Functional Networks Research Articles

Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.

Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling. To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature. To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue. With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined. KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at https://deepamahm.github.io/kiphynet_docs/ .

Network resilience of plant-bee interactions in the Eastern Afromontane Biodiversity Hotspot

Interaction network resilience can be defined as the ability of interacting organisms to maintain their functions, processes or populations after experiencing a disturbance. Studies on mutualistic interactions between plants and pollinators along environmental gradients are essential to understand the provision of ecosystem services and the mechanisms challenging their network resilience. However, it remains unknown to what level ecological changes along climatic gradients constrain the network resilience of mutualistic organisms, especially along elevation gradients. We surveyed bee species and recorded their interactions with plants throughout the four major seasons (i.e. long and short rainy, and long and short dry) on 50 study sites positioned along an elevation gradient (525 m to 2,530 m asl) in the Eastern Afromontane Biodiversity Hotspots in Kenya, East Africa. We calculated bee and plant network resilience using the network resilience parameter (βeff) and assessed changes in bee and plant network resilience along the elevation gradient using generalised additive models (gams). We quantified the effects of climate, bee and plant diversity, bee functional traits, network structure, and landscape configuration on bee and plant network resilience using a set of multi-model inference frameworks followed by structural equation models (SEM). We found that bee and plant species exhibited higher levels of network resilience at higher elevations. While bee network resilience increased linearly across the elevation gradient, plant network resilience increased exponentially from ∼1500 m and higher. Bee and plant network resilience increased in areas with reduced mean annual temperature (MAT) and decreased in areas with lower mean annual precipitation (MAP). Our SEM model showed that increasing temperatures indirectly influenced plant network resilience via network modularity and community assemblage of bees. We also found that MAP had a direct positive effect on plant diversity and network resilience, while the fragmentation of habitats reduced richness of plant communities and enhanced network modularity. In conclusion, we revealed that mutualistic networks showed higher network resilience at higher elevations. We also unveiled that climate and habitat fragmentation directly or indirectly influences the network resilience of plants and bees via the modulation of community assemblages and interaction networks. These influences are lower at higher elevations such that these systems seem better able to buffer against extinction cascades. We thus suggest that, management efforts should be geared at consolidating natural habitats. In contrast, restoration efforts should aim at mitigating climate change effects and harnessing the ability of mutualists to reconnect broken links to improve the network resilience and functioning of East-African montane ecosystems.

Metabolic Network Analysis Reveals Human Impact on Urban Nitrogen Cycles

Human interactions have led to the emergence of a higher complexity of urban metabolic networks; hence, traditional natural- or agriculture-oriented biogeochemical models might not be transferred well to urban environments. Increasingly serious environmental problems require the development of new concepts and models. Here, we propose a basic paradigm for urban–rural complex nitrogen (N) metabolic network reconstruction (NMNR) by introducing new concepts and methodologies from systems biology at the molecular scale, analyzing both local and global structural properties and exploring optimization and regulation methods. Using the Great Hangzhou Areas System (GHA) as a case study, we revealed that pathway fluxes follow a power law distribution, which indicates that human-dominated pathways constitute the principal part of the functions of the whole network. However, only 1.16% of the effective cycling pathways and an average hamming distance of only 5.23 between the main pathways indicate that the network lacks diverse pathways and feedback loops, which could lead to low robustness. Furthermore, more than half of the N fluxes did not pass through core metabolism, causing waste and pollution. We also provided strategies to design network structures and regulate system function: improving robustness and reducing pollution by referring to the characteristics of biochemical metabolic networks (e.g., the bow-tie structure). This method can be used to replace the trial-and-error method in system regulation and design. By decomposing the GHA N metabolic network into 4398 metabolic pathways and the corresponding fluxes with a power law distribution, NMNR helps us quantify the vulnerability in the current urban nitrogen cycle. The basic ideas and methodology in NMNR can be applied to coupled human and natural systems to advance global sustainable development studies, and they can also extend systems biology from the molecule to complex ecosystems and lead to the development of multi-scale unified theory in systems biology.

