (z = -1.03, P < 0.001) and total number of fibres (z = -0.44, P < 0.01), whereas in the short-range connections only fractional anisotropy was affected (z = -0.34, P = 0.03). Long-range connections also demonstrated a higher percentage of tract affected by lesions than short-range connections, independent of tract length (P < 0.001). Damage to long-range connections was more strongly related to structural network efficiency and cognition (fractional anisotropy: r = 0.329 and r = 0.447. number of fibres r = 0.321 and r = 0.278. and percentage of lesions: r = -0.219; r = -0.426, respectively) than damage to short-range connections. Only damage to long-distance connections correlated with a more abnormal functional network (fractional anisotropy: r = 0.226). Our findings indicate that long-range connections are more severely affected by multiple sclerosis-specific damage than short-range connections. Moreover compared to short-range connections, damage to long-range connections better explains network efficiency and cognition., (© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.)

The human brain develops rapidly in the first postnatal year, in which rewired functional brain networks could shape later behavioral and cognitive performance. Resting-state functional magnetic resonances imaging (rs-fMRI) and complex network analysis have been widely used for characterizing the developmental brain functional connectome. Yet, such studies focusing on the first year of postnatal life are still very limited. Leveraging normally developing longitudinal infant rs-fMRI scans from neonate to one year of age, we investigated how brain functional networks develop at a fine temporal scale (every 3 months). Considering challenges in the infant fMRI-based network analysis, we developed a novel algorithm to construct the robust, temporally consistent and modular structure augmented group-level network based on which functional modules were detected at each age. Our study reveals that the brain functional network is gradually subdivided into an increasing number of functional modules accompanied by the strengthened intra- and inter-modular connectivities. Based on the developing modules, we found connector hubs (the high-centrality regions connecting different modules) emerging and increasing, while provincial hubs (the high-centrality regions connecting regions in the same module) diminishing. Further region-wise longitudinal analysis validates that different hubs have distinct developmental trajectories of the intra- and inter-modular connections suggesting different types of role transitions in network, such as non-hubs to hubs or provincial hubs to connector hubs et al. All findings indicate that functional segregation and integration are both increased in the first year of postnatal life. The module reorganization and hub transition lead to more efficient brain networks, featuring increasingly segregated modular structure and more connector hubs. This study provides the first comprehensive report of the development of functional brain networks at a 3-month interval throughout the first postnatal year of life, which provides essential information to the future neurodevelopmental and developmental disorder studies., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Resting-state fMRI studies have demonstrated that Alzheimer's disease (AD) is associated with aberrant organization and function of large-scale brain networks. However, the nature of the disruption of cross-network interactions in the key neurocognitive networks in the brain remains unclear. In this paper, we examined the 'triple-network model', including the default mode network (DMN), salience network (SN), and central executive network (CEN), to identify the cross-network interactions in late mild cognitive impairment (LMCI) and AD. With resting-state fMRI, we tested cross-network functional connectivity among the DMN, SN, and CEN in 33 AD patients, 24 LMCI, and 25 well-matched normal control subjects. Then, we identified the most influential brain regions affected by AD and LMCI. Finally, we investigated the relationship between aberrant functional connectivity and clinical cognitive dysfunction. We found the cross-network functional connectivity of the SN-centered 'triple-network model' was significantly impaired in the AD group and the alterations were negatively correlated with the Mini-Mental State Examination (MMSE) scores. For the LMCI group, the functional connectivity of the SN-centered 'triple-network model' also changed compared to AD; however, we found no correlation with MMSE score. As predicted, the abnormal connections among the three networks mainly overlap with the key nodes of the three networks. Overall, our findings suggested that the interactions of the SN-centered 'triple-network model' are impaired in AD patients and that these alterations contribute to the decline in cognitive function. This 'triple-network model' provides new insights into AD and provides more information about the dysregulation of brain networks in AD.

