Brain Network Features Research Articles

Dynamic network features of functional and structural brain networks support visual working memory in aging adults

Abstract In this work, we investigated how the relationship between structural connectivity and the dynamics of functional connectivity changes with age to benefit cognitive ability. Visual working memory (VWM) is an important brain function that allows us to maintain a mental representation of the world around us, but its capacity and precision peak by around 20 years old and decreases steadily throughout the rest of our lives. This research examined the functional brain network dynamics associated with VWM throughout the lifespan and found that Default Mode Network and Fronto-Parietal Network states were more well represented in individuals with better VWM. Furthermore, transitions from the Visual/Somatomotor Network state to the Attention Network state were more well-represented in older adults, and a network control theory simulation demonstrated that structural connectivity differences supporting this transition were associated with better VWM, especially in middle-aged individuals. The structural connectivity of regions from all states was important for supporting this transition in younger adults, while regions within the Visual/Somatomotor and Attention Network states were more important in older adults. These findings demonstrate that structural connectivity supports flexible, functional dynamics that allow for better VWM with age and may lead to important interventions to uphold healthy VWM throughout the lifespan.

Abnormal resting-state brain networks and their relationship with cognitive reappraisal preferences in depressive tendencies.

Abnormal resting-state brain networks and their relationship with cognitive reappraisal preferences in depressive tendencies.

Analysis of Brain Functional Connectivity in Patients with Delayed Encephalopathy After Carbon Monoxide Poisoning Based on Functional Near-Infrared Spectroscopy Technology.

To observe the brain network characteristics of delayed encephalopathy after carbon monoxide poisoning (DEACMP) using functional near-infrared spectroscopy (fNIRS) technology. Fifteen patients with Carbon monoxide (CO) poisoning (DEACMP group) hospitalized in the Department of Neurology, Affiliated Hospital of North Sichuan Medical College, from November 2023 to March 2024 were selected. Fifteen healthy volunteers (the control group) were also recruited. Six-minute resting-state fNIRS data were collected from all subjects. Five cognitive-related key brain regions were selected as regions of interest (ROI): parietal cortex (PC), premotor cortex (PMC), frontopolar cortex (FPC), orbitofrontal cortex (OFC), and dorsolateral prefrontal cortex (DLPFC). NirSpark software was used to analyze the differences in whole-brain functional connectivity strength and functional connectivity strength within and between ROIs between the two groups. The functional connectivity strength of the left PMC, right PMC, and left FPC ,etc.In the DEACMP group was significantly lower than that in the control group (P<0.05, FDR corrected). Compared with the control group, the brain network of DEACMP patients showed heterogeneity from left PC to right PC; Left PC~Left PMC; Left PC~right PMC; Left PC~Left DLPFC ,etc.The functional connectivity strength between the left PC and the right DLPFC area has significantly decreased. All these differences were statistically significant (P<0.05, FDR adjusted). DEACMP exhibits abnormal functional connectivity in both whole-brain and cognitive-related key brain regions. This aberrant connectivity may represent the underlying neural network mechanisms responsible for the cognitive dysfunction observed in DEACMP.

Altered static and dynamic functional network connectivity and combined Machine learning in asthma.

Altered static and dynamic functional network connectivity and combined Machine learning in asthma.

Network centrality alterations in patients with moyamoya disease after combined revascularization surgery: a resting-state fMRI study.

