Functional Networks Research Articles

Nonlinear subspace clustering by functional link neural networks

Nonlinear subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. While this approach demonstrates impressive outcomes, it involves a balance between effectiveness and computational cost. In this study, we employ a functional link neural network to transform data samples into a nonlinear domain. Subsequently, we acquire a self-representation matrix through a learning mechanism that builds upon the mapped samples. As the functional link neural network is a single-layer neural network, our proposed method achieves high computational efficiency while ensuring desirable clustering performance. By incorporating the local similarity regularization to enhance the grouping effect, our proposed method further improves the quality of the clustering results. We name our method as Functional Link Neural Network Subspace Clustering (FLNNSC). Furthermore, we propose a convex combination subspace clustering scheme that combines a linear subspace clustering method with the functional link neural network subspace clustering approach. This combination method is named as Convex Combination Subspace Clustering (CCSC), which allows for a dynamic balance between linear and nonlinear representations. Extensive experiments conducted on four widely used datasets, including Extended Yale B, USPS, COIL20, and ORL, demonstrate that both FLNNSC and CCSC outperform several state-of-art subspace clustering methods in terms of clustering accuracy. Our affinity graph experiments reveal that FLNNSC exhibits clear block diagonal structures. We provide recommendations for hyperparameters in FLNNSC by performing a parameter sensitivity analysis, and empirically verify the convergence of FLNNSC. Additionally, we show that FLNNSC has a lower computational cost compared to two high-performing methods.

Associations between parental psychopathology and youth functional emotion regulation brain networks

Parental mental health is associated with children’s emotion regulation (ER) and risk for psychopathology. The relationship between parental psychopathology and children’s functional ER networks and whether connectivity patterns mediate the relationship between parent and youth psychopathology remains unexplored. Using resting-state functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study (N = 4202, mean age = 10.0) and a multilevel approach, we analyzed the relationship between self-reported parental psychopathology and their offsprings’ connectivity of four ER networks, as well as associations with self-reported youth psychopathology at a 3-year follow-up. Parental internalizing and total problems were associated with 1) higher connectivity between a subcortical-cortical integrative and ventrolateral prefrontal cortical (PFC) network, 2) lower connectivity between dorsolateral and ventrolateral PFC networks involved in cognitive aspects of ER, and 3) lower connectivity within a subcortical ER network (β = -0.05-0.04). Parental externalizing and total problems were associated with lower connectivity within the integrative network (βext = -0.05; βtot = -0.04). Mediation analyses yielded direct effects of parental to youth psychopathology, but no mediation effect of ER network connectivity. Overall, our results show that ER network connectivity in youth is related to parental psychopathology, yet do not explain intergenerational transmission of psychopathology.

A network method to analyze compound extreme events: Risk enhancement relationship and trigger causal relationship in high voice traffic and high data throughput events

Based on ideas from event coincidence analysis (ECA), we propose a network analysis method to study compound extreme events at different geographical locations. Integrating network modeling into statistical correlation research allows us to analyze potential risk enhancement relationship and trigger causal relationship between these events. In this approach, we consider different geographical locations as nodes and construct a directed edge from node i to node j when event A at location i occurs synchronously before event B at location j. Precursor coincidence analysis quantifies the risk enhancement relationship between two types of extreme events, while trigger coincidence analysis quantifies the trigger causal relationship between two types of extreme events. A directed weighted network can be constructed based on statistical correlations between these events at different geographical locations. Further analysis of network topology characteristics extends traditional ECA in method and application. Herein, we construct the precursor functional network and the trigger functional network of high voice traffic and high data throughput to analyze potential risk enhancement and trigger causal relationships between these events at different base stations within a communication system.

A digital twin framework for anomaly detection in industrial robot system based on multiple physics-informed hybrid convolutional autoencoder

The robot number in industry is growing up rapidly. Building anomaly detection system for them can improve the security of these expensive devices. The article implements an anomaly detection framework based on digital twin, which are built by a hybrid convolutional autoencoder. The framework shares those neural network weight files as digital assets, users can use them to estimate the possible output from real input. It approximates the dynamic relationship between motion, current, temperature and vibration with hybrid convolution. Considering the limited generalization performance of direct data-driven methods in practical physical systems, this article introduces physical information methods to improve the constraint function of neural network. The influence of multiple physical fields on current is established by a unified neural network. Terminals detect anomaly with KL divergence between really current and estimated current. The article collects operational data from real robots and verifies it, and the experiment shows that the RMSE for current estimation is below 1.5 %, the F1-score in anomaly detection is over 98.23 %, false positive is below 1 %, false negative is below 1.7 %. The relevant technologies are gradually being promoted and applied in enterprises.