Objectives: We investigated whether patients with complex interpersonal trauma engage neural networks that are commonly activated during cognitive reappraisal and responding naturally to affect-laden images. In this naturalistic study, we examined whether trauma treatment not only reduces symptoms but also changes neural networks involved in emotional control., Methods: Before and after eight weeks of phase-oriented inpatient trauma treatment, patients (n = 28) with complex posttraumatic stress disorder (cPTSD) and complex dissociative disorders (CDD) performed a cognitive reappraisal task while electroencephalography (EEG) was registered. Patients were measured as a prototypical dissociative part that aims to fulfill daily life goals while avoiding traumatic memories and associated dissociative parts. Matched healthy controls (n = 38) were measured twice as well. We examined task-related functional connectivity and assessed self-reports of clinical symptoms and emotion regulation skills., Results: Prior to treatment and compared to controls, patients showed hypoconnectivity within neural networks involved in emotional downregulation while reappraising affect-eliciting pictures as well as viewing neutral and affect-eliciting pictures. Following treatment, connectivity became normalized in these networks comprising regions associated with cognitive control and memory. Additionally, patients showed a treatment-related reduction of negative but not of positive dissociative symptoms., Conclusions: This is the first study demonstrating that trauma-focused treatment was associated with favorable changes in neural networks involved in emotional control. Emotional overregulation manifesting as negative dissociative symptoms was reduced but not emotional underregulation, manifesting as positive dissociative symptoms., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Motor stroke has been characterized by disruptions in multiple large-scale functional brain networks. However, it remains unclear whether stroke patients with good hand outcomes show different connectivity profiles within and between networks from those with poor hand outcomes. In this cross-sectional study, we recruited 52 chronic subcortical stroke patients [illness duration (mean ± SD): 16 ± 16.2 months] and 52 healthy controls from the local hospital and community from June 2010 to August 2016. We first performed independent component analysis (ICA) on resting-state fMRI data to extract fifteen resting-state networks. Then, we compared the functional connectivity within and between networks across 52 healthy controls, 26 patients with a partially paralyzed hand (PPH), and 26 patients with a completely paralyzed hand (CPH). Compared to the patients with a PPH, the patients with a CPH showed increased connectivity in the contralesional sensorimotor cortex within the contralesional sensorimotor network; the increased connectivity was negatively correlated with the performance of the paretic hand. Moreover, the patients with a CPH, compared to those with a PPH, showed decreased strengths of connectivity between the ipsilesional sensorimotor network and both the dorsal sensorimotor network and ventral visual network; the decreased strengths of connectivity were positively correlated with the performance of the paretic hand. Collectively, our findings suggest that stroke patients with different hand outcomes show distinct functional reorganization patterns in large-scale brain networks. These findings shed light on the network-level neuromechanisms that help explain why stroke survivors in the chronic stage show different hand outcomes., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The mammalian brain is a densely interconnected network that consists of millions to billions of neurons. Decoding how information is represented and processed by this neural circuitry requires the ability to capture and manipulate the dynamics of large populations at high speed and high resolution over a large area of the brain. Although the use of optical approaches by the neuroscience community has rapidly increased over the past two decades, most microscopy approaches are unable to record the activity of all neurons comprising a functional network across the mammalian brain at relevant temporal and spatial resolutions. In this review, we survey the recent development in optical technologies for Ca 2+ imaging in this regard and provide an overview of the strengths and limitations of each modality and its potential for scalability. We provide guidance from the perspective of a biological user driven by the typical biological applications and sample conditions. We also discuss the potential for future advances and synergies that could be obtained through hybrid approaches or other modalities.

