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. 2025 Mar 18;46:103769. doi: 10.1016/j.nicl.2025.103769

Increased glymphatic system activity and thalamic vulnerability in drug-naive somatic depression: Evidenced by DTI-ALPS index

Zipeng Deng a, Wei Wang a, Zhaowen Nie a, Simeng Ma a, Enqi Zhou a, Xinhui Xie a, Qian Gong a, Lihua Yao a, Lihong Bu b,⁎, Lijun Kang a,⁎⁎, Zhongchun Liu a,c,⁎⁎⁎
PMCID: PMC11998321  PMID: 40120532

Highlights

  • •

    Increased DTI-ALPS index observed in MDD, especially in somatic depression patients.

  • •

    DTI-ALPS index positively correlated with volume of thalamus nuclei, mainly in somatic depression patients.

  • •

    Findings suggest thalamus vulnerability to glymphatic system function changes in somatic depression patients.

Keywords: Somatic depression, Major depressive disorder, Glymphatic system, DTI-ALPS, Thalamus vulnerability

Abstract

Major depressive disorder (MDD) is a significant contributor to global disease burden, with somatic symptoms frequently complicating its diagnosis and treatment. Recent advances in neuroimaging have provided insights into the neurobiological underpinnings of MDD, yet the role of the glymphatic system remains largely unexplored. This study aimed to assess glymphatic function in drug-naïve somatic depression (SMD) patients using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. A total of 272 participants, including somatic depression patients (SMD), pure depression (PMD), and healthy controls (HC), were enrolled. We collected T1-weighted (T1w) and DTI (diffusion tensor image) scans and clinical data of all participants. The DTI-ALPS indices were calculated and compared among three groups. Gray matter regions associated with the DTI-ALPS index were identified by voxel-based morphometry analysis (VBM), revealing a cluster located in the thalamus. Then, we performed partial correlation analyses to further investigate the relationships between the DTI-ALPS index, thalamic volume, and clinical data. The DTI-ALPS index was significantly higher in the MDD group compared to the HC group, particularly in the SMD group. Furthermore, a significant positive correlation was observed between the DTI-ALPS index and thalamic volume, with lower DTI-ALPS values associated with reduced thalamic volumes, especially in the SMD group. Our findings suggest heightened glymphatic activity in MDD patients, especially SMD patients, and a potential link between glymphatic function and thalamic vulnerability. Therefore, the thalamus’ vulnerability to glymphatic system function may play a role in the pathophysiology of depression, particularly somatic depression, suggesting that both the glymphatic system and the thalamus could serve as potential therapeutic or intervention targets for future treatments.

1. Introduction

Major depressive disorder (MDD) was the second leading cause of years lived with disability (YLDs) worldwide in 2019, leading causes of global disease burden(GBD 2019 Mental Disorders Collaborators., 2022). The symptoms of MDD are clinically heterogeneous. Although the primary symptoms of MDD are emotional, somatic symptoms are frequently reported by patients. Approximately 83.0 % MDD patients experienced at least one somatic symptom, and 42.7 % report more than five somatic symptoms (Kop, 2012). The somatic symptoms are associated with the severity of depression, misdiagnosis, and inappropriate treatment. A research (Fournier et al., 2010) shows that 69.0 % of MDD patients initially present with somatic rather than emotional symptoms, with the majority seeking initial consultation at general hospitals, with a misdiagnosis rate for these patients exceeds 50.0 % (Bohman et al., 2010, Kop, 2012, Tylee and Gandhi, 2005). Cohort studies indicate that somatic symptoms can predict depression onset, treatment response, suicidal behavior, chronicity, and recurrence (Bohman et al., 2012, Liu et al., 2021). They are also correlated with treatment difficulty, serving as independent predictors of prognosis and major residual symptoms in MDD (Avery et al., 2014, Hung et al., 2010). Therefore, exploring the association between the neurobiological mechanisms and somatic depression may be crucial for providing valuable guidance on early diagnosis and prognosis. Silverstein, a leading researcher in somatic depression, considers it a distinct subtype of major depressive disorder (MDD). According to his study (Silverstein et al., 2017), somatic depression (SMD) was defined as met criteria for MDD with DSM-5 criteria plus more than two of: headaches, breathing difficulty, fatigue, body image problems (want to be thinner or unhappy with body shape), eating problems (regular fasting, binging, or intentional vomiting), and sleep problems (trouble falling or staying asleep), and pure depression (PMD) was defined as meeting criteria for depression but reporting less than three of the somatic criteria. This study adopts the same criteria as outlined in Silverstein’s work.

