Altered Resting Brain Connectivity in Persistent Cancer Related Fatigue Ⅰ

Abstract

There is an estimated 3 million women in the US living as breast cancer survivors and persistent cancer-related fatigue (PCRF) disrupts the lives of an estimated 30% of these women. PCRF is associated with decreased quality of life, decreased sleep quality, impaired cognition, and depression. The mechanisms of cancer-related fatigue are not well understood; however, preliminary findings indicate dysfunctional activity in the brain as a potential factor. Here we investigate the relationship between PCRF on intrinsic resting-state connectivity in this population. Twenty-three age-matched breast cancer survivors (15 fatigued and 8 non-fatigued) who completed all cancer-related treatments at least 12 weeks prior to the study, were recruited to undergo functional connectivity magnetic resonance imaging (fMRI). Intrinsic resting-state networks were examined with both seed-based and independent component analysis methods. Comparisons of brain connectivity patterns between groups as well as correlations with self-reported fatigue symptoms were performed. Fatigued patients displayed greater left inferior parietal lobule to superior frontal gyrus connectivity as compared to non-fatigued patients (P b 0.05 FDR corrected). This enhanced connectivity was associated with increased physical fatigue (P = 0.04, r = 0.52) and poor sleep quality (P = 0.04, r = 0.52) in the fatigued group. In contrast, greater connectivity in the non-fatigued group was found between the right precuneus to the periaqueductal gray as well as the left IPL to the subgenual cortex (P b 0.05 FDR corrected). Mental fatigue scores were associated with greater default mode network (DMN) connectivity to the superior frontal gyrus (P = 0.05 FDR corrected) among fatigued subjects (r = 0.82) and less connectivity in the non-fatigued group (r = −0.88). These findings indicate that there is enhanced intrinsic DMN connectivity to the frontal gyrus in breast cancer survivors with persistent fatigue. As the DMN is a network involved in self-referential thinking we speculate that enhanced connectivity between the DMN and the frontal gyrus may be related to mental fatigue and poor sleep quality. In contrast, enhanced connectivity between the DMN and regions in the subgenual cingulate and brainstem may serve a protective function in the non-fatigued group.

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1. Introduction

An estimated 3 million women in the United States are living as breast cancer (BC) survivors (American Cancer Society, 2012). While breast cancer is the most widespread type of cancer in women, more patients are in remission mainly because of early detection and important advances in treatment (American Cancer Society, 2014). However, in cancer survivors symptoms of fatigue, pain, poor sleep, and depression are common occurrences. 

Persistent cancer-related fatigue (PCRF) is one of the most troubling long-term side-effects of cancer treatment (Kim, Son et al., 2008; Alexander et al., 2009; Pearce et al., 2009) and continues to affect around 33% of BC survivors, persisting in some cases for years after completing cancer treatment. The mechanisms of PCRF are largely unknown. Since PCRF is associated with impaired cognition (Rodriguez et al., 2008), decreased sleep quality (Alexander et al., 2009), and depression (Bower, 2005; Kim, Son et al., 2008), it is possible that PCRF has a central neurobiological pathology. In support of this hypothesis, differences in brain metabolites between fatigued and non-fatigued survivors have been observed (Zick et al., 2014). It is unknown if a more widespread brain network disturbance may underlie fatigue in this population. Recent advances in neuroimaging methods have emerged that allow researchers to probe brain network activity and to study altered neural networks non-invasively. 

One such technique is resting-state functional connectivity magnetic resonance imaging (fMRI). Using this technique, aberrant brain connectivity patterns have been found in pain (Napadow et al., 2010), depression, and insomnia (Li, Wang et al., 2014), two common symptoms also seen in fatigued cancer survivors. However, no studies have investigated the role of fatigue in altered brain connectivity in this population. Previous fMRI studies on chronic fatigue syndrome (CFS) have shown changes in brain activations in the superior frontal cortex, premotor, and default mode network (DMN; see below), while performing fatiguing cognitive tasks (Lange et al., 2005; Caseras et al., 2006; Cook et al., 2007). Another study looking at structural changes also showed a bilateral decrease in gray matter volume in the prefrontal area among CFS patients as a region that regulates sensations of fatigue (Okada et al., 2004). To explore these regions (and networks) more thoroughly we used resting fcMRI in breast cancer survivors. There are two fundamental approaches to studying resting-state connectivity: a seed-based approach and a more data-driven method called independent component analysis (ICA). With both approaches, functional connectivity is inferred on the basis of correlation between brain regions for time series data from the blood-oxygen-level dependence (BOLD) signal. (i) With seed-based approaches, a specific region of the brain is chosen a priori based on previous work, and the time course of the BOLD signal in the seed is correlated with each voxel time course of the rest of the brain. Signifificant correlations are thought to arise when brain regions are “connected”. (ii) Data-driven multivariate approaches such as ICA do not require a specific prior hypothesis and instead use data from all regions of the brain to identify independent components that function as networks that are connected to other brain regions. With ICA separate resting-state networks that show correlated brain activity over time can be assessed for connectivity to other brain regions. Since previous MRI studies have investigated brain outcomes in CFS, we chose to examine similar brain regions using seeds from these studies. In addition, we also used ICA to identify novel connectivity patterns as they are not influenced by a prior hypothesis. One common resting-state network is the default mode network (DMN). This network is composed of the medial prefrontal cortex, the posterior cingulate cortex, inferior parietal lobule, and the precuneus (Buckner and Vincent, 2007; Fox and Raichle, 2007). Research over the past decade has shown that this network is activated when a person is at rest, having self-referential thoughts about themselves without engaging with their environment. 

