TY - JOUR
T1 - Ontological Dimensions of Cognitive-Neural Mappings
AU - Bolt, Taylor
AU - Nomi, Jason S.
AU - Arens, Rachel
AU - Vij, Shruti G.
AU - Riedel, Michael
AU - Salo, Taylor
AU - Laird, Angela R.
AU - Eickhoff, Simon B.
AU - Uddin, Lucina Q.
N1 - Funding Information:
The authors gratefully acknowledge Peter Fox for providing access to the BrainMap database. This work was supported by award R01MH107549 from the National Institute of Mental Health to LQU and award 1631325 from the National Science Foundation to ARL.
Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that ‘task paradigm’ categories explain more variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, ‘study ID’, or the study from which each activation map was reported, explained close to 50% of the variance in activation patterns. Using a clustering approach that allows for overlapping clusters, we derived data-driven latent activation states, associated with re-occurring configurations of the canonical frontoparietal, salience, sensory-motor, and default mode network activation patterns. Importantly, with only four data-driven latent dimensions, one can explain greater variance among activation maps than all conventional ontological dimensions combined. These latent dimensions may inform a data-driven cognitive ontology, and suggest that current descriptions of cognitive processes and the tasks used to elicit them do not accurately reflect activation patterns commonly observed in the human brain.
AB - The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that ‘task paradigm’ categories explain more variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, ‘study ID’, or the study from which each activation map was reported, explained close to 50% of the variance in activation patterns. Using a clustering approach that allows for overlapping clusters, we derived data-driven latent activation states, associated with re-occurring configurations of the canonical frontoparietal, salience, sensory-motor, and default mode network activation patterns. Importantly, with only four data-driven latent dimensions, one can explain greater variance among activation maps than all conventional ontological dimensions combined. These latent dimensions may inform a data-driven cognitive ontology, and suggest that current descriptions of cognitive processes and the tasks used to elicit them do not accurately reflect activation patterns commonly observed in the human brain.
KW - Cognitive ontology
KW - Lateral frontoparietal network
KW - Medial frontoparietal network
KW - Meta-analysis
KW - Midcingulo-insular network
KW - Pericentral network
KW - fMRI
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U2 - 10.1007/s12021-020-09454-y
DO - 10.1007/s12021-020-09454-y
M3 - Article
C2 - 32067196
AN - SCOPUS:85079821628
VL - 18
SP - 451
EP - 463
JO - Neuroinformatics
JF - Neuroinformatics
SN - 1539-2791
IS - 3
ER -