With the advancement of neuroimaging technology, the relationship between the pathophysiological mechanisms of SMD and brain structure and function has been partially characterized. In somatic depression across various brain regions, including the temporal (Geng et al., 2019, Sun et al., 2023), occipital (Geng et al., 2019, Sun et al., 2023, Zhang et al., 2021), parietal (Harshaw, 2015, McLaren et al., 2016), insular (Sun et al., 2023), and parahippocampal cortices (Geng et al., 2019), as well as subcortical nuclei such as the thalamus, striatum, and hippocampus (Stoeckel et al., 2018, Sun et al., 2023). Neuroimaging studies (Iliff et al., 2012, Stoeckel et al., 2018) exploring the relationship between interoception and depression have found that during interoceptive attention tasks, activity within the insula is negatively correlated with both depression severity and somatic symptom severity. Interoception refers to the perception and interpretation of bodily signals, playing a role in the construction of affective experience. Dysfunctional interoception leads to misinterpretation of bodily signals, contributing to the emergence of somatic symptoms (Perez et al., 2017, Witthöft and Hiller, 2010). Abnormal emotional processing also leads to somatic symptoms, making interoceptive dysfunction a potential bridge linking the construction of affective experience and somatic symptoms in depression (Harshaw, 2015, Kirmayer and Robbins, 1991), and the abnormal brain function in these patients could recover after the electroconvulsive therapy (ECT). A combined structural and functional study reported differences in grey matter volume (GMV) and grey matter density (GMD) between patients with and without somatic symptoms in regions such as the temporal, occipital, insular cortices, thalamus, and striatum. Additionally, these abnormal regions showed aberrant weighted degree centrality in SMD patients, with this abnormality being particularly pronounced in the thalamus. Therefore, these findings suggest an abnormal thalamus-centered cortical-striatal-thalamic-cortical circuit (Sun et al., 2023). Although previous studies have extensively investigated the neuroimaging characteristics of somatic symptoms, our understanding remains incomplete.

For a long time, it was believed that the brain lacked a lymphatic system. In 2012, Iliff et al. (2012) directly observed the exchange of substances between cerebrospinal fluid and the central nervous system in the perivascular spaces of mammals, which identified the glymphatic system in brain. This exchange occurs through astrocytes and their aquaporin-4 (AQP-4) channels. The glymphatic system has functions such as transporting fluids and metabolites, playing important roles in immune surveillance, and maintaining central nervous system (CNS) homeostasis. It is particularly efficient at clearing waste during sleep (Carlstrom et al., 2022). Previous in vivo neuroimaging studies of the glymphatic system often required invasive scanning techniques (Deike-Hofmann et al., 2019, Zhou et al., 2020), hindering research progress. However, in 2017, Taoka et al. (2017) proposed a non-invasive method, the diffusion tensor image analysis along the perivascular space (DTI-ALPS), which measures perivascular space to quantify glymphatic system function as an index. This method has simplified the measurement of glymphatic function, leading to increased attention in the field. Numerous studies have used the DTI-ALPS index as a marker for glymphatic system function, and cross-vendor studies have confirmed the test–retest reliability of this index (Liu et al., 2024, Taoka et al., 2017). Earlier research primarily focused on CNS diseases such as Alzheimer's disease (AD) (Chang et al., 2023, Siow et al., 2022), Parkinson's disease (PD) (Cai et al., 2023, Shen et al., 2022, Si et al., 2022), multiple sclerosis (MS) (Carotenuto et al., 2022), cerebral small vessel disease (Xu et al., 2023), and migraine (Zhang et al., 2023). Across these conditions, most disease groups exhibit glymphatic system dysfunction, which strongly correlates with clinical features such as GMV, cognition, and sleep. And some study show that the GMV in specific regions mediates the link between glymphatic function and cognition (Shokri-Kojori et al., 2018). In mental disorders, recent research by Tu et al. (2024) has identified potential glymphatic system dysfunction in schizophrenia patients, which correlates with cognitive impairment. Nonetheless, neuroimaging studies investigating the glymphatic system in patients with depression are still lacking.

The aim of our study is to evaluate the glymphatic system function in MDD patients and its subgroups using the DTI-ALPS index. Considering the strong correlation between sleep, glymphatic function (Shokri-Kojori et al., 2018) and clinical symptoms, we further explore the relationship between glymphatic function, GMV and clinical characteristics. We expect our study can provide the evidence for understanding the glymphatic system function in depressed patients, especially SMD patients, which can help us gain insights into the pathogenesis of depression, disease biomarkers, and potentially discover new treatment targets.

2. Materials and methods

2.1. Participants

This study utilized patient data collected from the Early-warning System and Comprehensive Intervention for Depression (ESCID) project (Ma et al., 2021). Participants were recruited from the outpatient psychiatric department at Renmin Hospital of Wuhan University and local colleges and communities. The exclusion criteria for the study included: 1) age under 18 or over 55 years; 2) presence or history of other mental illnesses or major neurological disorders; 3) substance dependence or abuse; 4) contraindications to MRI or abnormal structural findings in MRI scans; 5) pregnancy; 6) left-handed or mixed handed. All patients were diagnosed with MDD by two experienced physicians according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria (American Psychiatric Association et al., 2013).

The study was reviewed and approved by the Ethics Committee of Renmin Hospital of Wuhan University. All participants provided informed consent before participating in the research.

