To demonstrate that white matter tract shape is uniquely identifiable information (i.e., serves as a “Fingerprint”).

Imaging: Two cohorts were recruited. Group 1: 6 subjects imaged each 5 times at 1 week intervals with a b = 1200s/mm², 60 directions, 6 b0, 2.4mm isotropic protocol; Group 2: 248 unique subjects imaged once with a protocol matching Group 1.

Image Processing/Tractography: All images were corrected for motion, eddy currents and EPI distortions using ExploreDTI⁴ with in-house modifications to accommodate damped Richardson-Lucy based tractography⁵. Tracking parameters were a 1mm step size, 45^o angular threshold, 0.05 fODF threshold, 2mm isotropic seeding and [30 300]mm length thresholds.

Shape Categorisation: 30 individuals' FA maps (selected at random from Group 2) were affinely co-registered to an MNI (2mm FA) template and the transformation matrices reused to spatially normalise the corresponding whole brain tractography result. Streamlines were then re-parameterised to 30 knot-points (b-spline interpolation), translated to the origin (centre-of-mass subtraction), vectorised (coordinate concatenation) and the resultant feature descriptors subjected to principal component analysis. The first seven eigenvectors (encompassing ~98% of total shape variance) were then selected to form a set of shape basis functions, combinations of which should be sufficient to approximate any plausible streamline shape. Finally, the streamline feature descriptors were then decomposed onto the shape basis functions and the resultant weight vectors clustered (k-means) into 800 classes of shape, examples of which are presented in Figure 1.

Fingerprinting: To obtain "fingerprints", subject tractographies were spatially normalised (eliminating rotation as a confound) and reduced to weighted combinations of shape basis functions. For each streamline, the Euclidean distance between its weight vector and each of the 800 cluster centroids was calculated, assigning the (arbitrary, but unique) identifying number of the closest cluster to that streamline. A histogram of streamline identification numbers – i.e., the histogram of shapes (Figure 2) – then forms the "fingerprint". Similarity between fingerprints is assessed by vectorising all histogram bin-counts and computing cross-correlations between them.

Figure 3 shows the cross-correlation matrix of fingerprints within Group 1. Figure 4 displays a histogram of correlation coefficients resulting from the combination of Groups 1 and 2. Notice how the distribution of correlation coefficients derived from intra-individuals comparisons naturally separates from that arising from comparing different subjects. This allows same/different subject identification through simple thresholding of the correlation coefficient. Thresholding at 0.8 allowed, across the combined dataset (Group 1 & 2), identification of repeat-scan subject images (comparing each image in the combined cohorts, in turn, to all other images) with 93% precision.The work presented here indicates that tract shape does, indeed, carry uniquely identifiable information. Beyond subject identification – which is trivially achievable through other means – why is this important? Firstly, our technique opens up a new avenue for longitudinal study. Figure 3 demonstrates that, at least within the short term, streamline fingerprints are a highly consistent property. If this consistency can be shown to persist across longer periods, then sudden or progressive reductions in correlation coefficient (with respect to the subject's baseline) might be useful as a marker indicative of, for example, the appearance or progress of tumours, lesions or atrophy that are known to alter/disrupt white matter on a global scale. Secondly, our technique may serve as a new method for examining brain development. Many of the 800 shape parcellations here are specific to particular white matter structures but, importantly, individual clusters represent only one potential morphology and thus one fibre bundle is most likely represented by multiple clusters. By understanding the relationship between such clusters and studying the evolution of the fraction of assigned streamlines over time, brain fingerprints could be used to track, on both global and tract specific scales, the evolution of brain shape.