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

Multi-modality connectome-based predictive modeling of individualized compulsions in obsessive-compulsive disorder

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

A major challenge in neuroscience is to determine the rules that govern the structural and functional interdependence of cellular neural circuits and networks in the brain [1]. This challenge is a difficult one given the fact that brain networks are complex networks with emergent behaviors and properties. Understanding the underlying mechanisms that shape the intimate relationship between structure and function will help us better understand how functional brain states emerge from their underlying structural substrate, in addition to providing us with new insights into how the disruption of this function-structure interdependence will affect brain function. It is clear that structure and function are interrelated and that specific brain regions and even specific brain networks have well defined structural topologies, determined by the very specific neuron types that they recruit and the specific connections that these neurons project [2]. This fundamental property plays a major role in determining functions that are characteristic of certain brain networks. However, it is less clear how experience and exposure to external cues can lead to the emergence of fast and flexibly reconfigured functional networks that are bound by a well defined structural substrate, and how this structural substrate in turn, but at a slower time scale, may reshape functional networks. Even more intriguing is the fact that such functional networks, while highly dynamic, play a major role in remodeling, associating and storing information that is presented by external cues for time periods that can sometimes extend over an individual’s lifetime; in short they underlie learning and memory. Despite considerable advances in experimental techniques and computational paradigms, our understanding of this problem is still incomplete. We have begun to address this issue by developing a model that is composed of integrate-and-fire excitatory and inhibitory neurons that are connected to external inputs and project recurrent connections among themselves. It is based on the hypothesis that functional network dynamics are bound by three main rules: (1) Structural network connectivity is predetermined to a high extent and is a prerequisite for functional connectivity to take place between two neurons. (2) Synaptic plasticity that the network undergoes is Hebbian-like and weight change (following potentiation or depression) is proportional to the number of afferents of a certain neuron. (3) Heterosynaptic plasticity takes place to help regulate the neuronal firing rate. Our results show that structure and synaptic plasticity determine function at large and sets boundaries to the information capacity of the neuronal network. We also tested different network sizes and different physical connectivity constraints in order to determine how the emergent functional dynamics and network capacity scale with the network size. Here we present and discuss our data and results to date with this model. We aim, through this theoretical study and in the long run, to come full circle to a better and more integrated understanding of the intricate interdependence between structure and function in brain networks as shaped by learning and memory. Unraveling these dynamics will directly contribute to our understanding of how different parts of the mammalian brain function in health, how they fail in disease, and how best to guide nervous system related therapies.

Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major role in the communication within the brain. So far, for the functional brain network, only the average length of the shortest paths has been analyzed. In this article, we propose to construct the union of shortest path trees (USPT) as a new topology for the functional brain network. The minimum spanning tree, which has been successful in a lot of recent studies to comprise important features of the functional brain network, is always included in the USPT. After interpreting the link weights of the functional brain network as communication probabilities, the USPT of this network can be uniquely defined. Using data from magnetoencephalography, we applied the USPT as a method to find differences in the network topology of multiple sclerosis patients and healthy controls. The new concept of the USPT of the functional brain network also allows interesting interpretations and may represent the highways of the brain.

Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, and Neuroimaging Advancing the Field of Neurology.

For decades, task-based functional magnetic resonance imaging (tfMRI) has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a variety of brain network analysis methods for tfMRI data, including the general linear model (GLM), independent component analysis (ICA) and sparse representation methods. However, these shallow models are limited in faithful reconstruction and modeling of the hierarchical and temporal structures of brain networks, as demonstrated in more and more studies. Recently, recurrent neural networks (RNNs) exhibit great ability of modeling hierarchical and temporal dependency features in the machine learning field, which might be suitable for tfMRI data modeling. To explore such possible advantages of RNNs for tfMRI data, we propose a novel framework of deep recurrent neural network (DRNN) to model the functional brain networks for tfMRI data. Experimental results on the motor task tfMRI data of Human Connectome Project 900 subjects data release demonstrated that the proposed DRNN can not only faithfully reconstruct functional brain networks, but also identify more meaningful brain networks with multiple time scales which are overlooked by traditional shallow models. In general, this work provides an effective and powerful approach to identifying functional brain networks of multiple time scales from tfMRI data.

Chronic pain is associated with persistent alterations in brain structure and function. However, existing research has not fully explored the relationship between brain network topological properties and clinical symptoms in patients with chronic low back-related leg pain (cLBLP). In this study, we collected resting-state functional and structural magnetic resonance imaging data, along with clinical symptom evaluation data, from 32 cLBLP patients and 31 healthy controls. A large-scale complex network analysis was conducted to evaluate the global and nodal topological properties of functional and structural brain networks. Statistical analyses were performed to determine the associations between network properties and clinical variables. The results showed significant alterations in both global and nodal topological properties of functional and structural brain networks in cLBLP patients compared to healthy controls. Additionally, a direct correlation was found between structural network properties and spatial discrimination ability, measured by two-point tactile discrimination values, while no significant association was observed between functional connectivity and spatial discrimination. This study demonstrates that cLBLP patients exbibit a decreased local efficiency of functional connectivity network and increased compensatory global efficiency of structural connectivity network. Notably, alterations in the structural connectome, rather than the functional connectome, play a more significant role in deterioration of foot tactile spatial acuity in cLBLP patients.Trial registration: This trial was registered in the Chinese Clinical Trial Registry with the registration number ChiCTR2200055321 on 2022-01-06.

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Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain.

