Network Connectivity Research Articles

Does practice make perfect? Functional connectivity of the salience network and somatosensory network predicts response to mind-body treatments for fibromyalgia.

Mind-body treatments can improve coping mechanisms to deal with pain, improve the quality of life of patients with fibromyalgia syndrome (FMS), and reduce perceived pain in some cases. However, responses to these treatments are highly variable, the mechanisms underpinning them remain unclear, and reliable predictors of treatment response are lacking. We employed resting-state blood oxygen level-dependent (rsBOLD) functional magnetic resonance imaging (fMRI) to examine changes in brain functional connectivity (FC) following mind-body treatment that may relate to and predict pain relief. We recruited patients with FMS who underwent either mindfulness-based stress reduction (MBSR; n = 18) or a psychoeducational program (FibroQoL; n = 22) and a treatment-as-usual FMS group (TAU; n = 18). We collected rsBOLD data, alongside subjective pain, anxiety, depression, and catastrophizing measures prior to and following treatments. We examined behavioral changes and FC changes in the salience network (SN) and sensorimotor network (SMN) and performed regression analyses to identify predictors for treatment response. The MBSR and FibroQoL groups experienced significant reductions in pain catastrophizing. After treatment, the FC of the sensorimotor cortex with the rest of the SMN became significantly reduced in the MBSR group compared to the TAU group. The FC between the SN and the SMN at baseline was negatively correlated with pain reductions following MBSR but positively correlated with pain reductions in the FibroQoL group. These results yielded large to very large effect sizes. Following MBSR, only for those patients with lower baseline SMN-SN FC, minutes of mindfulness practice were positively associated with clinical improvement (small to medium effect size). Different mind-body treatments are underpinned by discrete brain networks. Measures of the functional interplay between SN and SMN have the potential as predictors of mind-body treatment response in patients with FMS.

Characterization of neural networks involved in transdiagnostic emotion dysregulation from a pilot randomized controlled trial of a neurostimulation-enhanced behavioral intervention

BackgroundEmotional dysregulation is a serious and impairing mental health problem. We examined functional activity and connectivity of neural networks involved in emotional dysregulation at baseline and following a pilot neurostimulation-enhanced cognitive restructuring intervention in a transdiagnostic clinical adult sample. MethodsNeuroimaging data were analyzed from adults who scored 89 or higher on the Difficulties with Emotion Regulation (DERS) scale and had at least one DSM-5 diagnosis. These participants were part of a pilot randomized, double-blind, placebo-controlled trial combining a single therapeutic session of cognitive restructuring with active or sham transcranial magnetic stimulation over the dorsolateral prefrontal cortex. During the study, participants engaged in an emotional regulation task using personalized autobiographical stressors while undergoing functional magnetic resonance imaging (fMRI) before and after the pilot intervention. The fMRI task required participants to either experience the emotions associated with the memories or apply cognitive restructuring strategies to reduce their distress. ResultsWhole-brain fMRI results during regulation at baseline revealed increased activation in the dorsal frontoparietal network but decreased activation in the supplementary motor area, cingulate cortex, insula, and ventrolateral prefrontal cortex (vlPFC). Emotion dysregulation was associated with greater vmPFC and amygdala activation and functional connectivity between these regions. The strength of functional connectivity between the dlPFC and other frontal regions was also a marker of emotional dysregulation. Preliminary findings from a subset of participants who completed the follow-up fMRI scan showed that active neurostimulation improved behavioral indices of emotion regulation more than sham stimulation. A whole-brain generalized psychophysiological interaction analysis indicated that active neurostimulation selectively increased occipital cortex connectivity with both the insula and the dlPFC. Region-of-interest functional connectivity analyses showed that active neurostimulation selectively increased dlPFC connectivity with the insula and orbitofrontal cortex (OFC). ConclusionInsufficient neural specificity during the emotion regulation process and over-involvement of frontal regions may be a marker of emotional dysregulation across disorders. OFC, vlPFC, insula activity, and connectivity are associated with improved emotion regulation in transdiagnostic adults. In this pilot study, active neurostimulation led to neural changes in the emotion regulation network after a single session; however, the intervention findings are preliminary, given the small sample size. These functional network properties can inform future neuroscience-driven interventions and larger-scale studies.

