Small-world human brain networks: Perspectives and challenges

Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction

Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10191-0.

Transdiagnostic network alterations and associated neurotransmitter signatures across major psychiatric disorders in adolescents: Evidence from edge-centric analysis of time-varying functional brain networks

BACKGROUND

Adolescence is a pivotal phase marked by heightened vulnerability to the onset of psychiatric disorders. However, there are few transdiagnostic studies of dynamic brain networks across major psychiatric disorders during this phase.

METHODS

We collected resting-state functional MRI data from 189 adolescent patients (61 with bipolar disorder, 73 with major depressive disorder, and 55 with schizophrenia) and 181 healthy adolescents. Functional networks were constructed using a state-of-art edge-centric dynamic functional connectivity (DFC) approach.

RESULTS

Four DFC states were identified for the healthy adolescents that were related to different behavioral and cognitive terms. Disorder-related alterations were observed in two states involving motor and somatosensory processing and one state involving various cognitive functions. Regardless of the state, the three patient groups exhibited lower FC that were mainly involved in edges between different functional subsystems and were predominantly linked to regions in the somatomotor network. The patients with major depressive disorder additionally showed increased FC that were primarily linked to default mode regions. Graph-based network analysis revealed different patterns of disrupted small-world organization and altered nodal degree in the disorders in a state-dependent manner. The nodal degree alterations were correlated with the concentration of various neurotransmitters. Intriguingly, the noradrenaline concentration was engaged in the nodal degree alterations in each patient group. Finally, decreased FC involving regions in the somatomotor network showed significant correlations with clinical variables in the major depressive disorder patients.

CONCLUSION

These findings may help understand the developmental pathways associated with the heightened vulnerability to major psychiatric disorders during adolescence.

Association of Bidirectional Network Cores in the Brain with Perceptual Awareness and Cognition

The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions. However, the link between such subnetworks and conscious perception remains unclear due to the complexity of brain networks. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions-termed the "cores" of a network-from brain activity. We applied this framework to resting-state and task-based human fMRI data from participants of both sexes to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. We found that the extracted central cores predominantly included cerebral cortical regions rather than subcortical regions. Additionally, regarding their relation to conscious perception, we demonstrated that the cores tend to include regions previously reported to be affected by electrical stimulation that altered conscious perception, although the results are not statistically robust due to the small sample size. Furthermore, in relation to cognitive functions, based on a meta-analysis and comparison of the core structure with a cortical functional connectivity gradient, we found that the central cores were related to unimodal sensorimotor functions. The proposed framework provides novel insights into the roles of network cores with strong bidirectional interactions in conscious perception and unimodal sensorimotor functions.

Altered functional connectivity of brainstem ARAS nuclei unveils the mechanisms of disorders of consciousness in sTBI: an exploratory study

OBJECTIVE

To investigate the functional connectivity (FC) characteristics of Ascending Reticular Activating System (ARAS) in patients with disorders of consciousness (DOC) following severe traumatic brain injury (sTBI), while introducing the Linear support vector machine (LSVM) to predict the recovery of consciousness.

METHODS

Resting-state MRI was used to measure FC changes between the brainstem ARAS nuclei and whole-brain voxels. We compared the differences in FC between sTBI patients and healthy controls, as well as between the wake and DOC groups. Furthermore, the LSVM model for consciousness recovery was developed based on the Z-values of regions of interest (ROIs) and/or scale to distinguish the prognosis of sTBI patients.

RESULTS

A total of 28 sTBI patients with DOC and 30 healthy controls were included, with no significant baseline differences (p > 0.05). Using the brainstem ARAS nuclei as the ROI, we observed increased FC in the subcortical regions compared to healthy controls. The strength of FC was significantly different between patients who recovered consciousness and those who did not at 6 months post-sTBI (AlphaSim corrected, p < 0.05, Cluster > 154). Furthermore, the LSVM model demonstrated strong predictive performance, with an area under the receiver operating characteristic curve of 0.81-0.98.

CONCLUSIONS

Our study suggest that the disruption FC of ARAS from the subcortex to the cortex may be associated with DOC and prognosis in sTBI patients. Furthermore, the LSVM model shows potential value in distinguishing the recovery of consciousness.

Resting-state functional brain networks in hypertensive retinopathy

OBJECTIVE

Hypertensive retinopathy (HR) is known to have effects on the brain's function. This neuroimaging investigation aimed to evaluate alterations in functional network connectivity and the topological properties of brain networks in in patients with HR.

METHODS

The study involved twenty patients with HR and forty-one healthy controls (HC), all of whom underwent resting-state functional MRI scans. Independent component analysis and graph theory analysis were calculated to identify functional connectivity and topological property abnormalities between the two groups.

RESULTS

Compared to HC, patients with HR demonstrated increased internetwork functional connectivity. Furthermore, these patients showed increased intranetwork functional connectivity within the right precuneus of the default mode network. Graph theory analysis revealed that both groups demonstrated a small-world topology. However, significant differences were observed in global and regional network metrics in HR patients compared to HC.

CONCLUSION

These findings highlight the alterations in functional connectivity and topological properties of brain networks in patients with HR, offering valuable insights into the potential neural mechanisms underlying their condition.

Cortical thickness and structural covariance network alterations in cerebral amyloid angiopathy: A graph theoretical analysis

AIMS

This study investigates large-scale brain network alterations in cerebral amyloid angiopathy (CAA) using structural covariance network (SCN) analysis and graph theory based on 7 T MRI.

METHODS

We employed structural covariance network (SCN) analysis based on cortical thickness data from ultra-high field 7 T MRI to investigate network alterations in CAA patients. Graph theoretical analysis was applied to quantify topological properties, including small-worldness, nodal centrality, and network efficiency. Between-group differences were assessed using permutation tests and false discovery rate (FDR) correction.

RESULTS

CAA patients exhibited significant alterations in small-world properties, with decreased Gamma (p = 0.002) and Sigma (p < 0.001), suggesting a shift toward a less optimal network configuration. Local efficiency was significantly different between groups (p = 0.045), while global efficiency remained unchanged (p = 0.127), indicating regionally disrupted rather than globally impaired network efficiency. At the nodal level, the right superior frontal gyrus exhibited increased betweenness centrality (p = 0.013), whereas the right banks of the superior temporal sulcus, left postcentral gyrus, and left superior temporal gyrus showed significantly reduced centrality (all p < 0.05). Additionally, nodal degree and efficiency were altered in key memory-related and association regions, including the entorhinal cortex, fusiform gyrus, and temporal pole.

CONCLUSION

SCN analysis combined with graph theory offers a valuable approach for understanding disease-related connectivity disruptions and may contribute to the development of network-based biomarkers for CAA.

