Alterations of White Matter Structure in Patients with Fibromyalgia: A Systematic Review of Diffusion Tensor Imaging Studies

doi:10.21203/rs.3.rs-4837537/v1

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Systematic Review

Alterations of White Matter Structure in Patients with Fibromyalgia: A Systematic Review of Diffusion Tensor Imaging Studies

https://doi.org/10.21203/rs.3.rs-4837537/v1

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Background

Fibromyalgia is a chronic pain condition with numerous and varied symptoms. Abnormal central pain processing underlies fibromyalgia, according to brain imaging studies. Diffusion Tensor Imaging (DTI) has effectively been used in pain research. In this research, we review studies that have used DTI for assessing white matter changes in patients with fibromyalgia.

Method

A systematic review on PubMed, Web of Science, and Scopus was conducted. Studies using DTI were included if they had compared the white matter changes in patients with fibromyalgia with controls. Studies with other imaging techniques and with languages other than English were excluded.

Results

The analysis included the results of 10 articles in which 215 patients were examined with DTI. The findings revealed widespread white matter brain abnormalities in regions such as the thalamus, frontal cortex, corpus callosum, and global white matter organization.

Discussion

This review provides primary evidence of white matter organization changes in patients with fibromyalgia. Further research is needed to better understand the relationship between these white matter changes and the pathophysiology of fibromyalgia, as well as to develop more effective treatment strategies for this debilitating condition.

Neurology

Fibromyalgia

Diffusion Tensor Imaging

Neuroimaging

Fibromyalgia (FM) is a chronic pain disorder that affects approximately 2% of the population (1). It accounts for 15% of rheumatology outpatient visits and 5% of primary care visits (2). Individuals with FM often exhibit abnormalities in various pain processing regions, the autonomic nervous system, neurotransmitter levels, and small fibers in the peripheral nervous system (3). Patients with FM typically experience increased sensitivity to various pain modalities, such as pressure (6), thermal (7), chemical (8), and electrical (9) stimuli, leading to allodynia and hyperalgesia (10, 11).

FM has received much attention and debate regarding its causes and physical basis. According to several studies, Chronic pain disorders, like FM, are linked to significant changes in the neural pain network of patients, including changes in structure, function, and metabolism (12). At present, there is no widely accepted and efficient treatment for FM, and patients have a slim chance of complete recovery. There is no definite agreement on the most effective treatment, and FM continues to be resistant to treatment (13, 14).

In recent years, neuroimaging techniques have been increasingly used to study FM (15). Various neuroimaging methods have been used in this condition, including functional magnetic resonance imaging (7, 18), multiple positron-emission tomography (PET) (17), single-photon emission computed tomography (SPECT) (16), as well as diffusion-tensor and volumetric imaging (19). Brain imaging studies suggest that abnormal central pain processing underlies FM (20). Patients displayed increased activity in insular and somatosensory cortices in reaction to both painful and nonpainful stimuli during functional MRI. Moreover, they demonstrated decreased functional connectivity in the pain-modulating network (18, 21). In addition to the brain’s response to experimentally induced pain, alterations in the regional cerebral blood flow (16, 24), the resting-state functional connectivity (22, 23), and neurotransmitter concentrations (25, 26) have been reported. Research findings propose that individuals with FM have atypical central pain processing that is tightly linked to enhanced nociception (27). The use of structural imaging has revealed the existence of dysfunctional central pain networks in individuals with FM. According to these studies, various regions of the brain, such as the thalamus, amygdala, hippocampus, insular, somatosensory, prefrontal, and anterior cingulate cortex, demonstrate reduced thickness or volume of gray matter (19, 23, 28). This suggests that prolonged pain might damage the pain-processing regions of the brain (27).

Neuroimaging studies also demonstrated that people with FM have higher levels of glutamate, an excitatory neurotransmitter, and lower levels of GABA, an inhibitory neurotransmitter. Additionally, there appears to be an increased concentration of GABA A receptors in the insula region of the brain in individuals with FM (26, 29, 30). Understanding the neurological basis of FM is crucial to developing effective therapy (31).