Disruption of bacterial interactions and community assembly in Babesia-infected Haemaphysalis longicornis following antibiotic treatment

BackgroundA previous study highlighted the role of antibiotic-induced dysbiosis in the tick microbiota, facilitating the transstadial transmission of Babesia microti from nymph to adult in Haemaphysalis longicornis. This study builds on previous findings by analyzing sequence data from an earlier study to investigate bacterial interactions that could be linked to enhanced transstadial transmission of Babesia in ticks. The study employed antibiotic-treated (AT) and control-treated (CT) Haemaphysalis longicornis ticks to investigate shifts in microbial community assembly. Network analysis techniques were utilized to assess bacterial interactions, comparing network centrality measures between AT and CT groups, alongside studying network robustness and connectivity loss. Additionally, functional profiling was conducted to evaluate metabolic diversity in response to antibiotic treatment.ResultsThe analysis revealed notable changes in microbial community assembly in response to antibiotic treatment. Antibiotic-treated (AT) ticks displayed a greater number of connected nodes but fewer correlations compared to control-treated (CT) ticks, indicating a less interactive yet more connected microbial community. Network centrality measures such as degree, betweenness, closeness, and eigenvector centrality, differed significantly between AT and CT groups, suggesting alterations in local network dynamics due to antibiotic intervention. Coxiella and Acinetobacter exhibited disrupted connectivity and roles, with the former showing reduced interactions in AT group and the latter displaying a loss of connected nodes, emphasizing their crucial roles in microbial network stability. Robustness tests against node removal showed decreased stability in AT networks, particularly under directed attacks, confirming a susceptibility of the microbial community to disturbances. Functional profile analysis further indicated a higher diversity and richness in metabolic capabilities in the AT group, reflecting potential shifts in microbial metabolism as a consequence of antimicrobial treatment.ConclusionsOur findings support that bacterial interaction traits boosting the transstadial transmission of Babesia could be associated with reduced colonization resistance. The disrupted microbial interactions and decreased network robustness in AT ticks suggest critical vulnerabilities that could be targeted for managing tick-borne diseases.

Frontostriatal salience network expansion in individuals in depression

Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2–4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Sex differences in functional cortical organization reflect differences in network topology rather than cortical morphometry

Differences in brain size between the sexes are consistently reported. However, the consequences of this anatomical difference on sex differences in intrinsic brain function remain unclear. In the current study, we investigate whether sex differences in intrinsic cortical functional organization may be associated with differences in cortical morphometry, namely different measures of brain size, microstructure, and the geodesic distance of connectivity profiles. For this, we compute a low dimensional representation of functional cortical organization, the sensory-association axis, and identify widespread sex differences. Contrary to our expectations, sex differences in functional organization do not appear to be systematically associated with differences in total surface area, microstructural organization, or geodesic distance, despite these morphometric properties being per se associated with functional organization and differing between sexes. Instead, functional sex differences in the sensory-association axis are associated with differences in functional connectivity profiles and network topology. Collectively, our findings suggest that sex differences in functional cortical organization extend beyond sex differences in cortical morphometry.

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

Ecological Network Construction in High-Density Water Network Areas Based on a Three-Dimensional Perspective: The Case of Foshan City

The acceleration of urbanization has resulted in varying degrees of impact on the stability and health of high-density urban ecosystems. Building urban ecological networks is crucial for safeguarding biodiversity and sustaining ecosystem vitality. In this study, the city of Foshan was selected as the study area, which is a prime representative of a high-density water network city. Additionally, a morphological spatial pattern analysis was employed to identify the ecological source. We built an ecological resistance surface using geographic, natural, and behavioral elements, adjusting it based on the density of the water network and the building height. Following this, the circuit theoretical model was utilized to create an ecological network by identifying ecological corridors. There were three key findings. First, the ecological network consisted of 30 ecological source sites and 53 ecological corridors, and 103 ecological “pinch points” and 193 ecological barrier points were identified. Second, the ecological sources were predominantly situated in the southwestern and northern parts of Foshan City. Meanwhile, the suburbs of Foshan City contained the primary ecological barrier points, mainly stemming from new construction sites, while the key ecological “pinch points” were concentrated at river junctions. The third outcome was the recommendations to (a) boost the connectivity of the ecological network in the suburbs, (b) improve the connection of the water network in urban areas, and (c) focus on enhancing landscape connectivity. The objective was to develop approaches for optimizing urban ecological networks, leading to better connectivity and improved ecological network quality.

