Skip to main content
Download PDF

Depression in patients with SAPHO syndrome and its relationship with brain activity and connectivity

Orphanet Journal of Rare Diseases volume 12, Article number: 103 (2017) Cite this article

Abstract

Background

Synovitis-acne-pustulosis-hyperostosis-osteitis (SAPHO) syndrome is a rare disease and there is no related literature concerning psychiatric symptoms in SAPHO patients. Thus, we believe that this will be the first paper to explore the episode and the neurobiological basis of depression symptoms in SAPHO patients using resting state functional magnetic resonance imaging (rs-fMRI). Twenty-eight SAPHO patients and fifteen age- and gender- matched normal controls (NC) were consecutively submitted to psychiatric evaluation and rs-fMRI scanning.

Results

46.2% (13/28) of SAPHO patients were diagnosed as depression. The local spontaneous activity study showed that depressed SAPHO (D-SAPHO) patients had decreased amplitude of low-frequency fluctuation (ALFF) in the bilateral ventrolateral prefrontal cortex (VLPFC, attributed to the anatomical structures of Brodmann’s area 47, 45 and 44) and right dorsolateral prefrontal cortex (DLPFC, attributed to the anatomical structures of Brodmann’s area 8, 9 and 46), increased ALFF in the bilateral middle temporal gyrus, when compared to non-depressed SAPHO (ND-SAPHO) patients. The functional connectivity (FC) study disclosed that D-SAPHO patients had an increased FC in the anterior portions of default mode network (DMN) (the bilateral inferior frontal cortex, anterior cingulate cortex and insula cortex), and a decreased FC in the posterior areas of DMN (left middle occipital cortex), when compared to ND-SAPHO patients. Furthermore, correlation analysis revealed that both ALFF and FC values were significantly correlated with depression scores of SAPHO patients.

Conclusion

These results prompt us to understand the underlying pathophysiological mechanism of depression in SAPHO syndrome, and demonstrate that abnormal brain functional areas may serve as effective biological indicators to monitor depression in the future.

Background

Synovitis-acne-pustulosis-hyperostosis-osteitis (SAPHO) syndrome is a special kind of clinical entity that characteristically affects the bones, joints, and skin [1]. Inflammatory osteitis with hyperostosis is the main feature of this disease and may occur without skin lesions. SAHPO syndrome is a rare disease and its prevalence is generally considered less than 1/10,000, although sufficient data on it is unavailable [2]. So far, the etiology, pathophysiological mechanisms, treatment and long-term prognosis of SAPHO syndrome have not been fully understood. Most of researchers only focus on dermatological and osteoarticular changes of SAPHO patients. Furthermore, there is no related literature concerning about depressive symptoms in SAPHO syndrome.

Resting state functional magnetic resonance imaging (rs-fMRI) can reveal intrinsic functional architecture of the brain by measuring the spontaneous fluctuations in blood oxygenation level dependent (BOLD) signals in brain during resting state [3]. Amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) are two common rs-fMRI data analysis methods. The ALFF reflects the extent of spontaneous neuronal activity [4], while the FC reveals the tendency of cortical networks to be co-activated [5]. Both methods have been applied effectively to detect the mechanisms of pathophysiology of major depressive disorder (MDD)[6,7,8,9,10,11,12] and other mental disorders, such as autism spectrum disorders[13], schizophrenia[14], obsessive-compulsive disorders[15] and so on. One of the most widely studied resting state networks is the default mode network (DMN), which plays an important role in self-referential, emotional processes, episodic memory and perceptual processing [16]. A great variety of abnormal regions have been revealed in MDD, mainly including the prefrontal cortex, anterior cingulate cortex, cerebellum, amygdala and so on [17, 18].

Spondyloarthritis (SpA), a family member of immune-mediated inflammatory disorders which includes ankylosing spondylitis (AS), psoriatic arthritis (PsA), reactive arthritis, and undifferentiated SpA, is considered to be associated with depression [19], and SAPHO syndrome may be one subtype of SpA. Baysal et al. [20] reported that the depression had interaction with disease activity and quality of life in AS patients. A recent population-based study revealed that MDD increased the risk of developing PsA among psoriasis patients. Therefore, it is important to identify depression in SAPHO patients as it may have similar effect on AS/psoriatic patients. Our research team is involved in the largest cohort study of SAPHO syndrome in the world [21]. When assessing the clinical, laboratory and radiological features of SAPHO syndrome, we also tried to explore the episode of depressive symptoms in SAPHO patients and revealed neurobiological basis of depression symptoms in these patients using rs-fMRI.

Methods

Subjects

Twenty-eight SAPHO patients (aged 16–65, mean 44.6 years, 15 females) were consecutively admitted from inpatient clinics in Peking Union Medical College Hospital from July 25, 2015 to October 20, 2015. All of the SAPHO patients met the diagnostic criteria proposed by Kahn and Khan [1] and had typical anterior chest wall and dermatological manifestations, detailed data shown in Additional file 1: Table S1. There are no uniform scoring criteria to evaluate the severity of SAPHO syndrome, and both SAPHO syndrome and ankylosing spondylitis (AS) are considered to belong to SpA. Therefore, we use the scoring criteria of AS to describe the severity of SAPHO syndrome, including Visual Analogue Scale (VAS) [22], Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) [23] and Bath Ankylosing Spondylitis Functional Index (BASFI) [24]. The drugs that SAPHO patients were using during our study included nonsteroidal anti-inflammatory drugs (NSAIDs), bisphosphonates, and disease-modifying antirheumatic drugs (DMARDs), detailed clinical data of which were shown in Additional file 1: Table S2. Fifteen age- and gender- matched normal controls (NC) (aged 25–65, mean 44.5, 8 females) were enrolled from the local community and they were drug-naïve.

The investigation of depression diagnose was tested by an experienced psychiatrist (Dr.Duan) using the Mini-International Neuropsychiatric Interview (M.I.N.I) [25], and severity of depressive symptoms was tested by the 17-item Hamilton Depression Rating Scale (HDRS) [26]. The group of the depressed SAPHO (D-SAPHO) patients should meet MDD criteria using M.I.N.I. intervention, and HDRS scores should be more than 7. The remaining patients were non-depressed SAPHO (ND-SAPHO) patients. At the same day of psychiatric evaluation, all participants went through MRI scans. The detailed clinical data of all subjects were shown in Table 1.

Table 1 Clinical and demographic characteristics
Full size table

Our study was approved by the local ethic committee of Peking Union Medical College Hospital. The written informed consents before psychometric and neurologic evaluations were signed by all of the subjects.

MRI Data Acquisition

All subjects were scanned with a 3-Tesla MRI scanner (MAGNETOM Skyra System, Siemens, Erlangen, Germany). Foam pads and the earplugs were used to minimize head motion and acoustic noise. The subjects were asked to stay still with their eyes closed and not think anything particular during the resting state scan. Functional images were collected using an echo-planar imaging sequence (repetition time [TR] =2510 ms, echo time [TE] =30 ms, flip angle = 90°, field of view [FOV] =240 mm × 240 mm, in-plane matrix = 80 × 80, slice thickness/gap = 3/0 mm). Additionally, subjects underwent structural imaging using a T1-weighted magnetization-prepared rapidly acquired gradient-echo sequence (176 slices, TR = 2300 ms, TE = 3.17 ms, TI = 900 ms, flip angle = 8°, FOV = 256 mm × 256 mm, in-plane matrix = 256 × 256, slice thickness/gap = 1/0 mm).

Rs-fMRI date analysis

The image preprocessing was performed using Data Processing Assistant for Resting-State fMRI (DPARSF) (http://www.restfmri.net) [27], which was based on Statistical Parametric Mapping (http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit 1.8 (REST) (http://www.restfmri.net) [28]. The first 10 volumes of the functional images were discarded to allow the magnetization to reach for a steady-state. Slice timing correction, head motion correction and nuisance covariate removal were performed. If the subjects’ head motion was more than 2.5 mm in the X, Y, Z-axis or rotation exceeding 2.5°, the subjects would be excluded. No subject was removed due to head motion in this study. Then functional images were normalized into the space of Montreal Neurological Institute template, using unified segmentation on T1 image, and were resampled to a voxel size of 3 × 3 × 3 mm3. Spatial smoothing was used with a 6 mm full-width at half maximum Gaussian smoothing kernel. The data were further processed with the linear detrending and temporally band-pass filtering (0.01–0.08 Hz).

ALFF analysis

The ALFF analysis was also conducted by using the DPARSF software. The time series of each voxel was first transformed into a frequency domain through using fast Fourier transform and the square root of the power spectrum was obtained. ALFF was calculated as the average over a predefined frequency interval (0.01 Hz to 0.08 Hz), performed on a voxel-by-voxel basis. To reduce the global effects of variability, the ALFF of each voxel was divided by the global mean ALFF value.

Statistics and ALFF-depression correlation analysis

An analysis of variance (ANOVA) was performed on the ALFF to identify brain areas with significant differences among D-SAPHO patients, ND-SAPHO patients and NC (voxel-level P < 0.01, cluster size > 1080 mm3/40 voxels, corresponding to a corrected P < 0.05 as determined by AlphaSim correction). Then these clusters were extracted as a mask. Two sample t-tests were conducted to compare the ALFF differences among the three groups within the mask (voxel-level P < 0.001, cluster size >108 mm3 /4 voxels, corresponding to a corrected P < 0.05 as determined by AlphaSim correction). The correction thresholds were determined by the Monte Carlo simulations based on the AlphaSim in Analysis of Functional Neuroimage [29], which were implemented on the REST software. To explore the correlation between depression and regional resting state activity, a post-hoc correlation analysis was performed between the HDRS scores and ALFF values which were extracted in regions where significant differences between D-SAPHO patients and ND-SAPHO patients were observed.

