Xi-Nian Zuo mostly deals with Neuroscience, Resting state fMRI, Brain mapping, Functional magnetic resonance imaging and Human brain. The study incorporates disciplines such as Connectome, Neuroimaging, Voxel, Artificial intelligence and Precuneus in addition to Resting state fMRI. Xi-Nian Zuo works mostly in the field of Neuroimaging, limiting it down to topics relating to Bioinformatics and, in certain cases, Discovery science, Functional neuroimaging and Computational biology.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. Xi-Nian Zuo interconnects Amplitude of low frequency fluctuations, Nerve net and Functional connectivity in the investigation of issues within Brain mapping. His study in the fields of Posterior cingulate and Functional Connectivity MRI under the domain of Functional magnetic resonance imaging overlaps with other disciplines such as Systems neuroscience.
Neuroscience, Resting state fMRI, Default mode network, Functional magnetic resonance imaging and Brain mapping are his primary areas of study. The Human brain, Connectome, Prefrontal cortex and Cognition research he does as part of his general Neuroscience study is frequently linked to other disciplines of science, such as Systems neuroscience, therefore creating a link between diverse domains of science. Within one scientific family, Xi-Nian Zuo focuses on topics pertaining to Neuroimaging under Connectome, and may sometimes address concerns connected to Cognitive psychology.
In his study, which falls under the umbrella issue of Resting state fMRI, Machine learning, Smoothing and Bioinformatics is strongly linked to Artificial intelligence. His research in Functional magnetic resonance imaging intersects with topics in Artificial neural network, Dorsolateral prefrontal cortex, Personality, Brain activity and meditation and Pattern recognition. His Brain mapping research includes elements of Amplitude of low frequency fluctuations and Nerve net.
Xi-Nian Zuo mainly investigates Artificial intelligence, Default mode network, Functional connectivity, Neuroscience and Pattern recognition. His Artificial intelligence research incorporates themes from Machine learning, Sample size determination and Big data. His research integrates issues of Major depressive disorder, Precuneus, Cortex and Salience in his study of Default mode network.
His studies in Major depressive disorder integrate themes in fields like Resting state fMRI, Internal medicine and Oncology. His work carried out in the field of Resting state fMRI brings together such families of science as Neuroimaging and Audiology. His study in Pattern recognition is interdisciplinary in nature, drawing from both Functional magnetic resonance imaging, Generalizability theory and Human Connectome Project.
His main research concerns Neuroimaging, Default mode network, Cognitive decline, Reliability and Neuroscience. His Neuroimaging research is multidisciplinary, incorporating elements of Growth chart, Brain network and School age child. His Reliability study integrates concerns from other disciplines, such as Big data, Neuroscience research, Connectomics and Reproducibility.
He performs integrative study on Neuroscience and Signal variability. His work deals with themes such as Resting state fMRI and Schizophrenia, which intersect with Temporal cortex. His study looks at the relationship between Resting state fMRI and topics such as Balance, which overlap with Audiology and Major depressive disorder.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
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)
REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing
Xiao-Wei Song;Zhang-Ye Dong;Xiang-Yu Long;Su-Fang Li.
PLOS ONE (2011)
An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF.
Qi-Hong Zou;Chao-Zhe Zhu;Yihong Yang;Xi-Nian Zuo.
Journal of Neuroscience Methods (2008)
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.
Chao-Gan Yan;Xin-Di Wang;Xi-Nian Zuo;Yu-Feng Zang.
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.
The oscillating brain: complex and reliable.
Xi-Nian Zuo;Adriana Di Martino;Clare Kelly;Zarrar E. Shehzad.
Network Centrality in the Human Functional Connectome
Xi-Nian Zuo;Ross Ehmke;Maarten Mennes;Davide Imperati.
Cerebral Cortex (2012)
Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.
Xi Nian Zuo;Clare Kelly;Jonathan S. Adelstein;Donald F. Klein;Donald F. Klein;Donald F. Klein.
Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI
Alexander Schaefer;Ru Kong;Evan M Gordon;Timothy O Laumann.
Cerebral Cortex (2018)
Growing Together and Growing Apart: Regional and Sex Differences in the Lifespan Developmental Trajectories of Functional Homotopy
Xi-Nian Zuo;Xi-Nian Zuo;Clare Kelly;Adriana Di Martino;Adriana Di Martino;Maarten Mennes.
The Journal of Neuroscience (2010)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: