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About 2,353 papers for “Connectomics”

Connectomics

Alison Calder · University of Regina Press eBooks · 2022

Cited by 0

Connectomics

Gareth Ball, Serena J. Counsell · Cambridge University Press eBooks · 2017

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Cited by 0

Connectomics

Dieter Jaeger, Ranu Jung · Encyclopedia of Computational Neuroscience · 2015

Cited by 0

Stroke connectomics

Schmitt, Oliver · Figshare · 2024

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the <i>neuroVIISAS</i> rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.The data available here allow the reproduction of the findings published in the paper "Connectome-based prediction of functional impairment in experimental stroke models" using the <i>neuroVIISAS</i> framework.

Cited by 0Open access

Human connectomics.

Behrens, TE, Sporns, O · Oxford University Research Archive (ORA) (University of Oxford) · 2012

Recent advances in non-invasive neuroimaging have enabled the measurement of connections between distant regions in the living human brain, thus opening up a new field of research: Human connectomics. Different imaging modalities allow the mapping of structural connections (axonal fibre tracts) as well as functional connections (correlations in time series), and individual variations in these connections may be related to individual variations in behaviour and cognition. Connectivity analysis has already led to a number of new insights about brain organization. For example, segregated brain regions may be identified by their unique patterns of connectivity, structural and functional connectivity may be compared to elucidate how dynamic interactions arise from the anatomical substrate, and the architecture of large-scale networks connecting sets of brain regions may be analysed in detail. The combined analysis of structural and functional networks has begun to reveal components or modules with distinct patterns of connections that become engaged in different cognitive tasks. Collectively, advances in human connectomics open up the possibility of studying how brain connections mediate regional brain function and thence behaviour.

Cited by 0

Statistical Connectomics

NeuroData · Figshare · 2015

Vogelstein JT, et al. Statistical Connectomics. Janelia Farm conference on Machine Learning, Statistical Inference and Neuroscience, 2012

Cited by 0Open access

Statistical Connectomics

Jaewon Chung, Eric Bridgeford, Jesús Arroyo et al. · 2020

The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

Cited by 7Open access

Statistical Connectomics

NeuroData · Figshare · 2014

Slides for talk I gave at Harvard University on February 2010.

Cited by 0Open access

Human connectomics

Timothy E.J. Behrens, Olaf Sporns · Current Opinion in Neurobiology · 2011

Cited by 239Open access

Stroke connectomics

Schmitt, Oliver · Figshare · 2024

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the <i>neuroVIISAS</i> rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.The data available here allow the reproduction of the findings published in the paper "Connectome-based prediction of functional impairment in experimental stroke models" using the <i>neuroVIISAS</i> framework.

Cited by 0Open access

Comparative Connectomics

Martijn P. van den Heuvel, Edward T. Bullmore, Olaf Sporns · Trends in Cognitive Sciences · 2016

Cited by 348

Brain Connectomics

Erik H. Middlebrooks, Sanjeet S. Grewal · Neuroimaging Clinics of North America · 2022

Cited by 10

Comparative Primate Connectomics

James K. Rilling, Martijn P. van den Heuvel · Brain Behavior and Evolution · 2018

A connectome is a comprehensive map of neural connections of a species nervous system. While recent work has begun comparing connectomes across a wide breadth of species, we present here a more detailed and specific comparison of connectomes across the primate order. Long-range connections are thought to improve communication efficiency and thus brain function but are costly in terms of energy and space utilization. Methods for measuring connectivity in the brain include measuring white matter volume, histological cell counting, anatomical tract tracing, diffusion-weighted imaging and tractography, and functional connectivity in MRI. Comparisons of global white matter connectivity suggest that larger primate brains are less well connected than smaller primate brains, but that humans have more connections than expected for our cortical neuron number, which may be concentrated in the prefrontal cortex. Although there is significant overlap in structural connectivity between humans and nonhuman primates, human-specific connections are found in cortical areas involved with language, imitation, and tool use. Similar to structural connectivity, there is also widespread overlap between humans and macaques in resting state functional connectivity. However, there are again a number of human-specific connections in cortical regions involved in language, tool use, and empathy. Comparative connectomics also offers the opportunity to detect specializations of connectivity in other primate species besides humans. Future research should capitalize on the ability of diffusion tractography to measure connectivity in postmortem brains that could expand the representation of species beyond humans, chimpanzees, and rhesus macaques, and facilitate identification of connectivity-based adaptations to different social and ecological niches. This work will require careful attention to establishing cortical homologies across species and to improving tractography methods to limit detection of false-positive and false-negative connections. Finally, it will be important to attempt to establish the functional significance of variation in connectivity profiles by examining how these covary with behavior and cognition both across and within species.

Cited by 39Open access

Google Connectomics Utilities

Blakely, Tim, Li, Peter H., Lueckmann, Jan-Matthis et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

Common code for analyzing volumetric datasets for connectomic reconstruction.

Cited by 0Open access