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Danilo Bzdok
Researcher at Montreal Neurological Institute and Hospital
Publications - 232
Citations - 12092
Danilo Bzdok is an academic researcher from Montreal Neurological Institute and Hospital. The author has contributed to research in topics: Medicine & Cognition. The author has an hindex of 41, co-authored 189 publications receiving 7940 citations. Previous affiliations of Danilo Bzdok include French Institute for Research in Computer Science and Automation & University of Düsseldorf.
Papers
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Activation likelihood estimation meta-analysis revisited.
TL;DR: The previous permutation algorithm is replaced with a faster and more rigorous analytical solution for the null-distribution and comprehensively address the issue of multiple-comparison corrections.
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Statistics versus machine learning
TL;DR: Statistics draws population inferences from a sample, and machine learning finds generalizable predictive patterns that can be applied to solve puzzles in medicine and science.
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Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy.
Danilo Bzdok,Leonhard Schilbach,Kai Vogeley,Karla Schneider,Angela R. Laird,Robert Langner,Simon B. Eickhoff +6 more
TL;DR: Investigating neural activity associated with different facets of moral thought provides evidence that the neural network underlying moral decisions is probably domain-global and might be dissociable into cognitive and affective sub-systems.
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Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation.
Simon B. Eickhoff,Thomas E. Nichols,Angela R. Laird,Felix Hoffstaedter,Katrin Amunts,Peter T. Fox,Danilo Bzdok,Claudia R. Eickhoff +7 more
TL;DR: This paper addressed two pressing questions related to ALE meta-analysis, and showed as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative.
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Machine Learning for Precision Psychiatry: Opportunities and Challenges.
TL;DR: This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice.