Predicting cognitive abilities across individuals using sparse EEG connectivity
Hakim, N.; Awh, E.; Vogel, E. K.; Rosenberg, M. D.
Show abstract
Human brains share a broadly similar functional organization with consequential individual variation. This duality in brain function has primarily been observed when using techniques that consider the spatial organization of the brain, such as MRI. Here, we ask whether these common and unique signals of cognition are also present in temporally sensitive, but spatially insensitive, neural signals. To address this question, we compiled EEG data from individuals performing multiple working memory tasks at two different data-collection sites (ns = 171 and 165). Results revealed that EEG connectivity patterns were stable within individuals and unique across individuals. Furthermore, models based on these connectivity patterns generalized across datasets to predict participants working memory capacity and general fluid intelligence. Thus, EEG connectivity provides a signature of working memory and fluid intelligence in humans and a new framework for characterizing individual differences in cognitive abilities.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks 95%
- Dissociable neural information dynamics of perceptual integration and differentiation during bistable perception 95%
- Resting state fluctuations underlie free and creative verbal behaviors in the human brain 95%
Similar papers in this journal
- Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention? 97%
- Mapping Cognitive Brain Functions at Scale 96%
- How the brain negotiates divergent executive processing demands: Evidence of network reorganization during fleeting brain states 96%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.