Different Modes of Low-Frequency Focused Ultrasound-Mediated Attenuation of Epilepsy Based on the Topological Theory

Epilepsy is common brain dysfunction, where abnormal synchronized activities can be observed across multiple brain regions. Low-frequency focused pulsed ultrasound has been proven to modulate the epileptic brain network. In this study, we used two modes of low-intensity focused ultrasound (pulsed-wave and continuous-wave) to sonicate the brains of KA-induced epileptic rats, analyzed the EEG functional brain connections to explore their respective effect on the epileptic brain network, and discuss the mechanism of ultrasound neuromodulation. By comparing the brain network characteristics before and after sonication, we found that two modes of ultrasound both significantly affected the functional brain network, especially in the low-frequency band below 12 Hz. After two modes of sonication, the power spectral density of the EEG signals and the connection strength of the brain network were significantly reduced, but there was no significant difference between the two modes. Our results indicated that the ultrasound neuromodulation could effectively regulate the epileptic brain connections. The ultrasound-mediated attenuation of epilepsy was independent of modes of ultrasound.

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Low-Intensity Focused Ultrasound-Mediated Attenuation of Acute Seizure Activity Based on EEG Brain Functional Connectivity

Brain Sciences ◽

10.3390/brainsci11060711 ◽

2021 ◽

Vol 11 (6) ◽

pp. 711

Author(s):

Minjian Zhang ◽

Bo Li ◽

Xiaodong Lv ◽

Sican Liu ◽

Yafei Liu ◽

...

Keyword(s):

Focused Ultrasound ◽

Low Frequency ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Connection Strength ◽

Functional Brain ◽

Low Intensity ◽

Functional Brain Network ◽

The Brain

(1) Background: Ultrasound has been used for noninvasive stimulation and is a promising technique for treating neurological diseases. Epilepsy is a common neurological disorder, that is attributed to uncontrollable abnormal neuronal hyperexcitability. Abnormal synchronized activities can be observed across multiple brain regions during a seizure. (2) Methods: we used low-intensity focused ultrasound (LIFU) to sonicate the brains of epileptic rats, analyzed the EEG functional brain network to explore the effect of LIFU on the epileptic brain network, and continued to explore the mechanism of ultrasound neuromodulation. LIFU was used in the hippocampus of epileptic rats in which a seizure was induced by kainic acid. (3) Results: By comparing the brain network characteristics before and after sonication, we found that LIFU significantly impacted the functional brain network, especially in the low-frequency band. The brain network connection strength across multiple brain regions significantly decreased after sonication compared to the connection strength in the control group. The brain network indicators (the path length, clustering coefficient, small-worldness, local efficiency and global efficiency) all changed significantly in the low-frequency. (4) Conclusions: These results revealed that LIFU could reduce the network connections of epilepsy circuits and change the structure of the brain network at the whole-brain level.

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Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

International Journal of Image and Graphics ◽

10.1142/s0219467822500292 ◽

2021 ◽

pp. 2250029

Author(s):

Geng Zhang ◽

Qi Zhu ◽

Jing Yang ◽

Ruting Xu ◽

Zhiqiang Zhang ◽

...

Keyword(s):

Brain Connectivity ◽

Medical Image Analysis ◽

Brain Regions ◽

Brain Network ◽

Brain Diseases ◽

Functional Brain ◽

Functional Brain Network ◽

Structural Brain Network ◽

The Brain ◽

Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

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Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/3609821 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 11

Author(s):

Xin Wang ◽

Yanshuang Ren ◽

Wensheng Zhang

Keyword(s):

Pearson Correlation ◽

Brain Regions ◽

Brain Network ◽

Classification Performance ◽

Low Rank ◽

Fmri Data ◽

Healthy Controls ◽

Functional Brain ◽

Functional Brain Network ◽

Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

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Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

Frontiers in Neurology ◽

10.3389/fneur.2021.715455 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhibao Li ◽

Chong Liu ◽

Qiao Wang ◽

Kun Liang ◽

Chunlei Han ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Functional Connectivity ◽

Frequency Band ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Functional Brain ◽

Frequency Bands ◽

The Brain ◽

Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

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Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Brain Sciences ◽

10.3390/brainsci10110777 ◽

2020 ◽

Vol 10 (11) ◽

pp. 777

Author(s):

Nicholas John Simos ◽

Stavros I. Dimitriadis ◽

Eleftherios Kavroulakis ◽

Georgios C. Manikis ◽

George Bertsias ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Lupus Erythematosus ◽

Statistical Power ◽

Functional Neuroimaging ◽

Brain Regions ◽

Brain Network ◽

Functional Brain ◽

Graph Metrics ◽

Functional Brain Network

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

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Construction and Application of Functional Brain Network Based on Entropy

Entropy ◽

10.3390/e22111234 ◽

2020 ◽

Vol 22 (11) ◽

pp. 1234

Author(s):

Lingyun Zhang ◽

Taorong Qiu ◽

Zhiqiang Lin ◽

Shuli Zou ◽

Xiaoming Bai

Keyword(s):