Objective: The purpose of this study was to investigate the relationship between alterations of functional brain network and cognition in patients with benign epilepsy with centrotemporal spikes (BECTS) as a function of spike-wave index (SWI) during slow wave sleep., Methods: Resting-state functional magnetic resonance imaging (RS-fMRI) data and Intelligence Quotient (IQ) were collected from two groups of patients with BECTS, including a SWI<50% group (5 cases) and a SWI≥50% group (7 cases). The SWI was calculated from the long-term video-electroencephalogram monitoring (one sleep cycle was included at least). The RS-fMRI data were analyzed by regional homogeneity (ReHo) method., Results: There were three main findings. Firstly, Full Intelligence Quotient (FIQ), Verbal Intelligence Quotient (VIQ), and Performance Intelligence Quotient (PIQ) of the SWI≥50% group were significantly lower than SWI<50% group (p<0.05). Secondly, there was a negative correlation between the FIQ, VIQ, PIQ, and SWI (p<0.05), and the FIQ, VIQ, and PIQ were not dependent on age, age of onset, disease course, years of education, and total number of seizures (p>0.05). Finally, compared with the SWI<50% group, the SWI≥50% group showed increased ReHo in the bilateral precentral gyrus, bilateral premotor area, bilateral subcortical structure, right temporal lobe, and bilateral insular lobe, while they showed decreased ReHo in the posterior cingulate cortex and posterior of right inferior temporal lobe., Conclusions: The alterations of functional brain network caused by the frequent discharges during slow wave sleep could affect cognition in patients with BECTS., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules. This process uses an information theoretic notion of energy (as opposed to a metabolic one) to produce an energy landscape in which local minima represent attractor states characterized by specific patterns of modular structure. The clustering of local minima highlights three classes of ROIs with similar patterns of allegiance to community states. Visual, attention, sensorimotor, and subcortical ROIs are well-characterized by a single functional community. The remaining ROIs affiliate with a putative executive control community or a putative default mode and salience community. We simulate the brain's dynamic transitions between these community states using a random walk process. We observe that simulated transition probabilities between basins are statistically consistent with empirically observed transitions in resting state fMRI data. These results offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in groups of brain regions, and that the strength of these attractors depends on the ongoing cognitive computations., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objective: The study was dedicated to investigating the change in information processing in brain networks of vascular dementia (VaD) patients during the process of decision making., Methods: EEG was recorded from 18 VaD patients and 19 healthy controls when subjects were performing a visual oddball task. The whole task was divided into several stages by using global field power analysis. In the stage related to the decision-making process, graph theoretical analysis was applied to the binary directed network derived from EEG signals at nine electrodes in the frontal, central, and parietal regions in δ (0.5-3.5Hz), θ (4-7Hz), α1 (8-10Hz), α2 (11-13Hz), and β (14-30Hz) frequency bands based on directed transfer function., Results: A weakened outgoing information flow, a decrease in out-degree, and an increase in in-degree were found in the parietal region in VaD patients, compared to healthy controls. In VaD patients, the parietal region may also lose its hub status in brain networks. In addition, the clustering coefficient was significantly lower in VaD patients., Conclusions: Impairment might be present in the parietal region or its connections with other regions, and it may serve as one of the causes for cognitive decline in VaD patients. The brain networks of VaD patients were significantly altered toward random networks., Significance: The present study extended our understanding of VaD from the perspective of brain functional networks, and it provided possible interpretations for cognitive deficits in VaD patients., (Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

In facial expression perception, a distributed network is activated according to stimulus context. We proposed that an interaction between brain activation and stimulus context in response to facial expressions could signify a pattern of interactivity across the whole brain network beyond the face processing network. Functional magnetic resonance imaging data were acquired for 19 young healthy subjects who were exposed to either emotionally neutral or negative facial expressions. We constructed group-wise functional brain networks for 12 face processing areas [bilateral inferior occipital gyri (IOG), fusiform gyri (FG), superior temporal sulci (STS), amygdalae (AMG), inferior frontal gyri (IFG), and orbitofrontal cortices (OFC)] and for 73 whole brain areas, based on partial correlation of mean activation across subjects. We compared the topological properties of the networks with respect to functional distance-based measures, global and local efficiency, between the two types of face stimulus. In both face processing and whole brain networks, global efficiency was lower and local efficiency was higher for negative faces relative to neutral faces, indicating that network topology differed according to stimulus context. Particularly in the face processing network, emotion-induced changes in network topology were attributable to interactions between core (bilateral IOG, FG, and STS) and extended (bilateral AMG, IFG, and OFC) systems. These results suggest that changes in brain activation patterns in response to emotional face stimuli could be revealed as changes in the topological properties of functional brain networks for the whole brain as well as for face processing areas.

