Functional Networks Research Articles

Altered brain-ventricle coupling modes over Alzheimer's disease progression detected with fMRI.

The origins of resting-state functional MRI (rsfMRI) signal fluctuations remain debated. Recent evidence shows coupling between global cortical rsfMRI signals and cerebrospinal fluid inflow in the fourth ventricle, increasing during sleep and decreasing with Alzheimer's disease (AD) progression, potentially reflecting brain clearance mechanisms. However, the existence of more complex brain-ventricle coupling modes and their relationship to cognitive decline remains unexplored. Analyzing 599 minimally-preprocessed rsfMRI scans from 163 elderly participants across the AD spectrum, we identified distinct brain-ventricle coupling modes that differentiate across groups and correlate with cognitive scores. Beyond the known anti-phase coupling between global brain signals and ventricles -more frequent in cognitively normal controls- we discovered additional modes where specific brain areas temporarily align with ventricle signals. At the cortical level, these modes form canonical resting-state networks, such as the Default Mode Network, which occurs less in AD or the Frontoparietal Network, which correlates positively with memory scores. The direct link between ventricle and brain signals challenges the common practice of removing CSF components from rsfMRI analyses and questions the origin of cortical signal fluctuations forming functional networks, which may reflect region-specific fluid inflow patterns. These findings provide new insights into the relationship between brain clearance mechanisms and network dysfunction in neurodegenerative diseases.

The effects of neurofeedback training on behavior and brain functional networks in children with autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with an unclear pathogenesis to date. Neurofeedback (NFB) had shown therapeutic effects in patients with ASD. In this study,we analyzed the brain functional networks of children with ASD and investigated the impact of NFB targeting the beta rhythm training on these networks. The Autism Behavior Checklist (ABC) and Social Response Scale (SRS) were employed to evaluate the effects of NFB training on the behavioral abilities of children with ASD. We compared the differences in static and dynamic brain functional networks between ASD and Typically Developing (TD) children, also explored the changes in these networks in ASD children after 20 sessions of NFB training. The Weighted Phase Lag Index (wPLI) was used to construct static functional networks, and the Fuzzy Entropy (FuzzyEn) algorithm was further employed to measure the complexity of static functional connectivity and construct dynamic functional networks. This allowed the analysis of functional connectivity and fluctuations in the static functional networks of ASD and TD children, as well as the time variability of the dynamic functional networks. Additionally, the study explored the changes in brain functional networks and behavioral scales before and after NFB training. Results from behavioral scales indicated significant improvements in cognitive, communication, language, and social scores in ASD children following NFB intervention. EEG analysis revealed that static functional connectivity was lower, connectivity variability was higher, and temporal variability was greater in ASD children compared to TD children. Following NFB training, increased functional connectivity, reduced connectivity variability in the Delta frequency band, and decreased temporal variability were observed in ASD children. The results revealed abnormalities in both static and dynamic functional networks in children with ASD, with NFB training showed potential to modulate these networks. While our results showed that NFB training can assist participants in regulating connectivity and temporal variability in specific brain regions, robust evidence for its effectiveness in alleviating core symptoms of ASD remained limited.

Data Driven-Based Machine Learning Modelling and Empirical Correlations for Predicting Snow-Covered Area in the Swat Region, Pakistan

In recent decades, global and regional climate change has emerged as a significant challenge with potential catastrophic consequences, including hurricanes, floods, sea level rise, and temperature shifts. Snow-covered area (SCA) serves as a crucial climatic parameter reflecting climate changes, yet accurately determining SCA proves to be a challenging and time-consuming task. This study aims to develop robust prediction models for SCA by employing three machine learning (ML) approaches using readily available climatic data from Swat, Pakistan, spanning two decades. The climate data encompass precipitation, daily maximum/minimum temperatures, and SCA measurements. Three ML methods—artificial neural networks (ANN), functional networks (FN), and adaptive neuro-fuzzy inference systems (ANFIS)—were employed to model SCA. Accuracy measures, including coefficient of determination (R2), average absolute percentage error (AAPE), and root mean squared error (RMSE) were utilized to evaluate model performance. All three ML models exhibited superior performance, with high R2 values and low errors. Accuracy indicators of the ANN model are better than FN and ANFIS models, yielding the highest R2 (0.956) and minimum RMSE and AAPE values (0.61 and 0.91). ANFIS demonstrated slightly better performance than FN, with RMSE, AAPE, and R2 values of 0.65, 1.1, and 0.950, respectively. FN yielded RMSE, AAPE, and R values of 1.14, 1.72, and 0.85, respectively. Additionally, two empirical correlations were derived from the optimized FN and ANN models for SCA prediction using the same input variables. This study underscores the efficacy of ML techniques in accurately and consistently predicting SCA parameters, offering valuable insights into climate change and its consequences.

Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity

Premature reproductive aging is linked to heightened stress sensitivity and psychological maladjustment across the life course. However, the brain dynamics underlying this relationship are poorly understood. Here, to address this issue, we analyzed multimodal data from female participants in the Adolescent Brain and Cognitive Development (longitudinal, N = 441; aged 9–12 years) and Human Connectome-Aging (cross-sectional, N = 130; aged 36–60 years) studies. Age-specific intrinsic functional brain network dynamics mediated the link between reproductive aging and perceptions of greater interpersonal adversity. The adolescent profile overlapped areas of greater glutamatergic and dopaminergic receptor density, and the middle-aged profile was concentrated in visual, attentional and default mode networks. The two profiles showed opposite relationships with patterns of functional neural network variability and cortical atrophy observed in psychosis versus major depressive disorder. Our findings underscore the divergent patterns of brain aging linked to reproductive maturation versus senescence, which may explain developmentally specific vulnerabilities to distinct disorders.

Static and dynamic connectivity structure of white-matter functional networks across the adult lifespan.

Aging of the human brain involves intricate biological processes, resulting in complex changes in structure and function. While the effects of aging on gray matter (GM) connectivity are extensively studied, white matter (WM) functional changes have received comparatively less attention. This study examines age-related WM functional dynamics using resting-state fMRI across the adult lifespan. We identified GM and WM functional networks (FNs) using k-means clustering. Static and dynamic analyses of WM functional network connectivity (FNC) were performed to explore age effects on WM-FNs and recurrent patterns of dynamic FNC. We identified 9 WM and 12 GM FNs. Age-related effects on WM FNC strength and WM-GM FNC dynamics included linear positive and U-shaped age trajectories in static FNC strength, and linear negative and inverted U-shaped trajectories in FNC temporal variability. Three distinct brain states with significant age-related differences were identified and validated. These findings were largely replicated in the validation analysis. High integration and low temporal variability in WM-GM FNC may indicate reduced adaptability of the network system in older adults, offering insights into brain aging processes.

Didactic properties and learning functions of neural networks

Introduction. The relevance of this research is based on the well-known fact that the challenges created by the digital revolution sharpened many problems within the humanities education. The object of our research is the process of integration of digital approaches into the traditional teaching of humanities as one of the most important challenges faced by contemporary higher education. Digital technologies have already changed and continue to change not only teaching methods and approaches but also the very concept of humanities as a specialized field of knowledge. Within this process, we face innovations that quickly transform into an everyday practice and become embedded into the pre-existing academic and educational processes (access to digital information sources, ease of information production, demonstration and presentation etc.). The aim of this paper is to analyze the process of integration of digital approaches in teaching humanities as one of the most important educational challenges faced by contemporary university. Materials and methods. Since this paper is interdisciplinary in character, its methodological research base relies on integrative conceptual approach allowing us to bring together general theoretical methods (analysis, synthesis, generalization) with cultural, historical, contextual and competence-based research methods. Such an approach provides ample opportunities to introduce existing theoretical and methodological toolkit developed by the humanities in order to understand problems and specific features of digital society and digital culture. Both in this methodology and in content, the concept of digital culture as a research object does not contradict practices established within humanities, but rather can be interpreted as a new area of study. Results. The results of our research demonstrate that digitalization of society led to the emergence of new themes in the humanities and within the education system: criticism of the “black box” character of contemporary digital infrastructure; issues of inequality and discrimination created or supported by digital tools; social effects of digitalization (including its impact on children and young adults); data-related problems: who, how and under what conditions creates, distributes and uses data in contemporary culture and society; anthropological approach that studies interactions between humans and digital technologies; cultural representations of digital technologies: images and narratives that circulate in society and are used to visualize and describe digital phenomena in film, literature, internet, social media etc. Conclusions. The authors conclude that the situation surrounding digitalization of humanities presents a field both of new problems and new opportunities. However, despite the existing issues, humanities research and education constantly produce new models and methods of interaction with the digital reality, which can and should be confidently introduced in education constantly experimenting with the new approaches.

Energy harvesting-based trust-aware medical data transmission model with multi-objective function in cognitive radio network

Energy harvesting-based trust-aware medical data transmission model with multi-objective function in cognitive radio network

Toward a functional future for the cognitive neuroscience of human aging.

