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Katie J. Paige, Mary Fiona Molloy, Mike Angstadt, Luke Williamson Hyde, Chandra Sripada, Alexander J Weigard
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10.1016/j.intell.2006.09.004
10.1016/j.intell.2006.09.004
10.1511/2013.104.352
10.1511/2013.104.352
10.1073/pnas.1010076108
10.1073/pnas.1010076108
On the Importance of Early-Life Cognitive Abilities in Shaping Later-Life Outcomes
10.1080/15427609.2014.936173 · 2014
An examination of reciprocal associations between substance use and effortful control across adolescence using a bifactor model of externalizing symptoms
10.1017/s0954579420000644 · 2021
Early childhood executive function predicts concurrent and later social and behavioral outcomes: A review and meta-analysis
10.1037/bul0000445 · 2024
10.1111/cdev.13383
10.1111/cdev.13383
10.1016/j.neuroimage.2017.01.060
10.1016/j.neuroimage.2017.01.060
10.1038/nrn3000
10.1038/nrn3000
10.1016/j.conb.2010.03.002
10.1016/j.conb.2010.03.002
10.1016/j.tics.2017.01.003
Provenance
crossref
Confidence 100%
europepmc
Confidence 96%
unpaywall
Confidence 95%
openalex
Confidence 95%
datacite
Confidence 0%
10.1016/j.tics.2017.01.003
10.1016/j.bandc.2009.08.005
10.1016/j.bandc.2009.08.005
The conception of the ABCD study: From substance use to a broad NIH collaboration
10.1016/j.dcn.2017.10.002 · 2018
10.1016/j.bandc.2009.08.005
10.1016/j.bandc.2009.08.005
10.1016/j.intell.2022.101654
10.1016/j.intell.2022.101654
10.1016/j.dcn.2024.101390
10.1016/j.dcn.2024.101390
10.1016/j.neuroimage.2022.119046
10.1016/j.neuroimage.2022.119046
10.1177/0956797620916786
10.1177/0956797620916786
10.1016/j.tics.2016.03.014
10.1016/j.tics.2016.03.014
A resting state functional connectivity analysis of human intelligence: Broad theoretical and practical implications for multiple intelligences theory
10.1037/pne0000200 · 2020
10.1038/s41586-022-04492-9
10.1038/s41586-022-04492-9
10.1098/rstb.2017.0284
10.1098/rstb.2017.0284
10.1038/nn.4179
10.1038/nn.4179
10.1101/2023.08.08.552448
10.1101/2023.08.08.552448
10.1038/s41380-019-0481-6
10.1038/s41380-019-0481-6
10.1073/pnas.0604187103
10.1073/pnas.0604187103
10.1016/j.neuroscience.2014.07.031
10.1016/j.neuroscience.2014.07.031
10.3389/fnhum.2021.757128
10.3389/fnhum.2021.757128
Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets
10.1007/s11682-019-00191-8 · 2020
Making individual prognoses in psychiatry using neuroimaging and machine learning
2018
10.1177/1745691617693393
10.1177/1745691617693393
The mythos of model interpretability
10.1145/3233231 · 2018
Toward a unified framework for interpreting machine-learning models in neuroimaging
10.1038/s41596-019-0289-5 · 2020
Neuroimaging of individual differences: A latent variable modeling perspective
10.1016/j.neubiorev.2018.12.022 · 2019
10.1002/hbm.25007
10.1002/hbm.25007
10.1038/s42003-024-06506-w
10.1038/s42003-024-06506-w
The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites
10.1016/j.dcn.2018.03.001 · 2018
10.1016/j.neuroimage.2013.05.041
10.1016/j.neuroimage.2013.05.041
10.1152/jn.90355.2008
10.1152/jn.90355.2008
10.3389/fnagi.2017.00152
10.3389/fnagi.2017.00152
