Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease

Abstract

Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest—crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier—FIX—cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment.

Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise

“…The consortium has established SMS imaging protocols for 3T of 8X acceleration to achieve 2 mm isotropic spatial resolution and 720 ms temporal resolution, and for 7T of 8X acceleration to achieve 1.6 mm isotropic spatial resolution and 1000 ms temporal resolution (Glasser et al, 2016). They have shown that these 2 mm protocols combined with ICA denoising significantly benefits resting state network analysis studies (Smith et al, 2013; Griffanti et al, 2015). Published results comparing task fMRI outcome metrics over a range of acceleration factors are more limited.…”

“…The reduction in data acquisition time achievable with SMS imaging can be used to acquire a greater number of thin slices to cover the entire brain within an acceptable scan time, or acquire standard resolution images at much higher frame rates. Several studies have shown the benefits of SMS imaging for higher spatial resolution fMRI studies (e.g., 2 mm isotropic/0.72 s volume acquisition time at 3T Glasser et al, 2016, 1.5 mm isotropic/1.65 s at 3T Todd et al, 2015, or 1.6 mm isotropic/1 s at 7T Glasser et al, 2016), and for improvements in fMRI resting state network analysis (Smith et al, 2013; Salimi-Khorshidi et al, 2014; Griffanti et al, 2015; Preibisch et al, 2015). For task fMRI studies carried out at moderate 3 mm isotropic spatial resolution that is common to many neuroimaging studies, there is less evidence in the literature detailing the benefits and drawbacks of SMS accelerated imaging.…”

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer’s Disease and bvFTD

“…Visual inspection of the data quality is important but that may not always be adequate. A recent study demonstrated that the finding of reduced functional connectivity (FC) in the DMN in AD could only be detected after aggressive data driven cleaning of the fMRI data using FMRIB’s ICA-based Xnoiseifier (FIX) (Griffanti et al, 2015). FIX attempts to auto-classify ICA components into RSNs and noise components.…”

Functional NeuroCognitive ImagingTM and Notus NeuroCogsTM: A Novel Use of fMRI in the Assessment of Cognitive Function in Major Neurocognitive Disorder

“…functional connectivity) when an individual is awake and alert but deactivated when the brain engages in task performance. Interestingly, DMN functional connectivity has been consistently shown to decrease in patients with MND, specifically Alzheimer's disease, and even those at risk for MND development [48,[51][52][53]. Though the present study was particularly interested in task-related functional changes, future studies investigating the use of our protocol and DMN changes in patients with MND are warranted.…”