Refined measure of functional connectomes for improved identifiability and prediction

Abstract

Brain functional connectome analysis is commonly based on population‐wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population‐wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject‐specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group‐wise inferences and refined connectomes are indeed desirable for brain mapping.

Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder

“…By checking the identification rates in Figure S3, we found that the accuracy for all the scenarios had been significantly improved, and the performance was almost the same as that described above. Note that inappropriate setting of the parameters involved in the SDL, that is, dictionary size and sparse, will induce overfitting and lower identification rates for the proposed framework (Cai et al, 2019). Thus, we believe that our results are not primarily influenced by data leakage.…”

“…In our previous study, we indicated that individual connectivity analysis benefits from group‐wise inferences and the refined connectomes are indeed useful for brain mapping (Cai et al, 2019). Thus, to further improve the inter‐subject variability across FCs, we implemented the same pipeline to reduce group‐wise contribution.…”

“…Here, the ksvdbox13 was implemented. More details about the SDL model can be found in our previous work (Cai et al, 2019).…”

“…To determine whether the refined FC profiles can better predict the individual cognitive behavior relative to those without using the AE network, we use the leave‐one‐subject‐out cross‐validation (LOOCV) strategy to estimate the prediction accuracy (Cai et al, 2019). For instance, to assess the ability of refined FCs to describe fluid intelligence, in each LOOCV fold, one participant is used as the test sample, and the remaining n − 1 subjects are considered as the training samples.…”

“…The studies mentioned above used a standard procedure to extract the functional profiles, but overlooked the influence from the group‐wise contribution. Inspired by this, we refined the measures of capturing individual connectomes by increasing inter‐subject variability across FC values in a population to improve the power of predicting individuality with FC fingerprints (Cai et al, 2019). In addition, Amico et al showed that the individual fingerprint of a human functional connectome could be improved from a reconstruction procedure based on a group‐wise decomposition (Amico & Goñi, 2018).…”