Diffusion Tensor Imaging (DTI) is another neuroimaging technique that has been effectively utilized in pain research (32). DTI is a method that studies the movement of water in the brain and measures the directionality of this movement, which is known as fractional anisotropy (FA). In a healthy brain, water moves preferentially along white-matter tracts, and the FA is higher in areas with greater axon compaction, fiber diameter, and myelination. This information can be used to estimate the anatomy of white-matter fiber bundles in the brain using tractography, which connects the principal direction of water diffusion to map the orientation of white-matter tracts (33).

DTI studies in FM have reported varying results, with some studies indicating widespread changes in white matter integrity, while others have found more localized alterations. These differences in findings can be attributed to several factors, including the choice of analysis method, sample size, and clinical characteristics of the participants. Therefore, it is crucial to consider these methodological factors when interpreting the findings of DTI studies in fibromyalgia. A systematic review was conducted to critically evaluate the existing literature on DTI use in fibromyalgia patients. The studies have identified regional differences in white matter microstructures that provide information about the most pronounced changes in FM in certain areas and tracts.

2.1. Search strategy

Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement (34), the PubMed, Scopus, and Web of Science databases were searched for studies published before February 2024. The search was limited to studies investigating brain white matter changes by DTI technique. The search was performed using the search terms DTI, Diffusion Tensor Imaging, and white matter crossed with Fibromyalgia. In addition to the systematic search, we also reviewed the included reference list of publications to identify any potential research that might have been missed. The studies selection process is outlined in the flow diagram (see Fig. 1).

2.2. Study selection

The article examines studies in which white matter abnormalities in adult patients with fibromyalgia have been investigated using DTI. The control group was healthy subjects or other patient groups, and the magnet strength of the scanner was at least 1.5 Tesla. If the articles were not original or were reported in a language other than English, they were excluded.

2.3. Data extraction

The following information was extracted and reported in Table 1: the authors' name, year of publication, age, gender, sample size, technical information about neuroimaging, and the results of the comparison between patients and healthy controls. Any discrepancies were discussed within the authors ' group to ensure accuracy.

Table 1

characteristics of studies
Author	Sample size of patients (female)	Sample size of control group (female)	Age of patients	Age of control	Scales
Mosck et al 2023	23 (all)	23 (all)	50.48 ± 9.89	46.62 ± 13.08	MPI, FIQ-G, FSQ
Tu et al 2023	20 (all)	20 (all)	46.4 ± 12.4	42.1 ± 12.5	VAS, PSP, PCS,
Tu et al 2023	20 (all)	20 (all)	46.4 ± 12.4	42.1 ± 12.5	VAS, PSP, PSC
Aster et al 2022	43 (all)	40 (all)	53.5 ± 6.5	52.5 ± 6.7	GCPS, NPSI, FIQ
Tu et al 2022	20 (all)	20 (all)	46.4 ± 12.4	42.1 ± 12.5	VAS, PSP, PSC
Kim et al 2015	42 (36)	63 (48)	45.3 ± 11.6	42.8 ± 13.7	BPI
Kim et al 2014	19 (not report)	18 (not report)	44.9 ± 8.3	44.7 ± 8.8	FIQ, SF-MPQ
Ceko et al 2013	28 (all)	28 (all)	48.7 ± 7.8	48.8 ± 7.7	VAS
Fayed et al 2010	10 (not report)	10 (not report)	40.0 ± 6.2	37.8 ± 8.7	PCS, PVAS, FIQ
Luts et al 2008	30 (all)	30 (all)	54 ± 8	49 ± 7	FIQ

