What is he best known for?
The fields of study he is best known for:
- Statistics
- Neuroscience
- Artificial intelligence
The scientist’s investigation covers issues in Neuroscience, Subthalamic nucleus, Deep brain stimulation, Parkinson's disease and Magnetoencephalography.
Neuroscience and Biological clock are two areas of study in which he engages in interdisciplinary research.
His Subthalamic nucleus research incorporates elements of Cerebral cortex and Local field potential.
His study looks at the intersection of Local field potential and topics like Dopamine with Beta Rhythm.
His Deep brain stimulation study combines topics from a wide range of disciplines, such as Motor cortex, Movement disorders and Electroencephalography.
His Magnetoencephalography study integrates concerns from other disciplines, such as Hippocampal formation, Functional magnetic resonance imaging and Encoding.
His most cited work include:
- Good practice for conducting and reporting MEG research (429 citations)
- EEG and MEG data analysis in SPM8. (416 citations)
- Preserved feedforward but impaired top-down processes in the vegetative state. (366 citations)
What are the main themes of his work throughout his whole career to date?
Neuroscience, Magnetoencephalography, Deep brain stimulation, Subthalamic nucleus and Electroencephalography are his primary areas of study.
As part of his studies on Neuroscience, Vladimir Litvak often connects relevant subjects like Parkinson's disease.
His Magnetoencephalography research integrates issues from Ictal, Epilepsy, Neuroimaging and Coherence.
His work deals with themes such as Pedunculopontine Tegmental Nucleus and Resting state fMRI, which intersect with Deep brain stimulation.
The study incorporates disciplines such as Cerebral cortex, Beta band, Beta Rhythm and Dopamine in addition to Subthalamic nucleus.
Vladimir Litvak interconnects Audiology, Transcranial magnetic stimulation, Artificial intelligence, Brain mapping and Pattern recognition in the investigation of issues within Electroencephalography.
He most often published in these fields:
- Neuroscience (57.14%)
- Magnetoencephalography (33.83%)
- Deep brain stimulation (29.32%)
What were the highlights of his more recent work (between 2019-2021)?
- Magnetoencephalography (33.83%)
- Neuroscience (57.14%)
- Causal model (12.78%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Magnetoencephalography, Neuroscience, Causal model, Dynamic causal modelling and Electroencephalography.
His research integrates issues of Ictal, Epilepsy, Artificial intelligence, Pattern recognition and Local field potential in his study of Magnetoencephalography.
The various areas that Vladimir Litvak examines in his Local field potential study include Granger causality and Autoregressive model.
His work carried out in the field of Neuroscience brings together such families of science as Deep brain stimulation, Parkinson's disease and Subthalamic nucleus.
The concepts of his Deep brain stimulation study are interwoven with issues in Stimulation and Set.
His Causal model research is multidisciplinary, incorporating perspectives in Econometrics and Bayesian inference.
Between 2019 and 2021, his most popular works were:
- Dynamic causal modelling of COVID-19. (24 citations)
- Bayesian fusion and multimodal DCM for EEG and fMRI (11 citations)
- Second waves, social distancing, and the spread of COVID-19 across America (9 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Neuroscience
- Artificial intelligence
Vladimir Litvak focuses on Causal model, Econometrics, Pattern recognition, Artificial intelligence and Bayesian probability.
In his research on the topic of Causal model, Time series is strongly related with Dynamic causal modelling.
His study looks at the relationship between Econometrics and fields such as Mortality rate, as well as how they intersect with chemical problems.
His biological study spans a wide range of topics, including Deep brain stimulation and Magnetoencephalography, Electroencephalography.
His Electroencephalography research includes themes of Estimator, Granger causality, Autoregressive model and Local field potential.
He combines subjects such as Test strategy and Test with his study of Bayesian probability.
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