Background: Dysconnectivity in schizophrenia can be understood in terms of dysfunctional integration of a distributed network of brain regions. Here we propose a new methodology to analyze complex networks based on semi-metric behavior, whereby higher levels of semi-metricity may represent a higher level of redundancy and dispersed communication. It was hypothesized that individuals with (increased risk for) psychotic disorder would have more semi-metric paths compared to controls and that this would be associated with symptoms., Methods: Resting-state functional MRI scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings and 72 controls. Semi-metric percentages (SMP) at the whole brain, hemispheric and lobar level were the dependent variables in a multilevel random regression analysis to investigate group differences. SMP was further examined in relation to symptomatology (i.e., psychotic/cognitive symptoms)., Results: At the whole brain and hemispheric level, patients had a significantly higher SMP compared to siblings and controls, with no difference between the latter. In the combined sibling and control group, individuals with high schizotypy had intermediate SMP values in the left hemisphere with respect to patients and individuals with low schizotypy. Exploratory analyses in patients revealed higher SMP in 12 out of 42 lobar divisions compared to controls, of which some were associated with worse PANSS symptomatology (i.e., positive symptoms, excitement and emotional distress) and worse cognitive performance on attention and emotion processing tasks. In the combined group of patients and controls, working memory, attention and social cognition were associated with higher SMP., Discussion: The results are suggestive of more dispersed network communication in patients with psychotic disorder, with some evidence for trait-based network alterations in high-schizotypy individuals. Dispersed communication may contribute to the clinical phenotype in psychotic disorder. In addition, higher SMP may contribute to neuro- and social cognition, independent of psychosis risk.

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 500[Formula: see text]ms 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 500[Formula: see text]ms 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).

The P300 is regarded as a psychosis endophenotype of schizophrenia and a putative biomarker of risk for schizophrenia. However, the brain activity (i.e., P300 amplitude) during tasks cannot always provide satisfying discrimination of patients with schizophrenia (SZs) from healthy controls (HCs). Spontaneous activity at rest indices the potential of the brain, such that if the task information can be efficiently processed, it provides a compensatory understanding of the cognitive deficits in SZs. In this paper, based on the resting and P300 task electroencephalogram (EEG) data sets, we constructed functional EEG networks and then extracted the inherent spatial pattern of network (SPN) features for both brain states. Finally, the combined SPN features of the rest and task networks were used to recognize SZs. The findings of this paper revealed that the combined SPN features could achieve the highest accuracy of 90.48%, with the sensitivity of 89.47%, and specificity of 91.30%. These findings consistently implied that the rest and task P300 EEGs could actually provide comprehensive information to reliably classify SZs from HCs, and the SPN is a promising tool for the clinical diagnosis of SZs. [ABSTRACT FROM AUTHOR]

Modelling 3D objects in CAD software requires special skills which require a novice user to undergo a series of training exercises to obtain. To minimize the training time for a novice user, the user-dependent factors must be studied. we have presented a comparative analysis of novice/expert information flow patterns. We have used Normalized Transfer Entropy (NTE) and Electroencephalogram (EEG) to investigate the differences. The experiment was divided into three cognitive states i.e., rest, drawing, and manipulation. We applied classification algorithms on NTE matrices and graph theory measures to see the effectiveness of NTE. The results revealed that the experts show approximately the same cognitive activation in drawing and manipulation states, whereas for novices the brain activation is more in manipulation state than drawing state. The hemisphere- and lobe-wise analysis showed that expert users have developed an ability to control the information flow in various brain regions. On the other hand, novice users have shown a continuous increase in information flow activity in almost all regions when doing drawing and manipulation tasks. A classification accuracy of more than 90% was achieved with a simple K-nearest neighbors (k-NN) to classify novice and expert users. The results showed that the proposed technique can be used to develop adaptive 3D modelling systems. [ABSTRACT FROM AUTHOR]