Frequency-band specific directed connectivity networks reveal functional disruptions and pathogenic patterns in temporal lobe epilepsy: a MEG study

This study investigates the network mechanisms of temporal lobe epilepsy (TLE) using MEG data, focusing on directed connectivity networks across different frequency bands. Unlike previous studies that primarily localize epileptogenic zones, this research aims to explore whole-brain network differences between left TLE (lTLE), right TLE (rTLE), and healthy controls (HCs). MEG data from 13 lTLE patients, 21 rTLE patients, and 14 HCs were source-reconstructed to 116 brain regions (AAL116). Directed Transfer Function (DTF) was used to construct directed connectivity networks, followed by networks and graph-theoretical analyses. The results indicate that, compared to HCs, TLE subjects exhibited a significant increase in average connectivity strength in the Low Gamma band. The connectivity patterns across frequency bands in TLE patients were found to be unstable. Both HC and TLE subjects demonstrated left hemisphere lateralization. In the mid-to-low frequency bands, TLE subjects showed increases in global clustering coefficient (GCC), global characteristic path length (GCPL), and local efficiency (LE) compared to HCs, which is attributed to enhanced synchronization between local brain regions in TLE subjects.

Characteristics of brain network after cardiopulmonary phase synchronization enhancement

The central neural mechanism plays an important role in cardiopulmonary coupling. How the brain stem affects the cardiopulmonary coupling is relatively clear, but there are few studies on the cerebral cortex activity of cardiopulmonary coupling. We aim to study the response of the cerebral cortex for cardiopulmonary phase synchronization enhancement. The method of brain network was used and Pearson correlation analysis performed on the global attributes and phase synchronization time (CRPST) in the spontaneous, 2/2 and 4/4 breathing modes. Furthermore, calculated the phase lag index (PLI) among 21 lead EEG signals, and then analyzed the correlation between PLI and the parameters of cardiovascular and respiratory systems. Our results show that the global brain network characteristic parameters are significantly different in the three breath modes in the α (8-14 Hz) band. The global efficiency and feature path length are significantly positively correlated with the phase synchronization and PLI indexes are widely related to CRPST and respiratory depth in the spontaneous breathing mode, while the brain network parameters and PLI indexes are not correlated with CRPST and PLI mainly positively correlated with respiratory rate in the controlled breathing modes. The differences of brain networks in the three modes are mainly caused by the physiological factors of cardiopulmonary coupling. These show that enhanced cardiopulmonary phase synchronization with controlled breathing based on heartbeat has a significant effect on the cardiopulmonary system and maybe provide some ideas for regulating cardiopulmonary function in the future.

A Fast Feedforward Small-World Neural Network for Nonlinear System Modeling

It is well-documented that cross-layer connections in feedforward small-world neural networks (FSWNNs) enhance the efficient transmission for gradients, thus improving its generalization ability with a fast learning. However, the merits of long-distance cross-layer connections are not fully utilized due to the random rewiring. In this study, aiming to further improve the learning efficiency, a fast FSWNN (FFSWNN) is proposed by taking into account the positive effects of long-distance cross-layer connections, and applied to nonlinear system modeling. First, a novel rewiring rule by giving priority to long-distance cross-layer connections is proposed to increase the gradient transmission efficiency when constructing FFSWNN. Second, an improved ridge regression method is put forward to determine the initial weights with high activation for the sigmoidal neurons in FFSWNN. Finally, to further improve the learning efficiency, an asynchronous learning algorithm is designed to train FFSWNN, with the weights connected to the output layer updated by the ridge regression method and other weights by the gradient descent method. Several experiments are conducted on four benchmark datasets from the University of California Irvine (UCI) machine learning repository and two datasets from real-life problems to evaluate the performance of FFSWNN on nonlinear system modeling. The results show that FFSWNN has significantly faster convergence speed and higher modeling accuracy than the comparative models, and the positive effects of the novel rewiring rule, the improved weight initialization, and the asynchronous learning algorithm on learning efficiency are demonstrated.

Graph theory analysis of a human body metabolic network: A systematic and organ-specific study

PURPOSES

Positron emission tomography (PET) imaging is widely used to detect focal lesions or diseases and to study metabolic abnormalities between organs. However, analyzing organ correlations alone does not fully capture the characteristics of the metabolic network. Our work proposes a graph-based analysis method for quantifying the topological properties of the network, both globally and at the nodal level, to detect systemic or single-organ metabolic abnormalities caused by diseases such as lung cancer.

METHODS

We used whole-body 18F-fluorodeoxyglucose (18F-FDG) standardized uptake value (SUV) images from 32 lung cancer patients and 20 healthy controls to construct two-organ glucose metabolism correlation networks at the population level. We calculated five global measures and three nodal centralities for these networks to explore the small-world, rich-club and modular organization in the metabolic network. Additionally, we analyzed the preference for connections significantly affected by lung cancer by dividing organs according to system level and spatial location.

RESULTS

In lung cancer patients, functional segregation in metabolic networks increased (increasedC p ${{C}_p}$ ,E loc ${{E}_{{\mathrm{loc}}}}$ , and Q $Q$ , t < 0), whereas functional integration decreased (increasedL p ${{L}_p}$ , t < 0, and decreasedE glob ${{E}_{{\mathrm{glob}}}}$ , t > 0), indicating more localized and dispersed metabolic activities. At the nodal level, certain organs, such as the pancreas, liver, heart, and right kidney, were no longer hubs in lung cancer patients (decreased nodal centralities, t > 0), whereas the left adrenal gland, left kidney, and left lung showed significantly increased centralities (increased nodal centralities, t < 0). This change suggests compensatory effects between organs. Connections between the nervous and urinary systems, as well as between the upper and middle organs, were more strongly affected by lung cancer (p < 0.05).

CONCLUSIONS

Our study demonstrates the utility of graph theory in analyzing PET imaging data to uncover metabolic network abnormalities. We identified significant topological changes and shifts in nodal roles in lung cancer patients, indicating a shift toward localized and segregated metabolic activities. These findings emphasize the need to consider systemic interactions and specific organ connections affected by disease. The impact on connections between the nervous and urinary systems and between the upper and middle regions underscores the modular nature of organ interactions, offering insights into disease mechanisms and potential therapeutic targets.

Network topology and metabolic alterations in early- and mid-stage Parkinson's disease: insights from fluorodeoxyglucose PET imaging

OBJECTIVES

Parkinson's disease (PD) is a neurodegenerative disorder with distinct metabolic alterations in the brain, which are detectable via 18 F-FDG PET. This study aims to delineate glucose metabolism patterns and network topology changes across early- and mid-stage PD patients.

METHODS

A total of 80 PD patients (Hoehn-Yahr stages 1-3) were retrospectively analyzed, including 40 early-stage and 40 mid-stage cases, along with 40 age-matched healthy controls. All participants underwent 18 F-FDG PET imaging. The brain metabolic activity was quantified, and network topology was assessed using graph theory metrics. Statistical comparisons between PD stages and control groups were performed to identify significant differences in metabolic patterns and network alterations.