2.2. Clinical assessment

We collected the self-reported demographic data of participants, including gender, age and educational level. All participants included in the study were evaluated or self-reported the following assessment scales: the Hamilton Depression Scale-17 (Hamilton, 1960) (HAMD-17), the Hamilton Anxiety Rating Scale (HAM-A), the Patient Health Questionnaire-9 (Kroenke et al., 2001) (PHQ-9), and the Patient Health Questionnaire-15 (Kroenke et al., 2002) (PHQ-15). Then, we calculated the three factors of HAMD-17: the anxiety/somatization factor (AS), the cognition factor (Cog), and the sleep factor (Sleep).

2.3. MRI acquisition

A General Electric scanner (GE Discovery MR750 3.0T, General Electric, Boston, MA) at the Renmin Hospital of Wuhan University were used to perform the scans. Before MRI scanning, all participants were instructed to keep their eyes closed, avoid complex thoughts, and refrain from falling asleep. We acquired high-resolution T1-weighted structural images (TR/TE = 8.5 ms/3.2 ms; Prep Time = 450 ms; FA = 12°; FOV = 256 × 256 mm; Matrix = 256 × 256; Slice Thickness = 1 mm; Locs per Slab = 180.) and DTI images (TR = 17000 ms; TE = 95.7 ms; NEX = 1; Slice Thickness = 2 mm; FOV = 256 × 256 mm; Matrix = 128 × 128; 25 directions; b = 0 and 1000 s/mm2; Flip Angle = 90°).

2.4. T1 image processing

2.4.1. Voxel-based morphometry analysis

We proceeded T1 images in voxel-level using the standard pipeline of the toolbox CAT12 (r2170, https://neuro-jena.github.io/cat/) based on SPM12 (v7771, http://www.fil.ion.ucl.ac.uk/spm/). The steps of the preprocessing were as follows: spatial adaptive non-local means denoising (Manjón et al., 2010) and resampling, bias correction, affine registration, SPM unified segmentation and skull-stripping, parcellation, local intensity correction, adaptive maximum a posteriori segmentation (Rajapakse et al., 1997), and spatial normalization through the DARTEL algorithm to the Montreal Neurological Institute (MNI) template. After preprocessing, the images were smoothed with an 8 mm full-width at half-maximum Gaussian kernel. In addition, estimated total intracranial volume (CAT12-eTIV) was obtained.

2.4.2. Segmentation of thalamic nuclei

All T1 image were processed using the standard FreesSurfer (v7.3.2, https://surfer.nmr.mgh.harvard.edu) recon-all pipeline, then a probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology were used for segmentation of thalamic nuclei (Iglesias et al., 2018). In addition, we obtained the estimated total intracranial volume (FS-eTIV).

2.5. DTI image processing

2.5.1. Preprocessing

The part 1 of an automated pipeline (https://github.com/Winniework/Spectrum-of-DTI-ALPS) based on MRtrix3, FSL and AFNI was used to preprocess individual DTI data with the following steps: denoising with Marchenko-Pastur distribution (Cordero-Grande et al., 2019, Veraart et al., 2016), removal of Gibbs-ringing artefact (Kellner et al., 2016), eddy current and motion correction (Andersson and Sotiropoulos, 2016), bias field correction, DTI parameter fitting, registering to fractional anisotropy (FA) template (JHU-ICBM-FA 2 mm, https://neurovault.org/images/1406/).

2.5.2. Calculation of the DTI-ALPS index

We used the DTI-ALPS method to evaluate glymphatic function in participants and obtained both FA and diffusivities along the three directions of the x-, y-, and z-axis (Dxx, Dyy and Dzz) maps from preprocessing. The definition and calculation of the DTI-ALPS Index was described in previous studies (Zhang et al., 2024) as summarized in Fig. 1. The FA and color-coded FA maps were generated by MRtrix3. We used the part 2 of the pipeline based on Python, csv, PIL, numpy and nibabel to place the ROI on association and projection area automatically. Subsequently, we obtained the value of Dxxproj, Dxxassoci, Dyyproj and Dzzassoci inside the ROIs. We calculated the ALPS indices according to the formula in Fig. 1 for both hemispheres and the indices were averaged.

Fig. 1.

Fig. 1

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Flow diagram of this study. CAT12: a toolbox based on SPM (Statistical Parametric Mapping) used for VMB (Voxel-based Morphometry Analysis); GMD: Grey Matter Density; DTI: Diffusion Tensor Imaging; ROI: Region of Interest; DTI-ALPS, diffusion tensor imaging along the perivascular space.

2.6. Quality control

2.6.1. T1

We conducted quality control on T1 images and processing through visual inspection and the IQR (Image Quality Rating) scores generated by the CAT12. Participants with poor image quality or an IQR score of less than 85 % were excluded. Additionally, we performed a visual inspection of the FreeSurfer segmentations using images generated by the fsqc toolbox (https://github.com/Deep-MI/fsqc). Participants with poor segmentation quality were also excluded.

