Gray matter abnormalities associated with fibromyalgia: A meta-analysis of voxel-based morphometric studies
Introduction
Fibromyalgia is a chronic and multidimensional disorder characterized mainly by widespread musculoskeletal pain but also by a constellation of other symptoms including fatigue, cognitive dysfunction, and sleep and mood disturbances [1], [2]. Fibromyalgia is a relatively common condition with an estimated prevalence of 2.7% in the general population worldwide [3], causing substantial health, economic, and social burden [4]. At present, the diagnosis of fibromyalgia is mainly based on clinical symptoms and no unbiased biomarkers that can confirm it are known [1], [2], [5], [6]. Treatment continues to be a challenge for patients and physicians [7], [8]. Fibromyalgia is currently recognized as the product of complex interactions of diverse factors including the physical, psychological, behavioral, social, genetic, neurobiological, and environmental, which complicates treatment [5], [7], [8], [9]. Considerable evidence suggests that the pathophysiological hallmark of fibromyalgia is sensitization of the central nervous system, although peripheral, cognitive-emotional, and interpersonal mechanisms might also be involved [8], [10], [11]. However, the precise underlying etiology and pathophysiological basis remain to be further explored.
During the last 2 decades, important advances in modern functional and structural neuroimaging techniques have provided a new avenue for understanding neurobiology in vivo, notably the central sensitization mechanisms of fibromyalgia [12], [13]. Human brain imaging data suggest that region-specific changes in gray matter (GM) volume, a decreased functional connectivity in the descending pain-modulating system, and an increased activity in the pain matrix related to central sensitization in fibromyalgia [12], [13], [14].
Voxel-based morphometry (VBM) is a technique for measuring the volumes of brain structures between groups of subjects that is whole-brain and non-theory driven, unlike manual methods that require regions of interest (ROI) to be drawn [15]. VBM has been widely used as a non-invasive tool to investigate the structural alterations occurring in the brain in neurological and psychiatric diseases [15], [16]. Diverse and variable findings regarding GM changes in fibromyalgia have been reported across VBM studies. Reduced GM volume or density has been reported in the frontal cortex [17], [18], [19], cingulate cortex [17], [18], [19], [20], [21], insular cortex [17], [20], premotor cortex [19], primary motor cortex [19], supplementary motor area [19], [22], temporooccipital cortex [19], precunes [23], hippocampus [24], parahippocampus [17], [21], amygdala [18], and brainstem [23]. Increased GM volume or density has been reported in the frontal cortex [19], primary somatosensory cortex [23], putamen [19], globus pallidus [19], nucleus accumbens [19], insular cortex [19], and cerebellum [25]. One study reported no consistent differences in GM volume between patients with fibromyalgia and healthy controls [26]. This divergence may be attributed to small samples and large clinical and demographic variability, particularly in age, gender, disease severity, disease duration, cognitive function, and neuropsychiatric comorbidities. Differences in the imaging protocols employed might also have contributed. Given this variability in the literature, a quantitative meta-analysis of the original studies to identify consistent findings of GM alterations in fibromyalgia is of particular importance.
To date, no such meta-analysis of VBM studies on fibromyalgia has been conducted. Therefore, the purpose of the present study was to establish the most prominent and replicable pattern of GM abnormality to advance our understanding of the neural structures associated with fibromyalgia. The Seed-based d Mapping (SDM) software package was used for a coordinate-based meta-analysis of the VBM studies [27], [28]. The SDM technique improves on the other existing meta-analytic methods for neuroimaging studies, by employing anisotropic kernels during the recreation of effect size maps to account for the anisotropy in the spatial covariance. This allows the present method to go beyond combining peak coordinates and statistical parametric maps, thus permitting a more exhaustive and accurate meta-analysis [27], [28]. This methodology has been fully validated in many studies [28], [29], [30], [31].
Section snippets
Literature search, study selection, and data extraction
A systematic and comprehensive search was performed in the PubMed database from January 2000 to September 2015. The search strategy for initial inclusion was (fibromyalgia or (fibromyalgia syndrome) or (chronic widespread pain)) and (voxel* or VBM or morphometry or (gray matter) or (grey matter)). The reference lists of the included studies, the relevant scholarly reviews, and Google Scholar were hand searched to obtain additional articles. Studies were considered for inclusion in the
Included studies and sample characteristics
After the initial discovery of 51 relevant documents using the search strategy, a total of 13 VBM studies on GM investigations in fibromyalgia patients were identified. Overall, 6 of the 13 studies were excluded for the following reasons: (1) use of a correlation analysis [37], [38]; (2) use of an ROI [24], [39] or volumes of interest approach [20]; and (3) not reporting whole-brain stereotactic coordinates [22]. Finally, seven eligible case–control studies [17], [18], [19], [21], [23], [25],
Discussion
This is, to our knowledge, the first meta-analysis of VBM studies to explore quantitatively GM abnormalities in patients with fibromyalgia. Individuals with fibromyalgia, compared with healthy controls, demonstrated decreased regional GM in bilateral anterior cingulate/paracingulate/medial prefrontal cortex, posterior cingulate/paracingulate cortex, and parahippocampal gyri, as well as increased GM in the left cerebellum. The findings remained largely unchanged during jackknife sensitivity
Conclusions
In summary, the prominent regions identified in the present meta-analysis are involved in several neurofunctional networks, mainly the medial pain system (ACC and mPFC), the DMN (mPFC, PCC, and parahippocampal gyrus), and the cerebro-cerebellar circuits (cerebellum). These findings further supports the concept that fibromyalgia is a symptom complex involving brain areas implicated in many other categories of neurological dysfunction, such as cognitive impairment and mood disturbance beyond those
Acknowledgments
We thank all the authors of the included studies. In particular, we thank Dr. Joaquim Radua for his kind help and suggestions. The authors are also very grateful to the reviewers for their valuable suggestions to the article.
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Both authors contributed equally to the work.