The cognitive neuroscience of human aging seeks to identify neural mechanisms behind the commonalities and individual differences in age-related behavioral changes. This goal has been pursued predominantly through structural or "task-free" resting-state functional neuroimaging. The former has elucidated the material foundations of behavioral decline, and the latter has provided key insight into how functional brain networks change with age. Crucially, however, neither is able to capture brain activity representing specific cognitive processes as they occur. In contrast, task-based functional imaging allows a direct probe into how aging affects real-time brain-behavior associations in any cognitive domain, from perception to higher-order cognition. Here, we outline why task-based functional neuroimaging must move center stage to better understand the neural bases of cognitive aging. In turn, we sketch a multi-modal, behavior-first research framework that is built upon cognitive experimentation and emphasizes the importance of theory and longitudinal design.

Seasonal instability of phytoplankton community in extreme arid lake basin during interval period of large-scale ecological water transport.

Large-scale water transport helps solve the imbalance in water resources. Studies on ecological benefits after long-term EWT mainly focus on vegetation restoration and increase in water surface. However, the maintenance of aquatic communities in the EWT context is a major challenge. This study studied the seasonal dynamics of phytoplankton communities during the interval period of 22nd and 23rd EWT in a newborn lake generated by long-term EWT in an extremely arid zone. The 22nd EWT was carried out from August 2021 to November 2021, and the 23rd EWT was implemented from July to September 2022. Our research indicates that during the interval period, water temperature (WT), pH, dissolved oxygen (DO), suspended solids (SS), turbidity, total nitrogen (TN), NO3-N, total phosphorus (TP), chemical oxygen demand (CODcr), SO42- showed seasonal dynamic changes. Meanwhile, the phytoplankton community also exhibits seasonal variations, including the succession of dominant phyla and dominant species, the decrease in density and α diversity, as well as the increased instability of functional groups and ecological networks. These phenomena are more pronounced in summer when the lake water evaporates and the water quality changes, suggesting that the phytoplankton community shows attenuation along with the changes of environmental factors during the interval period. Based on this, our study suggested that the coupling relationship between water quantity, water quality change and aquatic community succession should be considered for the precise control of EWT projects aiming at improving ecological benefits. Regular phytoplankton monitoring is recommended to track the evolution of aquatic ecosystems after long-term EWT.

Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters

Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters

Ergothioneine-rich Lentinula edodes mushroom extract restores mitochondrial functions in senescent HT22 cells

A decline in mitochondrial functions associated with ageing is the key factor of free radical generation which contributes to age-related pathologies. Protecting healthy functional mitochondrial networks with antioxidants is critical in promoting healthy ageing. This study aimed to investigate the protective effect of ergothioneine (EGT)-rich Lentinula edodes extract (LE-ETH) against tert-butyl hydroperoxide (t-BHP) assaulted senescent HT22 cells. Mitochondrial function was evaluated by measuring mitochondrial membrane potential (MMP), ATP levels and mitochondrial toxicity. The protective mechanisms were elucidated via the exploration of antioxidant and mitochondrial biogenesis signalling pathways. Our results revealed that a low dose of t-BHP increases mitochondrial toxicity. The pretreatment with 100 µg/mL of LE-ETH and the equimolar concentration of EGT for 8 h significantly improve the mitochondrial function and reduced inflammation. Through gene expression studies, we demonstrated that pretreatment of LE-ETH significantly improves the antioxidant and mitochondrial biogenesis pathway via Nrf2 signaling axis. However, the downstream genes of the mitochondrial biogenesis pathway were unaffected by equimolar EGT concentration. Gas chromatography-mass spectrum (GC–MS) analysis was carried out to identify the bioactive compounds that are present in LE-ETH extract which contributed to its efficacy in improving the mitochondrial functions. A total of 23 compounds consisting of phenols, fatty acids, and sterols were identified in the ethanolic extract. Pentanoic acid was the major compound identified in LE-ETH. These findings demonstrated that EGT-rich L.edodes mushroom is a potential neuroprotective agent which could serve as a potential therapeutic strategy for the preservation of mitochondrial functions in healthy ageing explorations.

Whole-brain functional connectivity and structural network properties in stroke patients with hemiplegia.

