Recognition memory performance can be estimated based on brain activation networks

Abstract

Background: Recognition memory is an essential ability for functioning in everyday life. Establishing robust brain networks linked to recognition memory performance can help to understand the neural basis of recognition memory itself and the interindividual differences in recognition memory performance.

Methods: We analysed behavioural and whole-brain fMRI data from 1'410 healthy young adults during the testing phase of a picture-recognition task. Using independent component analysis (ICA), we decomposed the fMRI contrast for previously seen vs. new (old-new) pictures into networks of brain activity. This was done in two independent samples (training sample: N = 645, replication sample: N = 665). Next, we investigated the relationship between the identified brain networks and interindividual differences in recognition memory performance by conducting a prediction analysis. We estimated the prediction accuracy in a third independent sample (test sample: N = 100).

Results: We identified 12 robust and replicable brain networks using two independent samples. Based on the activity of those networks we could successfully estimate interindividual differences in recognition memory performance with high accuracy in a third independent sample (r = 0.5, p = 1.29 × 10^-07).

Conclusion: Given the robustness of the ICA decomposition as well as the high prediction estimate, the identified brain networks may be considered as potential biomarkers of recognition memory performance in healthy young adults and can be further investigated in the context of health and disease.

Keywords: Independent component analysis (ICA); Memory; Prediction; Recognition; fMRI.