Metabolic network connectivity disturbances in Parkinson's disease: a novel imaging biomarker.

The diagnosis of Parkinson's Disease (PD) presents ongoing challenges. Advances in imaging techniques like 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have highlighted metabolic alterations in PD, yet the dynamic network interactions within the metabolic connectome remain elusive. To this end, we examined a dataset comprising 49 PD patients and 49 healthy controls. By employing a personalized metabolic connectome approach, we assessed both within- and between-network connectivities using Standard Uptake Value (SUV) and Jensen-Shannon Divergence Similarity Estimation (JSSE). A random forest algorithm was utilized to pinpoint key neuroimaging features differentiating PD from healthy states. Specifically, the results revealed heightened internetwork connectivity in PD, specifically within the somatomotor (SMN) and frontoparietal (FPN) networks, persisting after multiple comparison corrections (P < 0.05, Bonferroni adjusted for 10% and 20% sparsity). This altered connectivity effectively distinguished PD patients from healthy individuals. Notably, this study utilizes 18F-FDG PET imaging to map individual metabolic networks, revealing enhanced connectivity in the SMN and FPN among PD patients. This enhanced connectivity may serve as a promising imaging biomarker, offering a valuable asset for early PD detection.

Feature-aware ultra-low dimensional reduction of real networks

In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. This study introduces FiD-Mercator, a model-based ultra-low dimensional reduction technique that integrates node features with network structure to create D-dimensional maps of complex networks in a hyperbolic space. This embedding method efficiently uses features as an initial condition, guiding the search of nodes’ coordinates toward an optimal solution. The research reveals that downstream task performance improves with the correlation between network connectivity and features, emphasizing the importance of such correlation for enhancing the description and predictability of real networks. Simultaneously, hyperbolic embedding’s ability to reproduce local network properties remains unaffected by the inclusion of features. The findings highlight the necessity for developing network embedding techniques capable of exploiting such correlations to optimize both network structure and feature association jointly in the future.

Mindfulness Training in High-Demand Cohorts Alters Resting-State Electroencephalography: An Exploratory Investigation of Individual Alpha Frequency, Aperiodic 1/f Activity, and Microstates

BackgroundMindfulness training (MT) programs have demonstrated utility as cognitive training tools, but there is little consensus on the neurophysiological processes that may underlie its benefits. It has been posited that intrinsic brain activity recorded at rest reflects the functional connectivity of large-scale brain networks and may provide insight into neuroplastic changes that support MT. In the current study, we indexed changes in several resting-state electroencephalography (EEG) parameters to investigate the neurophysiological underpinnings of MT. MethodsResting-state EEG data were collected from active-duty U.S. military personnel (N = 80) at 2 testing sessions: before (time [T] 1) and after (T2) engaging in an 8-week MT or active comparison intervention (positivity training). We examined longitudinal and/or groupwise differences in several EEG parameters through parameterization of power spectra (individual alpha frequency and 1/f activity) and microstate analysis. ResultsWhile no significant group × time differences were observed in individual alpha frequency, significant group × time effects were observed in several EEG parameters from T1 to T2. Compared with MT, positivity training was associated with a steepening of the 1/f slope and higher 1/f intercepts together with decreased duration and increased global field power of microstates. ConclusionsTaken together, these results suggest that the effects of interventions may be differentiated in resting-state brain activity in a sample of military personnel. Such findings provide insight into the neural underpinnings of MT-related brain changes, but more research is required to elucidate how these may relate to task-related neural and performance changes with MT and whether results generalize to other mindfulness interventions in alternative cohorts and contexts.

PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network

BackgroundRecently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of orphans and other fewer evolution information proteins. (2) The other bottleneck is the method of predicting single-sequence-based proteins mainly focuses on selecting protein sequence features and tuning the neural network architecture, However, while the deeper neural networks improve prediction accuracy, there is still the problem of increasing the computational burden. Compared with other neural networks in the field of protein prediction, the graph neural network has the following advantages: due to the advantage of revealing the topology structure via graph neural network and being able to take advantage of the hierarchical structure and local connectivity of graph neural networks has certain advantages in capturing the features of different levels of abstraction in protein molecules. When using protein sequence and structure information for joint training, the dependencies between the two kinds of information can be better captured. And it can process protein molecular structures of different lengths and shapes, while traditional neural networks need to convert proteins into fixed-size vectors or matrices for processing.ResultsHere, we propose a single-sequence-based protein contact map predictor PCP-GC-LM, with dual-level graph neural networks and convolution networks. Our method performs better with other single-sequence-based predictors in different independent tests. In addition, to verify the validity of our method against complex protein structures, we will also compare it with other methods in two homodimers protein test sets (DeepHomo test dataset and CASP-CAPRI target dataset). Furthermore, we also perform ablation experiments to demonstrate the necessity of a dual graph network. In all, our framework presents new modules to accurately predict inter-chain contact maps in protein and it’s also useful to analyze interactions in other types of protein complexes.

A topological network connectivity design problem based on spectral analysis

How to improve network connectivity and which parts of the network are vulnerable are critical issues. We begin by defining an equal distribution problem, in which supplies are distributed equally to all nodes in the network. We then derive a topological network connectivity measure from the convergence speed, which is the second minimum eigenvalue of a Laplacian network matrix. Based on the equal distribution problem, we propose a method for identifying critical links for network connectivity using the derivative of the second minimum eigenvalue. Furthermore, we develop a network design problem that maximizes topological connectivity within a budget creating strengthening network links. The problem is convex programming, and the solution is global. Furthermore, it can be converted into an identical semidefinite programming problem, which requires less computational effort. Finally, we test the developed problems on road networks in the Japanese prefectures of Ishikawa and Toyama to determine their applicability and validity.

GCRL: a graph neural network framework for network connectivity robustness learning

Abstract The resilience and adaptability of complex networks is crucial in ensuring their functionality against disruptions. Particularly, maintaining network connectivity under various attack scenarios is a key aspect of such resilience. Network connectivity refers to the degree to which nodes within a network are interconnected and able to exchange information or resources. Its robustness reflects the ability of a network to maintain connectivity under various attacks. Such ability has profound physical significance, ensuring the stability and reliability of real-world systems. Currently, connectivity robustness assessments rely heavily on very time-consuming attack simulations. This paper introduces a graph neural network framework for network connectivity robustness learning (GCRL) to advance the study of network connectivity robustness. GCRL transforms initial degree distributions and network topology into informative embedding vectors, which are then processed by a robustness learning module mainly composed of multi-layer perceptron, achieving both high speed and precision. Our extensive experiments demonstrate the superior performance of GCRL obtained in less time compared to existing methods, especially in tough scenarios where test data distributions significantly differ from the training set. The framework also shows adaptability to networks of different sizes, making it a more generalized solution for complex network robustness learning.

Age-related changes of interoceptive brain networks: Implications for interoception and alexithymia.

Aging is known to be associated with a decline in interoceptive abilities and changes in emotional processing, including alexithymia. As the brain areas supporting interoceptive awareness participate in the perception of emotion, we suggested that interoceptive decline and alexithymia in older adults may share common neural ground. To test this hypothesis, we administered functional magnetic resonance imaging-based heartbeat detection task to 62 adults of diverse ages (range 18-73) and evaluated a larger sample of older and younger adults using questionnaires characterizing interoceptive sensibility, alexithymia, and depressive attitudes. We found that increasing age was linked to decreased activation during the interoceptive task, including the right insular-opercular and supplementary motor areas (SMAs). Age also affected task-based functional connectivity, with two major effects being a decrease in the connectivity of the SMA-insular network and an increase in the connectivity of the prefrontal-lateral occipital network. Path analysis performed for interoceptive accuracy as the endogenous variable revealed that the impact of age was mediated by the functional activation of the insular cortex and SMA and by the connectivity between these areas. Another path analysis using alexithymia as the endogenous variable while controlling for depressive attitudes showed that the effect of age was mediated by interoceptive decline. The study supports the role of central mechanisms in age-related interoceptive decline and shows its implications for alexithymia. Since alexithymia represents a risk factor for mental and cardiovascular diseases, the study findings may open an important direction toward maintaining older adults' well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Network Connectivity and Sensor Response of an OpenThread WSN Platform for Crop Monitoring

