What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Neuroscience
Mark W. Woolrich focuses on Artificial intelligence, Neuroscience, Pattern recognition, Functional magnetic resonance imaging and Machine learning.
His Artificial intelligence study integrates concerns from other disciplines, such as Tractography and Autoregressive model.
His Tractography research integrates issues from Human brain and Sensitivity.
Mark W. Woolrich works mostly in the field of Functional magnetic resonance imaging, limiting it down to concerns involving Neuroimaging and, occasionally, FMRIB Software Library.
His FMRIB Software Library research is multidisciplinary, incorporating perspectives in Uncinate fasciculus, Inferior longitudinal fasciculus, Neural tract and Superior longitudinal fasciculus.
He interconnects Genu of the corpus callosum, Arcuate fasciculus, Data science and Flexibility in the investigation of issues within Extreme capsule.
His most cited work include:
- Advances in functional and structural MR image analysis and implementation as FSL. (9202 citations)
- Advances in functional and structural MR image analysis and implementation as FSL. (9202 citations)
- Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? (2512 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Neuroscience, Magnetoencephalography, Pattern recognition and Resting state fMRI.
His Artificial intelligence research incorporates themes from Machine learning, Neuroimaging and Functional connectivity.
His Neuroscience study is mostly concerned with Cognition, Functional magnetic resonance imaging, Brain activity and meditation, Human brain and Electrophysiology.
His study looks at the relationship between Functional magnetic resonance imaging and fields such as Brain mapping, as well as how they intersect with chemical problems.
In his study, Cognitive psychology is inextricably linked to Working memory, which falls within the broad field of Magnetoencephalography.
His Pattern recognition research incorporates elements of Voxel, Computer vision and Bayesian probability.
He most often published in these fields:
- Artificial intelligence (43.75%)
- Neuroscience (38.97%)
- Magnetoencephalography (32.35%)
What were the highlights of his more recent work (between 2019-2021)?
- Neuroscience (38.97%)
- Magnetoencephalography (32.35%)
- Cognition (15.44%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Neuroscience, Magnetoencephalography, Cognition, Resting state fMRI and Artificial intelligence.
In his study, Lesion, Cardiology, Magnetic resonance imaging and Temporoparietal junction is strongly linked to Alpha, which falls under the umbrella field of Neuroscience.
His Magnetoencephalography research is multidisciplinary, incorporating elements of Brain activity and meditation, Hidden Markov model and Macaque.
His Resting state fMRI research includes themes of Intracranial Electroencephalography, Motor cortex, Neuroimaging, Electromyography and Epilepsy.
In his research, Biological system is intimately related to Functional connectivity, which falls under the overarching field of Neuroimaging.
Mark W. Woolrich is interested in Independent component analysis, which is a branch of Artificial intelligence.
Between 2019 and 2021, his most popular works were:
- Optimising network modelling methods for fMRI (47 citations)
- The role of transient spectral ‘bursts’ in functional connectivity: A magnetoencephalography study (13 citations)
- Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson's disease. (10 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Neuroscience
His primary areas of investigation include Neuroscience, Cognition, Magnetoencephalography, Resting state fMRI and Brain activity and meditation.
His Neuroscience study combines topics in areas such as Parkinson's disease and Subthalamic nucleus.
His studies in Magnetoencephalography integrate themes in fields like Precuneus, Orbitofrontal cortex and Hidden Markov model.
His Brain activity and meditation research is multidisciplinary, relying on both Cognitive development, Network dynamics and Brain mapping.
His work deals with themes such as Young adult, Electrophysiology, Human brain and Brain organization, which intersect with Functional brain.
His Functional connectivity research incorporates themes from Machine learning, Deep learning, Convolutional neural network and Neuroimaging.
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