Connectivity Metrics Research Articles

A new framework combining hydrological connectivity metrics and morphological spatial pattern analysis for the hydrological connectivity evaluation of wetlands.

The quantitative evaluation of wetland hydrological connectivity is essential to the hydrological connectivity restoration-oriented ecological conservation and environmental management of wetlands. We proposed a framework to evaluate wetland hydrological connectivity with a combination of hydrological connectivity metrics and morphological spatial pattern analysis and recognized potential sites and links that had been generally overlooked in previous studies. Variations in hydrological connectivity revealed a decreasing trend followed by a gradual recovery from the critical time node of 2005 in Baiyangdian Lake. The core, one of the most important landscape types, played a dominant role in maintaining wetland hydrological connectivity at both temporal and spatial scales, and its variations matched those of hydrological connectivity. More importantly, we redressed the conventional ignorance of peripheral patches and links and recognized their importance in improving the hydrological connectivity of wetlands. The proposed framework provides an effective and practical tool for the hydrological connectivity evaluation of wetlands, expanding new insights into maintaining the health and integrity of wetland ecosystems. Integr Environ Assess Manag 2023;19:1064-1078. © 2022 SETAC.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Objective. Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs. Approach. In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India. Main results. In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs. Significance. These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

Functionally and structurally distinct fusiform face area(s) in over 1000 participants

The fusiform face area (FFA) is a widely studied region causally involved in face perception. Even though cognitive neuroscientists have been studying the FFA for over two decades, answers to foundational questions regarding the function, architecture, and connectivity of the FFA from a large (N>1000) group of participants are still lacking. To fill this gap in knowledge, we quantified these multimodal features of fusiform face-selective regions in 1053 participants in the Human Connectome Project. After manually defining over 4,000 fusiform face-selective regions, we report five main findings. First, 68.76% of hemispheres have two cortically separate regions (pFus-faces/FFA-1 and mFus-faces/FFA-2). Second, in 26.69% of hemispheres, pFus-faces/FFA-1 and mFus-faces/FFA-2 are spatially contiguous, yet are distinct based on functional, architectural, and connectivity metrics. Third, pFus-faces/FFA-1 is more face-selective than mFus-faces/FFA-2, and the two regions have distinct functional connectivity fingerprints. Fourth, pFus-faces/FFA-1 is cortically thinner and more heavily myelinated than mFus-faces/FFA-2. Fifth, face-selective patterns and functional connectivity fingerprints of each region are more similar in monozygotic than dizygotic twins and more so than architectural gradients. As we share our areal definitions with the field, future studies can explore how structural and functional features of these regions will inform theories regarding how visual categories are represented in the brain.

Selective effects of acute and chronic stress on slow and alpha-theta cortical functional connectivity and reversal with subanesthetic ketamine.

Anxious, depressive, traumatic, and other stress-related disorders are associated with large scale brain network functional connectivity changes, yet the relationship between acute stress effects and the emergence of persistent large scale network reorganization is unclear. Using male Thy 1-jRGECO1a transgenic mice, we repeatedly sampled mesoscale cortical calcium activity across dorsal neocortex. First, mice were imaged in a homecage control condition, followed by an acute foot-shock stress, a chronic variable stress protocol, an acute on chronic foot-shock stress, and finally treatment with the prototype rapid acting antidepressant ketamine or vehicle. We derived functional connectivity metrics and network efficiency in two activity bands, namely slow cortical activity (0.3-4 Hz) and theta-alpha cortical activity (4-15 Hz). Compared to homecage control, an acute foot-shock stress induced widespread increases in cortical functional connectivity and network efficiency in the 4-15 Hz temporal band before normalizing after 24 h. Conversely, chronic stress produced a selective increase in between-module functional connectivity and network efficiency in the 0.3-4 Hz band, which was reversed after treatment with the rapidacting antidepressant ketamine. The functional connectivity changes induced by acute stress in the 4-15 Hz band were strongly related to those in the slow band after chronic stress, as well as the selective effects of subanesthetic ketamine. Together, this data indicates that stress induces functional connectivity changes with spatiotemporal features that link acute stress, persistent network reorganization after chronic stress, and treatment effects.