Causes of accidents in electrical distribution networks (using the example of the branch of PJSC Rossetti Volga – Saratov Distribution Networks)

The article is a continuation of a series of studies dedicated to the analysis of power transmission through distribution electric networks of PJSC ROSSETI. The presented material analyzes the functioning of the electrical networks of the branch of PJSC Rosseti Volga – Saratov Distribution Networks. An analytical assessment of the electric energy transport through distribution electric networks was carried out for the period 2018–2022. The structure of the company is considered, the structural characteristics of the main elements of the studied electrical networks are given, and the number of emergencies that occurred during the study period is estimated. A quantitative assessment of the magnitude of the electric energy undersupply caused by power supply interruptions is given. The classification of failures according to the degree of the intensity of undersupply was carried out and a quantitative assessment of each group of failures according to their intensity was made. The influence of the seasonal component on the number of emergencies during the study period is considered. Based on the data from 2022–2023, the analysis of the main causes of damage to the elements of electrical networks was carried out. The classification of failures by groups of failure causes is made, their main characteristics for this period are considered. The percentage ratio of the number of failures number for the most characteristic reasons to the total number of outages during the study period has been established. The information published in the open press was used as the source data. General scientific methods as well as numerical methods of analysis were used in the research. MATLAB graphics editor technologies were used to visualize the obtained analysis results. The results obtained may be of interest to the managers of power grid companies, as well as researchers and engineers engaged in research in the field of reliability of power supply.

Proteogenomic Analysis Identifies a Causal Association between Plasma Apolipoprotein B Levels and Liver Cancer Risk.

Liver oncogenesis is accompanied by discernible protein changes in the bloodstream. By employing plasma proteomic profiling, we can delve into the molecular mechanisms of liver cancer and pinpoint potential biomarkers. In this nested case-control study, we applied liquid chromatography-tandem mass spectrometry for proteome profiling in baseline plasma samples. Differential protein expression was determined and was subjected to functional enrichment, network, and Mendelian randomization (MR) analyses. We identified 193 proteins with notable differential levels between the groups. Of these proteins, MR analysis offered a compelling negative association between apolipoprotein B (APOB) and liver cancer. This association was further corroborated in the UK Biobank cohort: genetically predicted APOB levels were associated with a 31% (95% CI 19-42%) decreased risk of liver cancer; and phenotypic analysis indicated an 11% (95% CI 8-14%) decreased liver cancer risk for every 0.1 g/L increase of circulating APOB levels. Multivariable MR analysis suggested that the hepatic fat content might fully mediate the APOB-liver cancer connection. In summary, we identified some plasma proteins, particularly APOB, as potential biomarkers of liver cancer. Our findings underscore the intricate link between lipid metabolism and liver cancer, offering hints for targeted prophylactic strategies and early detection.

Axo-axonic synaptic input drives homeostatic plasticity by tuning the axon initial segment structurally and functionally.

Homeostatic plasticity maintains the stability of functional brain networks. The axon initial segment (AIS), where action potentials start, undergoes dynamic adjustment to exert powerful control over neuronal firing properties in response to network activity changes. However, it is poorly understood whether this plasticity involves direct synaptic input to the AIS. Here, we show that changes of GABAergic synaptic input from chandelier cells (ChCs) drive homeostatic tuning of the AIS of principal neurons (PNs) in the prelimbic (PL) region, while those from parvalbumin-positive basket cells do not. This tuning is evident in AIS morphology, voltage-gated sodium channel expression, and PN excitability. Moreover, the impact of this homeostatic plasticity can be reflected in animal behavior. Social behavior, inversely linked to PL PN activity, shows time-dependent alterations tightly coupled to changes in AIS plasticity and PN excitability. Thus, AIS-originated homeostatic plasticity in PNs may counteract deficits elicited by imbalanced ChC presynaptic input at cellular and behavioral levels.

Peripheral insulin resistance attenuates cerebral glucose metabolism and impairs working memory in healthy adults

People with insulin resistance are at increased risk for cognitive decline. Insulin resistance has previously been considered primarily a condition of ageing but it is increasingly seen in younger adults. It is possible that impaired insulin function in early adulthood has both proximal effects and moderates or even accelerates changes in cerebral metabolism in ageing. Thirty-six younger (mean 27.8 years) and 43 older (mean 75.5) participants completed a battery of tests, including blood sampling, cognitive assessment and a simultaneous PET/MR scan. Cortical thickness and cerebral metabolic rates of glucose were derived for 100 regions and 17 functional networks. Older adults had lower rates of regional cerebral glucose metabolism than younger adults across the brain even after adjusting for lower cortical thickness in older adults. Higher fasting blood glucose was also associated with lower regional cerebral glucose metabolism in older adults. In younger adults, higher insulin resistance was associated with lower rates of regional cerebral glucose metabolism but this was not seen in older adults. The largest effects of insulin resistance in younger adults were in prefrontal, parietal and temporal regions; and in the control, salience ventral attention, default and somatomotor networks. Higher rates of network glucose metabolism were associated with lower reaction time and psychomotor speed. Higher levels of insulin resistance were associated with lower working memory. Our results underscore the importance of insulin sensitivity and glycaemic control to brain health and cognitive function across the adult lifespan, even in early adulthood.