<sec><st>Introduction</st> Huntington’s disease (HD) is a complex and progressive disease, and long-term, individually tailored multidisciplinary healthcare is important to maintain patients’ functional ability and quality of life. In order to plan and improve provision of healthcare services it is essential to understand the met and unmet healthcare needs of individuals and families with HD. While previous research has described utilisation of healthcare services in HD in Europe, studies that systematically examine the health care needs of these patients are needed in order to advance our understanding. </sec> <sec><st>Aims</st> To identify met and unmet health services needs among HD patients and assess the relationship between unmet needs, functional disability, and health related quality of life. </sec> <sec><st>Methods</st> To date we included 80 patients with clinical diagnosis of HD residing in the South-East region of Norway. Measurements: Needs and Provision Complexity Scale (NPCS) (rater and patient version), a scale specifically developed to identify healthcare needs in patients with complex neurological disabilities. An algorithm is used to identify unmet healthcare needs. Additionally, the UHDRS Total Functional Capacity and Functional assessment scales and the EQ-5D a self-report measurement on quality of life scale are used. Demographic and patient characteristics are recorded. </sec> <sec><st>Results</st> We are currently analysing the collected data, and preliminary results will be presented at the conference. </sec> <sec><st>Conclusion</st> Modelling of met and unmet healthcare needs and it’s relation to functional disability and quality of life across all HD disease stages may provide important information when planning appropriate long term multidisciplinary healthcare services for HD patients and families. </sec>

In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.

Unprecedented 4D spatiotemporal infant regional cerebral blood flow framework and region-specific physiology–function coupling across infancy were elucidated, highlighting strong physiology–function coupling specifically at the default-mode network to meet extraneuronal metabolic demand for network emergence.

Facial palsy therapies based on cortical plasticity are in development, but facial synkinesis progress is limited. Studying neural plasticity characteristics, especially network organization and its constitutive elements (nodes/edges), is the key to overcome the bottleneck. We studied 55 participants (33 facial synkinesis patients, 22 healthy controls) with clinical assessments, functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI). We analyzed rich-club organization and metrics of structural brain networks (rich-club coefficients, strength, degree, density, and efficiency). Functional brain network metrics, including functional connectivity and its coupling with the structural network, were also computed. Patients displayed reduced strength and density of rich-club nodes and edges, as well as decreased global efficiency. All nodes exhibited decreased nodal efficiency in patients. Patients had significantly increased functional connectivity and decreased structural-functional coupling strength in rich-club nodes, rich-club edges, and feeder edges. Our study indicates that facial synkinesis patients have weakened structural connections but enhanced functional transmission from rich-club nodes. The loss of connections and efficiency in structural network may trigger compensatory increases in functional connectivity of rich-club nodes. Two potential biomarkers, rich-club edge density and structural-functional coupling strength, may serve as indicators of disease outcome. These findings provide valuable insights into synkinesis mechanisms and offer potential targets for cortical intervention.

Neuroimaging studies have identified alterations in functional connectivity between specific brain regions in patients with unilateral hearing loss (UHL) and different influence of the side of UHL on neural plasticity. However, little is known about changes of whole-brain functional networks in patients with UHL and whether differences exist in topological organization between right-sided UHL (RUHL) and left-sided UHL (LUHL). To address this issue, we employed resting-state fMRI (rs-fMRI) and graph-theoretical approaches to investigate the topological alterations of brain functional connectomes in patients with RUHL and LUHL. Data from 44 patients with UHL (including 22 RUHL patients and 22 LUHL patients) and 37 healthy control subjects (HCs) were collected. Functional brain networks were constructed for each participant, following by graph-theoretical network analyses at connectional and global (e.g., small-worldness) levels. The correlations between brain network topologies and clinical variables were further studied. Using network-based analysis, we found a subnetwork in the visual cortex which had significantly lower connectivity strength in patients with RUHL as compared to HCs. At global level, all participants showed small-world architecture in functional brain networks, however, significantly lower normalized clustering coefficient and small-worldness were observed in patients with RUHL than in HCs. Moreover, these abnormal network metrics were demonstrated to be correlated with the clinical variables and cognitive performance of patients with RUHL. Notably, no significant alterations in the functional brain networks were found in patients with LUHL. Our findings demonstrate that RUHL (rather than LUHL) is accompanied with aberrant topological organization of the functional brain connectome, indicating different pathophysiological mechanisms between RUHL and LUHL from a viewpoint of network topology.

Exploring topological properties of human brain network has become an exciting topic in neuroscience research. Large-scale structural and functional brain networks both exhibit a small-world topology, which is evidence for global and local parallel information processing. Meanwhile, resting state networks (RSNs) underlying specific biological functions have provided insights into how intrinsic functional architecture influences cognitive and perceptual information processing. However, topological properties of single RSNs remain poorly understood. Here, we have two hypotheses: i) each RSN also has optimized small-world architecture; ii) topological properties of RSNs related to perceptual and higher cognitive processes are different. To test these hypotheses, we investigated the topological properties of the default-mode, dorsal attention, central-executive, somato-motor, visual and auditory networks derived from resting-state functional magnetic resonance imaging (fMRI). We found small-world topology in each RSN. Furthermore, small-world properties of cognitive networks were higher than those of perceptual networks. Our findings are the first to demonstrate a topological fractionation between perceptual and higher cognitive networks. Our approach may be useful for clinical research, especially for diseases that show selective abnormal connectivity in specific brain networks.