Abstract
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵1 these authors share senior authorship
Main changes in the revised version of this manuscript include: Adaptation of the title to more precisely represent the results in our manuscript, revised preprocessing strategy of the primary sample including a recomputation of all analyses, additional CAPs analysis to test the number of coactivation patterns in our time frame selections, depiction of significance thresholds in the robustness analyses of our CMEP approach compared to previous prediction models, and a more detailed discussion on the change in prediction performance of single functional brain networks between the static connectivity and the selection of highest maxima. Also, additional Figures were added in the Supplement.