2.6.2. DTI

We visually inspected the DTI images and the color-coded FA maps for all subjects. In addition, we reviewed the quality control images generated by the automated pipeline to ensure the correct placement of ROIs. Participants with poor image quality or incorrect ROI placement were excluded.

A total of 106 SMD, 74 PMD patients and 92 healthy controls (HC) were enrolled in this study after quality control.

2.7. Statistical analysis

For demographic and clinical data, categorical variables were represented as counts (percentages) and compared using the chi-square test or Fisher's exact test. The Shapiro-Wilk test was used to assess the normality of continuous variables. Normally distributed continuous variables were expressed as mean ± standard deviation (SD) and compared between two groups using the Student’s t test.

Analysis of covariance (ANCOVA) was used to compare the group differences in the DTI-ALPS index. Sex, age, and educational level were included as covariates. Considering that age has been previously identified as independently associated with the DTI-ALPS index (Clark et al., 2024, Siow et al., 2022, Wang et al., 2023), we also included the age squared as a covariate to control the nonlinear effects of age (Mørch-Johnsen et al., 2023).

To investigate the relationship between the DTI-ALPS index and global GMV, voxel-based correlation analysis was performed using CAT12 basic model, with the same covariates above and CAT12-eTIV as covariates.

To understand the relationship between the DTI-ALPS index and the volume of the thalamus and its subregions, and to evaluate whether the DTI-ALPS index could serve as a potential marker for thalamic volume reduction in MDD patients, we performed partial correlation analysis. Thalamic volume was used as the dependent variable, with the aforementioned covariates and FS-eTIV as independent variables.

To explore the relationship between clinical characteristics and the DTI-ALPS index, we performed partial correlation analysis, incorporating the covariates used in the aforementioned ANCOVA into the model.

Python 3, along with the libraries: pandas, scipy, statsmodels, pingouin, matplotlib, seaborn, and ptitprince, was used for statistical analysis and plotting. To increase the reliability of the correlation interpretations, the robustness of the correlation results was evaluated based on extensive bootstrap iterations (n = 5,000). All reported statistical significance levels were two-sided. For voxel-based analysis, we set the voxel-wise p-value at 0.001, and the cluster-wise p-value at 0.05. Other statistical significance thresholds were set at 0.05, with Bonferroni correction (demographic, clinical data and ANCOVA model) and Benjamini-Hochberg (Benjamini and Hochberg, 1995) false discovery rate (FDR-BH) methods (partial correlation analysis) used for multiple comparisons correction where appropriate.

3. Results

3.1. Demographic and clinical characteristics

The demographic and clinical characteristics of the participants are shown in Table 1. Between MDD and HC group, there were no significant differences in demographics. However, in the SMD group, the proportion of females (n = 89, 84.0 %) was higher than males (n = 17, 16.0 %) and differed significantly from the number of females in the PMD group (n = 47, 63.5 %, p = 0.009). Age and educational level showed no significant differences in comparisons between groups. In terms of clinical characteristics, including the score of HAMD-17, AS, Cog, Sleep, HAMA, PHQ-9 and PHQ-15, the MDD group was significantly higher than HC group (p < 0.001), and the SMD group was significantly higher scores compared to the other two groups. Additionally, the PMD group had significantly higher scores compared to the HC group (p < 0.001).

Table 1.

Demographic and clinical data.

Group
 Subgroups
 MDD vs. HC
 SMD vs. PMD
 SMD vs. HC
 PMD vs. HC
MDD (n = 180) HC (n = 92) SMD (n = 106) PMD (n = 74) p-value p-value p-value p-value
Age 24.728 ± 6.456 24.728 ± 6.455 24.191 ± 6.456 23.770 ± 6.276 0.328 1.000 1.000 1.000
Sex Male 44 (24.4 %) 26 (28.3 %) 17 (16.0 %) 27 (36.5 %) 0.558 0.009* 0.120 0.945
Female 136 (75.6 %) 66 (71.7 %) 89 (84.0 %) 47 (63.5 %)
Educational level <Bachelor 6 (3.3 %) 2 (2.2 %) 3 (2.8 %) 3 (4.1 %) 0.080 0.867 0.149 0.234
=Bachelor 160 (88.9 %) 75 (81.5 %) 95 (89.6 %) 65 (87.8 %)
>Bachelor 14 (7.8 %) 15 (16.3 %) 8 (7.5 %) 6 (8.1 %)
HAMD-17 23.161 ± 4.215 1.337 ± 1.633 24.585 ± 4.209 21.122 ± 3.306 <0.001* <0.001* <0.001* <0.001*
AS 6.122 ± 1.942 0.315 ± 0.662 6.585 ± 1.871 5.459 ± 1.859 <0.001* <0.001* <0.001* <0.001*
Cog 5.278 ± 1.972 0.098 ± 0.333 5.557 ± 2.038 4.878 ± 1.813 <0.001* 0.016* <0.001* <0.001*
Sleep 3.639 ± 1.567 0.391 ± 0.695 4.047 ± 1.362 3.054 ± 1.663 <0.001* <0.001* <0.001* <0.001*
HAMA 20.328 ± 6.183 1.066 ± 1.645 21.925 ± 6.058 18.041 ± 5.653 <0.001* <0.001* <0.001* <0.001*
PHQ-9 18.606 ± 4.226 1.489 ± 1.872 20.000 ± 4.012 16.608 ± 3.712 <0.001* <0.001* <0.001* <0.001*
PHQ-15 15.289 ± 5.188 2.337 ± 2.387 17.764 ± 4.511 11.743 ± 3.882 <0.001* <0.001* <0.001* <0.001*
Headaches 52 (28.9 %) — 40 (37.7 %) 12 (16.2 %)
Breathing difficulty 57 (31.7 %) — 56 (52.8 %) 1 (1.35 %)
Fatigue 133 (73.9 %) — 94 (88.7 %) 39 (52.7 %)
Eating problems 127 (70.6 %) — 91 (85.8 %) 36 (48.6 %)
Sleep problems 132 (73.3 %) — 93 (87.7 %) 39 (52.7 %)
Body image problems 24 (13.3 %) — 22 (20.8 %) 2 (2.70 %)
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MDD: Major Depressive Disorder; HC: Healthy Control group; SMD: Somatic Major Depressive Disorder group; PMD: Pure Major Depressive Disorder group; HAMD-17: Hamilton Depression Scale-17; AS: Anxiety/Somatization factor of HAMD-17; Cog: Cognition factor of HAMD-17; Sleep: Insomnia factor of HAMD-17; HAMA: Hamilton Anxiety Scale; PHQ-9: Patient Health Questionnaire-9; PHQ-15: Patient Health Questionnaire-15.

