Depressive disorders are associated with brain structural injury in HIV-positive men who have sex with men

Abstract

Background

Depressive disorders (DD) are more prevalent among people with HIV (PWH) compared to the general population. Research in the general population has confirmed the association between depression and gray matter atrophy as well as reduced cortical thickness. However, there is a lack of neuroimaging studies investigating brain structural changes in PWH with comorbid DD (PWH-DD).

Methods

This cross-sectional study included 69 HIV-positive men who have sex with men, categorized into PWH-DD (n = 29) and PWH control (n = 40) groups based on the diagnosis of DD. Participants underwent clinical, neuropsychiatric, and MRI evaluations. Voxel-based and surface-based morphometry techniques were applied to analyze gray matter volume (GMV) and cortical anatomical characteristics in structural MRI data. The imaging findings were ultimately correlated with the results of clinical assessments.

Results

Compared to participants in the PWH control group, those in the PWH-DD group showed higher scores in evaluation of depression, anxiety, sleep disturbances, childhood trauma, and mental health symptoms, indicating a greater burden of psychological and emotional distress. Comparisons of brain structure showed that participants in the PWH-DD group exhibited lower GMV in the left middle frontal gyrus, shallower sulcal depth in the left supramarginal and bilateral superior parietal regions, and lower fractal dimension in multiple frontal and temporal lobe areas compared to those in the PWH control group. Among all participants, correlation analysis demonstrated that GMV of the left middle frontal gyrus was significantly negatively correlated with Self-Rating Depression Scale scores.

Conclusion

This study emphasizes that HIV-positive men who have sex with men with comorbid DD exhibit poorer mental health status and more severe brain structural alterations. However, further longitudinal studies are needed to explore the exact causal relationship between DD and brain structural injury in PWH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-025-07267-4.

Introduction

Depressive disorders (DD) are one of the most common psychiatric disorders among people with HIV (PWH). The prevalence of major depressive disorder is over twice as high in PWH as compared to the general population [1]. The high prevalence of DD among PWH may result from factors related to HIV infection, psychosocial factors, and exogenous factors associated with antiretroviral medications [2, 3]. This comorbidity not only exacerbates challenges in adherence to antiretroviral therapy (ART) and increases the risk of virological failure, but also contributes to a decline in physical health and quality of life [4–8]. In more severe cases, it may also lead to an increased risk of disability and mortality [9–11], underscoring the urgent need to elucidate its neurological mechanisms. An increasing body of evidence indicates that depression is associated with abnormalities in brain structure [12, 13]. Considering the potential involvement of distinct pathophysiological mechanisms, systematically examining structural and morphological brain changes in HIV-associated DD is crucial, as this may provide valuable biomarkers for early detection and therapeutic intervention. Nevertheless, research on these structural changes in PWH with comorbid DD (PWH-DD) remains limited.

Structural neuroimaging techniques, especially voxel-based morphometry (VBM) and surface-based morphometry (SBM), offer detailed insights into the structural brain alterations in DD across various populations. Among healthy individuals, DD are frequently linked to decreased gray matter volume (GMV), including the striatum, hippocampus, and thalamus [12, 14–16]. Morphological studies utilizing SBM analysis have also reported reduced cortical thickness in the frontal and parietal lobes among patients with major depressive disorder [17–19]. Several studies have reported associations between immunological/virological markers and alterations in brain structure. For instance, Nir et al. [20] found that reduced serum CD4⁺ T cell counts and elevated HIV RNA in PWH were significantly associated with decreased GMV. Sanford et al. [21] further reported a negative correlation between duration of HIV infection and cortical thickness. Moreover, research on individuals with depression has revealed GMV loss in the hippocampal gyrus associated with depressive symptoms [22]. These findings suggest that immune dysregulation, viral replication, and depressive symptomatology may all impact brain structure. However, studies on PWH-DD that include rigorous psychiatric diagnosis and structural morphology analyses still remain scarce. Moreover, the complex interplay of HIV-related neurotoxicity, psychological health status, and neurochemical alterations induced by ART may result in unique structural and morphological patterns distinct from those seen in the general population. Therefore, it is essential to prioritize research on brain structural changes in PWH with DD, as this may clarify the specific neuroanatomical consequences of HIV and its related factors on mood dysregulation, thereby enhancing our understanding of the neuropathological basis of DD in this vulnerable population.