Abnormal large-scale brain functional network dynamics in social anxiety disorder.

Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD. We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored. Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration. These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Efficient implementation of the functional links artificial neural networks with cross-terms for nonlinear active noise control

This paper proposes an efficient extension of functional links artificial neural networks (EE-FLANN) for the active noise control (ANC) application. The developed EE-FLANN controller can upgrade the model accuracy with the actual system thanks to adding the cross-terms to the trigonometric function. Unlike the method in the generalized FLANN (GFLANN) controller, the EE-FLANN exploits include cross-term symmetry. However, this causes the computational burden to increase remarkably. To reduce this disadvantage, we truncate the cross-terms appropriately based on the simplified strategy. Furthermore, the adaptive algorithm is designed to partially update the filter coefficients appropriately. Specifically, the cross-terms that do not satisfy certain magnitude conditions will be omitted during the update process to reduce costs. Experiments have shown that the proposed EE-FLANN controller can achieve comparable performance to the GFLANN controller but the complexity is reduced by up to 20%.

Prenatal protein malnutrition decreases neuron numbers in the parahippocampal region but not prefrontal cortex in adult rats

ABSTRACT Objective: Prenatal protein malnutrition produces anatomical and functional changes in the developing brain that persist despite immediate postnatal nutritional rehabilitation. Brain networks of prenatally malnourished animals show diminished activation of prefrontal areas and an increased activation of hippocampal regions during an attentional task [1]. While a reduction in cell number has been documented in hippocampal subfield CA1, nothing is known about changes in neuron numbers in the prefrontal or parahippocampal cortices. Methods: In the present study, we used unbiased stereology to investigate the effect of prenatal protein malnutrition on the neuron numbers in the medial prefrontal cortex and the cortices of the parahippocampal region that comprise the larger functional network. Results: Results show that prenatal protein malnutrition does not cause changes in the neuronal population in the medial prefrontal cortex of adult rats, indicating that the decrease in functional activation during attentional tasks is not due to a reduction in the number of neurons. Results also show that prenatal protein malnutrition is associated with a reduction in neuron numbers in specific parahippocampal subregions: the medial entorhinal cortex and presubiculum. Discussion: The affected regions along with CA1 comprise a tightly interconnected circuit, suggesting that prenatal malnutrition confers a vulnerability to specific hippocampal circuits. These findings are consistent with the idea that prenatal protein malnutrition produces a reorganization of structural and functional networks, which may underlie observed alterations in attentional processes and capabilities.

Disrupting glioblastoma networks with tumor treating fields (TTFields) in in vitro models.

This study investigates the biological effect of Tumor Treating Fields (TTFields) on key drivers of glioblastoma's malignancy-tumor microtube (TM) formation-and on the function and overall integrity of the tumor cell network. Using a two-dimensional monoculture GB cell network model (2DTM) of primary glioblastoma cell (GBC) cultures (S24, BG5 or T269), we evaluated the effects of TTFields on cell density, interconnectivity and structural integrity of the tumor network. We also analyzed calcium (Ca2+) transient dynamics and network morphology, validating findings in patient-derived tumoroids and brain tumor organoids. In the 2DTM assay, TTFields reduced cell density by 85-88% and disrupted network interconnectivity, particularly in cells with multiple TMs. A "crooked TM" phenotype emerged in 5-6% of treated cells, rarely seen in controls. Ca2+ transients were significantly compromised, with global Ca2+ activity reduced by 51-83%, active and periodic cells by over 50%, and intercellular co-activity by 52% in S24, and almost completely in BG5 GBCs. The effects were more pronounced at 200kHz compared to a 50kHz TTFields. Similar reductions in Ca2+ activity were observed in patient-derived tumoroids. In brain tumor organoids, TTFields significantly reduced tumor cell proliferation and infiltration. Our comprehensive study provides new insights into the multiple effects of Inovitro-modeled TTFields on glioma progression, morphology and network dynamics in vitro. Future in vivo studies to verify our in vitro findings may provide the basis for a deeper understanding and optimization of TTFields as a therapeutic modality in the treatment of GB.