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging modalities using deep learning remains a challenging yet crucial research topic. The integration of structural and functional modalities is particularly important for the diagnosis of various brain disorders, where structural information plays a crucial role in diseases such as Alzheimer's, while functional imaging is more critical for disorders such as schizophrenia. However, the combination of functional and structural imaging modalities can provide a more comprehensive diagnosis. In this work, we present MultiViT, a novel diagnostic deep learning model that utilizes vision transformers and cross-attention mechanisms to effectively fuse information from 3D gray matter maps derived from structural MRI with functional network connectivity matrices obtained from functional MRI using the ICA algorithm. MultiViT achieves an AUC of 0.833, outperforming both our unimodal and multimodal baselines, enabling more accurate classification and diagnosis of schizophrenia. In addition, using vision transformer's unique attentional maps in combination with cross-attentional mechanisms and brain function information, we identify critical brain regions in 3D gray matter space associated with the characteristics of schizophrenia. Our research not only significantly improves the accuracy of AI-based automated imaging diagnostics for schizophrenia, but also pioneers a rational and advanced data fusion approach by replacing complex, high-dimensional fMRI information with functional network connectivity, integrating it with representative structural data from 3D gray matter images, and further providing interpretative biomarker localization in a 3D structural space.

In silico screening of phytochemicals against chromatin modifier, SETD7 for remodeling of the immunosuppressive tumor microenvironment in renal cancer.

The tumor microenvironment and immune evasion function in a complex cellular network profoundly challenge the clinical outcome of promising therapies. Our recently published study reported that the subset of genes upregulated in ccRCC due to H3K4me1 and DNA demethylation potentially leads to an immunosuppressive environment. Thus, modulating H3K4me1 chromatin modifier SETD7 with a natural inhibitor in combination with immunotherapy might improve the immune landscape for a better therapeutic outcome.The present study was conducted via virtual screening and MD simulation using compounds from the literature, IMPPAT, and SuperNatural database. The phytochemical IMPHY002979 showed better binding affinity and lower energy than the reported R-PFI-2 and cyproheptadine inhibitors. The phytochemicals interact with the SET domain through H-bonding, as confirmed by MD simulation and molecular interaction analysis. Further, the compound was assessed using ADME parameters and free energy estimation, showing better pharmacokinetic properties. Therefore, the non-accessibility of the histone methyltransferase activity domain of SET7 with IMPHY002979 can downregulate H3K4me1 and, thereby, the expression of genes potentially responsible for immunosuppressive TME. Thus, patient stratification based on molecular markers for immunotherapy and combining epigenetic modulators with therapeutic drugs will improve the efficacy of immunotherapy in ccRCC.

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study

Disruption of macroscale functional network organisation in patients with frontotemporal dementia.

Neurodegenerative dementias have a profound impact on higher-order cognitive and behavioural functions. Investigating macroscale functional networks through cortical gradients provides valuable insights into the neurodegenerative dementia process and overall brain function. This approach allows for the exploration of unimodal-multimodal differentiation and the intricate interplay between functional brain networks. We applied cortical gradients mapping to resting-state functional MRI data of patients with frontotemporal dementia (FTD) (behavioural-bvFTD, non-fluent and semantic) and healthy controls. In healthy controls, the principal gradient maximally distinguished sensorimotor from default-mode network (DMN) and the secondary gradient visual from salience network (SN). In all FTD variants, the principal gradient's unimodal-multimodal differentiation was disrupted. The secondary gradient, however, showed widespread disruptions impacting the interactions among all networks specifically in bvFTD, while semantic and non-fluent variants exhibited more focal alterations in limbic and sensorimotor networks. Additionally, the visual network showed responsive and/or compensatory changes in all patients. Importantly, these disruptions extended beyond atrophy distribution and related to symptomatology in patients with bvFTD. In conclusion, optimal brain function requires networks to operate in a segregated yet collaborative manner. In FTD, our findings indicate a collapse and loss of differentiation between networks not solely explained by atrophy. These specific cortical gradients' fingerprints could serve as a functional signature for identifying early changes in neurodegenerative diseases or potential compensatory processes.

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images

Viruses are submicroscopic agents that can infect other lifeforms and use their hosts’ cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses are highly adaptable, resilient, and capable of causing severe complications in their hosts’ bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological threat both animal and plant species face. It is often challenging to promptly detect a virus in a host and accurately determine its type using manual examination techniques. However, computer-based automatic diagnosis methods, especially the ones using Transmission Electron Microscopy (TEM) images, have proven effective in instant virus identification. Using TEM images collected from a recent dataset, this article proposes a deep learning-based classification model to identify the virus type within those images. The methodology of this study includes two coherent image processing techniques to reduce the noise present in raw microscopy images and a functional Convolutional Neural Network (CNN) model for classification. Experimental results show that it can differentiate among 14 types of viruses with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable virus identification scheme subsidiary to the thorough diagnostic procedures.