MPI: West Haven-Yale Multidimensional Pain Inventory, FIQ-G: Fibromyalgia Impact Questionnaire, FSQ: Fibromyalgia Survey Questionnaire, VAS: Visual Analog Scale, PCS: Pain Catastrophizing Scale, PSP: Pain Self-Perception Scale, GCPS: Graded Chronic Pain Scale, NPSI: Neuropathic Pain Symptom Inventory, BPI: brief pain inventory, SF-MPQ: Short Form McGill Pain Questionnaire, PVAS: Pain Visual Analogue Scale

2.4. Risk of bias and quality of Studies

The quality of studies was evaluated using the Newcastle-Ottawa Scale (NOS) for non-randomized studies. This tool assesses the quality of sample selection, comparability of cases and controls, and outcome ascertainment method. The score range for NOS is 0–9, with high-quality studies receiving a score range of 7–9, medium-quality studies receiving a score range of 4–6, and low-quality studies receiving a score less than 4 (36) (Table 2).

Table 2

Quality assessment of studies
Study	Selection	Comparability	Exposure	score
Mosck et al (2023)	***	*	***	7
Tu et al (2023)	***	**	***	8
Tu et al (2023)	***	**	***	8
Aster et al (2022)	****	**	***	9
Tu et al (2022)	***	**	***	8
Kim et al (2015)	**	**	***	7
Kim et al (2014)	***	**	***	8
Ceko et al (2013)	***	*	***	7
Fayed et al (2010)	***	**	***	7
Luts et al (2008)	***	*	***	7

A total of 243 articles were identified. After removing duplications, abstract and titles of 73 papers were screened, from which 63 articles were excluded .Finally 10 articles were included. A total of 215 patients were compared with 233 healthy individuals. Eight studies used a 3T MRI scanner with a 32- or 64-channel head coil, and two studies used a 1.5 Tesla scanner.

3.1 Results of studies in details

Aster et al. (2022) compared the white matter structure of 43 women with FM with 40 healthy controls. Results of this study show that FA is generally increased in corticospinal tracts (including the corona radiata and regions of the limbic system), cingulum, the posterior thalamic radiation, the corpus callosum's body, and the corpus callosum's genu (3).
Mosch et al (2023) detected white matter changes in 23 patients compared with 21 healthy controls. Patients displayed decreased FA in bilateral medial lemniscus as well as tracts connecting the thalamus, such as thalamic radiation and dentato-rubro-thalamic tracts. A number of white matter tracts showing increased FA were located in and around the corpus callosum (12).
Tu et al. (2022) performed DTI on 20 FM patients and 20 healthy controls. Individuals with FM exhibited reduced hierarchy, synchronization, clustering coefficient, local efficiency, and increased normalized characteristic path length. Nodes with topological aberration were mainly located in the basal ganglia network and frontotemporal-thalamic network. In addition, compromised neural integration and segregation were found in patients (37).
Kim et al. (2015) compared 42 fibromyalgia patients with 63 controls. DTI methods demonstrated that the number of fibers between cerebellum, medial prefrontal/ orbitofrontal cortex, medial temporal lobe, and right inferior parietal lobule regions was associated with greater evoked pain hyperalgesia and clinical pain interference (27).
Fayed et al. (2010) compared ten FM patients with ten control subjects. There were no significant differences between diffusion-weighted images and diffusion tensor imaging in any area (38).
Kim et al. (2014) compared 19 FM patients with 21 matched healthy control subjects. FM group showed lower FA in the left body of the corpus callosum, which was connected to the bilateral sensorimotor cortices. In addition, FA values in the cluster were negatively associated with sensory pain (27).
Tu et al. (2023) compared 20 individuals with FM and 20 healthy controls. Patients with FM had significantly higher clustering coefficient volume and a lower DTI-ALPS index than controls. Higher clustering coefficient volume was associated with a lower DTI-ALPS index and longer disease duration (39).
Tu et al. (2023) compared 20 patients with FM and 20 healthy controls. FM patients had lower clustering coefficients, hierarchy, and synchronization, and higher normalized characteristic path length and local efficiency. Regionally, patients demonstrated a significant reduction in nodal efficiency and centrality. Furthermore, decreased structural connectivity in a subnetwork of the prefrontal cortex, basal ganglia, and thalamus was observed (31).
Ceko et al. (2013) compared 28 patients with 28 healthy controls. In younger patients, there was an increase in FA in the white matter tract that connects the insula and basal ganglia. This increase was caused by a decrease in radial diffusivity. On the other hand, older patients showed a decrease in FA in the white matter next to their gray matter (40).
Lutz et al. (2008) compared 30 patients with 30 healthy individuals and discovered that there was significant decrease in FA in the both thalami, the thalamocortical tracts, and both insular regions. On the other hand, an increase in FA was observed in the postcentral gyri, amygdalae, hippocampi, superior frontal gyri, and anterior cingulate gyri. The researchers found that changes in DTI measurements in the right superior frontal gyrus were correlated with increased pain intensity scores. Furthermore, higher intensity scores for stress symptoms were negatively correlated with diffusivity in the thalamus and FA in the left insular cortex (19).