Neuromodulation of brain power topography and network topology by prefrontal transcranial photobiomodulation

Objective. Transcranial photobiomodulation (tPBM) has shown promising benefits, including cognitive improvement, in healthy humans and in patients with Alzheimer’s disease. In this study, we aimed to identify key cortical regions that present significant changes caused by tPBM in the electroencephalogram (EEG) oscillation powers and functional connectivity in the healthy human brain. Approach. A 64-channel EEG was recorded from 45 healthy participants during a 13 min period consisting of a 2 min baseline, 8 min tPBM/sham intervention, and 3 min recovery. After pre-processing and normalizing the EEG data at the five EEG rhythms, cluster-based permutation tests were performed for multiple comparisons of spectral power topographies, followed by graph-theory analysis as a topological approach for quantification of brain connectivity metrics at global and nodal/cluster levels. Main results. EEG power enhancement was observed in clusters of channels over the frontoparietal regions in the alpha band and the centroparietal regions in the beta band. The global measures of the network revealed a reduction in synchronization, global efficiency, and small-worldness of beta band connectivity, implying an enhancement of brain network complexity. In addition, in the beta band, nodal graphical analysis demonstrated significant increases in local information integration and centrality over the frontal clusters, accompanied by a decrease in segregation over the bilateral frontal, left parietal, and left occipital regions. Significance. Frontal tPBM increased EEG alpha and beta powers in the frontal-central-parietal regions, enhanced the complexity of the global beta-wave brain network, and augmented local information flow and integration of beta oscillations across prefrontal cortical regions. This study sheds light on the potential link between electrophysiological effects and human cognitive improvement induced by tPBM.

Regional economic tightness from rural to urban regions

Regional economies are characterized by networks of interactions between individual elements and are thus quintessential complex systems. Analyzing the relatedness of various aspects of regional economies, such as exports, industries, occupations, and technologies, using methods from complexity science is becoming commonplace. However, current work has focused nearly exclusively on regional economic complexity of more urbanized regions within countries, if not entire countries themselves. Smaller urban areas are typically over-looked and rural regions are almost entirely absent from the dialog. This paper seeks to fill this gap by examining smaller urban areas and rural regions from a complexity economics perspective. Analyzing cross-sectional data provides initial insights into the transformation of regional economic connectedness from rural to urban regions. Using a previously developed metric of economic connectivity based the on co-occurrence of economic activities, called tightness, we examine the skills space and industry space of metropolitan, micropolitan, and rural regions in the United States. We find that the least and the most urbanized regions have the highest tightness, and that this is partially due to the share of specialty skills in a “socio-cognitive” lobe of skills space. However, we also find that the composition of skills in the least and most urbanized regions differs markedly. Findings suggest that planners seeking to increase the share of socio-cognitive skills in the local workforce may be constrained by population size, and that regions of moderate population size may be required to first grow industries that require less cognitive skills.Science Highlights• Regional economic tightness and regionaleconomic output are positively correlated, even when controlling for regionalpopulation.• Skills tightness is greatest in the leastpopulous and most populous regions while industry tightness is greatest in themost populous regions.• Higher skills tightness is driven partially bythe share of socio-cognitive skills in the regional workforce.• The most rural and most urban counties havethe highest share of specialty skills in the socio-cognitive lobe of skillsspace.Policy and PracticeRecommendations• Both skills and industrial tightness should befostered to increase regional per capita output.• Growing jobs that utilize socio-cognitiveskills may increase skills tightness and thus regional productivity.• Moderately-urbanized areas typically have a lowershare of workers with socio-cognitive skills, and may experience more difficultygrowing knowledge-intensive industries.

Structural network efficiency mediates the association between glymphatic function and cognition in mild VCI: a DTI-ALPS study.