RESULTS

Early-stage PD patients exhibited hypermetabolism in regions such as the pons and thalamus, with significant differences in metabolic activity compared with controls. Mid-stage PD patients showed more extensive hypermetabolism in the pons, right cerebellum, and putamen, alongside hypometabolism in the cuneus and calcarine regions. Hub node connectivity analysis revealed decreased connectivity in temporal and occipital lobes for both stages, while the limbic and frontal lobes showed enhanced connectivity. Compared with early-stage PD, mid-stage PD had reduced connectivity in the limbic system but increased in the frontal and occipital lobes.

CONCLUSIONS

18 F-FDG PET imaging reveals progressive metabolic disruptions and network changes in PD, offering potential biomarkers for disease staging and therapeutic targeting, while also aiding in the understanding of disease progression and guiding therapeutic interventions.

Neural architecture of social punishment: Insights from a queue-jumping scenario

Punishment in social settings is crucial for maintaining collective interests, yet the underlying mechanisms remain unclear. To address this, we developed a paradigm, the queue-jumping task, where participants imagine experiencing a queue-jumping event through vivid pictorial scenarios. Behavioral findings revealed that individuals prioritized collective interests over personal ones when punishing, highlighting the altruistic nature of social punishment. Neuroimaging results demonstrated that social punishment activated multiple neural circuits associated with social norms (e.g., fusiform gyrus and posterior cingulate cortex), self-related processing (e.g., ventromedial prefrontal cortex and middle cingulate cortex), and punishment implementation (e.g., anterior dorsolateral prefrontal cortex and middle temporal gyrus). Brain network analyses uncovered a social punishment network whose efficacy in information transmission forecasts individuals' tendency to punish. This study provides valuable insights into the cognitive and neural mechanisms involved in social punishment. The current paradigm closely reflects real-life queue-jumping situations and daily punitive behaviors, demonstrating its generalizability and validity.

Developmental timing of adversity and neural network organization: An fNIRS study of the impact of refugee displacement

This study investigated the neurodevelopmental impacts of displacement on resettled Syrian refugee children in Canada, focusing on how the timing and duration of adversity experienced during displacement influence neural network organization. Using graph theoretical approaches within a network neuroscience framework, we examined how the developmental timing of displacement (age of displacement, duration of displacement) related to functional integration, segregation, and small-worldness. Syrian refugee children (n = 61, MAge=14 Range = 8-18), completed a resting state scan using functional Near Infrared Spectroscopy (fNIRS) neuroimaging. Data were analyzed to assess the link between neural network properties and developmental timing of adversity. Results indicate that prolonged displacement experienced earlier in life was significantly linked with neural network organization, impacting the balance between the brain's functional integration and segregation as quantified by the overall reduced small worldness in comparison to experiencing displacement at an older age. This study leverages the experiences of refugee children to advance our understanding of how the timing of adversity affects development, providing valuable insights into the broader impacts of early adversity on neurodevelopment.

Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression

BACKGROUND

Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD.

METHODS

Functional networks of individuals with ScD (n = 26) and healthy controls (HCs) (n = 33) were constructed based on functional magnetic resonance imaging data. These networks were then optimized using a small-worldness and modular similarity-based network thresholding method to ensure the robustness of functional networks. Subsequently, graph-theoretic methods were employed to investigated both global and nodal topological metrics of these functional networks.

RESULTS

Compared to HCs, individuals with ScD exhibited significantly higher characteristic path length, clustering coefficient, and local efficiency, as well as a significantly lower global efficiency. Additionally, significantly lower nodal centrality metrics were found in the default mode network (DMN) regions (anterior cingulate cortex, superior frontal gyrus, precuneus) and occipital lobe in ScD, and the nodal efficiency of the left precuneus was negatively correlated with the severity of depression.

CONCLUSIONS

Altered global metrics indicate a disrupted small-world architecture and a typical shift toward regular configuration of functional networks in ScD, which may result in lower efficiency of information transmission in the brain of ScD. Moreover, lower nodal centrality in DMN regions suggest that DMN dysfunction is a neuroimaging characteristic shared by both ScD and major depressive disorder, and might serve as a vital factor promoting the development of depression.

Functional connectivity-based compensation in the brains of non-demented older adults and the influence of lifestyle: A longitudinal 7-year study

INTRODUCTION

The aging brain is characterized by structural decline and functional connectivity changes towards dedifferentiation, leading to cognitive decline. To some degree, the brain can compensate for structural deterioration. In this study, we aim to answer two questions: Where can we detect longitudinal functional connectivity-based compensation in the brains of cognitively healthy older adults? Can lifestyle predict the strength of this functional compensation?

METHODS

Using longitudinal data from 228 cognitively healthy older adults, we analyzed five measurement points over 7 years. Network-based statistics and latent growth modeling were employed to examine changes in structural and functional connectivity, as well as potential functional compensation for declines in processing speed and memory. Random forest and linear regression were used to predict the amplitude of compensation based on demographic, biological, and lifestyle factors.

RESULTS

Both functional and structural connectivity showed increases and decreases over time, depending on the specific connection and measure. Increased functional connectivity of 27 connections was linked to smaller declines in cognition. Five of those connections showed simultaneous decreases in fractional anisotropy, indicating direct compensation. The degree of compensation depended on the type of compensation and the cognitive ability, with demographic, biological, and lifestyle factors explaining 3.4-8.9% of the variance.

CONCLUSIONS

There are widespread changes in structural and functional connectivity in older adults. Despite the trend of dedifferentiation in functional connectivity, we detected both direct and indirect compensatory subnetworks that mitigated the decline in cognitive performance. The degree of compensation was influenced by demographic, biological, and lifestyle factors.

Structural covariance network patterns linked to neuropsychiatric symptoms in biologically defined Alzheimer's disease: Insights from the mild behavioral impairment checklist

BACKGROUND

The frequent presentation of Alzheimer's disease (AD) with neuropsychiatric symptoms (NPS) in the context of normal or minimally-impaired cognitive function led to the concept of Mild Behavioral Impairment (MBI). While MBI's impact on subsequent cognitive decline is recognized, its association with brain network changes in biologically-defined AD remains unexplored.

OBJECTIVE

To investigate the correlation of structural covariance networks with MBI-C checklist sub-scores in biologically-defined AD patients.

METHODS

We analyzed 33 biologically-defined AD patients, ranging from mild cognitive impairment to early dementia, all characterized as amyloid-positive through cerebrospinal fluid analysis or amyloid positron emission tomography scans. Regional network properties were assessed through graph theory.

RESULTS

Affective dysregulation correlated with decreased segregation and integration in the right inferior frontal gyrus (IFG). Impulse dyscontrol and social inappropriateness correlated positively with centrality and efficiency in the right posterior cingulate cortex (PCC). Global network properties showed a preserved small-world organization.

