Building a Science of Individual Differences from fMRI.

By J. Dubois, Ralph Adolphs. Trends in Cognitive Sciences, 2016

614 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/24a03920ec3d1e2206d0c26f804b90e7f11bbdee

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, background): “While alterations in connectivity can be associated with brain disorders, these could also be attributed to genetic variations (Dubois and Adolphs, 2016), topological differences between subjects (Langs et al.”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, neutral): “Larger datasets bring increased statistical power (Elliott et al., 2008), and many population-imaging studies use rest-fMRI to relate brain imaging to neuropathologies or other behavior and population phenotypes (Miller et al., 2016; Dubois and Adolphs, 2016).”
Can brain state be manipulated to emphasize individual differences in functional connectivity? (2017, background): “…in acquiring, analyzing and sharing fMRI data (Eickhoff et al., 2016; Poldrack et al., 2016), researchers are increasingly—and commendably—shifting focus from reporting group averages to discovering links between imaging measures and behavior in individual subjects (Dubois and Adolphs, 2016).”
Precision functional mapping of individual human brains (2017, neutral): “The observed differences between individual and group-average network descriptions carry important implications for future brain network analyses (Dubois and Adolphs, 2016; Satterthwaite and Davatzikos, 2015).”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, neutral): “Particularly, significant associations between this measure and behavior/cognition have been repeatedly observed (Dubois and Adolphs, 2016; Liu et al., 2017; Smith et al., 2015).”
A distributed brain network predicts general intelligence from resting-state human neuroimaging data (2018, background): “Current literature on the fMRI-derived substrates of intelligence suffers from the same caveats as most fMRI-based individual differences research to date [61]: small sample sizes, and lack of out-of-sample generalization.”
Predicting brain-age from multimodal imaging data captures cognitive impairment (2016, neutral): “However, these techniques are not currently standard practice and require further development for widespread application (e.g., see Dubois & Adolphs, 2016).”