FC analysis

FC analysis was performed to explore the changes of DMN in SAPHO patients. To identify the DMN map, a seed region of interests (ROIs) was placed in the posterior cingulate cortex (PCC; 0X,-53Y, 26Z; seed size of 10 mm × 10 mm × 10 mm). For each subject, the average BOLD time course of voxels within ROIs was plotted. Subsequently, we computed the correlation coefficient between that the BOLD time course of voxels within ROIs and the time course of all the other voxels in the brain. Then, the r-scored maps were converted to z-scores by using Fisher’s r-to-z transformation.

Statistics and FC-depression correlation analysis

The FC differences among groups were performed by using two sample t-tests (voxel-level P < 0.001, cluster size > 432 mm3/16 voxels, corresponding to a corrected P < 0.05 as determined by AlphaSim correction) after the ANOVA analysis. The FC values of these regions showing significant differences in DMN between D-SAPHO patients and ND-SAPHO patients were calculated separately. Subsequently, the correlation of the FC values and HDRS scores were analyzed.

Results

Clinical and Demographic characteristics

All of the patients had typical characteristics of syndrome, such as osteoarticular and dermatological lesions, one example shown in Fig. 1, detailed data shown in Additional file 1: Table S1. 46.4% (13/28) of SAPHO patients were diagnosed with depression. There were no significant differences in age (F = 1.891, P = 0.1642) and gender (χ2 = 0.803, P = 0.669) among D-SAPHO patients, ND-SAPHO patients and NC, whereas HDRS scores were significantly different among the three groups (F = 62.82,P < 0.0001). There was no significant differences in disease duration (t = 0.5515, P = 0.5860), VAS (t = 0.1303, P = 0.8974), BASDAI (t = 1.213, P = 0.2359) and BASFI (t =0.06762, P = 0.9466) between D-SAPHO patients and ND-SAPHO patients, as shown in Table 1. Meanwhile, differences in drug therapies were not significant between the two groups (χ2 = 2.610, P = 0.302), as shown in Additional file 1: Table S2. There were no significant differences in HDRS scores between male and female patients in either D-SAPHO patients (t = 1.852, P = 0.0910) or ND-SAPHO patients (t = 0.5331, P = 0.6030), and the severity of depression was not correlated with age in D-SAPHO patients (r = 0.2503, P = 0.4095).

Fig 1
figure 1

A 50-year-old woman with SAPHO syndrome presented with recurrent palmoplantar pustulosis for seven years and multiple joint pain for six years. (a) Erythema with pustules and scales on the right foot. (b) Coronal CT image of sternoclavicular joints demonstrates hyperostosis and erosive changes

Full size image

Altered ALFF in SAPHO patients

Significant differences of the ALFF were revealed by ANOVA among the D-SAPHO patients, ND-SAPHO patients and NC in the following regions: bilateral frontal cortex, anterior cingulate cortex, temporal cortex and left inferior partial gyrus (Fig. 2). Then two sample t-tests were conducted to compare the ALFF differences among the three groups (Fig. 2): (1) Compared with NC, decreased ALFF was detected in the bilateral prefrontal cortex, inferior temporal gyrus and anterior cingulate cortex in the D-SAPHO patients, and increased ALFF was detected in the bilateral superior temporal gyrus in the D-SAPHO patients. (2) Compared with NC, ND-SAPHO patients demonstrated decreased ALFF in the bilateral prefrontal cortex, anterior cingulate cortex and inferior temporal gyrus and increased ALFF in right superior temporal gyrus, left middle temporal gyrus and right inferior frontal gyrus. (3) Compared with ND-SAPHO patients, D-SAPHO patients showed decreased ALFF in the bilateral ventrolateral prefrontal cortex (VLPFC, attributed to the anatomical structures of Brodmann’s area 47, 45 and 44), right dorsolateral prefrontal cortex (DLPFC, attributed to the anatomical structures of Brodmann’s area 8, 9 and 46), increased ALFF in the bilateral middle temporal gyrus. More detailed information about the group comparisons was shown in Table 2.

Fig. 2
figure 2

Axial brain slices show the significant differences in the amplitude of low-frequency fluctuation (ALFF) among depressed SAPHO (D-SAPHO) patients, non-depressed SAPHO (ND-SAPHO) patients and normal controls (NC). The color bar represents the range of T values. R = right. L = left

Full size image
Table 2 Regions in which ALFFs were significantly different among D-SAPHO patients, ND-SAPHO patients and NC
Full size table

The relationships between HDRS scores and ALFF in regions showing significant difference (D-SAPHO patients and ND-SAPHO patients) were evaluated. HDRS scores of SAPHO patients (including D-SAPHO patients and ND-SAPHO patients) correlated with ALFF values in all of the regions that showed significant difference between D-SAPHO patents and ND-SAPHO patients (Fig. 3a-g). The left VLPFC (Brodmann’s area 47, Peak: x = −45 y = 42 z = −3) was the only region whose ALFF values correlated with HDRS scores of D-SAPHO patients (r = −0.7961, P = 0.0011) (Fig. 3h), and the other correlations were not significantly different (P > 0.05, no scatter-plot shown).