3.2. Group comparisons of DTI‑ALPS indices

The DTI-ALPS index in the MDD group was significantly higher than HC group (F = 5.734, p = 0.017). Further subgroup analyses revealed that this significant difference was only present in the comparison between SMD and HC group (1.548 ± 0.150 vs. 1.483 ± 0.162, F = 6.743, Bonferroni p = 0.030), with no significant differences found in other group comparisons (Fig. 2).

Fig. 2.

Fig. 2

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Distribution and group analysis of the DTI-ALPS index. (a), HC and MDD groups. (b), HC, PMD, and SMD groups. ALPS, diffusion tensor imaging along the perivascular space; p, p-value.

3.3. Correlations of the DTI‑ALPS index and the volumes of thalamus

In the correlation analysis of the global GMV in the MDD and SMD groups, a significant cluster in the left thalamus was found to be positively correlated with the DTI-ALPS index, and these clusters largely overlapped. However, no significant clusters were found in the PMD and HC groups. The position, cluster size, peak T value, and peak MNI coordinates of regions exhibiting differences in GMV are presented in Fig. 3.

Fig. 3.

Fig. 3

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Gray matter regions correlated with the DTI-ALPS index in MDD and SMD groups. MNI: Montreal Neurological Institute; T: peak T-statistics; p: SPM FWE-corrected P value.

As previously mentioned, we conducted VBM analysis and found a significant correlation between the DTI-ALPS index and thalamic volume. Therefore, we used the volumes of thalamic nuclei segmented by FreeSurfer for partial correlation analysis with the DTI-ALPS index. In the MDD group, significant positive correlations were found between the DTI-ALPS index and the volumes of the left whole thalamus (r = 0.196, p = 0.014), right whole thalamus (r = 0.168, p = 0.026), and the whole thalamus (r = 0.201, p = 0.014) (Fig. 4a–c), This finding was not replicated in the PMD group, but it was observed in the SMD group, showing significant positive correlations between the DTI-ALPS index and the volumes of the left whole thalamus (r = 0.294, p = 0.006), right whole thalamus (r = 0.220, p = 0.027), and the whole thalamus (r = 0.286, p = 0.006) (Fig. 4d–f). The correlation results between the DTI-ALPS index and the volume of thalamic subregions are shown in Table 2.

Fig. 4.

Fig. 4

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Correlations between the DTI-ALPS index and thalamus volume. The DTI-ALPS index was significantly correlated with left, right and whole thalamus volume in MDD (a–c) and SMD (d–f) groups.

Table 2.

Relationships between the DTI-ALPS index and thalamus subregions.

Subregion HC
 MDD
 SMD
 PMD
Left Right Left Right Left Right Left Right
Anterior −0.131 (0.998) −0.068 (0.772) 0.175 (0.031*) 0.118 (0.178) 0.369 (0.001*) 0.120 (0.280) −0.052 (0.803) 0.130 (0.575)
Lateral −0.104 (0.998) −0.115 (0.772) 0.061 (0.506) 0.060 (0.515) 0.232 (0.023*) 0.138 (0.251) −0.173 (0.803) −0.033 (0.786)
Ventral −0.030 (0.998) −0.054 (0.772) 0.179 (0.031*) 0.150 (0.140) 0.259 (0.013*) 0.177 (0.152) 0.055 (0.803) 0.153 (0.575)
Intralaminar 0.011 (0.998) 0.016 (0.883) 0.208 (0.017*) 0.137 (0.140) 0.298 (0.005*) 0.194 (0.152) 0.099 (0.803) 0.093 (0.672)
Medial −0.013 (0.998) 0.050 (0.772) −0.016 (0.836) 0.011 (0.890) 0.006 (0.956) 0.056 (0.581) 0.001 (0.996) 0.052 (0.786)
Posterior 0.000 (0.998) 0.098 (0.772) 0.212 (0.017*) 0.196 (0.056) 0.299 (0.005*) 0.230 (0.125) 0.063 (0.803) 0.182 (0.575)
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Results are expressed as correlation coefficient r (p-value).

