Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising

Abstract

The ability to make individual-level predictions from neuroanatomy has the potential to be particularly useful in child development. Previously, resting-state functional connectivity (RSFC) MRI has been used to successfully predict maturity and diagnosis of typically and atypically developing individuals. Unfortunately, submillimeter head motion in the scanner produces systematic, distance-dependent differences in RSFC and may contaminate, and potentially facilitate, these predictions. Here, we evaluated individual age prediction with RSFC after stringent motion denoising. Using multivariate machine learning, we found that 57% of the variance in individual RSFC after motion artifact denoising was explained by age, while 4% was explained by residual effects of head motion. When RSFC data were not adequately denoised, 50% of the variance was explained by motion. Reducing motion-related artifact also revealed that prediction did not depend upon characteristics of functional connections previously hypothesized to mediate development (e.g., connection distance). Instead, successful age prediction relied upon sampling functional connections across multiple functional systems with strong, reliable RSFC within an individual. Our results demonstrate that RSFC across the brain is sufficiently robust to make individual-level predictions of maturity in typical development, and hence, may have clinical utility for the diagnosis and prognosis of individuals with atypical developmental trajectories.

Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics

“…with the literature is difficult because of sensitivity to age range in the dataset. For example, many studies utilized either lifespan (Cole et al, 2017;Liem et al, 2017) or developmental (Sturmfels et al, 2018;Nielsen et al, 2019) cohorts, while the UK Biobank comprised older adults (more than 45 years old). Furthermore, many studies preferred to use structural MRI, instead of RSFC, for predicting age (Cole et al, 2017;Sturmfels et al, 2018;Varikuti et al, 2018).…”

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

“…These studies have examined both structural and functional network organization in a wide variety of samples, including healthy young adults (Power et al, 2013;Zanto and Gazzaley, 2013), developmental cohorts (Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), older adults (Baniqued et al, 2018;Gallen et al, 2016), and a plethora of neurological and psychiatric populations (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). We have gained a better understanding of typical and atypical human brain organization from these efforts.…”

“…These two ROI sets sample the cortex well, representing a diverse set of brain areas that can be organized into functional networks. Many investigators have used them to describe functional brain organization in a variety of healthy samples (Power et al, 2013;Zanto and Gazzaley, 2013), lifespan cohorts (Baniqued et al, 2018;Gallen et al, 2016;Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), as well as populations with neurologic and psychiatric diseases (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). However, the first set (264 volumetric ROIs) under-samples subcortical and cerebellar structures, as only 17 ROIs are non-cortical, and the second set (333 parcels) is restricted to the cortex only, similar to other popular ROI sets, e.g.…”

Bootstrapping promotes the RSFC-behavior associations: an application of individual cognitive traits prediction

“…A second way is to combine various kinds of information with RSFC to enhance the prediction, such as task-fMRI based FC (Elliott et al, 2019;Gao et al, 2019;Xiao et al, 2019) and dynamic FC (Liegeois et al, 2019;Lim et al, 2018;Park et al, 2018), which can provide complementary information to the conventional FC. A third way is to decrease the influence of the possible noise in rs-fMRI signal, for instance, global signal regression and motion artifact correction (Nielsen et al, 2019) have been reported to advance the RSFC-behavior prediction. The last way is to use the bagging strategy (Breiman, 1996) to improve the prediction with RSFC (Jollans et al, 2019).…”

“…Jiang et al, 2019;Sripada et al, 2019), attention , cocaine abstinence (Yip et al, 2019) and reading comprehension (Jangraw et al, 2018). SVR implements space conversion by some kernel function in order to achieve better prediction in new data space than in original data space (Basak et al, 2007), and has been utilized in prediction of mental disease (Rizk-Jackson et al, 2011), brain maturity (Dosenbach et al, 2010;Nielsen et al, 2019) and painful sensation (Tu et al, 2015). LASSO provides sparse representation by driving redundant features to zero-valued weights, and performs well in investigation of reward-related behavior (Ferenczi et al, 2016), mental state (Haufe et al, 2014) and various aspects of cognition (R. Jiang et al, 2019;R.…”