What is she best known for?
The fields of study she is best known for:
- Neuroscience
- Statistics
- Neuron
Her main research concerns Neuroscience, Dynamic causal modelling, Causal model, Electroencephalography and Local field potential.
She performs multidisciplinary study on Neuroscience and Psychedelic therapy in her works.
The Dynamic causal modelling study combines topics in areas such as Information flow, Cognitive science, Relation and Functional integration.
Her studies in Causal model integrate themes in fields like Bayes' theorem and Magnetoencephalography.
Her Electroencephalography research includes themes of Consciousness, Frontal lobe, Visual cortex and Unconsciousness.
Her research investigates the link between Local field potential and topics such as Electrophysiology that cross with problems in Phenomenological model, Linear system, Neurophysiology and Biological neural network.
Her most cited work include:
- Bayesian model selection for group studies. (1053 citations)
- Ten simple rules for dynamic causal modeling. (584 citations)
- Analysing connectivity with Granger causality and dynamic causal modelling. (407 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Neuroscience, Dynamic causal modelling, Electroencephalography, Causal model and Bayesian inference.
Her study in Sensory system, Electrophysiology, Magnetoencephalography, Inhibitory postsynaptic potential and Prefrontal cortex is carried out as part of her Neuroscience studies.
Her work deals with themes such as Mismatch negativity and Brain mapping, which intersect with Sensory system.
Her Dynamic causal modelling research integrates issues from Local field potential and Visual cortex.
In the field of Electroencephalography, her study on Wakefulness overlaps with subjects such as Generative model.
Research on Artificial intelligence and Bayesian probability is a part of her Bayesian inference study.
She most often published in these fields:
- Neuroscience (63.77%)
- Dynamic causal modelling (27.54%)
- Electroencephalography (22.46%)
What were the highlights of her more recent work (between 2019-2021)?
- Neuroscience (63.77%)
- Dynamic causal modelling (27.54%)
- Causal model (15.94%)
In recent papers she was focusing on the following fields of study:
Rosalyn J. Moran mostly deals with Neuroscience, Dynamic causal modelling, Causal model, Bayesian probability and Mortality rate.
In her study, which falls under the umbrella issue of Neuroscience, Anterior cingulate cortex is strongly linked to Nociception.
Her Dynamic causal modelling research incorporates themes from Stimulation and Functional neuroimaging.
Her research in Causal model focuses on subjects like Econometrics, which are connected to Trajectory, Test strategy and Test.
Rosalyn J. Moran has researched Bayesian probability in several fields, including Proprioception and Machine learning.
Her research in Mortality rate intersects with topics in Statistics and Bayesian inference.
Between 2019 and 2021, her most popular works were:
- Dynamic causal modelling of COVID-19. (24 citations)
- In Vivo Assay of Cortical Microcircuitry in Frontotemporal Dementia: A Platform for Experimental Medicine Studies. (9 citations)
- Co-registration of eye movements and neuroimaging for studying contextual predictions in natural reading (7 citations)
In her most recent research, the most cited papers focused on:
- Neuroscience
- Statistics
- Neuron
Her main research concerns Bayesian probability, Mortality rate, Causal model, Dynamic causal modelling and Pandemic.
The study incorporates disciplines such as Test strategy, Test and Bayesian inference in addition to Mortality rate.
Rosalyn J. Moran has included themes like Susceptible individual and Hidden Markov model in her Bayesian inference study.
Her Causal model study combines topics in areas such as Frontotemporal dementia, Neurodegeneration, Sensory system, Econometrics and Magnetoencephalography.
Her Econometrics research is multidisciplinary, incorporating elements of Trajectory and Time series.
Her Dynamic causal modelling study combines topics from a wide range of disciplines, such as Anterior cingulate cortex, Cortex and Brainstem.
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