No significant correlations were found between the DTI-ALPS index and clinical characteristics in any of the groups, including HAMD-17, AS, Cog, Sleep, HAMA, PHQ-9 and PHQ-15 scores. The details are shown in Table 3.

Table 3.

Relationships between the DTI-ALPS index and clinical characteristics.

MDD SMD PMD
HAMD-17 −0.020 (0.974) −0.023 (0.822) −0.138 (0.779)
AS 0.006 (0.940) −0.035 (0.924) −0.009 (0.940)
Cog 0.013 (0.940) −0.014 (0.924) 0.058 (0.940)
Sleep −0.008 (0.940) −0.010 (0.924) −0.099 (0.940)
HAMA −0.011 (0.974) −0.051 (0.814) −0.035 (0.994)
PHQ-9 0.014 (0.974) −0.066 (0.765) −0.001 (0.994)
PHQ-15 0.002 (0.974) −0.080 (0.765) −0.077 (0.790)
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Results are expressed as correlation coefficient r (p-value).

4. Discussion

This study applies the advanced, non-invasive DTI-ALPS index to explore glymphatic system function in depression and its subgroups, and its relationship with brain structure, providing further evidence for understanding the pathogenesis of depression. Our key findings are as follows: first, by comparing the demographic data of the SMD, PMD, and HC groups, we found that the demographic results of this study align with previous findings: a) SMD patients significantly outnumbered PMD patients (Liu et al., 2021, Sun et al., 2023); b) the prevalence of somatic depression is higher in females than in males (Silverstein, 1999, Silverstein, 2002, Silverstein et al., 2017). Compared with PMD group, there were more patients in the SMD group, and the proportion of females in the SMD group was significantly higher than males. These demographic differences related to disease characteristics may influence subsequent MRI analyses. Therefore, we used ANCOVA to examine sex differences in the DTI-ALPS index across subgroups, finding no significant differences within any subgroup: MDD (F = 1.220, P = 0.271), SMD (F = 0.930, P = 0.337), PMD (F = 0.028, P = 0.867), HC (F = 0.739, P = 0.392). To account for the potential impact of sex differences, we included sex as a covariate for subsequent analyses. In subsequent analyses, the DTI-ALPS index was significantly higher in the MDD group compared to the HC group, with this difference primarily driven by the SMD group. Notably, we observed a significant positive correlation between the GMV of a large cluster in the left thalamus, covering nearly the entire thalamus, and the DTI-ALPS index. Further analysis of thalamic subregions revealed that the DTI-ALPS index remained significantly positively correlated with the total volumes of the left, right, and bilateral thalami, as well as with the volume of all left thalamic nuclei except the medial nucleus. These positive correlations were primarily driven by the SMD group, indicating that in the MDD group, particularly in the SMD group, patients with lower DTI-ALPS Index values tend to have smaller volumes in most thalamic nuclei.

