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149 results for “network connectivity”
Data from: Road disturbance shifts root fungal symbiont types and reduces the connectivity of plant-fungal co-occurrence networks in mountains
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Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
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Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks
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Data and code from: The neglected pollinators: Settling moths are keystone floral visitors essential to network connectivity and tropical forest recovery
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Differences in network structure and connectivity of four (protected) Palearctic-Afrotropical flyways
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Invasion of freshwater ecosystems is promoted by network connectivity to hotspots of human activity
<p><b><span>Aim:</span></b> Hotspots of human activity are focal points for ecosystem disturbance and non-native introduction, from which invading populations disperse and spread. As such, connectivity to locations used by humans may influence the likelihood of invasion. Moreover, connectivity in freshwater ecosystems may follow the hydrological network. Here we tested whether multiple forms of connectivity to human recreational activities promotes biological invasion of freshwater ecosystems.</p> <p><b><span>Location:</span></b><span> England, UK.</span></p> <p><b><span>Time period:</span></b><span> 1990-2018.</span></p> <p><b><span>Major taxa studied:</span></b> 126 non-native freshwater birds, crustaceans, fish, molluscs and plants.</p> <p><b><span>Methods:</span></b> Machine learning was used to predict spatial gradients in human recreation and two high risk activities for invasion (fishing and water sports). Connectivity indices were developed for each activity, in which human influence decayed from activity hotspots according to Euclidean distance (spatial connectivity) or hydrological network distance (downstream, upstream and along-channel connectivity). Generalised linear mixed models identified the connectivity type most associated to invasive species richness of each group, while controlling for other anthropogenic and environmental drivers.</p> <p><b><span>Results:</span></b> Connectivity to humans generally had stronger positive effects on invasion than all other drivers except recording effort. Recreation had stronger influence than urban land cover, and for most groups high risk activities had stronger effects than general recreation. Downstream human connectivity was most important for invasion by most of the groups, potentially reflecting predominantly hydrological dispersal. An exception was birds, for which spatial connectivity was most important, possibly because of overland dispersal capacity.</p> <p><b><span>Main conclusions:</span></b> These findings support the hypothesis that freshwater invasion is partly determined by an interaction between human activity and species dispersal in the hydrological network. By comparing alternative connectivity types for different human activities, our approach could enable robust inference of specific pathways and spread mechanisms associated with particular taxa. This would provide evidence to support better prioritisation of surveillance and management for invasive non-native species.</p>
Integrating functional connectivity in designing networks of protected areas under climate change: a caribou case-study
<p>Land-use change and climate change are recognized as two main drivers of the current biodiversity decline. Protected areas help safeguard the landscape from additional anthropogenic disturbances and, when properly designed, can help species cope with climate change impacts. When designed to protect the regional biodiversity rather than to conserve focal species or landscape elements, protected areas need to cover a representative sample of the regional biodiversity and be functionally connected, facilitating individual movements among protected areas in a network to maximize their effectiveness. We developed a methodology to define effective protected areas to implement in a regional network using ecological representativeness and functional connectivity as criteria. We illustrated this methodology in the Gaspésie region of Québec, Canada. We simulated movements for the endangered Atlantic-Gaspésie caribou population (<i>Rangifer tarandus caribou</i>), using an individual-based model, to determine functional connectivity based on this large mammal. We created multiple protected areas network scenarios and evaluated their ecological representativeness and functional connectivity for the current and future conditions. We selected a subset of the most effective network scenarios and extracted the protected areas included in them. There was a tradeoff between ecological representativeness and functional connectivity for the created networks. Only a few protected areas among those available were repeatedly chosen in the most effective networks. Protected areas maximizing both ecological representativeness and functional connectivity represented suitable areas to implement in an effective protected areas network. These areas ensured that a representative sample of the regional biodiversity was covered by the network, as well as maximizing the movement over time between and inside the protected areas for the focal population.</p>
Data from: Phylogenetic signal in module composition and species connectivity in compartmentalized host-parasite networks
Across different taxa, networks of mutualistic or antagonistic interactions show consistent architecture. Most networks are modular, with modules being distinct species subsets connected mainly with each other and having few connections to other modules. We investigate the phylogenetic relatedness of species within modules and whether a phylogenetic signal is detectable in the within- and among module connectivity of species using 27 mammal-flea networks from the Palaearctic. In the 24 networks that were modular, closely-related hosts co-occurred in the same module more often than expected by chance; in contrast, this was rarely the case for parasites. The within- and among-module connectivity of the same host or parasite species varied geographically. However, among-module but not within-module connectivity of host and parasites was somewhat phylogenetically constrained. These findings suggest that the establishment of host-parasite networks results from the interplay between phylogenetic influences acting mostly on hosts and local factors acting on parasites, to create an asymmetrically constrained pattern of geographic variation in modular structure. Modularity in host-parasite networks seems to result from the shared evolutionary history of hosts and by trait convergence among unrelated parasites. This suggests profound differences between hosts and parasites in the establishment and functioning of bipartite antagonistic networks.