This study aimed to explore alterations in brain network characteristics among patients with moyamoya disease (MMD) before and after combined revascularization surgery (CRS). This investigation enrolled 20 MMD patients alongside 20 age- and sex-matched healthy controls (HCs). All participants underwent MRI scans. Additionally, MMD patients were subjected to comprehensive clinical assessments. Degree centrality (DC) analysis was utilized to assess the connectivity features of the entire brain network. The study also examined correlations between DC values in MMD patients before- (pre-CRS) and after-CRS (post-CRS) and various clinical indicators. Compared with HCs, pre-CRS MMD patients showed abnormal DC values in multiple brain regions, mainly including the cerebellum, frontal lobe, temporal lobe, and cingulate gyrus. One year after CRS treatment, the DC values of the bilateral cerebellum posterior lobe showed a reverse increase. In addition, the DC value of the right cerebellum posterior lobe in pre-CRS MMD patients was positively correlated with the Montreal cognitive assessment scores. CRS treatment can effectively improve the functional network damage of the bilateral cerebellum posterior lobes caused by MMD, and it is expected to provide a new neuroimaging marker for the evaluation of CRS treatment efficacy.

Topological Data Analysis for Early Detection of Neurodegenerative Disease

Abstract: Neurodegenerative diseases, such as Alzheimer's and Parkinson's, are characterized by progressive neuronal deterioration, leading to cognitive and motor impairments. Early detection is crucial for effective intervention and management. This study proposes a novel framework utilizing Topological Data Analysis (TDA), particularly persistent homology, to identify early biomarkers from neuroimaging and physiological data. This study explores the application of TDA techniques—specifically persistent homology and topological invariants—for the early detection of neurodegenerative diseases such as Alzheimer's. By analyzing functional brain connectivity, gait dynamics, and molecular structures associated with disease progression, the research demonstrates that TDA can capture subtle, yet critical, topological signatures indicative of early neurodegenerative changes. Layer-wise topological complexity in neural networks and persistent homology in clinical datasets reveal distinct structural degradation patterns. The integration of TDA with machine learning models enhances diagnostic accuracy, interpretability, and generalization, offering a promising direction for non-invasive, data-driven diagnostics in neurology.By capturing the intrinsic topological features of brain networks, the framework aims to distinguish between healthy and early-stage neurodegenerative conditions, offering a robust tool for early diagnosis.Topological Data Analysis (TDA) offers a robust mathematical framework for extracting shape-based features from high-dimensional and noisy biomedical data. Keywords: Topological Data Analysis, Persistent Homology, Neurodegenerative Disease, Alzheimer's, Brain Connectivity, Early Diagnosis, Betti Numbers, Gait Analysis, Functional Networks, Machine Learning, Biomedical Data

Cross-subject affective analysis based on dynamic brain functional networks.

Emotion recognition is crucial in facilitating human-computer emotional interaction. To enhance the credibility and realism of emotion recognition, researchers have turned to physiological signals, particularly EEG signals, as they directly reflect cerebral cortex activity. However, due to inter-subject variability and non-smoothness of EEG signals, the generalization performance of models across subjects remains a challenge. In this study, we proposed a novel approach that combines time-frequency analysis and brain functional networks to construct dynamic brain functional networks using sliding time windows. This integration of time, frequency, and spatial domains helps to effectively capture features, reducing inter-individual differences, and improving model generalization performance. To construct brain functional networks, we employed mutual information to quantify the correlation between EEG channels and set appropriate thresholds. We then extracted three network attribute features-global efficiency, local efficiency, and local clustering coefficients-to achieve emotion classification based on dynamic brain network features. The proposed method is evaluated on the DEAP dataset through subject-dependent (trial-independent), subject-independent, and subject- and trial-independent experiments along both valence and arousal dimensions. The results demonstrate that our dynamic brain functional network outperforms the static brain functional network in all three experimental cases. High classification accuracies of 90.89% and 91.17% in the valence and arousal dimensions, respectively, were achieved on the subject-independent experiments based on the dynamic brain function, leading to significant advancements in EEG-based emotion recognition. In addition, experiments with each brain region yielded that the left and right temporal lobes focused on processing individual private emotional information, whereas the remaining brain regions paid attention to processing basic emotional information.

Older cerebral small vessel disease and sleep disturbance: A diffusion tensor imaging study.

Older cerebral small vessel disease and sleep disturbance: A diffusion tensor imaging study.

