What is he best known for?
The fields of study he is best known for:
- Neuroscience
- Statistics
- Cognition
Neuroscience, Connectome, Functional magnetic resonance imaging, Human brain and Resting state fMRI are his primary areas of study.
His work in Functional magnetic resonance imaging tackles topics such as Brain activity and meditation which are related to areas like Posterior cingulate, Prefrontal cortex and Default mode network.
In his study, Cognitive neuroscience, Human Connectome, Human Connectomes and Blood-oxygen-level dependent is inextricably linked to Bioinformatics, which falls within the broad field of Human brain.
His research integrates issues of Clustering coefficient, Nerve net, Neuroimaging and Brain mapping in his study of Resting state fMRI.
His Brain mapping research integrates issues from DICOM, Visualization, Data pre-processing, Artificial intelligence and Pattern recognition.
His biological study spans a wide range of topics, including Amplitude of low frequency fluctuations, Magnetic resonance imaging and Positron emission tomography.
His most cited work include:
- REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing (1406 citations)
- DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. (963 citations)
- A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (961 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Neuroscience, Resting state fMRI, Default mode network, Functional magnetic resonance imaging and Functional connectivity.
His Resting state fMRI research incorporates themes from Audiology, Posterior cingulate, Artificial intelligence, Reliability and Precuneus.
His Artificial intelligence research includes elements of Machine learning, Magnetic resonance imaging, Data mining and Pattern recognition.
In his work, Functional brain, Internal medicine, Oncology, Neuroimaging and Clinical psychology is strongly intertwined with Major depressive disorder, which is a subfield of Default mode network.
His study in Functional magnetic resonance imaging is interdisciplinary in nature, drawing from both Functional neuroimaging, Connectome, Addiction and Bioinformatics.
His work carried out in the field of Bioinformatics brings together such families of science as Human Connectomes and Cognitive neuroscience.
He most often published in these fields:
- Neuroscience (67.83%)
- Resting state fMRI (59.13%)
- Default mode network (40.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Neuroimaging (20.00%)
- Default mode network (40.00%)
- Major depressive disorder (29.57%)
In recent papers he was focusing on the following fields of study:
Chao-Gan Yan mainly focuses on Neuroimaging, Default mode network, Major depressive disorder, Resting state fMRI and Artificial intelligence.
His Major depressive disorder research is within the category of Neuroscience.
His research on Neuroscience often connects related areas such as Chronic schizophrenia.
His work on Dynamic functional connectivity as part of general Resting state fMRI study is frequently linked to Network dynamics, bridging the gap between disciplines.
The study incorporates disciplines such as Magnetic resonance imaging and Pattern recognition in addition to Artificial intelligence.
His Functional magnetic resonance imaging research is multidisciplinary, incorporating elements of Neuropathology, Connectome, Functional brain and Cortex.
Between 2019 and 2021, his most popular works were:
- Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression (45 citations)
- Stability of dynamic functional architecture differs between brain networks and states. (7 citations)
- Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium. (6 citations)
In his most recent research, the most cited papers focused on:
- Neuroscience
- Statistics
- Cognition
His main research concerns Default mode network, Major depressive disorder, Neuroscience, Resting state fMRI and Prefrontal cortex.
While working in this field, he studies both Default mode network and Association.
Chao-Gan Yan integrates several fields in his works, including Association, Pattern recognition, Visual cortex, Voxel, Artificial intelligence and Property.
His work on Dynamic functional connectivity is typically connected to Dynamic network analysis as part of general Resting state fMRI study, connecting several disciplines of science.
His Dynamic functional connectivity study results in a more complete grasp of Functional magnetic resonance imaging.
His work investigates the relationship between Prefrontal cortex and topics such as Neuroimaging that intersect with problems in Neural correlates of consciousness.
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