Data from: Node-based measures of connectivity in genetic networks
At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal.
Data from: Loss of functional connectivity in migration networks induces population decline in migratory birds
Migratory birds rely on a habitat network along their migration routes by temporarily occupying stopover sites between breeding and non-breeding grounds. Removal or degradation of stopover sites in a network might impede movement, and thereby reduce migration success and survival. The extent to which the breakdown of migration networks, due to changes in land use, impacts the population sizes of migratory birds is poorly understood. We measured the functional connectivity of migration networks of waterfowl species that migrate over the East Asian-Australasian Flyway from 1992-2015. We analysed the relationship between changes in non-breeding population sizes and changes in functional connectivity, while taking into account other commonly-considered species traits, using a Phylogenetic Linear Mixed Model. We found that population sizes significantly declined with a reduction in the functional connectivity of migration networks; no other predictor variables were important. We conclude that the current decrease in functional connectivity, due to habitat loss and degradation in migration networks, can negatively and crucially impact population sizes of migratory birds. Our findings provide new insights into the underlying mechanisms that affect population trends of migratory birds under environmental changes. Establishment of international agreements leading to the creation of systematic conservation networks associated with migratory species' distributions and stopover sites may safeguard migratory bird populations.
Data from: Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks
Infection may modify the behaviour of the host and of its conspecifics in a group, potentially altering social connectivity. Because many infectious diseases are transmitted through social contact, social connectivity changes can impact transmission dynamics. Previous approaches to understanding disease transmission dynamics in wild populations were limited in their ability to disentangle different factors that determine the outcome of disease outbreaks. Here we ask how social connectivity is affected by infection and how this relationship impacts disease transmission dynamics. We experimentally manipulated disease status of wild house mice using an immune challenge and monitored social interactions within this free-living population before and after manipulation using automated tracking. The immune-challenged animals showed reduced connectivity to their social groups, which happened as a function of their own behaviour, rather than through conspecific avoidance. We incorporated these disease-induced changes of social connectivity among individuals into models of disease outbreaks over the empirically-derived networks. The models revealed that changes in host behaviour frequently resulted in the disease being contained to very few animals, as opposed to becoming widespread. Our results highlight the importance of considering the role that behavioural alterations during infection can have on social dynamics when evaluating the potential for disease outbreaks.
Data from: Ditch network sustains functional connectivity and influences patterns of gene flow in an intensive agricultural landscape
In intensive agricultural landscapes, plant species previously relying on semi-natural habitats may persist as metapopulations within landscape linear elements. Maintenance of populations' connectivity through pollen and seed dispersal is a key factor in species persistence in the face of substantial habitat loss. The goals of this study were to investigate the potential corridor role of ditches and to identify the landscape components that significantly impact patterns of gene flow among remnant populations. Using microsatellite loci, we explored the spatial genetic structure of two hydrochorous wetland plants exhibiting contrasting local abundance and different habitat requirements: the rare and regionally protected Oenanthe aquatica and the more commonly distributed Lycopus europaeus, in an 83 km2 agricultural lowland located in northern France. Both species exhibited a significant spatial genetic structure, along with substantial levels of genetic differentiation, especially for L. europaeus, which also expressed high levels of inbreeding. Isolation-by-distance analysis revealed enhanced gene flow along ditches, indicating their key role in effective seed and pollen dispersal. Our data also suggested that the configuration of the ditch network and the landscape elements significantly affected population genetic structure, with (i) species-specific scale effects on the genetic neighborhood and (ii) detrimental impact of human ditch management on genetic diversity, especially for O. aquatica. Altogether, these findings highlighted the key role of ditches in the maintenance of plant biodiversity in intensive agricultural landscapes with few remnant wetland habitats.