Fig. 3
figure 3

Scatter-plot (a-g) shows the significant correlation between Hamilton Depression Rating Scale (HDRS) scores of all SAPHO patients (including D-SAPHO patients and ND-SAPHO patients) and ALFF values in regions that show significant differences between D-SAPHO patients and ND-SAPHO patients. Scatter-plot (h) shows the significant correlation between HDRS scores of D-SAPHO patients and ALFF values in regions that show significant differences between D-SAPHO patients and ND-SAPHO patients

Full size image

Altered FC in D-SAPHO patients

An ANOVA revealed significant differences of the DMN FC among the D-SAPHO patients, ND-SAPHO patients, and NC in the following regions: bilateral prefrontal cortex, bilateral inferior parietal lobe, left posterior cingulate cortex,left inferior temporal cortex, and the right superior pole temporal cortex (Fig. 4). Then two sample t-tests were conducted to determine significant differences of DMN FC among the three group (Fig. 4): (1) The FC results showed that D-SAPHO patients had increased DMN in bilateral prefrontal cortex, inferior parietal cortex, precuneus cortex, left inferior temporal cortex, and middle occipital cortex, while decreased DMN in the bilateral orbital medial frontal cortex and left PCC compared with NC. (2) The results of the FC showed that ND-SAPHO patients had increased DMN FC in the left trigonal inferior frontal cortex, inferior temporal cortex, occipital cortex, inferior parietal cortex, right superior pole temporal cortex, and middle frontal cortex, while decreased DMN FC in the left orbital frontal cortex, PCC, bilateral anterior cingulate cortex, and right inferior parietal cortex compared with NC. (3) Compared with ND-SAPHO patients, D-SAPHO patients showed an increased DMN FC in anterior potions (the bilateral inferior frontal cortex, anterior cingulate cortex and insula cortex), and a decreased DMN FC in posterior areas (left middle occipital cortex). More detailed information about the group comparisons was shown in Table 3.

Fig. 4
figure 4

Axial brain slice displays the significant differences in the default mode network (DMN) functional connectivity (FC) among depressed SAPHO (D-SAPHO) patients, non-depressed SAPHO (ND-SAPHO) patients and normal controls (NC). The color bar represents the range of T values. R = right. L = left

Full size image
Table 3 Regions in which DMN FCs were significantly different among D-SAPHO patients, ND-SAPHO patients and NC
Full size table

The relationships between HDRS scores and FC in regions showing significant difference (D-SAPHO patients and ND-SAPHO patients) were evaluated. HDRS scores of SAPHO patients (including D-SAPHO patients and ND-SAPHO patients) correlated with FC values in DMN that showed significant difference between D-SAPHO patients and ND-SAPHO patients (Fig. 5a-e). Moreover, the FC between the PCC and left middle occipital cortex (Brodmann’s area 19, Peak: x = −−30 y = −84 z = 24) was significantly correlated with the HDRS scores of D-SAPHO patients (r = −0.6419 P = 0.0180) (Fig. 5f).

Fig. 5
figure 5

Scatter-plot (a-e) shows the significant correlation between Hamilton Depression Rating Scale (HDRS) scores of all SAPHO patients (including D-SAPHO patients and ND-SAPHO patients) and the default mode network (DMN) functional connectivity (FC) values in regions that show significant differences between D-SAPHO patients and ND-SAPHO patients. Scatter-plot (f) shows the significant correlation between HDRS scores and DMN FC values of D-SAPHO patients in regions that show significant differences between D-SAPHO patients and ND-SAPHO patients

Full size image

Discussion

To the best of our knowledge, this is the first study to reveal depressive symptoms in SAPHO syndrome. Our research team previously reported the largest cohort study of SAPHO syndrome in the world, including one hundred and sixty-four patients [21].In this study, psychiatric evaluation and MRI scans were performed on twenty-eight patients of these SAPHO patients, and fourteen SAPHO patients were diagnosed with depression. Therefore, we inferred that the prevalence of depression in SAPHO patients was at least 7.9% (13/164), and much higher than that in Chinese adults (6.40‰) [30]. A study using multivariate logistic regression analysis revealed that severity of disease (BASDAI), quality of life, and educational level were factors associated with the risk of depression in SpA [31]. Thus, we speculated that these factors might also contribute to the episode of depression in patients with SAPHO syndrome.

This study not only confirmed the existence of depression in SAPHO patients by psychiatric tests, but also revealed the abnormal brain activities of D-SAPHO patients by rs-fMRI. We found that D-SAPHO patients showed decreased ALFF in the bilateral VLPFC and right DLPFC, and disrupted FC in DMN.

