Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints

Abstract

Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently acquired data sets with repeated scans (total N = 213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns for repeat scans acquired within 3 months. The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time, whereas this change was not observed in the diffusivity measures. Moreover, the local connectome fingerprint can be used as a phenotypic marker, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings, relative to differences between unrelated subjects. This novel approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.

Subject-specificity of the correlation between large-scale structural and functional connectivity

“…M. Smith et al, 2015). Moreover, SC (Kumar, Desrosiers, Siddiqi, Colliot, & Toews, 2017;Munsell, 2017;Yeh et al, 2016) as well as FC (E. Amico, Goñi, J., 2017;Finn et al, 2015) can be used to identify individual connectome fingerprints. Nonetheless, the extent of this individual variability has been called into question (Marrelec, Messe, Giron, & Rudrauf, 2016;Waller et al, 2017), particularly for smaller sample sizes (Waller et al, 2017).…”

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

“…While numerous studies have focused on elucidating brain connectivity patterns that are shared across people, researchers have also acknowledged the high individual variability in brain struc-15 ture [16,17,18], function [19,20,21,22,23,24], and white matter geometry [25,26]. Motivated by this, the concept of connectome fingerprinting, which characterizes individuals using unique connectivity profiles, has recently drawn significant interest from the neuroscience community [27,28,29,30,31,32,33].…”

“…Recently, a novel approach was proposed for building individual connectome profiles based on dMRI data [33,35]. This approach uses the Spin Distribution Function (SDF) at each voxel to obtain a fingerprint encoding the diffusion density along a set of prominent directions in cerebral white matter.…”

Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors

“…This connective architecture is initially structured by genetics and then sculpted by experience over time Kochunov, Thompson, et al, 2016;Yeh, Vettel, et al, 2016). Recent advancements in neuroimaging techniques, particularly diffusion MRI (dMRI), have opened the door to mapping the macroscopic-level properties of the structural connectome in vivo (Le Bihan & Johansen-Berg, 2012).…”