CONCLUSIONS

This study reveals associations between MBI subdomains and structural brain network alterations in biologically-confirmed AD. The IFG's involvement is crucial for mood dysregulation, while the PCC could be involved in compensatory mechanisms for social cognition and impulse control. These findings underscore the significance of biomarker-based neuroimaging for the characterization of NPS across the AD spectrum.

How Can Graph Theory Inform the Dual-stream Model of Speech Processing? A Resting-state Functional Magnetic Resonance Imaging Study of Stroke and Aphasia Symptomology

The dual-stream model of speech processing describes a cortical network involved in speech processing. However, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream model's regions are related to speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI data sets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors collected at another site. We successfully identified an intrinsic functional network among the dual-stream model's regions in the control group using functional connectivity. We then used both standard functional connectivity analyses and graph theory approaches to determine how this connectivity may predict performance on clinical aphasia assessments. Our findings provide evidence that the dual-stream model of speech processing is an intrinsic network as measured via resting-state MRI and that functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. In addition, the functional connectivity of the hub nodes predicted linguistic impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemisphere's homologues of the left dorsal stream hubs to the left dorsal hubs, versus to the right ventral stream hubs, is a particularly strong predictor of poststroke aphasia severity and symptomology.

Identifying influential nodes in brain networks via self-supervised graph-transformer

BACKGROUND

Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood. In contrast, self-supervised deep learning dispenses with manual features, allowing it to learn meaningful representations directly from the data. This approach enables the exploration of I-nodes for brain networks, which is also lacking in current studies.

METHOD

This paper proposes a Self-Supervised Graph Reconstruction framework based on Graph-Transformer (SSGR-GT) to identify I-nodes, which has three main characteristics. First, as a self-supervised model, SSGR-GT extracts the importance of brain nodes to the reconstruction. Second, SSGR-GT uses Graph-Transformer, which is well-suited for extracting features from brain graphs, combining both local and global characteristics. Third, multimodal analysis of I-nodes uses graph-based fusion technology, combining functional and structural brain information.

RESULTS

The I-nodes we obtained are distributed in critical areas such as the superior frontal lobe, lateral parietal lobe, and lateral occipital lobe, with a total of 56 identified across different experiments. These I-nodes are involved in more brain networks than other regions, have longer fiber connections, and occupy more central positions in structural connectivity. They also exhibit strong connectivity and high node efficiency in both functional and structural networks. Furthermore, there is a significant overlap between the I-nodes and both the structural and functional rich-club.

CONCLUSIONS

Experimental results verify the effectiveness of the proposed method, and I-nodes are obtained and discussed. These findings enhance our understanding of the I-nodes within the brain network, and provide new insights for future research in further understanding the brain working mechanisms.

Exploring the transmission of cognitive task information through optimal brain pathways

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

A Machine learning classification framework using fused fractal property feature vectors for Alzheimer's disease diagnosis

Alzheimer's disease (AD) profoundly affects brain tissue and network structures. Analyzing the topological properties of these networks helps to understand the progression of the disease. Most studies focus on single-scale brain networks, but few address multiscale brain networks. In this study, the renormalization group approach was applied to rescale the gray matter brain networks of AD patients and cognitively normal (CN) into three scales: the original, once-renormalized, and twice-renormalized networks. Based on the fractal property of these networks at different scales, a novel framework for classifying Alzheimer's disease using fractal and renormalization group was proposed. We integrated the fractal metrics across different scales to create fused feature vectors, which served as inputs for the classification framework aimed at diagnosing Alzheimer's disease. The experimental result indicates that the original and once-renormalized networks of both CN and AD exhibit the fractal property. The classification framework performed best when using the fused feature vector, including the average connection ratio of the original and once-renormalized networks. Using the fused feature vector of the average connection ratio, the One-Dimensional Convolution Neural Network model achieved an accuracy of 92.59% and an F1 score of 91.19%. This marks an improvement of approximately 10% in accuracy and 5% in F1 score compared to results using feature fusion of the average degree, average path length, and clustering coefficient.

Developmental changes in brain-wide fear memory networks

Memory retrieval involves coordinated activity across multiple brain regions. Yet how the organization of memory networks evolves throughout development remains poorly understood. In this study, we compared whole-brain functional networks that are active during contextual fear memory recall in infant, juvenile, and adult mice. Our analyses revealed that long-term memory networks change significantly across postnatal development. Infant fear memory networks are dense and heterogeneous, whereas adult networks are sparse and have a small-world topology. While hippocampal subregions were highly connected nodes at all ages, the cortex gained many functional connections across development. Different functional connections matured at different rates, but their developmental timing fell into three major categories: stepwise change between two ages, linear change across all ages, or inverted-U, with elevated functional connectivity in juveniles. Our work highlights how a subset of brain regions likely maintain important roles in fear memory encoding, but the functional connectivity of fear memory networks undergoes significant reorganization across development. Together, these results provide a blueprint for studying how correlated cellular activity in key areas distinctly regulates memory storage and retrieval across development.

A novel method for functional brain networks based on static cerebral blood flow

Cerebral blood flow (CBF) offers a quantitative and reliable measurement for brain activity and is increasingly used to study functional networks. However, current methods evaluate inter-regional relations mainly based on CBF temporal dynamics, which suffers from low signal-to-noise ratio and poor temporal resolution. Here we proposed a method to construct functional brain networks by estimating shape similarity (index by Jensen-Shannon divergence) in probability distributions of regional static CBF measured by arterial spin labeling perfusion imaging over a scanning period. Based on CBF data of 30 healthy participants from 10 visits, we found that the CBF networks exhibited non-trivial topological features (e.g., small-world organization, modular architecture, and hubs) and showed low-to-fair test-retest reliability and high between-subject consistency. We further found that interregional CBF similarities were depended on anatomical distance and differed between high- and lower-order subnetworks. Moreover, interregional CBF similarities within high-order subnetworks showed significantly lower reliability than those within low-order subnetworks. Finally, we showed that nodal degree of the CBF networks were related to regional sizes and CBF levels and spatially aligned with maps of the dopamine transporter and metabolic glutamate receptor 5 intensities, expression levels of genes primarily enriched in cholesterol-related pathways and endothelial cells, and meta-analytic activations related to memory, language, and executive function. Altogether, our proposed method provide a novel, relatively reliable, and neurobiologically meaningful means to study functional network organization of the human brain.

Dysfunction in the hierarchy of morphometric similarity network in Alzheimer's disease and its correlation with cognitive performance and gene expression profiles

BACKGROUND

Previous research has shown abnormal functional network gradients in Alzheimer's disease (AD). Structural network gradient is capable of capturing continuous changes in brain morphology and has the ability to elucidate the underlying processes of neurodevelopment. However, it remains unclear whether structural network gradients are altered in AD and what associations exist between these changes and cognitive function, and gene expression profiles.