In our current study, we found decreased local spontaneous activity in the bilateral VLPFC and right DLPFC in D-SAPHO patients compared with both ND-SAPHO patents and NC. As is known, the VLPFC is considered to be negatively correlated with negative affect [32] and inhibitive controls of negative emotions and cognitions, such as reappraisal [33]. Meanwhile, a recent research showed that depression patients had decreased activation of left VLPFC compared to normal controls when processing negative information such as loss [34]. Furthermore, decreased ALFF values in the left VLPFC were negatively correlated to the HDRS score of D-SAPHO patients in our study, which suggested that ALFF measurement in left VLPFC would be a good marker to detect and evaluate the severity of depression in SAPHO patients. The DLPFC has primarily been correlated with cognitive and executive functions and it plays an important role in MDD [35]. Previous studies on MDD patients showed changes of metabolite concentrations in DLPFC [36, 37]. Moreover, a previous study demonstrated that the gray matter density (GMD) of right DLPFC decreased in the MDD patients compared with controls and the GMD values of right DLPFC were negatively correlated with the HDRS scores[38]. In contrast to decreased activity in the bilateral prefrontal cortex, increased ALFF in the bilateral middle temporal gyrus was observed in D-SAPHO patients compared to ND-SAPHO patients. In addition to language comprehension, the temporal lobe also contributes to social cognition and emotional processing [39]. Two previous studies using regional homogeneity (ReHo) analysis [40] and morphometric MRI [41] identified local activity and structure abnormalities in MDD, which were consistent with the pattern of our present findings. Wu et al. [40] demonstrated high ReHo in the right middle temporal gyrus in MDD patients and Ramezani et al. [41] revealed the abnormal structure of medial temporal regions in MDD patients.

Compared to the NC group, decreased ALFFs in the ventromedial prefrontal cortex (VMPFC) were demonstrated in D-SAPHO patients and ND-SAPHO patients separately, nevertheless there was no significant difference in spontaneous activity of VMPFC between D-SAPHO patients and ND-SAPHO patients. As is known, the VMPFC, which is anatomically synonymous with the orbitofrontal cortex, is considered to be involved with the control of emotional, cognitive and social behavior [42]. Firstly, we speculated that rs-fMRI could reveal the abnormal brain activity related to depression of SAPHO patients prior to clinical criteria. In other word, ND-SAPHO patients had the potential to merge depressive symptoms which could be detected by the decreased ALFF in the VMPFC. Secondly, we hypothesized that decreased ALFF in the VMPFC in SAPHO patients could be a SAPHO-specific marker rather than a depression-specific marker.

Further procession by the method of FC in our study, one kind of network level analysis, demonstrated disrupted DMN in SAPHO patients. Moreover, D-SAPHO patients had more FC impairment than ND-SAPHO patients compared to NC. The FC study disclosed that D-SAPHO patients had an increased DMN FC in anterior portions (the bilateral inferior frontal cortex, anterior cingulate cortex and insula cortex), and a decreased DMN FC in posterior areas (left middle occipital cortex), when compared to ND-SAPHO patients. Specially, the FC values between PCC and all of the regions showing significant differences in FC between D-SAPHO patients and ND-SAPHO patients were significantly correlated with the HDRS scores of all SAPHO patients (including D-SAPHO patients and ND-SAPHO patients) in our study, and the FC values between the PCC and left middle occipital cortex was negatively correlated with the HDRS scores of D-SAPHO patients. Our finding indicates that abnormal FCs in DMN are involved in the symptomatology of depression in SAPHO patients. Although we lacked studies exploring the neural mechanisms underlying the functional impairment of DMN in D-SAPHO patients, there are a number of studies in MDD corroborating our results. Similar to our results, Coutinho et al. [43] also described that the FCs of the anterior areas of DMN were positively correlated with depression scores, whereas posterior portions of DMN were negatively correlated with depression scores. These results could be interpreted as dissociation between anterior and posterior FCs in DMN, in which anterior regions could be involved in self-referential and emotional processes and posterior potions could be involved in episodic memory and perceptual processing [16]. A previous review found that patients with MDD exhibited changed connectivity between the anterior DMN and posterior DMN [44], in consistent to our understanding that disrupted DMN was involved in depression in SAPHO patients.

We recognize some limitations in this study. Firstly, our study was limited by the relatively small SAPHO samples, whereas SAHPO syndrome is a rare disease and previous reports about it usually involved dozens of patients. Secondly, SAPHO patients in our study were not drug-naive, which might lead to a potential confusion on rs-fMRI. It is also worthwhile to mention that differences in drug therapies are not distinguishable between D-SAPHO patients and ND-SAPHO patients in this study.