Characteristics of brain network after cardiopulmonary phase synchronization enhancement.

Characteristics of brain network after cardiopulmonary phase synchronization enhancement.

Intrinsic brain mapping of cognitive abilities: A multiple-dataset study on intelligence and its components.

Intrinsic brain mapping of cognitive abilities: A multiple-dataset study on intelligence and its components.

Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.

Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.

Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models.

Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models.

Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.

TDCS Modulates Brain Functional Networks in Children with Autism Spectrum Disorder: A Resting-State EEG Study.

This study aimed to investigate the effects of transcranial direct current stimulation (tDCS) on brain functional networks in children with autism spectrum disorder (ASD). We constructed brain functional networks using phase-locking value (PLV) and assessed the temporal variability of these networks using fuzzy entropy. Graph theory was applied to analyze network characteristics. Resting-state electroencephalography (EEG) data were used to compare differences in brain functional connectivity, temporal variability, and network properties between children with ASD and typically developing (TD) children. Additionally, we examined the changes in functional connectivity, temporal variability, and network properties in children with ASD after 20 sessions of tDCS intervention. The study revealed that children with ASD exhibited lower connectivity in the alpha band and higher connectivity in the beta band. In the delta and theta bands, ASD children demonstrated a mixed pattern of both higher and lower connectivity. Furthermore, ASD children exhibited higher temporal variability across all four frequency bands, particularly in the delta and beta bands. After tDCS intervention, the total score of the Autism Behavior Checklist (ABC) significantly decreased. Additionally, functional connectivity in the delta and alpha bands increased, while temporal variability in the delta and beta bands decreased, indicating positive changes in brain network characteristics. These results suggest that tDCS may be a promising intervention for modulating brain functional networks in children with ASD. ChiCTR2400092790. Registered 22 November, 2024, https://www.chictr.org.cn/showproj.html?proj=249950.

Early Development and Co-Evolution of Microstructural and Functional Brain Connectomes: A Multi-Modal MRI Study in Preterm and Full-Term Infants.

Functional networks characterized by coherent neural activity across distributed brain regions have been observed to emerge early in neurodevelopment. Synchronized maturation across regions that relate to functional connectivity (FC) could be partially reflected in the developmental changes in underlying microstructure. Nevertheless, covariation of regional microstructural properties, termed "microstructural connectivity" (MC), and its relationship to the emergence of functional specialization during the early neurodevelopmental period remain poorly understood. We investigated the evolution of MC and FC postnatally across a set of cortical and subcortical regions, focusing on 45 preterm infants scanned longitudinally, and compared to 45 matched full-term neonates as part of the developing Human Connectome Project (dHCP) using direct comparisons of grey-matter connectivity strengths as well as network-based analyses. Our findings revealed a global strengthening of both MC and FC with age, with connection-specific variability influenced by the connection maturational stage. Prematurity at term-equivalent age was associated with significant connectivity disruptions, particularly in FC. During the preterm period, direct comparisons of MC and FC strength showed a positive linear relationship, which seemed to weaken with development. On the other hand, overlaps between MC- and FC-derived networks (estimated with Mutual Information) increased with age, suggesting a potential convergence towards a shared underlying network structure that may support the co-evolution of microstructural and functional systems. Our study offers novel insights into the dynamic interplay between microstructural and functional brain development and highlights the potential of MC as a complementary descriptor for characterizing brain network development and alterations due to perinatal insults such as premature birth.

The brain-gut microbiota network (BGMN) is correlated with symptom severity and neurocognition in patients with schizophrenia.

The brain-gut microbiota network (BGMN) is correlated with symptom severity and neurocognition in patients with schizophrenia.

Association of aberrant brain network connectivity with visual dysfunction in patients with nonarteritic anterior ischemic optic neuropathy: a pilot study.

