What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Law
The scientist’s investigation covers issues in Neuroscience, Artificial intelligence, Pattern recognition, Resting state fMRI and Brain mapping.
His work investigates the relationship between Neuroscience and topics such as Diffusion MRI that intersect with problems in White matter.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Magnetic resonance imaging and Computer vision.
His work in the fields of Pattern recognition, such as Independent component analysis, Principal component analysis, Brain segmentation and Segmentation, overlaps with other areas such as Group comparison.
His Resting state fMRI study incorporates themes from Nerve net, Functional connectivity, Functional imaging, Cognitive science and Human brain.
His studies in Brain mapping integrate themes in fields like Independence, Interpretability and Network model.
His most cited work include:
- Advances in functional and structural MR image analysis and implementation as FSL. (9202 citations)
- Fast robust automated brain extraction (7686 citations)
- IMPROVED OPTIMIZATION FOR THE ROBUST AND ACCURATE LINEAR REGISTRATION AND MOTION CORRECTION OF BRAIN IMAGES (7328 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Neuroscience, Pattern recognition, Neuroimaging and Resting state fMRI are his primary areas of study.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Computer vision and Human Connectome Project.
All of his Neuroscience and Functional magnetic resonance imaging, Cognition, Brain mapping, Default mode network and Human brain investigations are sub-components of the entire Neuroscience study.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Magnetic resonance imaging and Diffusion MRI.
His work in Diffusion MRI covers topics such as White matter which are related to areas like Pathology.
He interconnects Connectome and Functional connectivity in the investigation of issues within Resting state fMRI.
He most often published in these fields:
- Artificial intelligence (28.26%)
- Neuroscience (20.55%)
- Pattern recognition (16.33%)
What were the highlights of his more recent work (between 2017-2021)?
- Neuroimaging (14.13%)
- Artificial intelligence (28.26%)
- Human Connectome Project (9.17%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Neuroimaging, Artificial intelligence, Human Connectome Project, Pattern recognition and Neuroscience.
He has researched Neuroimaging in several fields, including White matter, Biobank, Cognition and Disease.
Artificial intelligence is closely attributed to Machine learning in his study.
His research integrates issues of Resting state fMRI, Diffusion MRI and Connectome in his study of Human Connectome Project.
His biological study spans a wide range of topics, including Graphical model, Magnetic resonance imaging and Cortical surface.
His work on Functional connectivity, Default mode network, Brain aging and Neurotransmitter as part of general Neuroscience study is frequently linked to Functional change, bridging the gap between disciplines.
Between 2017 and 2021, his most popular works were:
- Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank (357 citations)
- Genome-wide association studies of brain imaging phenotypes in UK Biobank (239 citations)
- Statistical Challenges in "Big Data" Human Neuroimaging. (146 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Law
The scientist’s investigation covers issues in Neuroimaging, Human Connectome Project, Artificial intelligence, Connectome and Resting state fMRI.
His Neuroimaging research includes themes of Biobank and Set.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
His Independent component analysis study in the realm of Pattern recognition interacts with subjects such as Noise.
His Resting state fMRI research incorporates elements of Connectomics, Cognition, Functional connectivity, Functional magnetic resonance imaging and Brain activity and meditation.
As a member of one scientific family, he mostly works in the field of Neuroscience, focusing on Multivariate statistics and, on occasion, White matter.
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.
