Local Connectome Fingerprinting Reveals the Uniqueness of Individual White Matter Architecture

It is generally assumed that the uniqueness of individual identity is reflected in the connective architecture of the human brain. Here we introduce local connectome fingerprinting, a noninvasive method that uses diffusion MRI to characterize white matter bundles as fingerprints. Using four independently acquired datasets (total n=213), we show that the local connectome fingerprint is highly specific to an individual, achieving 100% accuracy across 17,398 identification tests with an estimated classification error at 10-6. This uniqueness profile is higher than fingerprints derived from local fractional anisotropy or region-to-region connectivity patterns. We further illustrate that local connectome fingerprinting allows for quantifying similarity between genetically-associated individuals, e.g., monozygotic twins (12% connectomic similarity), and neuroplasticity with time, e.g., fingerprint uniqueness decreases 0.02% per day. This approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.