This cross-sectional study aimed to investigate the relationship between DD and brain structural changes in HIV-positive men who have sex with men (MSM), using VBM and SBM techniques, compared to a cohort without neuropsychiatric disorders. Furthermore, correlation analyses were performed to explore the associations between imaging alterations and clinical scale data. These findings may offer foundational data for future imaging research on depression comorbid with HIV.

Materials and methods

Participants

The cross-sectional study was approved by the Institutional Ethics Committee of Beijing Youan Hospital, Capital Medical University. The study cohort was drawn from the Infectious Diseases outpatient clinic of Beijing Youan Hospital, Capital Medical University. Before signing the written informed consent form, all participants received detailed information about the entire procedure and potential risks. The inclusion criteria were: (1) Chinese HIV-positive MSM; (2) aged 18 years or older; (3) not taking antidepressants; and (4) right-handed. The exclusion criteria were: (1) current or past opportunistic central nervous system infections; (2) a history of confounding neurological diseases, including Parkinson’s disease, multiple sclerosis, epilepsy, or dementia; (3) previous head injury with loss of consciousness for more than half an hour; (4) the presence of space-occupying brain lesions; (5) MRI contraindications or claustrophobia; and (6) abuse of psychoactive substances, such as central nervous system stimulants like amphetamines and hallucinogens like lysergic acid diethylamide.

From April 2022 to November 2022, a total of 69 eligible participants were recruited by the research team for this study (Supplementary Fig. 1). Participants were categorized into PWH with DD and PWH control groups based on the diagnosis of neuropsychiatric disorders. All participants underwent comprehensive clinical, neuropsychiatric, and structural imaging evaluations.

Clinical assessments

Demographic and clinical data collected from all participants included age, height, weight, years of education, and HIV-related variables. Peripheral blood biomarker specimens were obtained from fasting venous blood, with some data sourced from outpatient monitoring of PWH. The sampling time points included HIV diagnosis, ART initiation, and MRI assessment. Immunological parameters were assessed by flow cytometry using the BD Lyric™ flow cytometry analysis system (BD Biosciences, USA) and analyzed with FlowJo software (version 10.8.1), employing the BD Multitest™ CD3/CD8/CD45/CD4 reagent kit (BD Biosciences) in strict accordance with the manufacturer’s instructions.

Diagnosis of neuropsychiatric disorders

Psychiatric diagnoses were determined by psychiatrists using the diagnostic criteria established in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) [23]. The psychiatrists identified 19 relevant neuropsychiatric conditions among participants, including depressive disorders, bipolar disorder, anxiety disorders, sleep disorder, obsessive–compulsive disorder, and alcohol-related mental and behavioral disorders. Depression subtypes identified included major depressive disorder, persistent depressive disorder, and unspecified depressive disorder. Given our focus on the impact of core depressive disorders on brain structure and the limited sample size for each subtype, participants with any of the aforementioned depressive subtypes were grouped into the PWH-DD cohort, while those without any neuropsychiatric disorder diagnoses were assigned to the PWH control cohort.

Neurocognitive tests

The Montreal Cognitive Assessment (MoCA) was used to evaluate neurocognitive function in all participants [24]. The MoCA scale comprises assessment items across seven cognitive domains: visuospatial/executive, naming, attention, language, abstraction, memory, and orientation. The total score is 30 points, with a score of 26 or above considered normal.