Network organization of the brain has been a long-standing goal in neuroscience. Through the application of graph theory, we are beginning to unravel the complex network organization of the brain. The human brain is thought to have evolved to optimize the efficiency of information transfer, while minimizing connectivity costs. This phenomenon is similar to other behaviors of complex networks and is thought to occur at all scales of space and time (41). Graph analysis suggests that fibromyalgia is associated with challenges of brain integrity and segregation that may lead to clinical symptoms (31). Neural integration allows different brain regions to quickly incorporate specialized information. The communication and integration among brain regions can be measured using global efficiency and characteristic path length. Conversely, neural segregation refers to the capacity of interconnected clusters of brain regions to support specific processing (31). This can be quantified using measures such as the local efficiency and clustering coefficient (42). Additionally, the brains of individuals with FM exhibit the ability to process information in a low-cost parallel manner, consistent with previous findings in pain studies (43, 44), and further confirmed the viewpoint that small-world characteristics are able to endure the developmental changes and disorders (45, 46).

Patients with FM may have a higher density of myelinated axons, which may impede diffusion orthogonal to white matter fibers. This is indicated by increased FA and decreased radial diffusivity, mean diffusivity, and axial diffusivity (47). The denser packing is likely due to a decrease in surrounding tissue and an increase in the diameter of previously existing white matter fibers with greater directionality (47, 48). One of the symptoms of demyelination is a decrease in axial diffusivity (49). Nonetheless, based on murine studies,it is suggested that rather than demyelination, lower axial diffusivity is related to axonal damage (50). It was found that decreased axial diffusivity values were linked to axonal degeneration, while increased radial diffusivity values were linked to myelin injury (51). In a human study, a reduction was seen in axial diffusivity in initial lesions and areas with subsequent white matter degeneration (52). It is likely that a reduction in axial diffusivity can be used as a biomarker for axonal loss, which indicates a significant loss of axonal integrity in FM (37). White matter tracts with varying axial diffusivity are primarily associated with pain processing and motor control (53).

Decreased clustering coefficient, nodal degree, and efficiency indicated less communication and interaction in the basal ganglia, prefrontal cortex, and thalamic areas with aberrant nodal metrics belonging to “pain matrix” (31, 54). There is an increasing knowledge of the basal ganglia participation in pain processing. Thalamic-cortical-basal ganglia loops integrate various aspects of pain, such as autonomic, motor, cognitive responses, and emotions (55). Patients with lesions in the basal ganglia exhibit decreased pain sensitivity and diminished brain activity related to pain, indicating the significance of this area in pain's sensory component (56).

It is noteworthy that a decrease in FA was discovered in the tracts that surround and connect the thalamus, such as the thalamic radiation and dentato-rubro-thalamic tracts. This suggests that there may have been changes to the mentioned white matter connectivity.