Recent studies show increased connectivity to this network among chronic pain (Napadow et al., 2010) and depression (Greicius et al., 2007) patients; two symptoms often found in cancer survivors with fatigue. As such, we hypothesized that differences in brain connectivity patterns to the DMN, as well as other brain regions, in fatigued breast cancer survivors may be specifically associated with the subjective symptom of fatigue, and that these differences may be independent of other comorbid symptoms. Here we report an exploratory study which is the first to our knowledge that investigates differences in intrinsic brain connectivity patterns among breast cancer survivors with and without fatigue.

2. Methods

2.1. Participants

The study was approved by the University of Michigan Medical School Institutional Review Board and participants provided written informed consent. Study participants were identified through the University of Michigan Breast Cancer Clinics and from participants in former clinical trials conducted in breast cancer survivors. Eligible participants were women eighteen or older, who have a diagnosis of breast cancer (stage 0 to IIIA), and have completed all cancer-related treatments (i.e., surgery, chemotherapy, radiotherapy, immunotherapy, etc.), except hormonal therapy at least 12 weeks prior to the study. Participants were excluded if they: had cancer recurrence; were pregnant or lactating; were diagnosed with anemia with hemoglobin levels less than 12 g/dl or receiving treatment for anemia; or were diagnosed with unstable or untreated comorbidities likely to cause fatigue (i.e., moderate to severe heart failure, hypothyroidism); had a diagnosis of untreated DSM-IV-TR Axis-I or Axis-II disorders; had an initiation, cessation or change of treatment dose (up to 3 weeks prior to the study start) of any chronic medications or dietary supplements; or if they had metal implants (such as surgical clips or staples) or other contraindications with magnetic resonance imaging (MRI). During participant screening socio-demographics, height and weight (used to calculate BMI); concomitant medications and supplements; medical history; brief physical including vitals; blood draw for a complete blood count; and a urine pregnancy test were conducted. Menopausal status at time of breast cancer diagnosis was determined through women3s medical chart where women who had experienced at least 12 continuous months without a menstrual cycle were deemed post-menopausal. Participants were asked to fill out a battery of self-administered questionnaires such as the multidimensional fatigue inventory (MFI) (Smets et al., 1995), brief fatigue inventory (BFI) (Mendoza et al., 1999), hospital anxiety and depression scale (HADS) (Zigmond and Snaith, 1983), Pittsburgh sleeps quality index (PSQI) (Buysse et al., 1989), and the brief pain inventory (BPI) (Cleeland and Ryan, 1994). For 2 weeks after their initial screening visit, participants were contacted via phone once per week and their BFI score was determined over the phone. To be designated as fatigued BC survivor women needed to have an average BFI ≥ 4.0 based on the three BFI administered approximately 1 week apart from their screening visit and via phone contacts on the following 2 weeks. Non-fatigued BC survivors needed an average BFI b 4.0 administered on the same timeframe as fatigued survivors; as well as an average pain score b 4 on BPI, a PSQI total score b 7 and a HADS b 11 for anxiety and depression sub-scales. Non-fatigued patients with signifificant presentation of these symptoms were excluded as high levels of pain, depression, or sleep problems could influence brain connectivity in that group. These symptoms were not excluded from the fatigue group as cancer survivors with fatigue often have comorbid symptoms of pain, sleep disorders, depression and anxiety, thus making enrollment of purely fatigued patients problematic. To test if our brain connectivity patterns within the fatigued group were related to levels of pain, depression, or sleep problems, we performed bivariate correlations between each brain imaging outcome and comorbid symptom levels within the fatigued group. 

2.2. Data acquisition

Participants were recruited to undergo resting-state fcMRI on a 3 T Philips Achieva scanner (Best, Netherlands) using an 8-channel head coil. Ten minutes of resting-state fMRI data were acquired using a custom T2* weighted spiral-in sequence (repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle (FA) = 90°, matrix size 80 × 80 with 30 slices, field of view (FOV) = 217 cm, 2.75 × 2.75 × 4 mm voxels and 300 volumes) followed by a T1 weighted high resolution MPRAGE structural scan for normalization using the following parameters [TR = 9.78 ms, TE = 4.59 ms, FA = 90°, FOV = 219 mm, matrix size 240 × 240 matrix with 150 slices and 0.83 × 0.83 × 1 mm voxels]. During the resting state, fMRI subjects were instructed not to focus on any particular task and stay awake with their eyes open at a fixation cross. Since cardiac and respiratory fluctuations are known to influence brain connectivity within several networks (Murphy et al., 2013), subject physiological data were collected simultaneously using a chest plethysmograph for respiratory and an infrared pulse oximeter on subjects3 fingers for cardiac data. Only subject functional data of less than 2 mm of translation and less than 1° rotation head motion inside the scanner were included for the fMRI analysis. Whole-brain coverage was achieved including the midbrain and rostral brainstem.

