TY  - JOUR
A1  - Wehrheim, Maren H.
A1  - Faskowitz, Joshua
A1  - Sporns, Olaf
A1  - Fiebach, Christian J.
A1  - Kaschube, Matthias
A1  - Hilger, Kirsten
T1  - Few temporally distributed brain connectivity states predict human cognitive abilities
T2  - NeuroImage
N2  - Highlights
• Brain connectivity states identified by cofluctuation strength.
• CMEP as new method to robustly predict human traits from brain imaging data.
• Network-identifying connectivity ‘events’ are not predictive of cognitive ability.
• Sixteen temporally independent fMRI time frames allow for significant prediction.
• Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths.

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 10 min 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.
KW  - functional connectivity
KW  - resting state
KW  - machine learning
KW  - predictive modeling
KW  - general cognitive ability
Y1  - 2023
UR  - https://opus.bibliothek.uni-wuerzburg.de/opus4-wuerzburg/frontdoor/index/index/docId/34987
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-349874
VL  - 277
ER  -