This study explored structural and functional alterations in the whole brain of stroke patients with hemiplegia. We collected multimodal magnetic resonance images of 24 patients with ischaemic stroke and 16 age-matched controls. Resting-state functional connectivity (FC) for all brain regions was evaluated. Diffusion tensor imaging was used to construct white matter structural networks, and the graph properties of the structural network were analysed using graph theory to determine group differences. The ipsilesional posterior parietal cortex (PPC) in the frontoparietal network accounts for more than half of the 25 brain regions with altered FC in stroke patients. The nodal efficiency of multiple ipsilesional frontal lobes and cerebellar regions, such as the ipsilateral cerebellum 8, was reduced. The contralesional cerebellum 8 showed elevated FC with the lingual gyrus and the visual network. Our results suggest that the PPC and cerebellum 8 are regions worthy of in-depth study. The cerebellum 8 may supplement deficits in motor balance function by enhancing functional congruence with the visual area. This study identified key brain regions and characteristics that exhibit structural and functional changes following stroke injury.

Olfactory Network Disruptions as Mediators of Cognitive Impairment in De Novo Parkinson's Disease.

Parkinson's disease (PD) is characterized by olfactory dysfunction (OD) and cognitive deficits at its early stages, yet the link between OD and cognitive deficits is also not well-understood. This study aims to examine the changes in the olfactory network associated with OD and their relationship with cognitive function in de novo PD patients. A total of 116 drug-naïve PD patients and 51 healthy controls (HCs) were recruited for this study. Graph theoretical approaches were employed to reveal the abnormalities of topological characteristics in the olfactory network. Network-based statistics (NBS) analysis was employed to identify the abnormal subnetworks within the olfactory network. Moreover, partial correlation analysis and mediation analysis were performed to examine the relationship between the abnormal network metrics, olfactory function, and cognitive function. Graph theoretical approaches revealed reduced betweenness centrality of the left insula in PD patients with OD. NBS analysis identified a disrupted subnetwork with decreased functional connectivity, primarily involving limbic regions. The average functional connectivity of this subnetwork partially mediated the relationship between olfactory and cognitive performance. Higher-granularity network analysis further highlighted the insula's key role and revealed reduced efficiency of information integration within the olfactory network. OD was associated with specific changes in the functional olfactory network, which, in turn, affects cognitive function. These findings underscore the importance of assessing and addressing OD. Understanding the neural correlates of OD could provide novel insights into the management and comprehension of cognitive impairment in PD.

Effects of Anti-Seizure Medications on Resting-State Functional Networks in Juvenile Myoclonic Epilepsy: An EEG Microstate Analysis

ObjectiveJuvenile myoclonic epilepsy (JME) is associated with large-scale brain network dysfunction. This study aims to investigate how anti-seizure medication (ASM) treatment alters resting-state functional networks in JME patients through resting-state EEG microstate analysis. MethodsNinety-six subjects participated in this study: 24 healthy controls (HC), 29 newly diagnosed JME patients who had not started ASMs therapy (JME-NM), and 43 JME patients on ASMs treatment with effective seizure control (JME-M). EEG data were collected for 10 minutes while participants were awake and resting with their eyes closed, using a standard 19-channel recording system. EEG topographies were categorized into four microstate classes (A, B, C, D), and parameters such as mean duration, occurrence rate, time coverage, and transition probabilities between microstates were computed and compared among the three groups. Advanced statistical methods were employed to ensure the robustness and validity of the findings. ResultsSignificant alterations in EEG microstate characteristics were observed in untreated JME patients (JME-NM) compared to both healthy controls and treated patients. Microstate B had a markedly reduced mean duration in the JME-NM group, while microstate A displayed an increased occurrence rate and greater time coverage. Transition probabilities between specific microstates, such as from A to C, A to D, and B to C, were also significantly different in the JME-NM group. The normalization of these parameters in the JME-M group suggests that ASMs effectively stabilize altered brain networks, potentially mitigating the pathophysiological disruptions associated with JME. ConclusionThis study demonstrates that ASMs effectively normalize disruptions in sensory-motor and visual networks in JME patients. EEG microstate analysis provides a dynamic view of brain network alterations and offers potential as a biomarker for the diagnosis and monitoring of JME, as well as for evaluating treatment response. These findings advance our understanding of the neurophysiological mechanisms underlying JME.

Decade of TRAP Progress: Insights and Future Prospects for Advancing Functional Network Research in Epilepsy.

Targeted Recombination in Active Populations (TRAP) represents an effective and extensively applied technique that has earned significant utilization in neuroscience over the past decade, primarily for identifying and modulating functionally activated neuronal ensembles associated with diverse behaviors. As epilepsy is a neurological disorder characterized by pathological hyper-excitatory networks, TRAP has already been widely applied in epilepsy research. However, the deployment of TRAP in this field remains underexplored, and there is significant potential for further application and development in epilepsy-related investigations. In this review, we embark on a concise examination of the mechanisms behind several TRAP tools, introduce the current applications of TRAP in epilepsy research, and collate the key advantages as well as limitations of TRAP. Furthermore, we sketch out perspectives on potential applications of TRAP in future epilepsy research, grounded in the present landscape and challenges of the field, as well as the ways TRAP has been embraced in other neuroscience domains.

