Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent studies applying dimensionality reduction techniques to resting-state fMRI (R-fMRI) have unveiled neurocognitively meaningful connectivity gradients that are present in both human and primate brains, and appear to differ meaningfully among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209×2) and the Midnight scan club (n=9), we tested the following key biomarker traits – reliability, reproducibility and predictive validity – of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including <jats:italic>i</jats:italic>) dimensionality reduction algorithms (<jats:italic>i.e</jats:italic>., linear <jats:italic>vs</jats:italic>. non-linear methods), <jats:italic>ii</jats:italic>) input data types (<jats:italic>i.e</jats:italic>., raw time series, [un-]thresholded functional connectivity), and <jats:italic>iii</jats:italic>) amount of the data (R-fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (<jats:italic>e.g</jats:italic>., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.</jats:p><jats:sec><jats:title>H<jats:sc>ighlights</jats:sc></jats:title><jats:list list-type="simple"><jats:list-item><jats:p>- There is a growing need to identify benchmark parameters in advancing functional connectivity gradients into a reliable biomarker.</jats:p></jats:list-item><jats:list-item><jats:p>- Here, we explored multidimensional parameter space in calculating functional gradients to improve their reproducibility, reliability and predictive validity.</jats:p></jats:list-item><jats:list-item><jats:p>- We demonstrated that more reproducible and reliable gradient markers tend to have higher predictive power for unseen phenotypic scores across various cognitive domains.</jats:p></jats:list-item><jats:list-item><jats:p>- We showed that the low-dimensional connectivity gradient approach could outperform raw edge-based analyses in terms of predicting phenotypic scores.</jats:p></jats:list-item><jats:list-item><jats:p>- We highlight the necessity of optimizing parameters for new imaging methods before their widespread deployment.</jats:p></jats:list-item></jats:list></jats:sec>

Original publication

DOI

10.1101/2020.04.15.043315

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

17/04/2020