Dynamic Properties ◽

Recognition Rate ◽

Brain Network ◽

Eeg Signals ◽

Data Set ◽

Functional Brain ◽

Model Based ◽

Functional Brain Network ◽

Fatigue Driving ◽

The Brain

Functional brain network (FBN) is an intuitive expression of the dynamic neural activity interaction between different neurons, neuron clusters, or cerebral cortex regions. It can characterize the brain network topology and dynamic properties. The method of building an FBN to characterize the features of the brain network accurately and effectively is a challenging subject. Entropy can effectively describe the complexity, non-linearity, and uncertainty of electroencephalogram (EEG) signals. As a relatively new research direction, the research of the FBN construction method based on EEG data of fatigue driving has broad prospects. Therefore, it is of great significance to study the entropy-based FBN construction. We focus on selecting appropriate entropy features to characterize EEG signals and construct an FBN. On the real data set of fatigue driving, FBN models based on different entropies are constructed to identify the state of fatigue driving. Through analyzing network measurement indicators, the experiment shows that the FBN model based on fuzzy entropy can achieve excellent classification recognition rate and good classification stability. In addition, when compared with the other model based on the same data set, our model could obtain a higher accuracy and more stable classification results even if the length of the intercepted EEG signal is different.

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Age-related changes in functional connectivity between young adulthood and late adulthood

Analytical Methods ◽

10.1039/c5ay00699f ◽

2015 ◽

Vol 7 (10) ◽

pp. 4111-4122 ◽

Cited By ~ 4

Author(s):

Xin Xu ◽

Qifan Kuang ◽

Yongqing Zhang ◽

Huijun Wang ◽

Zhining Wen ◽

...

Keyword(s):

Functional Connectivity ◽

Young Adulthood ◽

Brain Network ◽

Late Adulthood ◽

Functional Brain ◽

Age Related ◽

Functional Brain Network ◽

Significant Difference ◽

Network Metrics ◽

Age Related Changes

The functional brain network in late adulthood has been found to show a significant difference from that in young adulthood using a variety of network metrics.

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Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2018/4835676 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Satoru Hiwa ◽

Shogo Obuchi ◽

Tomoyuki Hiroyasu

Keyword(s):

Working Memory ◽

Functional Connectivity ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Support Vector ◽

Neural Basis ◽

Functional Brain ◽

Functional Brain Network ◽

Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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Changes in the Functional Brain Network of Children Undergoing Repeated Epilepsy Surgery: An EEG Source Connectivity Study

Diagnostics ◽

10.3390/diagnostics11071234 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1234

Author(s):

Giulia Iandolo ◽

Nitish Chourasia ◽

Georgios Ntolkeras ◽

Joseph R. Madsen ◽

Christos Papadelis ◽

...

Keyword(s):

Epilepsy Surgery ◽

Brain Connectivity ◽

Epileptiform Activity ◽

Brain Regions ◽

Brain Network ◽

Rank Test ◽

Functional Brain ◽

Functional Brain Network ◽

Repeated Surgery ◽

Cortical Regions

About 30% of children with drug-resistant epilepsy (DRE) continue to have seizures after epilepsy surgery. Since epilepsy is increasingly conceptualized as a network disorder, understanding how brain regions interact may be critical for planning re-operation in these patients. We aimed to estimate functional brain connectivity using scalp EEG and its evolution over time in patients who had repeated surgery (RS-group, n = 9) and patients who had one successful surgery (seizure-free, SF-group, n = 12). We analyzed EEGs without epileptiform activity at varying time points (before and after each surgery). We estimated functional connectivity between cortical regions and their relative centrality within the network. We compared the pre- and post-surgical centrality of all the non-resected (untouched) regions (far or adjacent to resection) for each group (using the Wilcoxon signed rank test). In alpha, theta, and beta frequency bands, the post-surgical centrality of the untouched cortical regions increased in the SF group (p < 0.001) whereas they decreased (p < 0.05) or did not change (p > 0.05) in the RS group after failed surgeries; when re-operation was successful, the post-surgical centrality of far regions increased (p < 0.05). Our data suggest that removal of the epileptogenic focus in children with DRE leads to a gain in the network centrality of the untouched areas. In contrast, unaltered or decreased connectivity is seen when seizures persist after surgery.

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Gaussian embedding-based functional brain connectomic analysis for amnestic mild cognitive impairment patients with cognitive training

10.1101/779744 ◽

2019 ◽

Author(s):

Mengjia Xu ◽

Zhijiang Wang ◽

Haifeng Zhang ◽

Dimitrios Pantazis ◽

Huali Wang ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Stage ◽

Brain Networks ◽

Disease Diagnosis ◽

Brain Regions ◽

Brain Network ◽

Amnestic Mild Cognitive Impairment ◽

Functional Brain ◽

Functional Brain Network

AbstractIdentifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer’s disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a new quantitative functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration.

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