A neuromarker of individual general fluid intelligence from the white-matter functional connectome

Jiao Li,Xujun Duan,Bharat B Biswal,Huafu Chen,Yao Meng,Siqi Yang,Qian Cui,Wei Liao

doi:10.1038/s41398-020-0829-3

Abstract

Neuroimaging studies have uncovered the neural roots of individual differences in human general fluid intelligence (Gf). Gf is characterized by the function of specific neural circuits in brain gray-matter; however, the association between Gf and neural function in brain white-matter (WM) remains unclear. Given reliable detection of blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals in WM, we used a functional, rather than an anatomical, neuromarker in WM to identify individual Gf. We collected longitudinal BOLD-fMRI data (in total three times, ~11 months between time 1 and time 2, and ~29 months between time 1 and time 3) in normal volunteers at rest, and identified WM functional connectomes that predicted the individual Gf at time 1 (n = 326). From internal validation analyses, we demonstrated that the constructed predictive model at time 1 predicted an individual’s Gf from WM functional connectomes at time 2 (time 1 ∩ time 2: n = 105) and further at time 3 (time 1 ∩ time 3: n = 83). From external validation analyses, we demonstrated that the predictive model from time 1 was generalized to unseen individuals from another center (n = 53). From anatomical aspects, WM functional connectivity showing high predictive power predominantly included the superior longitudinal fasciculus system, deep frontal WM, and ventral frontoparietal tracts. These results thus demonstrated that WM functional connectomes offer a novel applicable neuromarker of Gf and supplement the gray-matter connectomes to explore brain–behavior relationships.

Highlights

Neuroimaging and psychological studies have investigated the neural basis of the cognitive processes that motivate novel insights about brain–behavior relationships[1]
We first verified that the observed general fluid intelligence (Gf) scores did not correlate with head micromovements during scanning (internal validation group, time 1: r(324) = −0.051, P = 0.358; time 2: r(103) = 0.095, P = 0.468; time 3: r(81) = 0.027, P = 0.812; external validation group: r(51) = −0.028, P = 0.841) and age (internal validation group, time 1: r(324) = −0.026, P = 0.635; time 2: r(103) = 0.126, P = 0.200; time 3: r(81) = 0.315, P = 0.112; external validation group: r(51) = 0.025, P = 0.861) (Fig. S3)
After performing the predictive model analyses, we found that WM functional connectivity could be used as a feature to predict individual Gf (r(324) = 0.238, P = 1.44 × 10−5) (Fig. 2a)

Summary

Introduction

Neuroimaging and psychological studies have investigated the neural basis of the cognitive processes that motivate novel insights about brain–behavior relationships[1]. An enduring aim of brain and cognitive sciences is to understand individual differences in human intelligence[2]. The neural correlates of individual differences in Gf may be associated with variations in brain size and connections[2]. The information flow among certain areas associated with Gf have been quantified by functional connectivity studies. These results showed that the variational relationships of regions engaging in common or related performance (even at rest) may be the basis of individual differences in Gf 9–11, and

Methods

Results

Conclusion