An Expansion of the Trait-State-Occasion Model: Accounting for Shared Method Variance
10.1080/10705510801922381 · doi-reference
10.1037/1082-989x.10.1.3
10.1037/1082-989x.10.1.3 · doi-reference
10.17615/t6ff-a088
10.17615/t6ff-a088 · doi-reference
Making sense of Cronbach’s alpha
10.5116/ijme.4dfb.8dfd · doi-reference
10.66752/1077-5315.7392
10.66752/1077-5315.7392 · doi-reference
10.1037/0021-9010.78.1.98
10.1037/0021-9010.78.1.98 · doi-reference
10.1037/met0000045
10.1037/met0000045 · doi-reference
10.1080/00223891.2015.1089249
10.1080/00223891.2015.1089249 · doi-reference
10.13023/edp.tool.01
10.13023/edp.tool.01 · doi-reference
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
10.1207/s15328007sem0902_5 · doi-reference
A cognitive model of response omissions in distraction paradigms
10.3758/s13421-021-01265-z · doi-reference
10.1214/ss/1177011136
10.1214/ss/1177011136 · doi-reference
10.1037/a0032222
10.1037/a0032222 · doi-reference
Dynamic models of choice
10.3758/s13428-018-1067-y · doi-reference
10.1016/j.tics.2016.01.007
10.1016/j.tics.2016.01.007 · doi-reference
10.1037//0033-295x.85.2.59
10.1037//0033-295x.85.2.59 · doi-reference
10.1038/s41592-018-0235-4
10.1038/s41592-018-0235-4 · doi-reference
10.1016/j.dcn.2021.101030
10.1016/j.dcn.2021.101030 · doi-reference
10.1016/j.sleep.2008.03.018
10.1016/j.sleep.2008.03.018 · doi-reference
10.1111/j.1365-2869.1996.00251.x
10.1111/j.1365-2869.1996.00251.x · doi-reference
Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: Rationale, description of measures, and early data
10.1016/j.dcn.2018.03.004 · doi-reference
Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery
10.1016/j.dcn.2018.02.006 · doi-reference
10.1212/wnl.0b013e3182872ded
10.1212/wnl.0b013e3182872ded · doi-reference
10.1037/t79359-000
10.1037/t79359-000 · doi-reference
10.1016/j.neuroimage.2013.05.033
10.1016/j.neuroimage.2013.05.033 · doi-reference
Back to Basics: The Importance of Measurement Properties in Biological Psychiatry
10.1016/j.neubiorev.2021.01.008 · doi-reference
10.1016/j.tins.2019.07.004
10.1016/j.tins.2019.07.004 · doi-reference
Limits to the generalizability of resting-state functional magnetic resonance imaging studies of youth: An examination of ABCD Study® baseline data
10.1007/s11682-022-00665-2 · doi-reference
10.1016/j.dcn.2023.101196
10.1016/j.dcn.2023.101196 · doi-reference
10.1017/s0140525x20001685
10.1017/s0140525x20001685 · doi-reference
10.1073/pnas.1310134110
10.1073/pnas.1310134110 · doi-reference
10.1037/met0000229
10.1037/met0000229 · doi-reference
Basic units of inter-individual variation in resting state connectomes
10.1038/s41598-018-38406-5 · doi-reference
10.1016/j.dcn.2023.101337
10.1016/j.dcn.2023.101337 · doi-reference
10.1016/s0893-133x(01)00278-0
10.1016/s0893-133x(01)00278-0 · doi-reference
10.1016/j.neuroimage.2016.09.008
10.1016/j.neuroimage.2016.09.008 · doi-reference
10.1002/hbm.26447
10.1002/hbm.26447 · doi-reference
10.1007/s11336-012-9294-0
10.1007/s11336-012-9294-0 · doi-reference
10.5665/sleep.4404
10.5665/sleep.4404 · doi-reference
10.1016/j.smrv.2025.102163
10.1016/j.smrv.2025.102163 · doi-reference