Background and objective: Vascular cognitive impairment (VCI) can be caused by multiple types of cerebrovascular pathology and is considered a network disconnection disorder. The heterogeneity hinders research progress in VCI. Glymphatic failure has been considered as a key common pathway to dementia recently. The emergence of a new method, Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS), makes it possible to investigate the changes of the glymphatic function in humans non-invasively. We aimed to investigate alterations of glymphatic function in VCI and its potential impact on network connectivity. Methods: We recruited 79 patients with mild VCI, including 40 with cerebral small vessel disease cognitive impairment (SVCI) and 39 with post-stroke cognitive impairment (PSCI); and, 77 normal cognitive (NC) subjects were recruited. All subjects received neuropsychological assessments and multimodal magnetic resonance imaging scans. ALPS-index was calculated and structural networks were constructed by deterministic tractography, and then, the topological metrics of these structural connectivity were evaluated. Results: The ALPS-index of VCI patients was significantly lower than that of NC subjects (P < 0.001). Multiple linear regression analysis showed that ALPS-index affects cognitive function independently (β = 0.411, P < 0.001). The results of correlation analysis showed that the ALPS-index was correlated with overall vascular risk factor burden (r = -0.263, P = 0.001) and multiple cerebrovascular pathologies (P < 0.05). In addition, global efficiency (Eg) of network was correlated with ALPS-index in both SVCI (r = 0.348, P = 0.028) and PSCI (r = 0.732, P < 0.001) patients. Finally, the results of mediation analysis showed that Eg partially mediated in the impact of glymphatic dysfunction on cognitive impairment (indirect effect = 7.46, 95% CI 4.08-11.48). Conclusion: In both major subtypes of VCI, the ALPS-index was decreased, indicating impaired glymphatic function in VCI. Glymphatic dysfunction may affect cognitive function in VCI by disrupting network connectivity, and, may be a potential common pathological mechanism of VCI. ALPS-index is expected to become an emerging imaging marker for VCI.

Accounting for functional connectivity in cross-realm conservation planning in a data poor context: The Cyprus case

In the past years, efforts have been made to include connectivity metrics in conservation planning in order to promote and enhance well-connected systems of protected areas. Connectivity is particularly important for species that rely on more than one realm during their daily or life cycle (multi-realm species). However, conservation plans for the protection of multi-realm species usually involve a single realm, excluding other realms from the prioritization process. Here, we demonstrate an example of cross-realm conservation planning application for the island of Cyprus by taking into account the terrestrial and marine realms and their interface (i.e. coast). Operating within a data-poor context, we use functional connectivity metrics to identify priority areas for the conservation of six multi-realm species, by setting conservation targets simultaneously for the terrestrial and marine realms. MARXAN decision-support tool was used for the identification of the priority areas.Four scenarios were developed to evaluate the impacts of including connectivity in the prioritization process and the effectiveness of the existing coastal/marine protected areas in the achievement of the conservation targets set for the species. All scenarios considered land and sea anthropogenic uses as surrogate costs to influence the prioritization process.Our findings show an increase in the area of the reserve network and, therefore, the cost, when connectivity is included, whilst reducing the total boundary length. Furthermore, the current reserve network fails to achieve conservation targets, particularly for the marine part, which has a substantially smaller protection coverage than the terrestrial part.We conclude that focus should be given in the expansion of the current coastal/marine reserve network following a cross-realm conservation approach. This approach is not only relevant for the conservation of multi-realm species, but also for islandscapes, in particular, where the interdependence between the hinterland and the coast is larger and therefore the magnitude of the impacts generated in one realm and affects the other.

Identification of essential tremor based on resting-state functional connectivity.

Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole-brain resting-state functional connectivity (RSFC) metrics combined with machine-learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine-learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello-thalamo-motor and non-motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine-learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Выявление устойчивых связей между показателями коннективности ЭЭГ и компонентами интеллекта

According to the “neural efficiency” hypothesis, intelligence indicators are associated with specific spatial features of the optimal functional activity of the brain. Since there are studies that do not confirm such a relationship, the study of the issue of its stability remains relevant. Therefore, the main task of the study is to find metrics of EEG connectivity at rest that are stably correlated with indicators of the verbal and visual-spatial components of intelligence. Pearson’s and Spearman’s correlation coefficients, polychoric correlation coefficient and their stable analogs calculated on the basis of truncation, the MCD method, and the sign method were chosen as potential measures of the relationship of the studied parameters. To assess the robustness to outliers, the “leave-one-out test” (LOOT) procedure was used, on the basis of which a weighted robust analog of the correlation coefficients was calculated. By the degree of deviation from its initial value, calculated for the entire sample, one can judge the sensitivity to outliers. It is shown that rank-based correlation coefficients using truncation are the most resistant to outliers. As a result, stable significant correlations were found between intelligence indicators and EEG connectivity at rest, indicating a potentially effective pre-tuning of functional neural networks with the combination of local and distantly distributed neural ensembles.