Claustrum modulation drives altered prefrontal cortex dynamics and connectivity

This study delves into the claustrum’s role in modulating spontaneous and sensory-evoked network activity across cortical regions. Using mesoscale calcium imaging and Gi and Gq DREADDs in anesthetized mice, we show that decreasing claustral activity enhances prefrontal cortical activity, while activation reduces prefrontal cortical activity. This claustrum modulation also caused changes to the brain’s large-scale functional networks, emphasizing the claustrum’s ability to influence long-range functional connectivity in the cortex. Claustrum inhibition increased the local coupling between frontal cortex areas, but reduced the correlation between anterior medial regions and lateral/posterior regions, while also enhancing sensory-evoked responses in the visual cortex. These findings indicate the claustrum can participate in orchestrating neural communication across cortical regions through modulation of prefrontal cortical activity. These insights deepen our understanding of the claustrum’s impact on prefrontal connectivity, large-scale network dynamics, and sensory processing, positioning the claustrum as a key node modulating large-scale cortical dynamics.

The cell-type underpinnings of the human functional cortical connectome.

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cerebral cortex. The cortical sheet can be broadly divided into distinct networks, which are embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here using microarray data from the Allen Human Brain Atlas and single-nucleus RNA-sequencing data from multiple cortical territories, we demonstrate that cell-type distributions are spatially coupled to the functional organization of cortex, as estimated through functional magnetic resonance imaging. Differentially enriched cells follow the spatial topography of both functional gradients and associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on postmortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

Tumor microtubes: A new potential therapeutic target for high-grade gliomas.

High-grade infiltrating gliomas are highly aggressive and fatal brain tumors that present significant challenges for research and treatment due to their complex microenvironment and tissue structure. Recent discovery of tumor microtubes (TMs) has provided new insights into how high-grade gliomas develop in the brain and resist treatment. TMs are unique, ultra-long, and highly functional membrane protrusions that form multicellular networks and play crucial roles in glioma invasiveness, drug resistance, recurrence, and heterogeneity. This review focuses on the different roles that TMs play in glioma cell communication, material transport, and tumor cell behavior. Specifically, non-connecting TMs primarily promote glioma invasiveness, likely related to their role in enhancing cell motility. On the other hand, interconnecting TMs form functional and communication networks by connecting with surrounding astrocytes and neurons, thereby promoting glioma malignancy. We summarize the factors that influence the formation of TMs in gliomas and current strategies targeting TMs. As the understanding of TMs advances, we are closer to uncovering whether they might be the long-sought Achilles' heel of treatment-resistant gliomas. By delving deeper into TMs research, we hope to develop more effective therapeutic strategies for patients with malignant gliomas.

Combined Mendelian randomization and quantitative proteomics analysis to study the influence of thyroid dysfunction on acute ischemic stroke

AbstractAcute ischemic stroke (AIS) is characterized by high morbidity and mortality, making it crucial to identify the risk factors that influence its occurrence and prognosis. Although individuals with thyroid dysfunction exhibit altered stroke patterns, evidence from observational studies remains inconsistent. Herein, we investigated the influence of thyroid dysfunction on stroke progression and prognosis. We combined Mendelian randomization (MR) and tandem mass tag (TMT)‐based quantitative proteomics analysis to study the influence of thyroid dysfunction on AIS. Differentially expression proteins (DEPs) were subsequently identified, functional enrichment analysis was performed, and a protein–protein interaction (PPI) network was constructed. Protein alterations were further validated by western blot. MR analysis revealed a causal association between thyroid disorders and ischemic stroke. DEP analysis identified 38 downregulated proteins and five upregulated proteins. Functional enrichment analysis and PPI network construction highlighted the importance of immune response activation and acute phase pathways, along with the suppression of focal adhesion, regulation of the actin cytoskeleton, and platelet activation pathways. Vasodilator‐stimulated phosphoprotein, MYL12B, MYL6, and TPM4 were identified as key DEPs significantly associated with pathological pathways and were verified by western blot. The identification of these key proteins and pathways provides new perspectives for investigating the progression and prognosis of AIS.

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.

Altered Dynamic Brain Functional Network Connectivity Related to Visual Network in Spinal Cord Injury.

