What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Neuroscience
- Electroencephalography
Cornelis J. Stam mainly investigates Neuroscience, Electroencephalography, Magnetoencephalography, Resting state fMRI and Small-world network.
The Neuroscience study combines topics in areas such as Alzheimer's disease, Alpha and Clustering coefficient.
His Electroencephalography research is multidisciplinary, incorporating elements of Schizophrenia, Communication and Nonlinear system.
His research integrates issues of Nerve net, Central nervous system disease, Power graph analysis, Parkinson's disease and Functional connectivity in his study of Magnetoencephalography.
The study incorporates disciplines such as Frontal lobe, Average path length, Functional magnetic resonance imaging, Default mode network and Brain activity and meditation in addition to Resting state fMRI.
His Small-world network research is multidisciplinary, incorporating perspectives in Graph theory, Temporal lobe, Path length and Scale-free network.
His most cited work include:
- Consistent resting-state networks across healthy subjects (3280 citations)
- Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. (1058 citations)
- Small-world networks and functional connectivity in Alzheimer's disease (1001 citations)
What are the main themes of his work throughout his whole career to date?
Cornelis J. Stam mainly focuses on Neuroscience, Electroencephalography, Magnetoencephalography, Cognition and Resting state fMRI.
Cornelis J. Stam mostly deals with Brain mapping in his studies of Neuroscience.
The various areas that he examines in his Electroencephalography study include Functional connectivity, Disease, Audiology and Epilepsy.
His Magnetoencephalography research integrates issues from Magnetic resonance imaging, Brain activity and meditation, Glioma and Cognitive decline.
Cornelis J. Stam focuses mostly in the field of Resting state fMRI, narrowing it down to matters related to Artificial intelligence and, in some cases, Machine learning.
Cornelis J. Stam has included themes like Graph theory, Graph and Clustering coefficient in his Small-world network study.
He most often published in these fields:
- Neuroscience (49.03%)
- Electroencephalography (43.00%)
- Magnetoencephalography (26.33%)
What were the highlights of his more recent work (between 2016-2021)?
- Magnetoencephalography (26.33%)
- Neuroscience (49.03%)
- Electroencephalography (43.00%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Magnetoencephalography, Neuroscience, Electroencephalography, Cognition and Functional connectivity.
The concepts of his Magnetoencephalography study are interwoven with issues in Centrality, Resting state fMRI, Magnetic resonance imaging, Cognitive decline and Default mode network.
His Neuroscience research includes elements of Multiple sclerosis and Parkinson's disease.
His Electroencephalography research incorporates themes from Alpha, Audiology, Cardiology and Dementia, Disease.
His Cognition study incorporates themes from Biomarker and Alzheimer's disease.
In general Functional connectivity, his work in Functional networks is often linked to Network analysis linking many areas of study.
Between 2016 and 2021, his most popular works were:
- Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: an MEG-based multiplex network study (65 citations)
- Functional connectivity and network analysis during hypoactive delirium and recovery from anesthesia. (41 citations)
- Clinical correlates of quantitative EEG in Parkinson disease : A systematic review (37 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Neuroscience
- Statistics
Cornelis J. Stam focuses on Magnetoencephalography, Neuroscience, Functional connectivity, Electroencephalography and Cognition.
Cornelis J. Stam interconnects Centrality, Sensitivity, Ictal, Resting state fMRI and Artificial intelligence in the investigation of issues within Magnetoencephalography.
His White matter research extends to the thematically linked field of Neuroscience.
His study in the field of Dynamic functional connectivity is also linked to topics like Network analysis.
His Electroencephalography research includes themes of Dementia, Disease, Pathology, Graph theory and Clinical psychology.
His studies deal with areas such as Alpha, Physical medicine and rehabilitation, Power graph analysis, Cognitive decline and Alzheimer's disease as well as Cognition.
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