Conclusion

In summary, our study demonstrates that SAPHO patients may have the potential to develop depressive symptoms. Abnormal brain functional areas revealed by rs-fMRI with the method of ALFF and FC helped to understand the underlying pathophysiological mechanism of depression in SAPHO syndrome, which may be good biological indicators to monitor depression in the future.

Abbreviations

SAPHO:

Synovitis-acne-pustulosis-hyperostosis-osteitis

rs-fMRI:

Resting state functional magnetic resonance imaging

BOLD:

Blood oxygenation level dependent

ALFF:

Amplitude of low-frequency fluctuation

FC:

Functional connectivity

MDD:

Major depressive episode

DMN:

The default mode network

SpA:

Spondyloarthritis

AS:

Ankylosing spondylitis

PsA:

Psoriatic arthritis

VAS:

Visual Analogue Scale

BASDAI:

Bath Ankylosing Spondylitis Disease Activity Index

BASFI:

Bath Ankylosing Spondylitis Functional Index

NSAIDs:

Nonsteroidal anti-inflammatory drugs

DMARDs:

Disease-modifying antirheumatic drugs

NC:

Normal controls

M.I.N.I:

The Mini-International Neuropsychiatric Interview

HDRS:

The 17-item Hamilton Depression Rating Scale

D-SAPHO:

Depressed SAPHO

ND-SAPHO:

Non-depressed SAPHO

TR:

Repetition time

TE:

Echo time

FOV:

Field of view

DPARSF:

Data Processing Assistant for Resting-State fMRI

REST:

Resting-State fMRI Data Analysis Toolkit 1.8

ANCOVA:

Analysis of covariance

VLPFC:

Ventrolateral prefrontal cortex

DLPFC:

Dorsolateral prefrontal cortex

GMD:

Gray matter density

ReHo:

Regional homogeneity

VMPFC:

Ventromedial prefrontal cortex

Reference

  1. Kahn MF, Khan MA. The SAPHO syndrome. Baillieres Clin Rheumatol. 1994;8:333–62.

    Article  CAS  PubMed  Google Scholar 

  2. Nguyen MT, Borchers A, Selmi C, Naguwa SM, Cheema G, Gershwin ME. The SAPHO syndrome. Semin Arthritis Rheum. 2012;42:254–65.

    Article  PubMed  Google Scholar 

  3. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8:700–11.

    Article  CAS  PubMed  Google Scholar 

  4. Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 2007;29:83–91.

    Article  PubMed  Google Scholar 

  5. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–41.

    Article  CAS  PubMed  Google Scholar 

  6. Cerullo MA, Eliassen JC, Smith CT, Fleck DE, Nelson EB, Strawn JR, et al. Bipolar I disorder and major depressive disorder show similar brain activation during depression. Bipolar Disord. 2014;16:703–12.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chen F, Lv X, Fang J, Yu S, Sui J, Fan L, et al. The effect of body-mind relaxation meditation induction on major depressive disorder: A resting-state fMRI study. J Affect Disord. 2015;183:75–82.

    Article  PubMed  Google Scholar 

  8. Chi KF, Korgaonkar M, Grieve SM. Imaging predictors of remission to anti-depressant medications in major depressive disorder. J Affect Disord. 2015;186:134–44.

    Article  PubMed  Google Scholar 

  9. Diler RS, de Almeida JR, Ladouceur C, Birmaher B, Axelson D, Phillips M. Neural activity to intense positive versus negative stimuli can help differentiate bipolar disorder from unipolar major depressive disorder in depressed adolescents: a pilot fMRI study. Psychiatry Res. 2013;214:277–84.

    Article  PubMed  Google Scholar 

  10. Huang XL, Huang PY, Li D, Zhang Y, Wang T, Mu J, et al. Early brain changes associated with psychotherapy in major depressive disorder revealed by resting-state fMRI: evidence for the top-down regulation theory. Int J Psychophysiol. 2014;94:437–44.

    Article  PubMed  Google Scholar 

  11. Liu J, Ren L, Womer FY, Wang J, Fan GG, Jiang WY, et al. Alterations in amplitude of low frequency fluctuation in treatment-naive major depressive disorder measured with resting-state fMRI. Hum Brain Mapp. 2014;35:4979–88.

    Article  PubMed  Google Scholar 

  12. Lueken U, Straube B, Yang Y, Hahn T, Beesdo-Baum K, Wittchen HU, et al. Separating depressive comorbidity from panic disorder: A combined functional magnetic resonance imaging and machine learning approach. J Affect Disord. 2015;184:182–92.

    Article  PubMed  Google Scholar 

  13. Dichter GS. Functional magnetic resonance imaging of autism spectrum disorders. Dialogues Clin Neurosci. 2012;14:319–51.

    PubMed  PubMed Central  Google Scholar 

  14. Cao H, Duan J, Lin D, Shugart YY, Calhoun V, Wang YP. Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs. Neuroimage. 2014;102(Pt 1):220–8.