As previously mentioned, earlier studies have primarily focused on CNS diseases, identifying glymphatic dysfunction in conditions such as AD (Chang et al., 2023, Siow et al., 2022), PD (Cai et al., 2023, Shen et al., 2022, Si et al., 2022), MS (Carotenuto et al., 2022), and cerebral small vessel disease (Xu et al., 2023). Our study extends glymphatic research to depression, suggesting that glymphatic system activity might be a crucial pathological mechanism. We observed an increased DTI-ALPS index in depression, particularly in SMD patients, may indicate heightened glymphatic activity. Recent studies (Chao et al., 2024, Yang et al., 2024) have reported a decrease in the DTI-ALPS index in MDD patients, indicating glymphatic system dysfunction, which contrasts with our findings. Previous research has demonstrated that glymphatic system function (DTI-ALPS index) is strongly associated with age (Clark et al., 2024, Hsiao et al., 2023, Siow et al., 2022, Taoka et al., 2022). In some studies, the effect of age on the DTI-ALPS index was shown to be non-linear, peaking at 30–40 years (Hsiao et al., 2023, Taoka et al., 2022) and subsequently declining. The average age of the participants in our study (24.7 years old) is considerably lower than in the aforementioned two studies (53 and 58.6 years old), which may be a key factor contributing to the contradictory results. We speculate that in older populations studied by Yang et al. (2024), the glymphatic system may play a dysfunctional role in depression. In contrast, in our younger cohort, the glymphatic system might play a different role in the pathophysiology of depression. Although there are no more studies on depression, our findings are unexpectedly similar to a study on migraine chronification (Zhang et al., 2023), which also found increased glymphatic activity rather than dysfunction. The function of the glymphatic system is mediated by AQP-4 and heavily dependent on endogenous circadian rhythms. It is sharply suppressed during wakefulness but significantly enhanced during sleep, especially during slow-wave sleep (Hablitz et al., 2020, Ma et al., 2019, Xie et al., 2013). Numerous clinical studies have shown that patients with depression commonly exhibit abnormalities in the serotonin (5-HT) system and circadian rhythm disruptions (Bunney et al., 2015, Murphy and Peterson, 2015), which may directly affect glymphatic function. In our study, we measured glymphatic function during wakefulness and observed significantly increased glymphatic activity in depressed patients, especially in SMD patients, compared to healthy controls. We speculate that the circadian rhythm disruption caused by 5-HT abnormalities may lead to a corresponding disruption in the glymphatic system, weakening its suppression during wakefulness, which could explain the enhanced glymphatic activity observed in depressed patients during this state. Additionally, 5-HT is involved in various physiological processes and behaviors, including pain, appetite, and respiration, which are closely related to somatic symptoms (Lesch and Waider, 2012). Norepinephrine (NE) may also independently influence glymphatic function (Berridge and Waterhouse, 2003a, Berridge and Waterhouse, 2003b, O’Donnell et al., 2012, Xie et al., 2013), with its surge during wakefulness being a key factor in the rapid suppression of glymphatic activity (Goldman et al., 2020, Zhang et al., 2022). Altered NE secretion in the brains of MDD patients could further contribute to the enhanced glymphatic function observed in this population. Pain, a common somatic symptom, is closely related to the glymphatic system. Studies have shown that chronic pain is associated with AQP-4, NE, and 5-HT, with AQP-4 deficiency reducing inflammatory and neuropathic pain (Peng et al., 2023). Furthermore, serotonin and norepinephrine reuptake inhibitors (SNRIs) are a treatment option for chronic pain, which may explain why antidepressants are effective in treating both depression and chronic pain. In conclusion, our study emphasizes that glymphatic system dysfunction may be linked to depression, particularly in SMD patients. Changes in multiple neurotransmitters may play a significant role in this disruption, but further research is needed to explore these mechanisms.

Additionally, there are currently no studies on the relationship between depression and the human glymphatic system, though a few animal studies have explored this connection. While the results are inconsistent, most studies (Liu et al., 2020, Xia et al., 2017) have found evidence of glymphatic dysfunction or downregulated AQP-4 expression in mice subjected to chronic unpredictable mild stress (CUMS) (Kinoshita et al., 2018, Taler et al., 2021). These findings seem to contradict the results of our study, which may be due to the lack of consideration of endogenous circadian rhythm effects. Our study measured glymphatic function in humans during wakefulness, and glymphatic activity during wakefulness does not represent the system's overall function. We speculate that, although glymphatic function is abnormally enhanced during wakefulness in depression patients, the overall function of the glymphatic system may still be impaired due to the prevalent sleep disturbances in these patients and the significant efficiency differences in the glymphatic system between wakefulness and sleep.

Research on neurodegenerative diseases (Chang et al., 2023, Hsu et al., 2023, Jiang et al., 2023, Siow et al., 2022) has suggested that glymphatic dysfunction can lead to widespread gray matter (GM) atrophy, affecting both cortical and subcortical structures. To explore brain regions associated with glymphatic function in depression, we evaluated whole-brain GMV and identified the thalamus as a region significantly correlated with the DTI-ALPS index, particularly in SMD patients. Several studies have also emphasized the thalamus's dependence on glymphatic function. One study highlighted GM regions vulnerable to atrophy in the context of glymphatic dysfunction, including the thalamus, and found through mediation analysis that the impact of glymphatic dysfunction on cognition is mediated by its role in maintaining GM integrity (Chang et al., 2023). The thalamus has also been shown to be susceptible to glymphatic dysfunction, with findings such as enhanced and delayed gadolinium clearance in the thalamus (Stoeckel et al., 2018), increased amyloid deposition after just one night of sleep deprivation, which was enough to impair neuronal function in rodents (Shokri-Kojori et al., 2018), and associations between thalamic GM volume and the DTI-ALPS index in community-dwelling elderly populations (Siow et al., 2022). Interestingly, this vulnerability was also observed in MDD patients, with a notable contribution from the SMD group.

