Abstract
Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.
Highlights
Resting-state functional MRI has been increasingly used in studies of neurodegenerative disorders in the recent years
Alzheimer’s disease (AD) is typically associated with memory decline, but especially in early onset AD executive dysfunction and visuospatial dysfunction are common (Mendez et al, 2007; Rohrer, 2012). behavioral variant frontotemporal dementia (bvFTD) is characterized by profound changes in behavior and personality, as well as executive dysfunction (Rascovsky et al, 2011)
We explore whether only a single 3D map of BOLD signal coefficient of variation (CV) could be used in data quality control, speeding up the visualization process in addition to normal visual inspection of the whole 4D fMRI-data
Summary
Resting-state functional MRI (rs-fMRI) has been increasingly used in studies of neurodegenerative disorders in the recent years. It offers the benefit of the patient not having to be able to perform any specific tasks in the scanner and it suits well in, e.g., dementia research. Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the two most common forms of early onset dementia. AD is typically associated with memory decline, but especially in early onset AD executive dysfunction and visuospatial dysfunction are common (Mendez et al, 2007; Rohrer, 2012). The two disorders are anatomically and histopathologically distinct, considerable clinical overlapping exist, and the differential diagnosis may be difficult especially in the early stages of the disease. At present there are no reliable biomarkers and the diagnosis is based on clinical criteria
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