How much data do we need? Lower bounds of brain activation states to predict human cognitive ability

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Despite their low frequency of occurrence, states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture (derived from resting-state fMRI) and to be highly subject-specific. However, it is currently unclear whether such network-defining states of high cofluctuation also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, an eigenvector-based prediction framework, we show that functional connectivity estimates from as few as 20 temporally separated time frames (< 3% of a 10 min resting-state fMRI scan) are significantly predictive of individual differences in intelligence (N = 281, p < .001). In contrast and against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not achieve significant prediction of 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 brain connectivity, temporally distributed information is necessary to extract information about cognitive abilities from functional connectivity time series. This information, however, 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. Significance Statement Neuroimaging has significantly contributed to our understanding about human cognition. Individual differences in cognitive ability can be assessed with intelligence tests and resulting scores predict important life outcomes. Previous research revealed that few states of high brain-wide connectivity determine individuals functional connectomes. We show that these states do not predict intelligence but identify alternative states which contain intelligence-predictive information. These states comprise a minimum of 20 time frames (< 3% of 10 min resting-state fMRI), include a distributed network of brain regions, and their temporal independence is a critical prerequisite. Means to find these states are relevant for future neuroimaging research on complex human traits and the here introduced prediction framework has potential for broad applications across scientific disciplines.