Role of green roofs in urban connectivity, an exploratory approach using landscape graphs in the city of Paris, France

Green roofs provide many ecosystem services, but little is known about the way they contribute to urban functional connectivity. This paper has the following four objectives: (1) to compare the potential green roofs’ role to connectivity in relation to other urban green spaces, (2) to specify the green roofs contribution’s type, (3) to explore the influence of building height integration method and finally (4) to assess the impact on connectivity of simulated greening new roofs. Using a landscape graph approach, we modeled ecological networks of three species groups with different dispersion capacities in the Paris region (France). Then, we computed several connectivity metrics to assess the potential contribution of green roofs to functional connectivity. At a large scale (metropole scale), our results show that green roofs can slightly improve the global connectivity largely through the connections rather than the addition of habitat area. More than a stepping stone function, green roofs would have a dispersion flux function at a local scale. Furthermore, when the difficulty of crossing movement is exponential to the height of buildings, green roofs over 20 m high are mostly disconnected from the ecological networks. In addition to the green roof’s height, our analysis highlights the very strong role played by buildings’ configuration. This study raises promising directions for the integration of building height into the analysis of urban connectivity. Detailed research and long-term biological data from green roofs and green spaces are needed to confirm our results.

Aberrant resting-state functional connectivity and topological properties of the subcortical network in functional dyspepsia patients.

Functional dyspepsia (FD) is a disorder of gut-brain interaction. Previous studies have demonstrated a wide range of abnormalities in functional brain activity and connectivity patterns in FD. However, the connectivity pattern of the subcortical network (SCN), which is a hub of visceral information transmission and processing, remains unclear in FD patients. The study compared the resting-state functional connectivity (rsFC) and the global and nodal topological properties of SCN between 109 FD patients and 98 healthy controls, and then explored the correlations between the connectivity metrics and clinical symptoms in FD patients. The results demonstrated that FD patients manifested the increased rsFC in seventeen edges among the SCN, decreased small-worldness and local efficiency in SCN, as well as increased nodal efficiency and nodal degree centrality in the anterior thalamus than healthy controls (p < 0.05, false discovery rate corrected). Moreover, the rsFC of the right anterior thalamus-left nucleus accumbens edge was significantly correlated with the NDSI scores (r = 0.255, p = 0.008, uncorrected) and NDLQI scores (r = −0.241, p = 0.013, uncorrected), the nodal efficiency of right anterior thalamus was significantly correlated with NDLQI scores (r = 0.204, p = 0.036, uncorrected) in FD patients. This study indicated the abnormal rsFC pattern, as well as global and nodal topological properties of the SCN, especially the bilateral anterior thalamus in FD patients, which enhanced our understanding of the central pathophysiology of FD and will lay the foundation for the objective diagnosis of FD and the development of new therapies.

Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.

Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (E g ), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed. Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8. Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.

One Tree at a Time: Restoring Landscape Connectivity through Silvopastoral Systems in Transformed Amazon Landscapes

Due to the continued expansion of pastures and illicit crops, the Andes-Amazon foothills in Colombia are one of most threatened biodiversity hotspots in the country. Halting and restoring the connectivity of the landscapes transformed over the last 40 years and now dominated by extensive cattle ranching practices, represents a challenge. Silvopastoral systems (SPSs) have been proposed as a strategy to help conserve the biodiversity by improving landscape connectivity. However, understanding the contributions of SPSs to biodiversity conservation still requires additional research. At the farm scale (here called farmscape), we compared different landscape fragmentation and connectivity metrics under two SPS conditions (with and without). Overall, the adoption of SPSs increased the probability of connectivity (PC) index in all cases. However, the contributions of SPSs to landscape connectivity were not linear. Greater PC increases were observed in highly degraded farmscapes (ΔPc = 284) compared to farmscapes containing patches that were better connected and had larger habitat areas (ΔPc = 6). These variables could play a fundamental role in enhancing the landscape connectivity through restoration activities that seek to improve biodiversity conservation. Even if they are relatively small and scattered, in highly degraded cattle ranching systems, SPSs could significantly improve the landscape connectivity, which in turn could improve wildlife conservation.

