His scientific interests lie mostly in Neuroscience, Resting state fMRI, Artificial intelligence, Brain mapping and Functional magnetic resonance imaging. His works in Default mode network, Cognition, Prefrontal cortex, Working memory and Brain activity and meditation are all subjects of inquiry into Neuroscience. His Resting state fMRI research includes themes of Nerve net, Connectome, Functional connectivity, Human Connectome Project and Cognitive science.
He interconnects Classifier and Neuroimaging in the investigation of issues within Connectome. His Artificial intelligence research incorporates themes from Machine learning, Data mining and Pattern recognition. His study looks at the relationship between Functional magnetic resonance imaging and fields such as Human brain, as well as how they intersect with chemical problems.
Christian F. Beckmann mostly deals with Neuroscience, Resting state fMRI, Artificial intelligence, Functional magnetic resonance imaging and Neuroimaging. His study in Default mode network, Cognition, Brain mapping, Temporal lobe and Prefrontal cortex is done as part of Neuroscience. His research in Default mode network intersects with topics in Precuneus and Posterior cingulate.
His Resting state fMRI study combines topics in areas such as Nerve net, Connectome, Functional connectivity, Human brain and Salience. Christian F. Beckmann combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence. In his study, Somatosensory system is strongly linked to Sensory system, which falls under the umbrella field of Functional magnetic resonance imaging.
The scientist’s investigation covers issues in Neuroscience, Neuroimaging, Artificial intelligence, Autism and Resting state fMRI. His Neuroscience course of study focuses on Adaptation and Visual hierarchy and Visual processing. His Neuroimaging research is multidisciplinary, incorporating elements of White matter, Computational biology, Disease and Attention deficit hyperactivity disorder.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition. His study looks at the intersection of Resting state fMRI and topics like Functional connectivity with Set and Brain organization. His research integrates issues of Functional magnetic resonance imaging and Audiology in his study of Autism spectrum disorder.
Christian F. Beckmann spends much of his time researching Neuroscience, Neuroimaging, Cognition, Artificial intelligence and Machine learning. His Neuroimaging research includes elements of Neocortex, Temporal cortex, Temporal lobe and Tractography. His Cognition research incorporates elements of Functional connectivity, Human Connectome Project and Set.
By researching both Artificial intelligence and Network architecture, he produces research that crosses academic boundaries. Christian F. Beckmann has researched Machine learning in several fields, including Range, Variation and Bayesian linear regression. His work in Somatosensory system addresses subjects such as Cerebellum, which are connected to disciplines such as Autism, Autism spectrum disorder and Functional magnetic resonance imaging.
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Advances in functional and structural MR image analysis and implementation as FSL.
S M Smith;M Jenkinson;M W Woolrich;M W Woolrich;C F Beckmann.
Correspondence of the brain's functional architecture during activation and rest.
Stephen M. Smith;Peter T. Fox;Karla L. Miller;David C. Glahn.
Proceedings of the National Academy of Sciences of the United States of America (2009)
Consistent resting-state networks across healthy subjects
J. S. Damoiseaux;S. A. R. B. Rombouts;F. Barkhof;P. Scheltens.
Proceedings of the National Academy of Sciences of the United States of America (2006)
Investigations into resting-state connectivity using independent component analysis
Christian F Beckmann;Marilena DeLuca;Joseph T Devlin;Stephen M Smith.
Philosophical Transactions of the Royal Society B (2005)
Toward discovery science of human brain function
Bharat B. Biswal;Maarten Mennes;Xi Nian Zuo;Suril Gohel.
Proceedings of the National Academy of Sciences of the United States of America (2010)
Probabilistic independent component analysis for functional magnetic resonance imaging
C.F. Beckmann;S.M. Smith.
IEEE Transactions on Medical Imaging (2004)
Bayesian analysis of neuroimaging data in FSL.
Mark William Woolrich;Saâd Jbabdi;Brian Patenaude;Michael A. Chappell.
A multi-modal parcellation of human cerebral cortex
Matthew F. Glasser;Timothy S. Coalson;Emma C. Robinson;Emma C. Robinson;Carl D. Hacker.
Network modelling methods for FMRI.
Stephen M. Smith;Karla L. Miller;Gholamreza Salimi-Khorshidi;Matthew Webster.
An anatomically comprehensive atlas of the adult human brain transcriptome
Michael J. Hawrylycz;Ed S. Lein;Angela L. Guillozet-Bongaarts;Elaine H. Shen.
Profile was last updated on December 6th, 2021.
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