METHODS

By constructing an individualized structural network gradient decomposition framework, we calculated the morphological similarity network (MSN) gradients for 404 subjects (186 AD patients and 218 normal controls). We investigated AD-related alterations in MSN gradients, along with the associations between MSN gradients and cognitive function, MSN topological properties, and gene expression profiles.

RESULTS

Our findings indicated that the principal MSN gradient alterations in AD were primarily characterized by an increase in the primary and secondary sensory cortices and a decrease in the association cortex 1. The primary and higher-order cortices exhibited opposite associations with cognition, including executive function, language skills, and memory processes. Moreover, the principal MSN gradients were found to significantly predict cognitive function in AD. The altered gradient pattern was 14.8% attributable to gene expression profiles, and the genes demonstrating the highest correlation are involved in metabolic activity and synaptic signaling.

CONCLUSIONS

Our results offered novel insights into the underlying mechanisms of structural brain network impairment in AD patients, enhancing our understanding of the neurobiological processes responsible for impaired cognition in patients with AD, and offering a new dimensional structural biomarker for AD.

Typical and disrupted small-world architecture and regional communication in full-term and preterm infants

Understanding the emergence of complex cognition in the neonate is one of the great frontiers of cognitive neuroscience. In the adult brain, small-world organization enables efficient information segregation and integration and dynamic adaptability to cognitive demands. It remains unknown, however, when functional small-world architecture emerges in development, whether it is present by birth and how prematurity affects it. We leveraged the world's largest fMRI neonatal dataset-Developing Human Connectome Project-to include full-term neonates (n = 278), and preterm neonates scanned at term-equivalent age (TEA; n = 72), or before TEA (n = 70), and the Human Connectome Project for a reference adult group (n = 176). Although different from adults', the small-world architecture was developed in full-term neonates at birth. The key novel finding was that premature neonates before TEA showed dramatic underdevelopment of small-world organization and regional communication in 9/11 networks, with disruption in 32% of brain nodes. The somatomotor and dorsal attention networks carry the largest spatial effect, and visual network the smallest. Significant prematurity-related disruption of small-world architecture and reduced efficiency of regional communication in networks related to high-order cognition, including language, persisted at TEA. Critically, at full-term birth or by TEA, infants exhibited functional small-world architecture, which facilitates differentiated and integrated neural processes that support complex cognition. Conversely, this brain infrastructure is significantly underdeveloped before infants reach TEA. These findings improve understanding of the ontogeny of functional small-world architecture and efficiency of neural communication, and of their disruption by premature birth.

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

A Taxonomy of Neuroscientific Strategies Based on Interaction Orders

In recent decades, neuroscience has advanced with increasingly sophisticated strategies for recording and analysing brain activity, enabling detailed investigations into the roles of functional units, such as individual neurons, brain regions and their interactions. Recently, new strategies for the investigation of cognitive functions regard the study of higher order interactions-that is, the interactions involving more than two brain regions or neurons. Although methods focusing on individual units and their interactions at various levels offer valuable and often complementary insights, each approach comes with its own set of limitations. In this context, a conceptual map to categorize and locate diverse strategies could be crucial to orient researchers and guide future research directions. To this end, we define the spectrum of orders of interaction, namely, a framework that categorizes the interactions among neurons or brain regions based on the number of elements involved in these interactions. We use a simulation of a toy model and a few case studies to demonstrate the utility and the challenges of the exploration of the spectrum. We conclude by proposing future research directions aimed at enhancing our understanding of brain function and cognition through a more nuanced methodological framework.

Therapeutic exploration potential of adenosine receptor antagonists through pharmacophore ligand-based modelling and pharmacokinetics studies against Parkinson disease

Parkinson's Disease (PD) is a neurodegenerative disorder that primarily affects persons aged 65 and older. It leads to a decline in motor function as a result of the buildup of abnormal protein deposits called Lewy bodies in the brain. Existing therapies exhibit restricted effectiveness and undesirable side effects. The objective was to discover potent medications that have demonstrated effectiveness in treating PD by employing computational methods. This work employed a comprehensive approach to evaluate 70 pyrimidine derivatives for their potential in treating PD. The evaluation involved the use of QSAR modelling, virtual screening, molecular docking, MD simulation, ADMET analysis, and antagonist inhibitor creation. Six compounds passed all the evaluation, while for MD simulation, carried out between the compound with best docking score and the reference drug, compound 57 was discovered to possess more stability compared to theophylline which is the reference drug, and it functions as a primary inhibitor of the adenosine A2A receptor. Additionally, the study determined that compound 57 satisfied the Rule of Five (Ro5) standards and exhibited robust physicochemical characteristics. The compound exhibited moderate to low levels of hERG inhibition. The conducted investigations highlighted promising outcomes of natural compounds that can orient towards the rational development of effective Parkinson's disease inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00305-9.

Atypical brain network topology of the triple network and cortico-subcortical network in autism spectrum disorder

The default mode network (DMN), salience network (SN), and central executive control network (CEN) form the well-known triple network, providing a framework for understanding various neurodevelopmental and psychiatric disorders. However, the topology of this network remains unclear in autism spectrum disorder (ASD). To gain a more profound understanding of ASD, we explored the topology of the triple network in ASD. Additionally, the striatum and thalamus are pivotal centres of information transmission within the brain, and the realization of various brain functions requires the coordination of cortical and subcortical structures. Therefore, we also investigated the topology of the cortico-subcortical network in ASD, which consists of the DMN, SN, CEN, striatum, and thalamus. Resting-state functional magnetic resonance imaging data on 208 ASD patients and 278 typically developing (TD) controls (8-18 years old) were obtained from the Autism Brain Imaging Data Exchange database. We performed graph theory analysis on the triple network and the cortico-subcortical network. The results showed that the triple network's clustering coefficient, lambda, and network local efficiency values were significantly lower in ASD, and the nodal degree and efficiency of the medial prefrontal cortex also decreased. For the cortico-subcortical network, the sigma, clustering coefficient, gamma, and network local efficiency showed the same reduction, and the altered clustering coefficient negatively correlated with ASD manifestations. In addition, the interaction between the DMN and CEN was more robust in ASD patients. These findings enhance our understanding of ASD and suggest that subcortical structures should be more considered in future ASD related studies.

