His primary scientific interests are in Artificial intelligence, Resting state fMRI, Neuroscience, Causal model and Bayesian inference. Artificial intelligence is often connected to Dynamic causal modelling in his work. His work carried out in the field of Resting state fMRI brings together such families of science as Cognition, Default mode network and Brain mapping.
His work in the fields of Resting fmri and Functional connectivity overlaps with other areas such as Rest and Event. As part of one scientific family, he deals mainly with the area of Bayesian inference, narrowing it down to issues related to the Machine learning, and often Pattern recognition and Directed graph. Adeel Razi has included themes like Electrophysiology, Algorithm, Visual motion and Premovement neuronal activity in his Bayesian probability study.
Adeel Razi mainly investigates Neuroscience, Dynamic causal modelling, Causal model, Resting state fMRI and Artificial intelligence. His Neuroscience course of study focuses on Huntington's disease and Neurodegeneration and Atrophy. His Dynamic causal modelling research includes elements of Divergence, Time series and Disinhibition.
His biological study spans a wide range of topics, including Econometrics, Bayesian probability, Vaccination, Pandemic and Brain mapping. His work deals with themes such as Algorithm, Cognitive neuroscience, Default mode network and Human Connectome Project, which intersect with Resting state fMRI. His work on Bayesian inference as part of general Artificial intelligence research is frequently linked to Graph, thereby connecting diverse disciplines of science.
His primary areas of study are Dynamic causal modelling, Causal model, Neuroscience, Econometrics and Technical report. His Dynamic causal modelling study combines topics in areas such as Cortex, Positron emission tomography and Divergence. Adeel Razi has researched Divergence in several fields, including Computational neuroscience, Resting state fMRI, Cognitive neuroscience and Regression.
Adeel Razi combines subjects such as Marginal likelihood, Time series, Electroencephalography, Vaccination and Pandemic with his study of Causal model. His Resting fmri and Functional connectivity study in the realm of Neuroscience connects with subjects such as Rest. His Econometrics research focuses on Bayesian probability and how it relates to Convolutional neural network, Deep learning and Machine learning.
Adeel Razi focuses on Causal model, Econometrics, Neuroscience, Bayesian probability and Dynamic causal modelling. His research integrates issues of Artificial intelligence, Electroencephalography and Pattern recognition in his study of Causal model. While the research belongs to areas of Econometrics, Adeel Razi spends his time largely on the problem of Pandemic, intersecting his research to questions surrounding Severe acute respiratory syndrome coronavirus 2.
His work on Functional connectivity and Resting fmri as part of general Neuroscience research is often related to Rest and Structure function, thus linking different fields of science. His Bayesian probability research incorporates themes from Test strategy and Time horizon. As a part of the same scientific family, Adeel Razi mostly works in the field of Dynamic causal modelling, focusing on Time series and, on occasion, Prior probability and Mismatch negativity.
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A DCM for resting state fMRI
Karl J. Friston;Joshua Kahan;Bharat B. Biswal;Adeel Razi;Adeel Razi.
Bayesian model reduction and empirical Bayes for group (DCM) studies.
Karl J. Friston;Vladimir Litvak;Ashwini Oswal;Adeel Razi;Adeel Razi.
Construct validation of a DCM for resting state fMRI.
Adeel Razi;Adeel Razi;Joshua Kahan;Geraint Rees;Geraint Rees;Karl J. Friston.
Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding
G. Geraci;M. Egan;Jinhong Yuan;A. Razi.
IEEE Transactions on Communications (2012)
Leveraging Data Science to Combat COVID-19: A Comprehensive Review
Siddique Latif;Muhammad Usman;Sanaullah Manzoor;Waleed Iqbal.
IEEE Transactions on Artificial Intelligence (2020)
Dynamic causal modelling revisited
Karl J. Friston;Katrin H. Preller;Katrin H. Preller;Christoph Mathys;Christoph Mathys;Hayriye Cagnan;Hayriye Cagnan.
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Daniel J. Lurie;Daniel Kessler;Danielle S. Bassett;Richard F. Betzel.
Network Neuroscience , 4 (1) pp. 30-69. (2020) (2020)
Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation
Kamen A. Tsvetanov;Kamen A. Tsvetanov;Richard N.A. Henson;Lorraine K. Tyler;Adeel Razi.
The Journal of Neuroscience (2016)
Compensation in Preclinical Huntington's Disease: Evidence From the Track-On HD Study
Stefan Klöppel;Sarah Gregory;Elisa Scheller;Lora Minkova.
Large-scale DCMs for resting-state fMRI
Adeel Razi;Mohamed L. Seghier;Yuan Zhou;Yuan Zhou;Peter McColgan.
Network Neuroscience , 1 (3) pp. 222-241. (2017) (2017)
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