Network Connectivity and Sensor Response of an OpenThread WSN Platform for Crop Monitoring

Exploring Social Networks: An Analysis of Intra-organizational Networks

This research investigates intra-organizational social networks by employing network science methods, aiming to provide actionable insights for both managers and employees. Using datasets from the Colorado Index of Complex Networks (ICON), this study examines four intra-organizational networks from a consulting firm and a research team, focusing on advice requests and skill awareness. The analysis includes network topology, degree distribution, community detection, correlation between communities and node attributes, maximal clique detection, and multilayer network analysis. The findings reveal that intra-organizational networks are intricate and significantly impact organizational efficiency and individual career development. Key insights include the importance of mid-level employees in network connectivity, the existence of "bad edges" where advice is sought from less competent peers, and the influence of regional and locational factors on communication patterns. The study emphasizes the need to enhance knowledge sharing and inter-region communications. For employees, understanding the structure of these networks can guide them to more effectively leverage their social connections for career advancement. This interdisciplinary effort bridges gaps in the existing literature and showcases the potential of integrating network science methods into social science research.

Physical activity and frontoparietal network connectivity in traumatic brain injury.

Prolonged changes to functional network connectivity as a result of a traumatic brain injury (TBI) may relate to long-term cognitive complaints reported by TBI survivors. No interventions have proven to be effective at treating long-term cognitive complaints after TBI but physical activity has been shown to promote cognitive function and modulate functional network connectivity in non-injured adults. Therefore, the objective of this study was to test if physical activity engagement was associated with functional connectivity of the cognitively relevant frontoparietal control network (FPCN) in adults with a TBI history. In a case-control study design, resting state function magnetic resonance imaging and physical activity data from a subset of participants (18-81 years old) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study was analyzed. Fifty-seven participants reported a prior head injury with loss of consciousness and 57 age and sex matched controls were selected. Seed-based functional connectivity analyses were performed using seeds in the dorsolateral prefrontal cortex and the inferior parietal lobule, to test for differences in functional connectivity between groups, associations between physical activity and functional connectivity within TBI as well as differential associations between physical activity and functional connectivity between TBI and controls. Seed-based connectivity analyses from the dorsolateral prefrontal cortex showed that those with a history of TBI had decreased positive connectivity between dorsolateral prefrontal cortex and intracalcarine cortex, lingual gyrus, and cerebellum, and increased positive connectivity between dorsolateral prefrontal cortex and cingulate gyrus and frontal pole in the TBI group. Results showed that higher physical activity was positively associated with increased connectivity between the dorsolateral prefrontal cortex and inferior temporal gyrus. Differential associations were observed between groups whereby the strength of the physical activity-functional connectivity association was different between the inferior parietal lobule and inferior temporal gyrus in TBI compared to controls. Individuals with a history of TBI show functional connectivity alterations of the FPCN. Moreover, engagement in physical activity is associated with functional network connectivity of the FPCN in those with a TBI. These findings are consistent with the evidence that physical activity affects FPCN connectivity in non-injured adults; however, this effect presents differently in those with a history of TBI.