Temporoparietal structural-functional coupling abnormalities in drug-naïve first-episode major depressive disorder

IntroductionMajor depressive disorder (MDD) is a debilitating and heterogeneous disease. Many MDD patients experience concurrent anxiety symptoms, often referred to as anxious depression (MDD-ANX). The relationships between network alterations in structural connectivity (SC) and functional connectivity (FC) in MDD and its anxiety-related subtype remain areas that require further investigation. MethodsWe investigated SC-FC coupling at the system and regional levels in 80 never-treated first-episode MDD patients and 80 healthy control (HC) subjects. For brain systems and regions showing significant between-group coupling differences, we further conducted subgroup comparisons between MDD-ANX, non-anxious depression (MDD-NANX) and HC. We also investigated topological features at the corresponding levels, and assessed the correlation patterns between significant coupling alterations and the topological and clinical characteristics. ResultsRelative to HC, MDD patients showed increased SC-FC coupling in the temporal system (right hippocampus and left superior temporal gyrus [STG]) but decreased coupling in the parietal system (right postcentral gyrus and left angular gyrus). These systems and regions were further characterized by disturbed inter-module connections and impaired structural network efficiency in MDD. Notably, SC-FC coupling of the right hippocampus was significantly increased in MDD-ANX compared to MDD-NANX, which further showed distinct correlation patterns with structural network efficiency. ConclusionsAlterations in both SC-FC coupling and topological properties in the temporal and parietal regions provide insights into the interplay between the structural and functional network abnormalities in MDD. SC-FC coupling alterations in the right hippocampus, associated with structural nodal efficiency, may be implicated in the neuropathology of anxious depression.

Neural Rewiring of Resilience: The Effects of Combat Deployment on Functional Network Architecture

Neural Rewiring of Resilience: The Effects of Combat Deployment on Functional Network Architecture

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.

Impact of Different Auditory Environments on Task Performance and EEG Activity

Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload. For example, the ‘Mozart effect’ has been shown to enhance performance in spatial reasoning tasks. However, the impact of auditory stimuli on task performance and brain activity remains unclear. This study examined the effects of three different environments—quiet, music, and white noise—on task performance and EEG activities. The N-back task was employed to induce mental workload, and the Psychomotor Vigilance Task assessed participants’ alertness. We proposed a novel, statistically-based method to construct the brain functional network, avoiding issues associated with subjective threshold selection. This method systematically analyzed the connectivity patterns under different environments. Our analysis revealed that white noise negatively affected participants, primarily impacting brain activity in high-frequency ranges. This study provided deeper insights into the relationship between auditory stimuli and mental workload, offering a robust framework for future research on mental workload regulation.

Passive hyperthermia alters the resting-state functional connectivity of mouse brain

Purpose To investigate how passive hyperthermia affect the resting-state functional brain activity based on an acute mouse model after heat stress exposure. Materials and methods Twenty-eight rs-fMRI data of C57BL/6J male mice which weighing about 24 ∼ 29 g and aged 12 ∼ 16 weeks were collected. The mice in the hyperthermia group (HT, 40 °C ± 0.5 °C, 40 min) were subjected to passive hyperthermia before the anesthesia preparation for scanning. While the normal control group (NC) was subjected to normothermia condition (NC, 20 °C ± 2 °C, 40 min). After data preprocessing, we performed independent component analysis (ICA) and region of interested (ROI)-ROI functional connectivity (FC) analyses on the data of both HT (n = 13) and NC (n = 15). Results The group ICA analysis showed that the HT and the NC both included 11 intrinsic connectivity networks (ICNs), and can be divided into four types of networks: the cortical network (CN), the subcortical network (SN), the default mode network (DMN), and cerebellar networks. CN and SN belongs to sensorimotor network. Compared with NC, the functional network organization of ICNs in the HT was altered and the overall functional intensity was decreased. Furthermore, 13 ROIs were selected in CN, SN, and DMN for further ROI-ROI FC analysis. The ROI-ROI FC analysis showed that passive hyperthermia exposure significantly reduced the FC strength in the overall brain represented by CN, SN, DMN of mice. Conclusion Prolonged exposure to high temperature has a greater impact on the overall perception and cognitive level of mice, which might help understand the relationship between neuronal activities and physiological thermal sensation and regulation as well as behavioral changes.