Topological features of brain functional networks are reorganized during chronic tinnitus: A graph-theoretical study

This study aimed to investigate the topological properties of brain functional networks in patients with tinnitus of varying durations. A total of 51 tinnitus patients (divided into recent-onset tinnitus (ROT) and persistent tinnitus (PT) groups) and 27 healthy controls (HC) were recruited. All participants underwent resting-state functional MRI and audiological assessments. Graph theory was used to examine brain network topology. The results showed that the ROT group exhibited lower clustering coefficient, gamma, sigma and local efficiency compared to both the HC and PT groups (all P < 0.05). Significant reductions in nodal clustering coefficient and local efficiency were found in the left caudate nucleus and left olfactory cortex, while increased nodal centralities were observed in the left orbital middle frontal gyrus and left postcentral gyrus in ROT patients (all P < 0.05). Furthermore, the ROT group had decreased nodal clustering in the right lenticular putamen and reduced nodal efficiency in the left olfactory cortex compared to both PT patients and HCs (all P < 0.05). Additionally, PT patients showed weaker functional connectivity between the subcortical and occipital lobe modules, as well as between the prefrontal and intra-frontal modules, compared to ROT patients. However, intra-module connectivity in the subcortical module was stronger in PT patients than in HCs. These findings suggest that recent-onset tinnitus is associated with alterations in brain network topology, but many of these changes are restored with the persistence of tinnitus.

Altered Connectome Topology in Newborns at Risk for Cognitive Developmental Delay: A Cross-Etiologic Study

The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD. Our study aim is to address this knowledge gap by using a multi-etiologic neonatal dataset to reveal potential commonalities and distinctions in the structural brain connectome and their associations with DD. We used diffusion tensor imaging of 187 newborns (42 controls, 51 with CHD, 51 with prematurity, and 43 with SBA). Structural weighted connectomes were constructed using constrained spherical deconvolution-based probabilistic tractography and the Edinburgh Neonatal Atlas. Assessment of brain network topology encompassed the analysis of global graph features, network-based statistics, and low-dimensional representation of global and local graph features. The Cognitive Composite Score of the Bayley scales of Infant and Toddler Development 3rd edition was used as outcome measure at corrected 2 years for the preterm born individuals and SBA patients, and at 1 year for the healthy controls and CHD. We detected differences in the connectomic structure of newborns across the four groups after visualizing the connectomes in a two-dimensional space defined by network integration and segregation. Further, analysis of covariance analyses revealed differences in global efficiency (p < 0.0001), modularity (p < 0.0001), mean rich club coefficient (p = 0.017), and small-worldness (p = 0.016) between groups after adjustment for postmenstrual age at scan and gestational age at birth. Moreover, small-worldness was significantly associated with poorer cognitive outcome, specifically in the CHD cohort (r = -0.41, p = 0.005). Our cross-etiologic study identified divergent structural brain connectome profiles linked to deviations from optimal network integration and segregation in newborns at risk for DD. Small-worldness emerges as a key feature, associating with early cognitive outcomes, especially within the CHD cohort, emphasizing small-worldness' crucial role in shaping neurodevelopmental trajectories. Neonatal connectomic alterations associated with DD may serve as a marker identifying newborns at-risk for DD and provide early therapeutic interventions. Trial Registration: ClinicalTrials.gov identifier: NCT00313946.

Weak brain function and anxiety-related loop in harm-avoidance personality: A resting-state functional magnetic resonance imaging study

BACKGROUND

Personality is a unique and relatively stable psychological concept that defines individual human beings. It strongly influences long-term behavioral styles such as emotional expression. This study aims to elucidate the brain functional underpinning behind personality.

METHODS

A total of 97 young subjects were included. All subjects completed personality, emotion, and cognition scales, and resting-state functional magnetic resonance imaging scan. All subjects were divided into subtypes of harm avoidance (HA) and reward dependence (RD) by clustering analysis. Graph theory analysis and network-based analysis were used to explore the brain functional configurations of personalities.

RESULTS

HA subjects showed lower network metrics (P = 0.018) and node metrics (P < 0.009). A negative component network was observed in HA subjects (P < 0.001). Functional topology metrics were negatively correlated with the HA score. The amygdala-IPG functional connectivity mediated the positive correlation between personality HA and state anxiety.

CONCLUSION

Personality HA is associated with decreased functional configuration, which could influence emotion by downregulating amygdala-IPG coupling. These findings provide insight into how the brain shapes personality and related emotions.

Brain-scale theta band functional connectome as signature of slow breathing and breath-hold phases

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain have been investigated during attentive breathing at 2 cycles per minute (cpm). The study presents its novelty through three main aspects. First, it explores the complex breathing circuitry beyond the brain stem, specifically examining how higher brain regions interact with respiratory cycles. Second, unlike previous studies that treated respiratory phases as a singular phenomenon, this research analyses inhalation, exhalation, and breath-holds separately, providing a deeper understanding of their individual dynamics and FC in the brain. Finally, the breathing protocol is designed to include inhale-hold and exhale-hold sessions alongside symmetric breathing, allowing for testing on healthy subjects rather than specialized cohorts, which were used in earlier studies. An experimental protocol involving equal durations of inhale, inhale-hold, exhale, and exhale-hold conditions, synchronized to a visual metronome, was designed and administered to 20 healthy subjects (9 females and 11 males, age: 32.0 ± 9.5 years (mean ± SD)). EEG data were collected during these sessions using the 64-channel eego™ mylab system from ANT Neuro. Further, FC was estimated for all possible pairs of EEG time series data, for 7 EEG bands. Feature selection using a genetic algorithm (GA) was performed to identify a subset of functional connections that would best distinguish the inhale, inhale-hold, exhale, and exhale-hold phases using a random committee classifier. The best accuracy of 95.056% was obtained when 403 theta-band functional connections were fed as input to the classifier, highlighting the efficacy of the theta-band functional connectome in distinguishing these phases of the respiratory cycle. This functional network was further characterized using graph measures, and observations illustrated a statistically significant difference in the efficiency of information exchange through the network during different respiratory phases.

Cortical Morphological Networks Differ Between Gyri and Sulci

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

Relationship between δ-catenin expression and whole-brain small-world network in breast cancer patients before chemotherapy

Our study aimed to investigate the relationship between δ-catenin expression and whole-brain small-world network in breast cancer patients before chemotherapy using rs-fMRI. The study was based on the hypothesis that different δ-catenin expression levels correspond to distinct brain imaging characteristics. A total of 105 pathologically confirmed breast cancer patients were collected and categorized into high δ-catenin expression (DH, 52 cases) and low expression (DL, 53 cases) groups. Additionally, 36 age-matched healthy women were enrolled as a healthy control group (HC). The results demonstrated differences in several network topology attributes among the three groups. Furthermore, in addition to differences in nodal efficiency, betweenness, and degree centrality metrics observed between the patient groups and HCs across multiple brain regions, significant alterations were also identified between the DL and DH groups, particularly in the supramarginal gyrus and inferior frontal gyrus. Correlation analysis revealed associations between cognitive and memory-related brain regions, such as the caudate nucleus and frontal lobe, and scores on cognitive and verbal memory scales (all P < 0.05). This study concludes that high and low expression levels of δ-catenin in breast cancer patients are associated with distinct whole-brain network topology patterns, and that these differences in network segregation and integration functions are associated with alterations in cognition and verbal memory.

