His primary scientific interests are in Connectome, Resting state fMRI, Neuroimaging, Neuroscience and Artificial intelligence. R. Cameron Craddock combines subjects such as Functional magnetic resonance imaging and Bioinformatics with his study of Connectome. His Neuroimaging research includes themes of Data mining, Neuropsychology, Medical imaging, Datasets as Topic and Amplitude of low frequency fluctuations.
His studies in Neuroscience integrate themes in fields like Diffusion MRI and Traumatic brain injury. His Artificial intelligence research incorporates themes from Reliability, Brain mapping and Pattern recognition. His work deals with themes such as Motion, Computer vision, Cognitive psychology and Spectral clustering, which intersect with Brain mapping.
R. Cameron Craddock spends much of his time researching Neuroimaging, Artificial intelligence, Resting state fMRI, Connectome and Neuroscience. His Neuroimaging research includes elements of Stroke, Stroke recovery, Voxel and Cognition. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Reliability, Machine learning, Computer vision and Pattern recognition.
His research investigates the connection between Resting state fMRI and topics such as Functional connectivity that intersect with problems in Brain function. The Connectome study combines topics in areas such as Functional magnetic resonance imaging, Data mining and Regression. In the subject of general Neuroscience, his work in Cortex and Human brain is often linked to Extramural and Identification, thereby combining diverse domains of study.
Artificial intelligence, Neuroimaging, Pattern recognition, Neuroscience and Reproducibility are his primary areas of study. His Artificial intelligence study combines topics in areas such as Reliability, Machine learning, Neural correlates of consciousness and Computer vision. His Machine learning study also includes
R. Cameron Craddock performs multidisciplinary study in Neuroimaging and Data quality in his work. His Neuroscience research is mostly focused on the topic Functional connectivity. R. Cameron Craddock has researched Deep learning in several fields, including Resting state fMRI and Identification.
His primary areas of study are Artificial intelligence, Neuroimaging, Neuroscience, Functional connectivity and Pattern recognition. His Artificial intelligence study combines topics from a wide range of disciplines, such as Lesion and Stroke recovery. His Neuroimaging study combines topics from a wide range of disciplines, such as Stroke and Gold standard.
Many of his research projects under Neuroscience are closely connected to Motor cortex with Motor cortex, tying the diverse disciplines of science together. His Functional connectivity research includes elements of Deep learning, Autism spectrum disorder and Identification. His Pattern recognition research incorporates themes from Brain development, Connectome and Test set.
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A whole brain fMRI atlas generated via spatially constrained spectral clustering
R. Cameron Craddock;G.Andrew James;Paul E. Holtzheimer;Xiaoping P. Hu.
Human Brain Mapping (2012)
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics
Chao-Gan Yan;Brian Cheung;Clare Kelly;Stanley J. Colcombe.
Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression.
Andres M. Lozano;Helen S. Mayberg;Helen S. Mayberg;Peter Giacobbe;Clement Hamani.
Biological Psychiatry (2008)
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.
Krzysztof J. Gorgolewski;Tibor Auer;Vince D. Calhoun;R. Cameron Craddock.
Scientific Data (2016)
Toward a Neuroimaging Treatment Selection Biomarker for Major Depressive Disorder
Callie L. McGrath;Mary E. Kelley;Paul E. Holtzheimer;Boadie W. Dunlop.
JAMA Psychiatry (2013)
Identification of autism spectrum disorder using deep learning and the ABIDE dataset.
Anibal Sólon Heinsfeld;Alexandre Rosa Franco;R. Cameron Craddock;Augusto Buchweitz.
NeuroImage: Clinical (2018)
Neuroimaging after mild traumatic brain injury: Review and meta-analysis
Cyrus Eierud;R. Cameron Craddock;Sean Fletcher;Manek Aulakh.
NeuroImage: Clinical (2014)
Disease state prediction from resting state functional connectivity
R. Cameron Craddock;R. Cameron Craddock;Paul E. Holtzheimer;Xiaoping P. Hu;Helen S. Mayberg.
Magnetic Resonance in Medicine (2009)
Imaging human connectomes at the macroscale
R Cameron Craddock;Saad Jbabdi;Chao-Gan Yan;Chao-Gan Yan;Chao-Gan Yan;Joshua T Vogelstein.
Nature Methods (2013)
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham;Michael P. Milham;Adriana Di Martino;R. Cameron Craddock.
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