    Article  PubMed  Google Scholar 

  15. Thorsen AL, van den Heuvel OA, Hansen B, Kvale G. Neuroimaging of psychotherapy for obsessive-compulsive disorder: A systematic review. Psychiatry Res. 2015;233:306–13.

    Article  PubMed  Google Scholar 

  16. Zhu X, Wang X, Xiao J, Liao J, Zhong M, Wang W, et al. Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biol Psychiatry. 2012;71:611–7.

    Article  PubMed  Google Scholar 

  17. Dutta A, McKie S, Deakin JF. Resting state networks in major depressive disorder. Psychiatry Res. 2014;224:139–51.

    Article  PubMed  Google Scholar 

  18. Kerestes R, Davey CG, Stephanou K, Whittle S, Harrison BJ. Functional brain imaging studies of youth depression: a systematic review. Neuroimage Clin. 2014;4:209–31.

    Article  PubMed  Google Scholar 

  19. Chan CY, Tsang HH, Lau CS, Chung HY. Prevalence of depressive and anxiety disorders and validation of the Hospital Anxiety and Depression Scale as a screening tool in axial spondyloarthritis patients. Int J Rheum Dis. 2014; doi: 10.1111/1756-185X.12456.

  20. Baysal O, Durmus B, Ersoy Y, Altay Z, Senel K, Nas K, et al. Relationship between psychological status and disease activity and quality of life in ankylosing spondylitis. Rheumatol Int. 2011;31:795–800.

    Article  PubMed  Google Scholar 

  21. Li C, Zuo YZ, Wu N, Li L, Li F, Zhang WH, et al. Synovitis, acne, pustulosis, hyperostosis and osteitis syndrome: a single centre study of a cohort of 164 patients. Rheumatology (Oxford). 2016;55:1023–30.

    Article  Google Scholar 

  22. Carlsson AM. Assessment of chronic pain. I. Aspects of the reliability and validity of the visual analogue scale. Pain. 1983;16:87–101.

    Article  CAS  PubMed  Google Scholar 

  23. Garrett S, Jenkinson T, Kennedy LG, Whitelock H, Gaisford P, Calin A. A new approach to defining disease status in ankylosing spondylitis: the Bath Ankylosing Spondylitis Disease Activity Index. J Rheumatol. 1994;21:2286–91.

    CAS  PubMed  Google Scholar 

  24. Calin A, Garrett S, Whitelock H, Kennedy LG, O'Hea J, Mallorie P, et al. A new approach to defining functional ability in ankylosing spondylitis: the development of the Bath Ankylosing Spondylitis Functional Index. J Rheumatol. 1994;21:2281–5.

    CAS  PubMed  Google Scholar 

  25. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59 Suppl 20:22–33. quiz 4–57.

    PubMed  Google Scholar 

  26. Hamilton M. Development of a rating scale for primary depressive illness. Br J Soc Clin Psychol. 1967;6:278–96.

    Article  CAS  PubMed  Google Scholar 

  27. Yan CG, Zang YF. DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Front Syst Neurosci. 2010;4:13. doi:10.3389/fnsys.2010.00013.

    Google Scholar 

  28. Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, et al. REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One. 2011;6:e25031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29:162–73.

    Article  CAS  PubMed  Google Scholar 

  30. Yu C, Lv Y, Chen Y, Guo Y, Paul S, Bian Z, et al. Epidemiology of major depressive episodes among Chinese adults aged 30–79 years: data from the China Kadoorie Biobank. Zhonghua Liu Xing Bing Xue Za Zhi. 2015;36:52–6.

    PubMed  Google Scholar 

  31. Meesters JJ, Bremander A, Bergman S, Petersson IF, Turkiewicz A, Englund M. The risk for depression in patients with ankylosing spondylitis: a population-based cohort study. Arthritis Res Ther. 2014;16:418.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kret ME, Denollet J, Grezes J, de Gelder B. The role of negative affectivity and social inhibition in perceiving social threat: an fMRI study. Neuropsychologia. 2011;49:1187–93.

    Article  PubMed  Google Scholar 

  33. Payer DE, Baicy K, Lieberman MD, London ED. Overlapping neural substrates between intentional and incidental down-regulation of negative emotions. Emotion. 2012;12:229–35.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Mori A, Okamoto Y, Okada G, Takagaki K, Jinnin R, Takamura M, et al. Behavioral activation can normalize neural hypoactivation in subthreshold depression during a monetary incentive delay task. J Affect Disord. 2016;189:254–62.

    Article  PubMed  Google Scholar 

  35. Koenigs M, Grafman J. The functional neuroanatomy of depression: distinct roles for ventromedial and dorsolateral prefrontal cortex. Behav Brain Res. 2009;201:239–43.