Aberrant network topological structure of sensorimotor superficial white-matter system in major depressive disorder

White-matter tracts play a pivotal role in transmitting sensory and motor information, facilitating interhemispheric communication and integrating different brain regions. Meanwhile, sensorimotor disturbance is a common symptom in patients with major depressive disorder (MDD). However, the role of aberrant sensorimotor white-matter system in MDD remains largely unknown. Herein, we investigated the topological structure alterations of white-matter morphological brain networks in 233 MDD patients versus 257 matched healthy controls (HCs) from the DIRECT consortium. White-matter networks were derived from magnetic resonance imaging (MRI) data by combining voxel-based morphometry (VBM) and three-dimensional discrete wavelet transform (3D-DWT) approaches. Support vector machine (SVM) analysis was performed to discriminate MDD patients from HCs. The results indicated that the network topological changes in node degree, node efficiency, and node betweenness were mainly located in the sensorimotor superficial white-matter system in MDD. Using network nodal topological properties as classification features, the SVM model could effectively distinguish MDD patients from HCs. These findings provide new evidence to highlight the importance of the sensorimotor system in brain mechanisms underlying MDD from a new perspective of white-matter morphological network.

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Fluid intelligence is an individual's innate ability to cope with complex situations and is gradually reduced across adults aging. The realization of fluid intelligence requires the simultaneous activity of multiple brain regions and depends on the structural connection of distributed brain regions. Uncovering the structural features of brain connections associated with fluid intelligence decline will provide reference for the development of intervention and treatment programs for cognitive decline. Using structural magnetic resonance imaging data of 454 healthy participants (18-87 years) from the Cam-CAN dataset, we constructed structural similarity network for each participant and calculated the node degree. Spearman correlation analysis showed that age was positively correlated with degree centrality in the cingulate cortex, left insula and subcortical regions, while negatively correlated with that in the orbito-frontal cortex, right middle temporal and precentral regions. Partial least squares (PLS) regression showed that the first PLS components explained 32 % (second PLS component: 20 %, p perm < 0.001) of the variance in fluid intelligence. Additionally, the degree centralities of anterior insula, supplementary motor area, prefrontal, orbito-frontal and anterior cingulate cortices, which are critical nodes of the multiple-demand network (MDN), were linked to fluid intelligence. Increased degree centrality in anterior cingulate cortex and left insula partially mediated age-related decline in fluid intelligence. Collectively, these findings suggest that the structural stability of MDN might contribute to the maintenance of fluid intelligence.

MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

Brain network analysis of benign childhood epilepsy with centrotemporal spikes: With versus without interictal spikes

Brain networks provided powerful tools for the analysis and diagnosis of epilepsy. This paper performed a pairwise comparative analysis on the brain networks of Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS): spike group (spike), non‐spike group (non‐spike), and control group (control). In this study, fragments with and without interictal spikes in electroencephalograms of 13 BECTS children during non‐rapid eye movement sleep stage I (NREMI) were selected to construct dynamic brain function networks to explore the functional connectivity (FC). Graph theory and statistical analysis were exploited to investigate changes in FC across different brain regions in different frequency bands. From this study, we can draw the following conclusions: (1) Both spike and non‐spike have lower energy in each brain region on the γ band. (2) With the increase of the frequency band, the FC strength of spike, non‐spike and control groups are all weakened. (3) Spikes are correlated with brain network efficiency and the small‐world property. (4) Spikes increase the FC of temporal, parietal and occipital regions except in the γ band and the absence of spikes weakens the FC of the entire brain region.

Abnormal topological structure of structural covariance networks based on fractal dimension in noise induced hearing loss

The topological attributes of structural covariance networks (SCNs) based on fractal dimension (FD) and changes in brain network connectivity were investigated using graph theory and network-based statistics (NBS) in patients with noise-induced hearing loss (NIHL). High-resolution 3D T1 images of 40 patients with NIHL and 38 healthy controls (HCs) were analyzed. FD-based Pearson correlation coefficients were calculated and converted to Fisher's Z to construct the SCNs. Topological attributes and network hubs were calculated using the graph theory. Topological measures between groups were compared using nonparametric permutation tests. Abnormal connection networks were identified using NBS analysis. The NIHL group showed a significantly increased normalized clustering coefficient, normalized characteristic path length, and decreased nodal efficiency of the right medial orbitofrontal gyrus. Additionally, the network hubs based on betweenness centrality and degree centrality were both the right transverse temporal gyrus and left parahippocampal gyrus in the NIHL group. The NBS analysis revealed two subnetworks with abnormal connections. The subnetwork with enhanced connections was mainly distributed in the default mode, frontoparietal, dorsal attention, and somatomotor networks, whereas the subnetwork with reduced connections was mainly distributed in the limbic, visual, default mode, and auditory networks. These findings demonstrate the abnormal topological structure of FD-based SCNs in patients with NIHL, which may contribute to understand the complex mechanisms of brain damage at the network level, providing a new theoretical basis for neuropathological mechanisms.

Exploring the pathophysiology of restless leg syndrome: focus on white matter structure and function

BACKGROUND

Restless legs syndrome (RLS) is a sleep disorder characterized by an irresistible urge to move the legs, with pathogenesis involving genetic, environmental, and neurobiological factors. Recent advancements in imaging techniques have provided valuable insights into the pathophysiological mechanisms of RLS.

OBJECTIVE

To synthesize recent research on white matter fiber alterations in RLS and their role in disease pathology.

MATERIALS AND METHODS

This review synthesized recent research on RLS, focusing on neuroimaging findings, particularly white matter fiber alterations, and their implications for disease mechanisms. Studies involving structural and functional MRI were analyzed.

RESULTS

Imaging studies suggested that RLS was associated with white matter integrity changes, affecting areas linked to sensory and motor control. These alterations may reflect disruptions in central nervous system pathways regulating movement.

CONCLUSION

White matter changes provide valuable insights into the pathophysiology of RLS, enhancing our understanding of the disorder and potentially guiding future therapeutic strategies.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease

Objective

Explore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD).

Materials and methods

Utilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups.

Results

In Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group's brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks.

Conclusion

This study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis

OBJECTIVES

To investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT).

METHODS

21 stroke patients and 20 healthy subjects were enrolled, with the stroke patients randomized to either control group (n = 11) or robot group (n = 10), and resting-state functional magnetic resonance imaging data were collected. The global network metrics were obtained using graph theory analysis and compared between stroke patients and healthy subjects, and the effect of the RAGT on the whole-brain networks was explored.