Sex-Specific Effects of Anxiety on Cognition and Activity-Dependent Neural Networks: Insights from (Female) Mice and (Wo)Men

Neuropsychiatric symptoms (NPS), such as depression and anxiety, are observed in 90% of Alzheimer's disease (AD) patients, two-thirds of whom are women. NPS usually manifest long before AD onset creating a therapeutic opportunity. Here, we examined the impact of anxiety on AD progression and the underlying brain-wide neuronal mechanisms. To gain mechanistic insight into how anxiety impacts AD progression, we performed a cross-sectional analysis on mood, cognition, and neural activity utilizing the ArcCreERT2 x enhanced yellow fluorescent protein (eYFP) x APP/PS1 (AD) mice. The ADNI dataset was used to determine the impact of anxiety on AD progression in human subjects. Female APP/PS1 mice exhibited anxiety-like behavior and cognitive decline at an earlier age than control (Ctrl) mice and male mice. Brain-wide analysis of c-Fos+ revealed changes in regional correlations and overall network connectivity in APP/PS1 mice. Sex-specific eYFP+/c-Fos+ changes were observed; female APP/PS1 mice exhibited less eYFP+/c-Fos+ cells in dorsal CA3 (dCA3), while male APP/PS1 mice exhibited less eYFP+/c-Fos+ cells in the dorsal dentate gyrus (dDG). In the ADNI dataset, anxiety predicted transition to dementia. Female subjects positive for anxiety and amyloid transitioned more quickly to dementia than male subjects. While future studies are needed to understand whether anxiety is a predictor, a neuropsychiatric biomarker, or a comorbid symptom that occurs during disease onset, these results suggest that there are sex differences in AD network dysfunction, and that personalized medicine may benefit male and female AD patients rather than a one size fits all approach.

Bridging climate refuges for climate change adaptation: A spatio-temporal connectivity network approach

Enhancing the spatio-temporal connectivity of dynamic landscapes is crucial for species to adapt to climate change. However, a spatio-temporal connectivity network approach considering climate change and species movement is often overlooked. Taking Tibetan wild ass on the Qinghai-Xizang Plateau as an example, we simulated species distribution under current (2019) and future scenarios (2100), constructed spatio-temporal connectivity networks, and assessed the spatio-temporal connectivity. The results show that under the current, SSP2-4.5 and SSP3-7.0 scenarios, the suitable habitats for Tibetan wild ass account for 21.11%, 21.34%, and 20.95% of the total area, respectively, which show more fragmentation in 2100. 78.35% of habitats which are predicted to be suitable under current conditions will remain suitable in the future, which can be regarded as stable climate refuges. With the increase in future emission intensity, the number percentage of auxiliary connectivity corridors increases from 27.65% to 33.57%. This indicates that more patches will function as temporary refuges and the auxiliary connectivity corridors will gradually weaken the dominance of direct connectivity corridors. Under different SSP-RCP scenarios, the internal spatio-temporal connectivity is the highest, accounting for 42%–43%. Compared with the spatio-temporal perspective, the purely spatial perspective overestimates network connectivity by about 28% considering all current and future patches, and underestimates network connectivity by 16%–21% when only considering all current or future patches. In this study, a new approach of spatio-temporal connectivity network is proposed to bridge climate refuges, which contributes to the long-term effectiveness of conservation networks for species’ adaptation to climate change.

Physiology-inspired bifocal fronto-parietal tACS for working memory enhancement

Aging populations face significant cognitive challenges, particularly in working memory (WM). Transcranial alternating current stimulation (tACS) offer promising avenues for cognitive enhancement, especially when inspired by brain physiology. This study (NCT04986787) explores the effect of multifocal tACS on WM performance in healthy older adults, focusing on fronto-parietal network modulation. Individualized physiology-inspired tACS applied to the fronto-parietal network was investigated in two blinded cross-over experiments. The first experiment involved monofocal/bifocal theta-tACS to the fronto-parietal network, while in the second experiment cross-frequency theta-gamma interactions between these regions were explored. Participants have done online WM tasks under the stimulation conditions. Network connectivity was assessed via rs-fMRI and multichannel electroencephalography. Prefrontal monofocal theta tACS modestly improved WM accuracy over sham (d = 0.30). Fronto-parietal stimulation enhanced WM task processing speed, with the strongest effects for bifocal in-phase theta tACS (d = 0.41). Cross-frequency stimulations modestly boosted processing speed with or without impairing task accuracy depending on the stimulation protocol. This research adds to the understanding of physiology-inspired brain stimulation for cognitive enhancement in older subjects.