Stalled data flows in digital innovation networks: Underlying mechanisms and the role of related variety

Data flows across organizational boundaries are vital for creating and capturing value from data-intensive digital technologies, such as Artificial Intelligence. To achieve this, organizations increasingly engage in digital innovation networks, i.e., constellations of relations among dispersed, loosely coupled actors, who seek to collaborate for combining heterogeneously distributed domain expertise to train and leverage emerging digital technologies that learn from data. Yet, data flows remain stalled within digital innovation networks, and organizations fail to achieve sought-after benefits from data-intensive digital technologies. To date, research has paid limited attention to what contributes to stalled data flows and what strategies are required to facilitate seamless data flows. Our in-depth qualitative study of a digital innovation network within the Swedish forestry identified four key mechanisms underlying stalled data flows and hampering firms in leveraging value from data-intensive digital technologies and revealed the key role of brokerage functions in digital innovation networks for establishing what we call related variety.

Non-voltage-gated Ca2+ channel signaling in glomerular cells in kidney health and disease.

Positioned at the head of the nephron, the renal corpuscle generates a plasma ultrafiltrate to initiate urine formation. Three major cell types within the renal corpuscle, the glomerular mesangial cells, podocytes, and glomerular capillary endothelial cells, communicate via endocrine- and paracrine-signaling mechanisms to maintain the structure and function of the glomerular capillary network and filtration barrier. Ca2+ signaling mediated by several distinct plasma membrane Ca2+ channels impacts the functions of all three cell types. The past two decades have witnessed pivotal advances in understanding of non-voltage-gated Ca2+ channel function and regulation in the renal corpuscle in health and renal disease. This review summarizes the current knowledge of the physiological and pathological impact of non-voltage-gated Ca2+ channel signaling in mesangial cells, podocytes and glomerular capillary endothelium. The main focus is on transient receptor potential and store-operated Ca2+ channels, but ionotropic N-methyl-d-aspartate receptors and purinergic receptors also are discussed. This update of Ca2+ channel functions and their cellular signaling cascades in the renal corpuscle is intended to inform the development of therapeutic strategies targeting these channels to treat kidney diseases, particularly diabetic nephropathy.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.

Peripheral and central pathogenesis of postherpetic neuralgia.

Postherpetic neuralgia (PHN) is a classic chronic condition with multiple signs of peripheral and central neuropathy. Unfortunately, the pathogenesis of PHN is not well defined, limiting clinical treatment and disease management. To describe the peripheral and central pathological axes of PHN, including peripheral nerve injury, inflammation induction, central nervous system sensitization, and brain functional and structural network activity. A bibliographic survey was carried out, selecting relevant articles that evaluated the characterization of the pathogenesis of PHN, including peripheral and central pathological axes. Currently, due to the complexity of the pathophysiological mechanisms of PHN and the incomplete understanding of the exact mechanism of neuralgia. It is essential to conduct in-depth research to clarify the origins of PHN pathogenesis and explore effective and comprehensive therapies for PHN.

Brain network localization of gray matter atrophy, neurocognitive and social cognitive dysfunction in schizophrenia

BackgroundNumerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, there is still a dearth of literature examining brain network localization of gray matter atrophy, neurocognitive and social cognitive dysfunction in schizophrenia. MethodsTo address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 schizophrenia individuals and 9275 healthy controls. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia. ResultsThe gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks. ConclusionsOur findings suggest shared and unique brain network substrates of gray matter atrophy, neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective, but also potentially contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.

Deciphering Host-Parasite Interplay in Leishmania Infection through a One Health View of Proteomics Studies on Drug Resistance.

Recent efforts in the study of vector-borne parasitic diseases (VBPDs) have emphasized an increased consideration for preventing drug resistance and promoting the environmental safety of drugs, from the beginning of the drug discovery pipeline. The intensive use of the few available antileishmanial drugs has led to the spreading of hyper-resistant Leishmania infantum strains, resulting in a chronic burden of the disease. In the present work, we have investigated the biochemical mechanisms of resistance to antimonials, paromomycin, and miltefosine in three drug-resistant parasitic strains from human clinical isolates, using a whole-cell mass spectrometry proteomics approach. We identified 14 differentially expressed proteins that were validated with their transcripts. Next, we employed functional association networks to identify parasite-specific proteins as potential targets for novel drug discovery studies. We used SeqAPASS analysis to predict susceptibility based on the evolutionary conservation of protein drug targets across species. MATH-domain-containing protein, adenosine triphosphate (ATP)-binding cassette B2, histone H4, calpain-like cysteine peptidase, and trypanothione reductase emerged as top candidates. Overall, this work identifies new biological targets for designing drugs to prevent the development of Leishmania drug resistance, while aligning with One Health principles that emphasize the interconnected health of people, animals, and ecosystems.

High segregation and diminished global integration in large-scale brain functional networks enhances the perceptual binding of cross-modal stimuli.

