Elsevier

Magnetic Resonance Imaging

Volume 36, February 2017, Pages 56-67
Magnetic Resonance Imaging

Original contribution
Abnormal dynamics of cortical resting state functional connectivity in chronic headache patients

https://doi.org/10.1016/j.mri.2016.10.015Get rights and content

Abstract

The goals of this study are to characterize the temporal dynamics of inter-regional connectivity of the brain in chronic headache (CH) patients versus their age/gender matched controls (CONCH, n = 28 pairs), and to determine whether dynamic measures reveal additional features to static functional connectivity and correlate with psychometric scores. Cortical thickness and inter-regional resting state fMRI connectivity were quantified and compared between CH and CONCH groups. Six cortical regions of interest (ROI) pairs that exhibited correlated cortical thickness and static functional connectivity abnormalities were selected for temporal dynamic analysis. Two methods were used: temporal sliding-window (SW) and wavelet transformation coherence (WTC). SW analyses using three temporal windows of 30, 60, 120 s revealed that all six ROI pairs of CH exhibited higher percentage of strong connectivity (high r values), and smaller fast Fourier transform (FFT) amplitudes at a very low frequency range (i.e., 0.002–0.01 Hz), compared to those of CONCH. These features were particularly prevalent in the 120 s window analysis. Less variable dynamic fluctuation (i.e., smaller standard deviation of r values) was identified in two out of six ROI pairs in CH. WTC analysis revealed that time-averaged coherence was generally greater in CH than CONCH between wavelet decomposition scales 20 to 55 (0.018–0.05 Hz), and was statistically significant in three out of six ROI pairs. Together, the most robust and significant differences in temporal dynamics between CH and CONCH were detected in two ROI pairs: left medial-orbitofrontal–left posterior-cingulate and left medial-orbitofrontal–left inferior-temporal. The high degrees of sleep disturbance (high PSQI score), depression (high HRSD score) and fatigue (low SF-36 score) were associated with high degree of inter-regional temporal coherence in CH. In summary, these dynamic functional connectivity (dFC) measures uncovered a temporal “lock-down” condition in a subset of ROI pairs, showing static functional connectivity changes in CH patients. This study provides important evidence for the presence of associated psychological wellbeing and abnormal temporal dynamics in between specific cortical regions in CH patients.

Introduction

Chronic headache (CH) is a disabling neurological condition [12], [20], [29], [50]. Due to an incomplete understanding of the underlying pathological mechanism, a limited number of effective medications and management tools are available in clinical practice. The importance of the brain's contribution, from the circuit's perspective, to symptoms of CH has gained increasing recognitions in recent years [1], [2], [18], [22], [41], [42]. A better understanding of the functional network changes in the brain will provide new insights about the pathology underlying the generation and maintenance of chronic pain states, and ultimately promote novel therapeutic ideas.

Structural and functional MRI (fMRI) are widely used imaging methods for studying abnormalities in brain structure and functions in various disease conditions including chronic pain [9], [23], [29], [42], [47], [48], [64], [68], [69]. In the area of fMRI research, resting state functional connectivity (rsFC) has proven to be an effective measure for probing functional abnormality at the circuit level, as well as parcellating intrinsic networks of brain functions [19], [22], [40], [53], [54]. The typical rsFC analysis assumes that low frequency fMRI signal fluctuations are stationary over a period of time (e.g., 10 min). We term this analysis as static functional connectivity (sFC) in order to differentiate it from temporal dynamic analysis of functional connectivity (dFC). It is known in human EEG/MEG literature that the brain's state at rest is never stationary but dynamic over time, and these dynamic features are associated with cognition and behavior [24], [36], [55]. Importantly, similar temporal dynamics were also present in resting state fMRI signals, although occurring in a much slower frequency range and lasted from a few seconds to minutes [10], [35], [55], [56], [67]. Furthermore, dFC fluctuations not only exist in humans but also in monkeys and other mammals, indicating its fundamental role in execution and maintenance of normal brain functions [11], [25], [34], [35], [37], [45]. To date, however, few studies have explored dynamic features of the brain's functional network changes in CH patients [22], [67].

To fill in this knowledge gap, this study aimed to characterize the temporal dynamic features of inter-regional functional connectivity, to examine whether dynamic measures provide additional information to sFC measures, and to evaluate whether dynamic features of FC are linked to psychological wellbeing of the patients. To date, there is no consensus on what is the most sensitive and reliable method for characterizing dynamic features. Thus, in this study we applied two commonly used temporal dynamic analysis methods: sliding-window (SW) [32], [33], [35] and wavelet transform coherence (WTC) [15], [26], [66]. SW is sensitive in detecting time-dependent variations, whereas WTC analysis characterizes time-frequency dynamics of rsFC. Our results demonstrate that dynamic changes are both time-varying and frequency-dependent, with differences in both dFC measures between patients and control groups. Abnormal temporal dynamics in specific cortical regions were linked to compromised psychological wellbeing in CH. These temporal dynamic features may be used as imaging biomarkers for quantifying functional reorganization of brain circuits in a chronic pain state.

Section snippets

Research participants

Volunteers who participated in this research study included 56 CH patients and healthy subjects with matching ages and genders (14 males, 42 females; mean age: 40.4 ± 13.4 yrs). All participants were recruited from Shanghai Clinical Research Center/Xuhui Central Hospital, Chinese Academy of Sciences. CH patients were classified as migraine (n = 13), primary headache (n = 7) and tension type headache (n = 8) patients, according to the International Classification of Headache Disorders (3rd ed., ICHD-3

Quantification of questionnaires

Table 1 and Fig. 1 show the differences of psychological scores between CH and CONCH. Generally, presentation of the CONCH was healthier than the CH patients across the five questionnaire measures. The CH patients showed more fatigue problems (SF-36), depression (BDI, STAI, HRSD) and poorer sleeping quality (PSQI) than their age and gender matched CONCH. The CH patients also showed greater variations of reported scores than those of CONCH, except in the STAI scores. Specifically, significant

Significance of temporal dynamic features of rsFC

Since the discovery of the rsFC that is indicated by correlated fluctuations of low frequency resting state fMRI signals, it has become a common tool in probing changes of intrinsic brain functional circuits in both healthy and pathological conditions [8], [13], [16], [17], [22], [44], [59]. Compared to task or stimulation related fMRI studies, resting state fMRI studies have advantages of robustness in detecting changes and ease of implementation (without requesting a specific task) [17]. In

Conclusion

Building upon the identifiable structural and sFC alterations, we found universal differences in all four temporal dynamic measures of the resting state fMRI signals between headache patient and control groups, regardless of ROI pairs. Dynamic functional connectivity measures uncovered additional features in a subset of ROI pairs, showing sFC changes. In conclusion, dynamic features of resting state signals provide additional sensitive measures that may be used as potential imaging biomarkers

Acknowledgments

We thank Mr. Yong Zhu, Dr. Qingji Zhang and Mrs. Youyun Li for their assistances in data collection and analysis. We also thank Dr. Lixia Yang and Dr. Yonghua Xu for their work on patient selection. This study is supported by the “Strategic Priority Research Program (B)” “Mapping Brain Functional Connection” project of the Chinese Academy of Sciences, grant no. XDB02010400.

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