A simple and practical measure of the connectivity of protected area networks: TheProNetmetric

AbstractMeasuring connectivity is key to track progress toward broad conservation goals, such as the United Nations Convention on Biological Diversity's proposed Post‐2020 Global Biodiversity Framework. The framework includes an area‐based target for the protection of 30% of lands and seas globally—through well‐connected systems of protected areas. Although the field of connectivity science has grown rapidly, limited progress has been made in tracking conservation connectivity in practice. This is in part due to the lack of a standardizing framework to clarify different purposes, approaches, and datasets—particularly in differentiating a metric from its application within a broader connectivity framework—as well as a benchmark to quantitatively compare alternative approaches. To address this science‐practice gap, we developed a novel metric of connectivity called the Protected Network metric (ProNet). ProNet is designed to assess the structural connectivity of a protected area network in a way that can be easily described, clearly communicated, and rapidly computed at high resolution. We evaluated how ProNet adheres to fundamental conservation science principles using a library of hypothetical landscapes, compared it to two commonly used existing connectivity metrics, and demonstrated its performance in assessing connectivity for a set of real‐world landscapes selected across the gradient of human modification. More broadly, ProNet is a powerful tool to galvanize emerging connectivity conservation as a countermeasure to increasing fragmentation of global ecosystems.

Towards Liveable Urban Densities Using a GIS-Based Assessment Methodology

Urban density and densi fication are hotly debated topics of sustainable urban development. On the one hand, international landmark agreements such as the Sustainable Development Goals and the New Urban Agenda highlight the importance of good quality urban environments with low pollution, easy access to green spaces, walkability, and active mobility. On the other hand, densi fication can increase pollution, decrease available green spaces, and degrade walkability by concentrating vehicles and their operations. The result can be a degradation of urban liveability, de fined as a city's capacity to promote the wellbeing of residents. Yet higher urban density can also result in more effi cient urban infrastructures and networks and provide more housing (including more aff ordable housing) in more appropriate mixed-use locations. The challenge, then, is to maintain the liveable quality of this denser urban fabric. The GIS-based approach presented in this paper uses a basic liveability assessment by calculating connectivity, greenery, and urban form complexity metrics to be employed in the context of densi fication, aiming to optimize sustainability and liveability aspects. Using our study area in Salzburg, Austria, we demonstrate how such a GIS-based liveability assessment, relying on spatial data, can aid urban planners in quantifying and achieving both urban density and liveability.

Differences in brain connectivity of meditators during assessing neurocognition via gamified experimental logic task: A machine learning approach.

Meditation practices increase attention, memory, and self-awareness. The neuroscientific study of meditation has helped gain useful insights into the functional changes in the brain. In this study, we have assessed the performance of meditators with different years of practice while performing an engaging task rather than studying the meditation practice itself. This task helps assess many neural processes simultaneously and represents task performance in presence of multiple audio-visual distractors as in a real-life scenario. The long-term practice of meditation could bring neuroplastic changes in the way cognitive processing is carried out. It could be conscious and effortful in short-term practitioners and relatively unconscious and effortless in long-term practitioners. Our goal is to understand if it is possible to differentiate between long-term and short-term meditators solely based on their cognitive processing. A group of proficient Rajayoga meditators from the Brahma Kumaris were recruited based on their meditation experience-Long-Term Practitioners (n = 12, mean 13,596h) and Short-Term Practitioners (n = 10, mean 1095h). A task-based functional Magnetic Resonance Imaging was acquired while the subjects performed the task. Functional Connectivity Analysis was performed to derive the correlation measures to be used as features for classification. Five supervised Machine Learning algorithms Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosted Tree were used for classification. Among all the classifiers Gradient Boosted Tree performed the best with an accuracy of 77% when all the four Functional Connectivity Metrics were used. Connectivity in visual areas, cerebellum, left rostral prefrontal cortex, and middle frontal gyrus was found to be higher in long-term meditators. Such a classification demonstrates that long-term meditation practice brings about neuroplastic changes that influence cognitive processing.

Upper-Limb Amputation Disrupts the Interhemispheric Structural Rather than Functional Connectivity.