Speech perception requires the binding of spatiotemporally disjoint auditory-visual cues. The corresponding brain network-level information processing can be characterized by two complementary mechanisms: functional segregation which refers to the localization of processing in either isolated or distributed modules across the brain, and integration which pertains to cooperation among relevant functional modules. Here, we demonstrate using functional magnetic resonance imaging recordings that subjective perceptual experience of multisensory speech stimuli, real and illusory, are represented in differential states of segregation-integration. We controlled the inter-subject variability of illusory/cross-modal perception parametrically, by introducing temporal lags in the incongruent auditory-visual articulations of speech sounds within the McGurk paradigm. The states of segregation-integration balance were captured using two alternative computational approaches. First, the module responsible for cross-modal binding of sensory signals defined as the perceptual binding network (PBN) was identified using standardized parametric statistical approaches and their temporal correlations with all other brain areas were computed. With increasing illusory perception, the majority of the nodes of PBN showed decreased cooperation with the rest of the brain, reflecting states of high segregation but reduced global integration. Second, using graph theoretic measures, the altered patterns of segregation-integration were cross-validated.

In vivo Mapping of the Mouse Galnt3-specific O-Glycoproteome

The UDP-N-acetylgalactosamine polypeptide:N-acetylgalactosaminyltransferase (GalNAc-T) family of enzymes initiates O-linked glycosylation by catalyzing the addition of the first GalNAc sugar to serine or threonine on proteins destined to be membrane-bound or secreted. Defects in individual isoforms of the GalNAc-T family can lead to certain congenital disorders of glycosylation (CDG). The GALNT3-CDG, is caused by mutations in GALNT3, resulting in hyperphosphatemic familial tumoral calcinosis (HFTC) due to impaired glycosylation of the phosphate-regulating hormone FGF23 within osteocytes of the bone. Patients with hyperphosphatemia present altered bone density, abnormal tooth structure and calcified masses throughout the body. It is therefore important to identify all potential substrates of GalNAc-T3 throughout the body to understand the complex disease phenotypes. Here, we compared the Galnt3-/- mouse model, which partially phenocopies GALNT3-CDG, with wild-type mice and employed a multi-component approach utilizing chemoenzymatic conditions, a product-dependent method constructed using EThcD triggered scans in a mass spectrometry workflow, quantitative O-glycoproteomics, and global proteomics to identify 663 Galnt3-specific O-glycosites from 269 glycoproteins across multiple tissues. Consistent with the mouse and human phenotypes, functional networks of glycoproteins that contain GalNAc-T3-specific O-glycosites involved in skeletal morphology, mineral level maintenance and hemostasis were identified. This library of in vivo GalNAc-T3-specific substrate proteins and O-glycosites will serve as a valuable resource to understand the functional implications of O-glycosylation and to unravel the underlying causes of complex human GALNT3-CDG phenotypes.

The abnormalities of brain function in females with primary insomnia: a resting-state functional magnetic resonance imaging study

BackgroundThe neuropathologic mechanism of primary insomnia (PI) of females remains unclear. This study aims to investigate the features of amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) in females with PI using functional magnetic resonance imaging (fMRI), and then explore the abnormalities of functional connectivity (FC).Materials and methodsA total of 39 female PI patients and 31 female healthy controls (HC) were enrolled in the study. The sleep condition was assessed using the Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI), and their depressive symptom was evaluated using the Hamilton Depression Scale (HAMD-24). The rs-fMRI was once conducted for every subject. ReHo, ALFF, and ROI-based FC were used to analyze the changes of brain function.ResultsALFF values were increased in the Cerebelum_4_5_L, as well as decreased ALFF in the bilateral Frontal_Sup_Medial (SFGmed), Angular_L (ANG.L), Parietal_Inf_R (IPL.R), SupraMarginal_R (SMG.R), and Postcentral_R (PoCG.R). ReHo values were increased in the Temporal_Pole_Mid_R (TPOsup.R), as well as decreased ReHo in the Insula_R (INS.R), Frontal_Inf_Oper_R (ORBinf.R), Putamen_R (PUT.R), Rolandic_Oper_R (ROL.R), bilateral Cingulum_Post (PCG), bilateral Frontal_Sup_Medial (SFGmed), bilateral anterior cingulate and paracingulate gyri (ACG), and the bilateral precuneus (PCUN). Across the entire brain, there was a decline in the FC between Angular_R and Frontal_Sup_Medial_L.ConclusionAlterations in brain regions of female patients with PI involved multiple functional networks, including the default mode network, the salience network, the central executive network, and the limbic network. Reduced coordination between functional networks may be an important mechanism for insomnia and may lead to reduced cognitive function and decision-making ability.