RESULTS

Compared to healthy subjects, area under the curve (AUC) for small-worldness (σ), clustering coefficient (Cp), global efficiency (Eg) and mean local efficiency (Eloc) were significantly lower in stroke patients, whereas AUC for characteristic path length (Lp) were significantly higher. Compared with the control group, patients in robot group showed significant improvement in lower limb motor function, balance function and walking function after intervention, with a significant reduction in the AUC of Cp. Moreover, the improvement of walking function was positively correlated with the changes of AUC of σ and Eg, and negatively correlated with the changes of AUC of Cp.

CONCLUSIONS

Small-worldness and network efficiency were significantly reduced after stroke, whereas RAGT decreased characteristic path length and promoted normalization of the whole-brain network, and this change was associated with improvement in walking function. Our findings reveal the mechanism by which RAGT regulates network reorganization and neuroplasticity after stroke.

Trajectories of human brain functional connectome maturation across the birth transition

Understanding the sequence and timing of brain functional network development at the beginning of human life is critically important from both normative and clinical perspectives. Yet, we presently lack rigorous examination of the longitudinal emergence of human brain functional networks over the birth transition. Leveraging a large, longitudinal perinatal functional magnetic resonance imaging (fMRI) data set, this study models developmental trajectories of brain functional networks spanning 25 to 55 weeks of post-conceptual gestational age (GA). The final sample includes 126 fetal scans (GA = 31.36 ± 3.83 weeks) and 58 infant scans (GA = 48.17 ± 3.73 weeks) from 140 unique subjects. In this study, we document the developmental changes of resting-state functional connectivity (RSFC) over the birth transition, evident at both network and graph levels. We observe that growth patterns are regionally specific, with some areas showing minimal RSFC changes, while others exhibit a dramatic increase at birth. Examples with birth-triggered dramatic change include RSFC within the subcortical network, within the superior frontal network, within the occipital-cerebellum joint network, as well as the cross-hemisphere RSFC between the bilateral sensorimotor networks and between the bilateral temporal network. Our graph analysis further emphasized the subcortical network as the only region of the brain exhibiting a significant increase in local efficiency around birth, while a concomitant gradual increase was found in global efficiency in sensorimotor and parietal-frontal regions throughout the fetal to neonatal period. This work unveils fundamental aspects of early brain development and lays the foundation for future work on the influence of environmental factors on this process.

A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI

BACKGROUND

There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods.

NEW METHODS

We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation.

RESULTS

The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data.

COMPARISON WITH EXISTING METHODS

Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC.

CONCLUSION

We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.

Inverse stochastic resonance in adaptive small-world neural networks

Inverse stochastic resonance (ISR) is a counterintuitive phenomenon where noise reduces the oscillation frequency of an oscillator to a minimum occurring at an intermediate noise intensity, and sometimes even to the complete absence of oscillations. In neuroscience, ISR was first experimentally verified with cerebellar Purkinje neurons [Buchin et al., PLOS Comput. Biol. 12, e1005000 (2016)]. These experiments showed that ISR enables a locally optimal information transfer between the input and output spike train of neurons. Subsequent studies have further demonstrated the efficiency of information processing and transfer in neural networks with small-world network topology. We have conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bi-metastable regime consisting of a metastable fixed point and a metastable limit cycle. Our results show that the degree of ISR is highly dependent on the value of the FHN model's timescale separation parameter ε. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter P or homeostatic structural plasticity (HSP) with rewiring frequency F. We demonstrate that both STDP and HSP amplify the effect of ISR when ε lies within the bi-stability region of FHN neurons. Specifically, at larger values of ε within the bi-stability regime, higher rewiring frequencies F are observed to enhance ISR at intermediate (weak) synaptic noise intensities, while values of P consistent with depression-domination (potentiation-domination) consistently enhance (deteriorate) ISR. Moreover, although STDP and HSP control parameters may jointly enhance ISR, P has a greater impact on improving ISR compared to F. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize local information transfer between input and output spike trains in neuromorphic systems and prompt venues for experiments in neural networks.

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10-4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10-3; p = 7.67 × 10-4) and right insula (p = 0.02; p = 8.99 × 10-6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10-5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = -0.36, p = 8.45 × 10-3) and small-worldness (r = -0.35, p = 9.89 × 10-3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

Graph Metrics Reveal Brain Network Topological Property in Neuropathic Pain Patients: A Systematic Review

Neuropathic pain (NP) is a common and persistent disease that leads to immense suffering and serious social burden. Incomplete understanding of the underlying neural basis makes it difficult to achieve significant breakthroughs in the treatment of NP. We aimed to review the functional and structural brain topological properties in patients with NP and consider how graph measures reveal potential mechanisms and are applied to clinical practice. Related studies were searched in PubMed and Web of Science databases. Topological property changes in patients with NP, including small-worldness, functional separation, integration, and centrality metrics, were reviewed. The findings suggest that NP was characterized by retained but declined small-worldness, indicating an insidious imbalance between network integration and segregation. The global-level measures revealed decreased global and local efficiency in the NP, implying decreased information transfer efficiency for both long- and short-range connections. Altered nodal centrality measures involve various brain regions, mostly those associated with pain, cognition, and emotion. Graph theory is a powerful tool for identifying topological properties of patients with NP. These specific brain changes in patients with NP are very helpful in revealing the potential mechanisms of NP, developing new treatment strategies, and evaluating the efficacy and prognosis of NP.

Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder

BACKGROUND

Although brain structural covariance network (SCN) abnormalities have been associated with suicidal thoughts and behaviors (STBs) in individuals with major depressive disorder (MDD), previous studies have reported inconsistent findings based on small sample sizes, and underlying transcriptional patterns remain poorly understood.

METHODS

Using a multicenter magnetic resonance imaging dataset including 218 MDD patients with STBs, 230 MDD patients without STBs, and 263 healthy control participants, we established individualized SCNs based on regional morphometric measures and assessed network topological metrics using graph theoretical analysis. Machine learning methods were applied to explore and compare the diagnostic value of morphometric and topological features in identifying MDD and STBs at the individual level. Brainwide relationships between STBs-related connectomic alterations and gene expression were examined using partial least squares regression.

RESULTS

Group comparisons revealed that SCN topological deficits associated with STBs were identified in the prefrontal, anterior cingulate, and lateral temporal cortices. Combining morphometric and topological features allowed for individual-level characterization of MDD and STBs. Topological features made a greater contribution to distinguishing between patients with and without STBs. STBs-related connectomic alterations were spatially correlated with the expression of genes enriched for cellular metabolism and synaptic signaling.

CONCLUSIONS

These findings revealed robust brain structural deficits at the network level, highlighting the importance of SCN topological measures in characterizing individual suicidality and demonstrating its linkage to molecular function and cell types, providing novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of suicide.

Differential network knockoff filter with application to brain connectivity analysis

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease

BACKGROUND

Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear.

OBJECTIVE

The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD).

METHODS

Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs.

RESULTS

A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics.

CONCLUSIONS

These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.