Thalamus is widely recognized as being critically involved in the processing and distribution of nociceptive information (12). There are some abnormalities related to the thalamus in another region called the internal capsule. The internal capsule is made up of axonal fibers that connect the cortical cortex to the spinal cord and thalamus. The fiber bundles in the anterior part of the internal capsule connect the prefrontal cortex and medial thalamus. This tract is thought to be involved in the attentional and cognitive aspects of pain perception processing (57). This reduction in axial diffusivity of the internal capsule may be caused by abnormal processing of somatosensory input or reduced physical activity (37). The cerebral peduncles, anterior corona radiata, and corticospinal tracts, are not involved in the processing of pain perception. These tracts are generally believed to communicate information related to movement. In individuals with FM, pain often worsens with excessive exercise or a sudden increase in physical activity. This can lead to an aversion to physical activity (59).

Research suggests that the dorsolateral prefrontal cortex modulates the cognitive and emotional aspects of pain processing (60). Individuals with FM have been reported to exhibit a decreased activation in the dorsolateral prefrontal cortex (61). Non-invasive stimulation of the dorsolateral prefrontal cortex can alleviate pain and improve daily function in people with FM (62). The discovery of reduced nodal efficiency in the dorsolateral prefrontal cortex in patients with FM provides further evidence of the region's involvement in pain regulation and highlights its role in FM (31).

Motor areas are involved in FM. The supplementary motor area is a critical region of the medial brain and is involved in the generation and/or regulation of motor behavior (63, 64). However, the supplementary motor area could also be hypo-active in cognitive circumstances, such as anticipating pain and completing executive tasks in FM individuals (65–67). Chronic neuropathic pain can affect M1 function. M1 is associated with FM and may be a non-invasive pain relief option (68). This stimulation-induced pain relief may be linked to changes in the functional connectivity between cerebral regions involved in sensory, emotional, and cognitive pain processing as well as serum endorphin levels (69–72). Reduced nodal efficiency in M1 suggests that structural abnormalities may contribute to FM (31).

In FM patients, certain white matter tracts exhibit increased FA. These tracts are located in and around the corpus callosum, which includes the tapetum corporis callosi, forceps major, and forceps minor. This area is strongly connected to the bilateral sensorimotor cortex and is responsible for interhemispheric transfer (73). Therefore, changes in the structure of the corpus callosum may indicate a significant alteration in the connectivity between the two hemispheres (12).

Limitations

The studies mentioned in the text were designed as cross-sectional, which means that they only provide a snapshot of the data at a particular point in time. Therefore, it can be difficult to determine the reasons for and the effects of the detected group differences. Additionally, the informative value of diffusion indices such as FA might be a critical factor to consider. Furthermore, since the number of participants enrolled in the studies was relatively small, the statistical significance of the results might be reduced. It is also important to note that only women with FM were recruited for the studies, so the results cannot be generalized to the entire FM population. Moreover, it is important to consider that white matter changes might be influenced by analgesic or antidepressant medication, which are known to exert neuroplastic changes in brain structure and function. Therefore, the observed changes in white matter might not be solely due to FM, and medication use should be taken into account when interpreting the results.

DTI imaging plays a crucial role in understanding FM as it provides detailed insights into the structural connectivity of the brain. After examining the evidence, it can be concluded that FM patients experience changes in the organization of white matter in their brain, including neural integration and segregation, radial diffusivity, clustering coefficients, hierarchy, synchronization, local efficiency, higher normalized characteristic path length, nodal efficiency, and centrality. Specific brain regions, such as the thalamus, basal ganglia, corpus callosum, and frontal cortex, seem to be more susceptible to these changes than others.

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The authors declare no competing interests.

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Alterations of White Matter Structure in Patients with Fibromyalgia: A Systematic Review of Diffusion Tensor Imaging Studies

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Abstract

Background

Method

Results

Discussion

Figures

1. Introduction

2. Method

2.1. Search strategy

2.2. Study selection

2.3. Data extraction

2.4. Risk of bias and quality of Studies

3. Results

4. Discussion

Conclusion

References

Additional Declarations

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