    Article  PubMed  PubMed Central  Google Scholar 

  36. McNamara RK, Jandacek R, Rider T, Tso P, Chu WJ, Weber WA, et al. Effects of fish oil supplementation on prefrontal metabolite concentrations in adolescents with major depressive disorder: a preliminary 1H MRS study. Nutr Neurosci. 2016;19:145–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Lirng JF, Chen HC, Fuh JL, Tsai CF, Liang JF, Wang SJ. Increased myo-inositol level in dorsolateral prefrontal cortex in migraine patients with major depression. Cephalalgia. 2015;35:702–9.

    Article  PubMed  Google Scholar 

  38. Ye T, Peng J, Nie B, Gao J, Liu J, Li Y, et al. Altered functional connectivity of the dorsolateral prefrontal cortex in first-episode patients with major depressive disorder. Eur J Radiol. 2012;81:4035–40.

    Article  PubMed  Google Scholar 

  39. Wang L, Dai W, Su Y, Wang G, Tan Y, Jin Z, et al. Amplitude of low-frequency oscillations in first-episode, treatment-naive patients with major depressive disorder: a resting-state functional MRI study. PLoS One. 2012;7:e48658.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wu QZ, Li DM, Kuang WH, Zhang TJ, Lui S, Huang XQ, et al. Abnormal regional spontaneous neural activity in treatment-refractory depression revealed by resting-state fMRI. Hum Brain Mapp. 2011;32:1290–9.

    Article  PubMed  Google Scholar 

  41. Ramezani M, Johnsrude I, Rasoulian A, Bosma R, Tong R, Hollenstein T, et al. Temporal-lobe morphology differs between healthy adolescents and those with early-onset of depression. Neuroimage Clin. 2014;6:145–55.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Uylings HB, Sanz-Arigita EJ, de Vos K, Pool CW, Evers P, Rajkowska G. 3-D cytoarchitectonic parcellation of human orbitofrontal cortex correlation with postmortem MRI. Psychiatry Res. 2010;183:1–20.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Coutinho JF, Fernandesl SV, Soares JM, Maia L, Goncalves OF, Sampaio A. Default mode network dissociation in depressive and anxiety states. Brain Imaging Behav. 2016;10:147–57.

    Article  PubMed  Google Scholar 

  44. Mulders PC, van Eijndhoven PF, Schene AH, Beckmann CF, Tendolkar I. Resting-state functional connectivity in major depressive disorder: A review. Neurosci Biobehav Rev. 2015;56:330–44.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

National Natural Science Foundation of China [No. 81271545]; the National Key Research and Development Program of China [No.2016YFC0901501]; the Scientific Research Foundation for the Returned Overseas Chinese Scholars; and the Science and Technology Foundation for the Selected Returned Overseas Chinese Scholars.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

JL, YD and WZ involved in the study design and manuscript drafting. ZZ, XZ and JL participated in data analysis. YD, WX and CL performed clinical evaluation of the patients. RX and HL involved in the critical evaluation of the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests

Consent for publication

All the authors agreed.

Ethics approval and consent to participation

Our study was approved by the local ethic committee of Peking Union Medical College Hospital (Number of Ethics documents: ZS-944). The written informed consents before psychometric and neurologic evaluations were signed by all of the subjects.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Author notes

    Authors and Affiliations

    1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No1. Shuaifuyuan Street, Dongcheng District, Beijing, China

      Jie Lu, Wenrui Xu, Xuewei Zhang & Weihong Zhang

    2. Department of Psychology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China

      Yanping Duan

    3. State Key Laboratory of Brain and Cognitive Science, Beijing MR Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

      Zhentao Zuo & Rong Xue

    4. Department of Interventional Radiology, China Meitan General Hospital, Beijing, China

      Xuewei Zhang

    5. Department of Traditional Chinese Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China

      Chen Li

    6. Beijing Institute for Brain Disorders, Beijing, China

      Rong Xue

    7. The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA

      Hanzhang Lu

    Authors
    1. Jie Lu
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Yanping Duan
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Zhentao Zuo
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Wenrui Xu
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Xuewei Zhang
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Chen Li
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Rong Xue
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Hanzhang Lu
      View author publications

      You can also search for this author in PubMed Google Scholar

    9. Weihong Zhang
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Weihong Zhang.

    Additional file

    Additional file 1: Table S1.

    Clinical characteristics of the SAPHO patients. Table S2. Drugs which SAPHO patients were using in this study. (DOCX 14 kb)

    Rights and permissions

    Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

    Reprints and Permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Lu, J., Duan, Y., Zuo, Z. et al. Depression in patients with SAPHO syndrome and its relationship with brain activity and connectivity. Orphanet J Rare Dis 12, 103 (2017). https://doi.org/10.1186/s13023-017-0658-5

    Download citation

    • Received:

    • Accepted:

    • Published:

    • DOI: https://doi.org/10.1186/s13023-017-0658-5

    Keywords

    • SAPHO syndrome
    • Depression
    • Resting state functional magnetic resonance imaging (rs-fMRI)
    • Default mode network (DMN)

    Orphanet Journal of Rare Diseases

    ISSN: 1750-1172