Background: Recent neuroimaging studies on upper-limb amputation have revealed the reorganization of bilateral sensorimotor cortex after sensory deprivation, underpinning the assumption of changes in the interhemispheric connections. In the present study, using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we aim to explore the alterations in the interhemispheric functional and structural connectivity after upper-limb amputation. Methods: Twenty-two upper-limb amputees and 15 age- and sex-matched healthy controls were recruited for MRI scanning. The amputees were further divided into subgroups by amputation side and residual limb pain (RLP). DTI metrics of corpus callosum (CC) subregions and resting-state functional connectivity (FC) between the bilateral sensorimotor cortices were measured for each participant. Linear mixed models were carried out to investigate the relationship of interhemispheric connectivity with the amputation, amputation side, and RLP. Results: Compared with healthy controls, upper-limb amputees showed lower axial diffusivity (AD) in CC subregions II and III. Subgroup analyses showed that the dominant hand amputation induced significant microstructural changes in CC subregion III. In addition, only amputees with RLP showed decreased fractional anisotropy and AD in CC, which was also correlated with the intensity of RLP. No significant changes in interhemispheric FC were found after upper-limb amputation. Conclusion: The present study demonstrated that the interhemispheric structural connectivity rather than FC degenerated after upper-limb amputation, and the degeneration of interhemispheric structural connectivity was shown to be relevant to the amputation side and the intensity of RLP. Impact statement Neuroimaging studies have revealed the functional reorganization of bilateral sensorimotor cortex after amputation, with expanded activation from the intact hemisphere to the deprived hemisphere. Our findings indicated a degeneration of interhemispheric white matter connections in upper-limb amputees, unveiling the underlying structural basis for bilateral functional reorganization after amputation.

How much can highway stormwater ponds contribute to amphibian ecological network connectivity?

ABSTRACT Several studies assert that transportation infrastructure rights-of-way can play a positive role for many animal species, especially in highly fragmented and human-dominated landscapes. This study examines the contribution of highway stormwater ponds to amphibian ecological networks, questioning both the influence of their location on species richness and their capacity to increase landscape connectivity. The ecological networks of amphibian species were modeled by graph theory around highway A77 (France). Stormwater ponds as well as natural ponds were included as potential habitats and several connectivity metrics were calculated at different scales. The connectivity values of the stormwater ponds were analyzed in relation to species presence data collected in 2018. While most of these ponds contribute little to regional connectivity, some of them represent important connector patches. Statistical analysis also suggests that highway ponds are used by amphibians in areas with few natural pools. This raises questions about how to improve the management of ecological networks by considering the capacity of artificial habitats to serve as a refuge for biodiversity.

Interplay of tau and functional network connectivity in progressive supranuclear palsy: a [18F]PI-2620 PET/MRI study.

Progressive supranuclear palsy (PSP) is primary 4-repeat tauopathy. Evidence spanning from imaging studies indicate aberrant connectivity in PSPs. Our goal was to assess functional connectivity network alterations in PSP patients and the potential link between regional tau-burden and network-level functional connectivity using the next-generation tau PET tracer [18F]PI-2620 and resting-state functional MRI (fMRI). Twenty-four probable PSP patients (70.9 ± 6.9years, 13 female), including 14 Richardson syndrome (RS) and 10 non-RS phenotypes, underwent [18F]PI-2620 PET/MRI imaging. Distribution volume ratios (DVRs) were estimated using non-invasive pharmacokinetic modeling. Resting-state fMRI was also acquired in these patients as well as in thirteen older non-AD MCI reference group (64 ± 9years, 4 female). The functional network was constructed using 141 by 141 region-to-region functional connectivity metrics (RRC) and network-based statistic was carried out (connection threshold p < 0.001, cluster threshold pFDR < 0.05). In total, 9870 functional connections were analyzed. PSPs compared to aged non-AD MCI reference group expressed aberrant connectivity evidenced by the significant NBS network consisting of 89 ROIs and 118 connections among them (NBS mass 4226, pFDR < 0.05). Tau load in the right globus pallidus externus (GPe) and left dentate nucleus (DN) showed significant effects on functional network connectivity. The network linked with increased tau load in the right GPe was associated with hyperconnectivity of low-range intra-opercular connections (NBS mass 356, pFDR < 0.05), while the network linked with increased tau load in the left cerebellar DN was associated with cerebellar hyperconnectivity and cortico-cerebellar hypoconnectivity (NBS mass 517, pFDR < 0.05). PSP patients show altered functional connectivity. Network incorporating deep gray matter structures demonstrate hypoconnectivity, cerebellum hyperconnectivity, while cortico-cortical connections show variable changes. Tau load in the right GPe and left DN is associated with functional networks which strengthen low-scale intra-opercular and intra-cerebellar connections and weaken opercular-cerebellar connections. These findings support the concept of tau load-dependent functional network changes in PSP, by that providing evidence for downstream effects of neuropathology on brain functionality in this primary tauopathy.