Visual feedback training (VFT) plays an important role in the motor rehabilitation of patients with spinal cord injury (SCI). However, the neural mechanisms are unclear. We aimed to investigate the changes in dynamic functional network connectivity (FNC) related to visual networks (VN) in patients with SCI and to reveal the neural mechanism of VFT promoting motor function rehabilitation. Dynamic FNC and the sliding window method were performed in 18 complete SCI (CSCI), 16 patients with incomplete SCI (ISCI), and 42 healthy controls (HCs). Then, k-mean clustering was implemented to identify discrete FNC states, and temporal properties were computed. The correlations between these dynamic features and neurological parameters in all patients with SCI were calculated. The majority of aberrant FNC was manifested between VN and executive control network (ECN). In addition, compared with HCs, temporal metrics derived from state transition vectors were decreased in patients with CSCI including the mean dwell time and the fraction of time spent in state 3. Furthermore, the disrupted FNC between salience network and ECN in state 2 and the number of transitions were all positively correlated with neurological scores in patients with SCI. Our findings indicated that SCI could result in VN-related FNC alterations, revealing the possible mechanism for VFT in rehabilitation of patients with SCI and increasing the training efficacy and promoting rehabilitation for SCI.

Drug–target interaction prediction by integrating heterogeneous information with mutual attention network

BackgroundIdentification of drug–target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug–target interaction prediction.MethodsHere, we propose DrugMAN, a deep learning model for predicting drug–target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug–target prediction.ResultsDrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN’s learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug–target interactions and can be a powerful tool for drug discovery and drug repurposing.

Proteomic and metabolomic insights into the mechanisms of calcium-mediated salt stress tolerance in hemp.

Industrial hemp (Cannabis sativa L.) is a multifaced crop that has the potential to be exploited for many industrial applications, and making use of salt lands is considered to be a sustainable development strategy for the hemp industry. However, no elite salt-tolerant hemp varieties have been developed, and therefore supplementing appropriate exogenous substances to saline soil is one possible solution. Calcium-containing compounds are well-known for their salt tolerance enhancing effects, but the underlying molecular mechanisms remain largely unclear. Here, we first assessed the ameliorative effects of calcium amendments on salt-stressed hemp plants and then investigated these mechanisms on hemp using integrative analysis of proteomics and metabolomics. The stress phenotypes could be lessened by Ca2+ treatment. Certain concentrations of Ca2+ maintained relative electrical conductivity and the contents of malondialdehyde and chlorophyll. Ca2+ treatment also generally led to greater accumulations of soluble proteins, soluble carbohydrates and proline, and enhanced the activities of superoxide dismutase and peroxidase. Through functional classification, pathway enrichment, and network analysis, our data reveal that accumulation of dipeptides is a prominent metabolic signature upon exogenous Ca2+ treatment, and that changes in mitochondrial properties may play an important role in enhancing the salt tolerance. Our results outline the complex metabolic alternations involved in calcium-mediated salt stress resistance, and these data and analyses would be useful for future functional studies.

Alterations of Sensory-related Functional Brain Network Connectivity in Nrsn2 Homozygous Knockout Mice

Alterations of Sensory-related Functional Brain Network Connectivity in Nrsn2 Homozygous Knockout Mice

A light-temperature neuron and its adaptive regulation

Abstract The appropriate firing modes for a neuron can be excited under the external stimulus. From the viewpoint of physical, the intrinsic biophysical effects, functional encoding, and the mechanism for the transcription of external signals play an extremely important role in building reliable neuron models. In this paper, a light-temperature neuron model is proposed by connecting a phototube and a thermistor into a nonlinear circuit for investigating the information encoding and responses of neurons under the external optical signals and temperature signals. In this neuron model, a phototube is used to encode external light signals, similar to artificial eyes, and a thermistor can encode temperature intensity. Furthermore, the Hamilton energy (HE) function of neurons is calculated based on the Helmholtz’s theorem, and a self-regulation method is designed by applying the ratio of electric field energy to magnetic field energy to estimate the self-regulation of neurons. The results show that the proposed neuron can reproduce the main characteristics of biological neurons by adjusting the external stimulus. The double coherence resonance is induced under noise temperature. These results could be helpful for researching the collective behaviors in functional neural networks.

Методи та засоби оптимізації використання обчислювальних ресурсів в корпоративній мережі закладу вищої освіти

The paper considers the problems of using computing resources in the corporate network of a higher education institution. An approach to assessing the efficiency of the load of computer workstations is proposed, which is based on the indicators of the load of individual computing resources and their implementation in the general system of computing resources for individual applied resource-intensive tasks. It is established that to minimize costs when building a university CM, it is necessary to use non-standard approaches that could dynamically distribute computing resources, in particular terminal and cluster technologies for building corporate networks. A number of experimental studies have been conducted based on data on the functioning of the computer network of a higher education institution. A mathematical model of optimal load distribution on terminal servers has been constructed, which describes the dependence between individual network nodes and the file access system.