Fig. 3. Haplotype network for 419 in Across mountains and ocean: species delimitation and historical connectivity in Holarctic and Arctic-Alpine wolf spiders (Lycosidae, Pardosa)
Fig. 3. Haplotype network for 419 specimens of P. saltuaria species group based on mitochondrial COI data. Haplotypes are colored based on morphological identification. Each line is 1 mutation step.
Map of the Burgundian Connectivity within the Network of Western- and Central European Marriages between the Nobility, 1350-1550
<p>Map of the Burgundian Connectivity within the Network of Western- and Central European Marriages between the Nobility, 1350-1550. ArcGIS (Esri). Lines indicate a marital relationship between two individuals based on their place of birth. Red lines indicate marital relationships between the Burgundian dukes and their spouses.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
Social network connections are positively related to temperature in winter flocks of black-capped chickadees
<p>Social species often share with conspecifics the responsibilities of finding food, defending against predators or caring for young. Within a social group, individuals' roles can be influenced by age, sex, and personality. Black-capped chickadees, <em>Poecile atricapillus</em>, are nonmigratory passerine birds that spend their winters living in social flocks. Due to the intense metabolic stress placed on chickadees by low winter temperatures, the role of flocking in resource localization and allocation among flockmates is an important aspect of individual fitness, especially during winter. Understanding changes in flock structure in response to the environment may provide insight into the consequences of social behaviour variability on overwinter survival. Here, we used social network analysis to quantify the effects of ambient temperature on social foraging behaviour in winter flocks of black-capped chickadees over two winters in Massachusetts, USA. Contrary to our expectations, we found that two measures of social connection were higher on warm days than on cold days. We found no evidence that sex or dominance rank influenced birds' behavioural responses to temperature. Furthermore, contrary to previous research, we found male-biased sex ratios in most of our flocks. Our findings that birds had more and stronger social connections on warm days and that an individual's response to temperature was not related to their status in a flock beg further investigation as to the specific costs and benefits of flocking in chickadees and other birds.</p>
Functional connectivity and graph theory of self networks in toodlers with ASD
<p>The study collected fMRI data from children with autism and typical developmental disorders at 18-24 months and 25-48 months, and analyzed the brain's self network using functional connectivity and graph theory methods</p>
Data from: Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas
Landscape connectivity is essential for maintaining viable populations, particularly for species restricted to fragmented habitats or naturally arrayed in metapopulations and facing rapid climate change. The importance of assessing both structural connectivity (the physical distribution of favorable habitat patches) and functional connectivity (how species move among habitat patches) for managing such species is well understood. However, the degree to which functional connectivity for a species varies among landscapes, and the resulting implications for conservation, have rarely been assessed. We used a landscape genetics approach to evaluate resistance to gene flow and, thus, to determine how landscape and climate-related variables influence gene flow for American pikas (Ochotona princeps) in eight federally managed sites in the western United States. We used those empirically-derived, individual-based landscape resistance models in conjunction with predictive occupancy models to generate patch-based network models describing functional landscape connectivity. Metareplication across landscapes enabled identification of limiting factors for dispersal that would not otherwise have been apparent. Despite the cool microclimates characteristic of pika habitat, south-facing aspects consistently represented higher resistance to movement, supporting the previous hypothesis that exposure to relatively high temperatures may limit dispersal in American pikas. We found that other barriers to dispersal included areas with a high degree of topographic relief, such as cliffs and ravines, as well as streams and distances greater than one to four kilometers depending on the site. Using the empirically-derived network models of habitat patch connectivity, we identified habitat patches that were likely disproportionately important for maintaining functional connectivity, areas in which habitat appeared fragmented, and locations that could be targeted for management actions to improve functional connectivity. We concluded that climate change, besides influencing patch occupancy as predicted by other studies, may alter landscape resistance for pikas, thereby influencing functional connectivity through multiple pathways simultaneously. Spatial autocorrelation among genotypes varied across study sites and was largest where habitat was most dispersed, suggesting that dispersal distances increased with habitat fragmentation, up to a point. This study demonstrates how landscape features linked to climate can affect functional connectivity for species with naturally fragmented distributions, and reinforces the importance of replicating studies across landscapes.
Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks
<p>The processed data and codes in the study are included. </p> <p><strong>Source data:</strong></p> <p>The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder.</p> <ul> <li><em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station</li> <li><em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km).</li> <li><em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre</li> <li><em>The meteorological simulation data</em> comes from the simulation results of the WRF model</li> </ul> <p><strong>Codes:</strong></p> <ul> <li>preprocessing of raw CMAQ data, observed pollution data and meteorological data</li> <li>bulid and train process of fully connected neural networks</li> <li>calculation of correlation between variables</li> <li>feature selection method</li> <li>contribution analysis</li> </ul>
Changes in patterns of age-related network connectivity are associated with risk for schizophrenia
<p>This is the online data repository accompanying the following manuscript:</p> <p><strong>Changes in patterns of age-related network connectivity are associated with risk for schizophrenia</strong></p> <p>Roberta Passiatore<sup>a,b,c</sup>, Linda A. Antonucci<sup>a</sup>, Thomas P. DeRamus<sup>b</sup>, Leonardo Fazio<sup>d</sup>, Giuseppe Stolfa<sup>a</sup>, Leonardo Sportelli<sup>a,e</sup>, Gianluca C. Kikidis<sup>a,e</sup>, Giuseppe Blasi<sup>a,f</sup>, Qiang Chen<sup>e</sup>, Juergen Dukart<sup>c,g</sup>, Aaron L. Goldman<sup>e</sup>, Venkata S. Mattay<sup>e,h</sup>, Teresa Popolizio<sup>i</sup>, Antonio Rampino<sup>a,f</sup>, Fabio Sambataro<sup>j</sup>, Pierluigi Selvaggi<sup>a,f</sup>, William Ulrich<sup>e</sup>, Apulian Network on Risk for Psychosis<sup>a,k,l,m,n,o</sup>, Daniel R. Weinberger<sup>e,h,p,q,r</sup>, Alessandro Bertolino<sup>a,f1</sup>, Vince D. Calhoun<sup>b1</sup>, Giulio Pergola<sup>a,e,p1</sup></p> <p><sup>a</sup> Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124 Bari, Italy</p> <p><sup>b</sup> Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303 Atlanta, GA</p> <p><sup>c</sup> Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428 Jülich, Germany</p> <p><sup>d</sup> Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010 Casamassima, Italy</p> <p><sup>e </sup>Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, MD</p> <p><sup>f</sup> Psychiatric Unit, University Hospital, 70124 Bari, Italy</p> <p><sup>g</sup> Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany</p> <p><sup>h</sup> Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287 Baltimore, MD</p> <p><sup>i</sup> Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Foggia, Italy</p> <p><sup>j</sup> Section of Psychiatry, Department of Neuroscience, University of Padova, 35121 Padova, Italy</p> <p><sup>k</sup> Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121 Foggia, Italy</p> <p><sup>l </sup>Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy</p> <p><sup>m </sup>Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123 Andria, Italy</p> <p><sup>n </sup>Department of Mental Health, Azienda Sanitaria Locale Bari, 70132 Bari, Italy</p> <p><sup>o </sup>Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100 Brindisi, Italy</p> <p><sup>p</sup> Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205 Baltimore, MD</p> <p><sup>q</sup> Department of Neuroscience, Johns Hopkins University School of Medicine, 21287 Baltimore, MD<br> <sup>r</sup> Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287 Baltimore, MD</p> <p> </p> <p>Members of the Apulian Network on Risk for Psychosis include Ileana Andriola, Lucia Mare, Nicolò Parente, Alessandra Raio, Veronica D. Toro, (Department of Translational Biomedicine and Neuroscience – University of Bari Aldo Moro, Bari, IT), Mario Altamura, Marimar Castrigno, Melania Difino (Department of Mental Health, ASL Foggia, Foggia, IT; Department of Clinical and Experimental Medicine, University of Foggia, Foggia, IT), Flora Brudaglio, Domenico S. Savino, Rossana Vista (Department of Mental Health, ASL Barletta-Andria-Trani, Andria, IT), Angela Carofiglio, Barbara Gelao, Marina Mancini (Department of Mental Health, ASL Bari, Bari, IT), Alessandro Saponaro, Anna Manzari, Domenico Suma (Department of Mental Health, ASL Brindisi, Brindisi, IT).