Nonarteritic anterior ischemic optic neuropathy (NAION) is often accompanied by degeneration of optic nerve axons and ganglion cell apoptosis, but the mechanism of its effects on the cerebral cortex and visual centers is not clear. Graph theory analysis, as a quantitative tool for complex networks, has made it possible to characterize the topological alterations of brain networks in patients with NAION. The objective of this pilot study was to investigate the topological characteristics of functional brain networks in patients with NAION and to analyze their potential correlation with visual dysfunction. This prospective, cross-sectional study recruited 25 patients with NAION and 24 matched healthy controls (HCs) from Dongfang Hospital, Beijing University of Chinese Medicine. Following resting-state functional magnetic resonance imaging (rs-fMRI) scans, large-scale functional connectivity matrices of 90 regions were constructed. Graph theory was then used to compare global and local network parameters. Subsequently, network-based statistics (NBS) analysis was employed to detect differences in functional connectivity across the brain. Finally, correlations were assessed between the network topological properties and clinical variables. Individuals with NAION, as compared to controls, exhibited significant decreases in normalized clustering coefficient (gamma; P=0.021), small-worldness (sigma; P=0.043), and local efficiency (Eloc; P=0.030), as well as a significant increase in the size of the largest connected component (LCC; P=0.039) of the network. Additionally, the LCC showed a negative association with gamma, sigma and global efficiency (Eg) but a positive correlation with the normalized characteristic path length (lambda) of the two groups (all P values <0.05). Regionally, patients exhibited changes in nodal centralities, particularly affecting the attention, visual, and salience networks. NBS analysis identified an interconnected subnetwork consisting of 49 nodes and 77 edges (P<0.001, NBS-corrected) that showed significantly higher connectivity in patients with NAION. The mean connectivity of this subnetwork was negatively correlated with the global topological parameters gamma, sigma, and Eg in the NAION group and gamma and sigma in the HCs but positively correlated with the LCC in both groups (all P values <0.05). Moreover, the nodal betweenness centrality of the left dorsolateral superior frontal gyrus exhibited a significant positive correlation with the visual field (VF) mean deviation (MD) in the NAION group (P<0.001). This study initially identified aberrant topological and connectivity changes in the functional brain networks associated with visual impairment in patients with NAION, thus expanding our existing understanding of the neurobiological mechanisms of NAION.

Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning.

Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning.

Distinct brain network features predict internalizing and externalizing traits in children, adolescents and adults

Distinct brain network features predict internalizing and externalizing traits in children, adolescents and adults

Complex Spiking Neural Network Evaluated by Injury Resistance Under Stochastic Attacks.

Brain-inspired models are commonly employed for artificial intelligence. However, the complex environment can hinder the performance of electronic equipment. Therefore, enhancing the injury resistance of brain-inspired models is a crucial issue. Human brains have self-adaptive abilities under injury, so drawing on the advantages of the human brain to construct a brain-inspired model is intended to enhance its injury resistance. But current brain-inspired models still lack bio-plausibility, meaning they do not sufficiently draw on real neural systems' structure or function. To address this challenge, this paper proposes the complex spiking neural network (Com-SNN) as a brain-inspired model, in which the topology is inspired by the topological characteristics of biological functional brain networks, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with time delay co-regulated by excitatory synapses and inhibitory synapses. To evaluate the injury resistance of the Com-SNN, two injury-resistance metrics are investigated and compared with SNNs with alternative topologies under the stochastic removal of neuron models to simulate the consequence of stochastic attacks. In addition, the injury-resistance mechanism of brain-inspired models remains unclear, and revealing the mechanism is crucial for understanding the development of SNNs with injury resistance. To address this challenge, this paper analyzes the synaptic plasticity dynamic regulation and dynamic topological characteristics of the Com-SNN under stochastic attacks. The experimental results indicate that the injury resistance of the Com-SNN is superior to that of other SNNs, demonstrating that our results can help improve the injury resistance of SNNs. Our results imply that synaptic plasticity is an intrinsic element impacting injury resistance, and that network topology is another element that impacts injury resistance.