Danilo Bzdok
Danilo Bzdok
Canada CIFAR AI Chair at McGill University & Mila Quebec AI Institute, Montreal
Verified email at - Homepage
Cited by
Cited by
Activation likelihood estimation meta-analysis revisited
SB Eickhoff, D Bzdok, AR Laird, F Kurth, PT Fox
Neuroimage 59 (3), 2349-2361, 2012
Statistics versus Machine Learning
D Bzdok, N Altman, M Krzywinski
Nature Methods, 1-7, 2018
Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy
D Bzdok, L Schilbach, K Vogeley, K Schneider, AR Laird, R Langner, ...
Brain Structure and Function 217, 783-796, 2012
Machine learning for precision psychiatry: opportunities and challenges
D Bzdok, A Meyer-Lindenberg
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 3 (3), 223-230, 2018
Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation
SB Eickhoff, TE Nichols, AR Laird, F Hoffstaedter, K Amunts, PT Fox, ...
Neuroimage 137, 70-85, 2016
The default mode network in cognition: a topographical perspective
J Smallwood, BC Bernhardt, R Leech, D Bzdok, E Jefferies, DS Margulies
Nature Reviews Neuroscience 22 (8), 503-513, 2021
Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation
SB Eickhoff, D Bzdok, AR Laird, C Roski, S Caspers, K Zilles, PT Fox
Neuroimage 57 (3), 938-949, 2011
An investigation of the structural, connectional, and functional subspecialization in the human amygdala
D Bzdok, AR Laird, K Zilles, PT Fox, SB Eickhoff
Human brain mapping 34 (12), 3247-3266, 2013
The role of the right temporoparietal junction in attention and social interaction as revealed by ALE meta-analysis
SC Krall, C Rottschy, E Oberwelland, D Bzdok, PT Fox, SB Eickhoff, ...
Brain Structure and Function 220, 587-604, 2015
An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings
PN Alves, C Foulon, V Karolis, D Bzdok, DS Margulies, E Volle, ...
Nature Communications biology 2 (1), 370, 2019
Machine learning: Supervised methods
D Bzdok, M Krzywinski, N Altman
Nature Methods, 2018
Connectivity‐based parcellation: Critique and implications
SB Eickhoff, B Thirion, G Varoquaux, D Bzdok
Human brain mapping 36 (12), 4771-4792, 2015
The neurobiology of social distance
D Bzdok, RIM Dunbar
Trends in cognitive sciences 24 (9), 717-733, 2020
Characterization of the temporo-parietal junction by combining data-driven parcellation, complementary connectivity analyses, and functional decoding
D Bzdok, R Langner, L Schilbach, O Jakobs, C Roski, S Caspers, ...
Neuroimage 81, 381-392, 2013
Introspective minds: using ALE meta-analyses to study commonalities in the neural correlates of emotional processing, social & unconstrained cognition
L Schilbach, D Bzdok, B Timmermans, PT Fox, AR Laird, K Vogeley, ...
PloS one 7 (2), e30920, 2012
Autism spectrum heterogeneity: fact or artifact?
L Mottron, D Bzdok
Molecular psychiatry 25 (12), 3178-3185, 2020
Microstructural Parcellation of the Human Cerebral Cortex – From Brodmann's Post-Mortem Map to in vivo Mapping with High-Field Magnetic Resonance Imaging
S Geyer, M Weiss, K Reimann, G Lohmann, R Turner
Frontiers in human neuroscience 5, 19, 2011
Definition and characterization of an extended social-affective default network
M Amft, D Bzdok, AR Laird, PT Fox, L Schilbach, SB Eickhoff
Brain Structure and Function 220, 1031-1049, 2015
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
MA Schulz, BTT Yeo, JT Vogelstein, J Mourao-Miranada, JN Kather, ...
Nature Communications 11 (1), 1-15, 2020
ALE meta-analysis on facial judgments of trustworthiness and attractiveness
D Bzdok, R Langner, S Caspers, F Kurth, U Habel, K Zilles, A Laird, ...
Brain Structure and Function 215, 209-223, 2011
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