</p> <p><strong>Abstract</strong></p> <p>Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single- session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age-stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlate with genetic risk for SCZ and are detectable with MRI in young participants.</p> <p><em><strong>DOI: 10.1073/pnas.2221533120</strong></em></p> <p> </p> <p>Files:</p> <p>- <strong>NeuroMark_1.0_template </strong></p> <ol> <li><strong><em>NeuroMark_1.0.nii</em>: </strong> 4D Nifti containing the 100 Independent Components (ICs) spatial maps that was used for individual IC extraction based on Du Y, Fu Z, Sui J, Gao S, Xing Y, Lin D, Salman M, Abrol A, Rahaman MA, Chen J, Hong LE, Kochunov P, Osuch EA, Calhoun VD; Alzheimer's Disease Neuroimaging Initiative. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. Neuroimage Clin. 2020;28:102375. doi: 10.1016/j.nicl.2020.102375. Epub 2020 Aug 11. PMID: 32961402; PMCID: PMC7509081.</li> <li><em><strong>NeuroMark_1.0_Label.doc</strong></em>: document containing labels for each IC extracted including brain regions, peak coordinates of ICs, corresponding functional network in which ICs are arranged, and the corresponding image number in the NeuroMark_1.0.nii file. NOTE</li> </ol> <p>- <strong>AveragedMatrix</strong></p> <ol> <li><em><strong>vectorized_FNC_LIBD.RData, </strong></em><strong><em>vectorized_FNC_UNIBA1.RData, </em></strong><strong><em>vectorized_FNC_UNIBA1.RData:</em></strong></li> <li><strong><em>FNC_all_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects averaged across groups, sessions and cohorts</li> <li><strong><em>FNC_all_rest (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during resting state fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during encoding (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during retrieval (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during working memory (2-Back) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix across subjects during emotion recognition (Faces/FMT) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_yNC_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC averaged across sessions</li> <li><strong><em>FNC_yNC_rest (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during resting state fMRI</li> <li><strong><em>FNC_yNC_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during encoding (RISE/PEAR) fMRI</li> <li><strong><em>FNC_yNC_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during retrieval (RISE/PEAR) fMRI</li> <li><strong><em>FNC_yNC_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during working memory (2-Back) fMRI</li> <li><strong><em>FNC_yNC_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during emotion recognition (Faces/FMT) fMRI</li> <li><strong><em>FN_oNC_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC averaged across sessions</li> <li><strong><em>FNC_oNC_rest (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during resting state fMRI</li> <li><strong><em>FNC_oNC_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during encoding (RISE/PEAR) fMRI</li> <li><strong><em>FNC_oNC_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during retrieval (RISE/PEAR) fMRI</li> <li><strong><em>FNC_oNC_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during working memory (2-Back) fMRI</li> <li><strong><em>FNC_oNC_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during emotion recognition (Faces/FMT) fMRI</li> <li><strong><em>FNC_ySIB_averaged (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB averaged across sessions</li> <li><strong><em>FNC_ySIB_rest (LIBD): </em></strong>mean FNC matrix on ySIB during resting state fMRI</li> <li><strong><em>FNC_ySIB_enc (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB during encoding (PEAR) fMRI</li> <li><strong><em>FNC_ySIB_ret (LIBD): </em></strong>mean FNC matrix on ySIB during retrieval (PEAR) fMRI</li> <li><strong><em>FNC_ySIB_nback (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB during working memory (2-Back) fMRI</li> <li><strong><em>FNC_ySIB_emo (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB during emotion recognition (Faces/FMT) fMRI</li> <li><strong><em>FNC_oSIB_averaged (UNIBA1, LIBD): </em></strong>mean FNC matrix on oSIB averaged across sessions</li> <li><strong><em>FNC_oSIB_rest (LIBD): </em></strong>mean FNC matrix on oSIB during resting state fMRI</li> <li><strong><em>FNC_oSIB_enc (UNIBA1, LIBD): </em></strong>mean FNC matrix on oSIB during encoding (PEAR) fMRI</li> <li><strong><em>FNC_oSIB_ret (LIBD): </em></strong>mean FNC matrix on oSIB during retrieval (PEAR) fMRI</li> <li><strong><em>FNC_oSIB_nback (UNIBA1, LIBD): </em></strong>mean FNC matrix on oSIB during working memory (2-Back) fMRI</li> <li><strong><em>FNC_oSIB_emo (LIBD): </em></strong>mean FNC matrix on oSIB during emotion recognition (FMT) fMRI</li> <li><strong><em>FNC_yPSY_averaged (UNIBA2): </em></strong>mean FNC matrix on yPSY averaged across sessions</li> <li><strong><em>FNC_yPSY_rest (UNIBA2): </em></strong>mean FNC matrix on yPSY during resting state fMRI</li> <li><strong><em>FNC_yPSY_enc (UNIBA2): </em></strong>mean FNC matrix on yPSY during encoding (RISE) fMRI</li> <li><strong><em>FNC_yPSY_ret (UNIBA2): </em></strong>mean FNC matrix on yPSY during retrieval (RISE) fMRI</li> <li><strong><em>FNC_yPSY_nback (UNIBA2): </em></strong>mean FNC matrix on yPSY during working memory (2-Back) fMRI</li> <li><strong><em>FNC_yPSY_emo (UNIBA2): </em></strong>mean FNC matrix on yPSY during emotion recognition (Faces) fMRI</li> <li><strong><em>FNC_oSCZ_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ averaged across sessions</li> <li><strong><em>FNC_oSCZ_rest (UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during resting state fMRI</li> <li><strong><em>FNC_oSCZ_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during encoding (PEAR/RISE) fMRI</li> <li><strong><em>FNC_oSCZ_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during retrieval (PEAR/RISE) fMRI</li> <li><strong><em>FNC_oSCZ_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during working memory (2-Back) fMRI</li> <li><strong><em>FNC_oSCZ_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during emotion recognition (Faces/FMT) fMRI</li> <li><strong>PlotMatrixFNC.m (Script): </strong>code used to generate FNC plots. Example of FNC plots are included in Figure 1.</li> </ol> <p>- <strong>Connectogram</strong></p> <ol> <li><em><strong>MeanMatrix.mat: </strong></em>matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Matlab object.</li> <li><em><strong>MeanMatrix.txt: </strong></em>matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Text file.</li> <li><em><strong>ConnectogramNeuroMark_1.0.m (Script)</strong></em>: code used to generate Figure 2. Graphical and statistical options are provided. Please refer to <a href="https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf">https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf</a> for further details.</li> </ol> <p>- <strong>ResFNCAgeDifferences</strong></p> <ol> <li><em><strong>data_example.RData (Data): </strong></em>example data frame for <em>AgeDiff </em>code.</li> <li><strong><em>AgeDiff_real.R (Script)</em></strong>: code to test age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li><em><strong>AgeDiff_permutation.R (Script): </strong></em>code to permute age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li><strong><em>ResFNCAgeDifference.RData: </em></strong>summary statistics obtained on UNIBA1, UNIBA2, LIBD cohorts included in the manuscript.</li> <li><em><strong>boxplot_permutation_p_LIBD.pdf, boxplot_permutation_p_UNIBA1.pdf, boxplot_permutation_p_UNIBA2.pdf: </strong></em>error bars showing results obtained after permutation (10,000 iteration) across IC pairs.</li> </ol> <p><strong>- dataFNC</strong></p> <ol> <li><strong><em>data_final.RData: </em></strong>de-identified individuals' aggregate FNC data for each cohort (UNIBA1, UNIBA2, LIBD, PNC, ABCD, UKB)</li> <li><em><strong>plotData.R (Script): </strong></em>code used to plot FNC differences across groups (Figure 3). Code used to assess group differences - Wilcoxon Rank sum test - is included.