What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Neuroscience
Thomas E. Nichols focuses on Neuroscience, Artificial intelligence, Neuroimaging, Algorithm and Data mining.
His Neuroscience research includes elements of Genome-wide association study and Diffusion MRI.
His Artificial intelligence research incorporates elements of Machine learning, Magnetic resonance imaging, Bioinformatics and Pattern recognition.
His studies in Pattern recognition integrate themes in fields like Image processing, Fractional anisotropy, Spatial analysis and Smoothing.
His work investigates the relationship between Algorithm and topics such as Permutation that intersect with problems in Random field, Resampling, Inference, Statistics and Nonparametric statistics.
In his papers, he integrates diverse fields, such as Data mining and Sensitivity.
His most cited work include:
- Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. (4658 citations)
- Nonparametric permutation tests for functional neuroimaging: A primer with examples (4654 citations)
- Thresholding of statistical maps in functional neuroimaging using the false discovery rate. (4239 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Neuroimaging, Neuroscience, Inference and Machine learning.
His Artificial intelligence study combines topics in areas such as Functional magnetic resonance imaging and Pattern recognition.
His Neuroimaging research includes themes of Genome-wide association study, Field, Meta-analysis, Data science and Brain mapping.
His biological study deals with issues like Heritability, which deal with fields such as Fractional anisotropy.
The concepts of his Inference study are interwoven with issues in Statistics, Resampling, Null hypothesis, Algorithm and Statistic.
His studies deal with areas such as Nonparametric statistics and Permutation as well as Algorithm.
He most often published in these fields:
- Artificial intelligence (37.23%)
- Neuroimaging (30.89%)
- Neuroscience (23.17%)
What were the highlights of his more recent work (between 2017-2021)?
- Neuroimaging (30.89%)
- Artificial intelligence (37.23%)
- Inference (22.57%)
In recent papers he was focusing on the following fields of study:
Thomas E. Nichols mostly deals with Neuroimaging, Artificial intelligence, Inference, Resting state fMRI and Machine learning.
His biological study spans a wide range of topics, including White matter, Cognitive psychology, Meta-analysis, Functional magnetic resonance imaging and Data science.
The study incorporates disciplines such as Statistical hypothesis testing, Contrast, Statistical power and Pattern recognition in addition to Artificial intelligence.
His Inference research is multidisciplinary, incorporating perspectives in Sample size determination, Statistical inference, Statistics, Resampling and Null hypothesis.
His Resting state fMRI research is multidisciplinary, relying on both Developmental psychology, Null and Socioeconomic status.
Within one scientific family, he focuses on topics pertaining to Data sharing under Machine learning, and may sometimes address concerns connected to Software.
Between 2017 and 2021, his most popular works were:
- Ten simple rules for neuroimaging meta-analysis. (212 citations)
- Variability in the analysis of a single neuroimaging dataset by many teams (147 citations)
- Statistical Challenges in "Big Data" Human Neuroimaging. (146 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Thomas E. Nichols spends much of his time researching Neuroimaging, Data science, Artificial intelligence, Resting state fMRI and Field.
His Neuroimaging study integrates concerns from other disciplines, such as Biobank, Sample size determination, Cognition, Set and Algorithm.
Thomas E. Nichols works mostly in the field of Artificial intelligence, limiting it down to topics relating to Machine learning and, in certain cases, Data sharing, as a part of the same area of interest.
His study in Resting state fMRI is interdisciplinary in nature, drawing from both Null, Inference, Functional connectivity, Human Connectome Project and Pattern recognition.
The Pattern recognition study which covers Sampling distribution that intersects with Autocorrelation.
His Field study deals with Flexibility intersecting with Pipeline, Variation and Statistical hypothesis testing.
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