Initially, the thalamus was primarily considered the central relay and integration hub for sensory information in the brain (Nakagawa, 2019, Schmid et al., 2016). It relays all sensory input, except olfaction, also including signals from the visceral motor system (Liu et al., 2016), and projects them to the corresponding cortical areas for further analysis and integration. Later studies revealed that the thalamus is not only involved in cognitive processes (Haber and Calzavara, 2009), but also plays a role in regulating motivation and emotions (Nakajima and Halassa, 2017). These diverse functions make the thalamus a key structure in the brain, crucial for normal physiological processes as well as in the pathophysiology of various mental disorders. In depression, thalamic abnormalities have been closely linked to the manifestation of multiple symptoms. For instance, studies have shown that the lateral and ventral nuclei—known as relay stations for motor and sensory processing—are strongly associated with somatic symptoms in depression (Chibaatar et al., 2023). The intralaminar nuclei have also been reported to be involved in cognitive and behavioral impairments in depression (Cover and Mathur, 2021, Schiff, 2008). Additionally, the pulvinar nuclei (Pu), the largest thalamic nuclei within the posterior nuclei, have been shown to participate in emotional processing and executive function, potentially playing a role in the development of depression (Hakamata et al., 2016). In our study, all four of these nuclei exhibited positive correlations with glymphatic function, with the SMD group contributing most significantly. This aligns with the theory proposed by a large-scale multimodal neuroimaging study (Harshaw, 2015), which suggested that abnormalities in the cortical-striatal-thalamic-cortical circuit, centered on the thalamus, may form the biological basis of somatic symptoms in depression. We propose that glymphatic system dysfunction could influence thalamus nuclei volume, thereby contributing to somatic symptoms in depression. This may explain why the glymphatic system dysfunction and its correlation with GM volume were more pronounced in somatic depression patients in our study.

Unfortunately, this study has some limitations. First, while the method used to measure glymphatic function along the perivascular space is non-invasive, it is not as direct as using gadolinium-based contrast agents. We did not employ contrast agents for a more direct comparison of glymphatic function, and we also lack neuroimaging data to assess glymphatic function during slow-wave sleep. Consequently, we were unable to fully evaluate overall glymphatic function, highlighting the need for more comprehensive assessments in future research. Second, we could not measure accurate, real-time in vivo monoamine concentrations, limiting our ability to directly assess the impact of the monoamine system on glymphatic function in depression. Experimental studies are needed to validate the hypotheses and speculations we have proposed. Third, the demographic results of this study showed differences, including differences in the number of patients across groups, gender distribution within groups, and the age distribution compared to other studies. These factors may limit the generalizability of our findings. Finally, our study is cross-sectional, and longitudinal studies are required to confirm our findings.

5. Conclusion

We found the DTI-ALPS index was significantly increased in MDD patients, particularly those somatic depression patients, possibly due to the glymphatic activity increasing. And the DTI-ALPS index was positive correlated with the volume of thalamic nuclei. This suggests that the thalamus's vulnerability to glymphatic system function is also present in depression, particularly in somatic depression, and may be one of the biological foundations of its pathophysiology. The interaction between glymphatic function and the thalamus in the context of depression warrants further investigation.

Funding sources

This work was supported by grants from the National Natural Science Foundation of China (U21A20364), the National Key R&D Program of China (2018YFC1314600), the Natural Science Foundation of Hubei Province (2023AFB213), the Fundamental Research Funds for the Central Universities (2042023kf0014).

CRediT authorship contribution statement

Zipeng Deng: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation. Wei Wang: Investigation, Formal analysis, Data curation. Zhaowen Nie: Investigation, Formal analysis, Data curation. Simeng Ma: Writing – review & editing, Investigation. Enqi Zhou: Writing – review & editing, Data curation. Xinhui Xie: Investigation. Qian Gong: Data curation. Lihua Yao: Investigation, Data curation. Lihong Bu: Writing – review & editing, Resources, Methodology. Lijun Kang: Writing – review & editing, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. Zhongchun Liu: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization.

Acknowledgements

Some numerical calculations in this study have been done on the supercomputing system in the Supercomputing Center of Wuhan University. The remote access and file transfer network solution was sponsored by WJY Cloud, Wuhan.

Ethics approval statement

The Ethics Committee of Renmin Hospital of Wuhan University approved the study protocol (WDRY2020-K191).

Contributor Information

Zipeng Deng, Email: zp.deng@whu.edu.cn.

Wei Wang, Email: 851551307@qq.com.

Zhaowen Nie, Email: zhaowen.nie@whu.edu.cn.

Simeng Ma, Email: simeng.ma@whu.edu.cn.

Enqi Zhou, Email: zeqzeq1998@163.com.

Xinhui Xie, Email: xxh.med@gmail.com.

Qian Gong, Email: q1an_gong@whu.edu.cn.

Lihua Yao, Email: 523961646@qq.com.

Lihong Bu, Email: bulihongs@126.com.

Lijun Kang, Email: LijunnKang@126.com.

Zhongchun Liu, Email: zcliu6@whu.edu.cn.

Data availability

The data that support the findings of this study are available on request from the corresponding author, upon reasonable request. The code we used in our DTI data processing can be found in the following repository: [https://github.com/Winniework/Automated-DTI-ALPS-pipeline].

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author, upon reasonable request. The code we used in our DTI data processing can be found in the following repository: [https://github.com/Winniework/Automated-DTI-ALPS-pipeline].

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