</li> </ol> <p><strong>- ResFNCRiskDifferences</strong></p> <ol> <li><em><strong>res_cerebellar_occipitoparietal_ICs.csv: </strong></em>summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted<sub>FDR</sub><0.05) on the cerebellar-occipitoparietal FNC</li> <li><strong><em>res_dorsolateralPFC_sensorimotor_ICs.csv: </em></strong>summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted<sub>FDR</sub><0.05) on medial PFC-sensorimotor FNC</li> <li><strong><em>res_medialPFC_sensorimotor_ICs.csv: </em></strong>summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted<sub>FDR</sub><0.05) on dorsolateral PFC-sensorimotor FNC</li> </ol> <p>- <strong>ResFNCxPRS</strong></p> <ol> <li><strong><em>ResMetanalysisPRS_FNC_R.csv: </em></strong>summary statistics of metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups</li> <li><strong><em>ResMetanalysisPRS_FNC_children_R.csv: </em></strong>summary statistics of metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li><strong><em>t2r.R (Script):</em></strong> code for t to R<sup>2</sup> transform</li> <li><em><strong>MetanalysisAveragedFNC_PRS.R (Script): </strong></em>code used perform metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> <li><strong><em>MetanalysisAveragedFNC_PRS_children.R (Script): </em></strong>code used perform metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li><strong><em>MetanalysisPRS_FNC_sessions_R.R (Script): </em></strong>code used perform metanalysis on the session-specific FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> </ol> <p> </p> <p>The present work includes data from UK Biobank (ID #41655), Adolescence Brain Cognitive Development (ABCD; ID #12036) Study, and Philadelphia Neurodevelopmental Cohort (PNC; ID #20998). Data are publicly accessible under approved request at the following links:</p> <p>- UK Biobank: <a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a></p> <p>- ABCD: <a href="https://abcdstudy.org/">https://abcdstudy.org/</a></p> <p>- PNC: <a href="https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html">https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html</a></p> <p>The ABCD data used in this report came from NIMH Data Archive DOI: https://doi.org/10.15154/1528793.</p> <p>Additional code for MRI data processing and Independent Component Analysis are publicly available at the following link: <a href="https://trendscenter.org/software/">https://trendscenter.org/software/</a></p> <p> </p> <p>Please, check https://doi.org/10.5281/zenodo.7853030 for future updates.</p> <p>For any data inquiries please contact:</p> <ul> <li><strong>Roberta Passiatore: </strong>Roberta.Passiatore@uniba.it</li> <li><strong>Giulio Pergola: </strong>Giulio.Pergola@uniba.it - Giulio.Pergola@libd.org</li> <li><strong>Vince D Calhoun:</strong> VCalhoun@gsu.edu</li> <li><strong>Alessandro Bertolino:</strong> Alessandro.Bertolino@uniba.it</li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 798181 awarded to G.P. (PI) and A.B., D.R.W.; from the European Union funding within the MUR PNRR Extended Partnership initiative on Neuroscience and Neuropharmacology (Project no. PE00000006 CUP H93C22000660006 “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) to G.B., A.R., A.B., and G.P.; from the funding initiative Horizon Europe Seeds 2021 (Next Generation 8 EU–MUR D.M. 737/2021) for the project S68 CUP: H99J21017550006 to G.P. (PI) and L.A.A., G.B., A.R., A.B.; from the Apulian regional government for the project: “Early Identification of Psychosis Risk” to A.B.; from the Research Projects of National Relevance (PRIN) 2017 Prot. 2017K2NEF4 awarded to G.P.; from a Collaboration Grant from Exprivia Spa to G.P. under the ministerial decree D.M. n. 352/22 and A.B. under a collaboration agreement; from a Collaboration Grant from ITEL Telecomunicazioni Srl awarded to A.B.; from the 2021 Helmholtz Information and Data Science Academy Grant No. 12429 awarded to R.P. and J.D.; and from the NIH grant #R01MH118695 awarded to V.D.C. The collection of the MRI and genetic data for the LIBD cohort was supported by direct funding from the Intramural Research Program of the NIMH to the Clinical Brain Disorders Branch (PI: D.R.W., protocol 95-M-0150).</p> <p> </p>
Enhancing Function in Later Life: Exercise and Functional Network Connectivity
ClinicalTrials.gov study NCT02068612. IPD Sharing: Not stated. Countries: 1. Publications: 4.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.