Background/Purpose: Examining neural activity in the absence of task (i.e. resting state) is an active area of research. Functional connectivity, defined as correlations in BOLD signal between two brain regions, is a promising component of fatigue/pain research. Seed to voxel analyses are one approach used to estimate functional connectivity between brain areas. This approach takes the BOLD signal time course of a priori defined seeds and compares their signals to all other voxels in the brain. We determined brain areas of chronic fatigue syndrome (CFS) patients as seeds for connectivity analysis that demonstrated abnormal resting cerebral blood flow during arterial spin labeling (ASL) functional MRI.

Methods:   CFS was determined using the CDC Criteria. 15 CFS patients (age = 50.5±13.0)) and 12 HC (age = 49.2±12.2) were MRI scanned with a 3 Tesla Achieva during rest using a pseudo-continuous arterial spin labeling (pCASL) sequence.  ASL data were corrected for rigid body motion and smoothed in SPM8. Label and control images were subtracted to create a perfusion time series. The perfusion time series was used to quantify cerebral blood flow (CBF) using the software aslTBX. A mean CBF image was created which was normalized to MNI space and resampled into 2mm isotropic voxels. An independent samples t-test was used to examine voxel-wise differences in CBF between CFS patients and HC. Resulting t-maps were thresholded with a t-statistic > 4.0 and a cluster size > 120 mm³. 2 distinct clusters passed this threshold and were used to create seed masks for subsequent BOLD functional connectivity analyses.  BOLD resting state data were slice-time corrected, realigned and resliced into 3mm isotropic voxels, co-registered to the anatomic volume, warped into MNI standard space and spatially smoothed. Data were spike-corrected to reduce the impact of artifacts using the post-processing Artifact Detection Tool. The final processing steps were then carried out using the functional connectivity toolbox Conn that implements the component-based noise correction method strategy for physiological and other noise source reduction, which included: Temporal (band-pass) filtering, and removal of several nuisance variables, such as CSF and white matter signal, rigid body motion parameters, and outlier data points.

Results: Significantly decreased blood flow was observed in the right parahippocampal  gyrus of CFS patients [27.69 (6.22)] compared to HC [40.51 (7.89) (ml/100g/min)]]. Subsequent analyses showed increased connectivity between the parahippocampal seed and the supramarginal gyrus of CFS patients compared to HC.

Conclusion: Our novel method of generating seeds for functional connectivity analyses, using multi-modal neuroimaging data, demonstrated increased connectivity between brain areas involved in memory and language processing of CFS patients.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/resting-state-functional-connectivity-differs-between-chronic-fatigue-syndrome-patients-and-healthy-controls/