2.3. Data analysis

fMRI data were preprocessed and analyzed using statistical parametric mapping (SPM) software package version 8 (Wellcome Department of Cognitive Neurology, London, United Kingdom), Conn (Cognitive and Affective Neuroscience Laboratory, Massachusetts Institute of Technology, Cambridge, USA) functional connectivity toolbox, and GIFT (Group ICA of fMRI Toolbox) toolbar running on MATLAB 7.10 (Mathworks, Sherborn, MA, USA). Upon collection of resting-state fMRI data, physiological artifacts were removed using a custom Matlab algorithm and slice time corrected using FSL 4.1.9 (FMRIB3s Software Library, http://www.fmrib. ox.ac.uk/fsl) software. Preprocessing steps included motion correction, re-alignment, registration, normalization to standard MNI (Montreal Neurological Institute) template, and smoothing (FWHM Gaussian kernel of 8 mm) using SPM8. 

2.3.1. Seed connectivity analysis 

Seed to whole-brain functional connectivity analysis was done using the Conn toolbox (Whitfifield-Gabrieli and Nieto-Castanon, 2012). Seed regions were identified from previously published fMRI studies on chronic fatigue syndrome (Lange et al., 2005; Caseras et al., 2006; Cook et al., 2007; Caseras et al., 2008) and created as spheres (5 mm radius) around peak voxel coordinates (Supplementary Table S1). White matter, CSF, and motion parameters were entered into the analysis as covariates of no interest. A band pass filter (frequency window: 0.01–0.1 Hz) was applied to remove linear drifts and high-frequency noise from the data. First-level analysis was done correlating time course from the seed to whole brain voxels creating connectivity maps for each seed region, using bivariate correlations. These connectivity maps were then passed up to group-level analyses comparing differences in connectivity among fatigued versus non-fatigued BC survivors using age as a covariate of no interest. The resulting maps were threshold at whole-brain P b 0.001 (or P b 0.0001) uncorrected voxel threshold and P ≤ 0.05 FDR cluster corrected for multiple comparisons. As multiple seeds were chosen (n = 8) for our analysis, we also performed a more stringent Bonferroni correction for our results. This threshold was set at P b 0.0063 (i.e., 0.05/8 tests). Correlation of brain connectivity outcomes to participant behavioral data was achieved by obtaining the average finisher transformed r values of the resulting signifificant clusters using Marsbar toolbox (Poldrack, 2007), and then correlated with behavioral measures (MFI, BFI, and PSQI) in SPSS 21 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY). Group difference to fatigue measure correlations were done controlling for both pain and depression using linear regression in SPSS. A Bonferroni correction of P b 0.017 (i.e., 0.05/3 tests) was also performed on symptom correlations. 

2.3.2. Independent component analysis

Group ICA was performed using the GIFT toolbar (Calhoun et al., 2004). Component estimates were validated using ICASSO software (Himberg et al., 2004) for 10 iterations to ensure the reliability of ICA algorithm and to increase the robustness of the results. The number of independent components (ICs) was limited to 25 to minimize splitting into subcomponents. Subject-specific spatial maps and time courses were back reconstructed using Spatio-temporal regression (STR) or dual regression options available in GIFT. STR regresses (i) the original subject data onto the combined ICA spatial maps to estimate subject-specific time courses for each component; and (ii) then regresses the individual subject data back onto these time course matrices to estimate subject-specific spatial maps. Thus, the original combined spatial map and the later estimated spatial maps represent the best approximation for the individual subject-specific Z-score component maps. These Z values reflect the degree of connectivity between each voxel and the group averaged time course of the component. Component maps representing resting-state networks were identified by spatial correlation with templates provided by Beckmann et al. (2005) and Smith et al. (2009). These individual resting-state network maps were then passed onto group second-level analyses in SPM where differences in resting-state network connectivity between participants with fatigue and non-fatigued participants were performed. We also performed a whole-brain covariate of interest interaction analysis using a 2-way ANOVA model with brain connectivity and behavioral measure as factors to assess the differential associations between fatigue symptom levels (MFI and BFI scores) and network connectivity across groups. For all ICA analyses, signifificant clusters were identified by thresholding resultant brain maps at P b 0.0001 uncorrected voxel threshold and P ≤ 0.05 FDR or FWE cluster corrected significance for multiple comparisons. Since pain and depression are major comorbid symptoms, signifificant fatigue symptom findings were controlled for both pain and depression in SPM as regressors of no interest.

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