What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Machine learning
His primary scientific interests are in Diffusion MRI, Neuroscience, Brain mapping, Connectome and Neuroimaging.
His Diffusion MRI research is multidisciplinary, relying on both Smoothing, White matter, Functional connectivity and Pattern recognition.
His Neuroscience research focuses on Functional magnetic resonance imaging and Cerebral cortex.
The various areas that he examines in his Brain mapping study include Recall, Video tracking, Working memory, Long-term memory and Artificial intelligence.
He has included themes like Machine learning and Computer vision in his Artificial intelligence study.
His Connectome research incorporates elements of Posttraumatic stress and Resting state fMRI.
His most cited work include:
- An open science resource for establishing reliability and reproducibility in functional connectomics (290 citations)
- Review of methods for functional brain connectivity detection using fMRI (186 citations)
- Representing and Retrieving Video Shots in Human-Centric Brain Imaging Space (142 citations)
What are the main themes of his work throughout his whole career to date?
Kaiming Li focuses on Artificial intelligence, Diffusion MRI, Neuroscience, Resting state fMRI and Neuroimaging.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Working memory, Cortical surface, Computer vision, Machine learning and Pattern recognition.
The concepts of his Diffusion MRI study are interwoven with issues in White matter, Neuropsychology, Stimulus, Human brain and Voxel.
His study in Brain mapping, Functional connectivity, Cerebral cortex, Functional magnetic resonance imaging and Macaque falls under the purview of Neuroscience.
His studies deal with areas such as Schizophrenia, Connectome, Connectomics, State and Default mode network as well as Resting state fMRI.
The Neuroimaging study which covers Similarity that intersects with Smoothing and Data mining.
He most often published in these fields:
- Artificial intelligence (58.68%)
- Diffusion MRI (60.33%)
- Neuroscience (57.02%)
What were the highlights of his more recent work (between 2016-2021)?
- Major depressive disorder (8.26%)
- Default mode network (12.40%)
- Neuroscience (57.02%)
In recent papers he was focusing on the following fields of study:
Kaiming Li mainly focuses on Major depressive disorder, Default mode network, Neuroscience, Functional connectivity and Internal medicine.
His Default mode network research includes elements of Resting state fMRI, Neuroimaging and Functional brain.
Kaiming Li studied Resting state fMRI and Connectome that intersect with Functional magnetic resonance imaging, Identification, Recurrent neural network and Visualization.
Kaiming Li frequently studies issues relating to Pattern analysis and Neuroscience.
His biological study spans a wide range of topics, including Convolutional neural network, Pattern recognition, Depression and Recurrent major depressive disorder.
His White matter research is multidisciplinary, relying on both Corpus callosum and Diffusion MRI.
Between 2016 and 2021, his most popular works were:
- Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. (91 citations)
- Microstructural brain abnormalities in medication-free patients with major depressive disorder: a systematic review and meta-analysis of diffusion tensor imaging. (63 citations)
- Volume alteration of hippocampal subfields in first-episode antipsychotic-naïve schizophrenia patients before and after acute antipsychotic treatment. (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Machine learning
His primary areas of study are Major depressive disorder, Internal medicine, First episode, Neuroscience and Antipsychotic.
His studies deal with areas such as Functional connectivity, Precuneus, Functional brain and Oncology as well as Major depressive disorder.
His Internal medicine research is multidisciplinary, incorporating elements of White matter, Fractional anisotropy, Corpus callosum and Cardiology.
His White matter study incorporates themes from Meta-analysis, Psychiatry and Diffusion MRI.
As part of his studies on Neuroscience, Kaiming Li often connects relevant subjects like Grey matter.
His Antipsychotic research incorporates elements of Corona radiata, Internal capsule and Drug-naïve.
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