de Campos, B. M. | Search bioRxiv

bioRxiv2022

Classification of Human Chronotype Based on fMRINetwork-Based Statistics

Chronotype - the relationship between the internal circadian physiology of an individual and the external 24-hour light-dark cycle - is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9-5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health are not well understood. To address this issue, we use resting state fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype to study if differentiable information about chronotype is embedded in functional brain networks. We develop a classifier utilising the Network Based-Statistic (NBS) methodology, using rigorous selection criteria to select t-statistic thresholds within the NBS approach. We find significant differences in functional networks measured in early and late chronotypes and describe conditions under which the classifier achieves 97.3% accuracy. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterise the relationship between internal physiology, external perturbations, brain networks and disease.

neuroscience↗

bioRxiv2021

Evaluating denoising strategies in resting-state fMRI in traumatic brain injury (EpiBioS4Rx)

ObjectiveResting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity of traumatic brain injury patients. MethodsWe applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 traumatic brain injury patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial (EpiBioS4Rx). Pipelines were evaluated by three quality control metrics across three exclusion regimes based on the participants head movement profile. ResultsWhile no pipeline eliminated noise effects on functional connectivity, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data driven methods performed comparatively worse. ConclusionIn this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related functional connectivity in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as traumatic brain injury datasets.

neuroscience↗