Mood assessment

The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to measure depression and anxiety levels among all participants [25, 26]. The SAS scores range from 20 to 80, with a standard cutoff of 50: 50–59 indicates mild anxiety, 60–69 moderate anxiety, and scores ≥ 70 severe anxiety; higher scores denote greater anxiety severity. The SDS ranges from 20 to 80, with a standard cutoff of 53: 53–62 indicates mild depression, 63–72 moderate depression, and scores ≥ 73 severe depression; higher scores indicate greater depression severity. Mental health status was assessed by the Symptom Checklist 90 (SCL-90) [27]. The SCL-90 total score ranges from 90 to 450; higher scores reflect greater overall psychological distress. The most commonly used metrics are the total score and the average factor score. Sleep quality was evaluated by the Pittsburgh Sleep Quality Index (PSQI) [28]. The PSQI total score ranges from 0 to 21, with higher scores reflecting poorer sleep quality.

Other assessments

Childhood maltreatment history was assessed with the Childhood Trauma Questionnaire (CTQ) [29]. Subscale scores on the CTQ range from 5 to 25, where higher scores denote more severe childhood trauma experiences. Alcohol craving was assessed with the Alcohol Urge Questionnaire (AUQ) and the Visual Analogue Scale (VAS) [30, 31]. The AUQ has a top score of 56 and evaluates the degree of alcohol craving in participants. The VAS ranges from 0 to 10; like the AUQ, higher scores indicate greater alcohol craving.

MRI data acquisition

Structural imaging data were collected using a 1.5 T MRI scanner (Philips, Amsterdam, The Netherlands) at the Second Hospital of Beijing, with foam cushions used to minimize head movements. Participants were instructed to lie in a relaxed position, close their eyes, and avoid focusing on specific thoughts without falling asleep. Structural scans were acquired using the same MRI machines with consistent acquisition parameters. High-resolution T1-weighted anatomical images were obtained using a 3D MPRAGE sequence with the following parameters: shortest repetition time/echo time = 8.3/3.9 ms, matrix = 256 × 227, flip angle = 12°, slice thickness = 1 mm, no interslice gap, 384 slices, and a field of view of 256 mm × 256 mm.

Image preprocessing

VBM and SBM analyses were performed using the Computational Anatomy Toolbox 12 (CAT12, http://dbm.neuro.uni-jena.de/cat12/) implemented in Statistical Parametric Mapping 12 (SPM12, Welcome Department of Imaging Neuroscience, London, UK) running on MATLAB 2022b (The MathWorks, Natick, MA, USA).

The raw imaging data were initially examined for artifacts and anatomical anomalies. All image data acquired in Digital Imaging and Communications in Medicine format were converted to Neuroimaging Informatics Technology Initiative format for processing and analysis. The preprocessing steps included bias-field correction, tissue segmentation, and cortical surface reconstruction. Each normalized bias-corrected volume was visually inspected to identify and exclude volumes with artifacts or suboptimal orientations. Modulated, normalized gray matter segments were inspected to detect outliers and ensure sample homogeneity. An image was deemed qualified if the weighted average of the image and preprocessing quality was ≥ 80% [32]. Finally, all scans were resampled, with GMV smoothed using an 8-mm full-width at half-maximum (FWHM) Gaussian kernel, cortical thickness smoothed using a 12-mm FWHM Gaussian kernel, and cortical complexity smoothed using a 20-mm FWHM Gaussian kernel.

MRI data analysis

GMV, cortical thickness, and complexity were compared between the two groups of PWH. Cortical complexity, including the gyrification index, sulcal depth, and fractal dimension, was assessed using SBM via CAT12/SPM12. Cortical thickness and complexity were analyzed with a general linear model in the CAT12 toolbox within the SPM12 framework. Age, body mass index, and years of education were included as covariates in the design matrix. Following processing, a study-specific gray matter mask was applied to the VBM analysis. To exclude artifacts along the gray matter border, an absolute gray matter threshold of 0.2 was applied. As an exploratory study, statistical significance was set at P < 0.001 (uncorrected). Additionally, statistical analyses were corrected for multiple comparisons using Gaussian Random Field (GRF) theory, with a voxel-wise threshold of (P < 0.001) and a cluster-level threshold of (P < 0.05). Anatomical descriptions of regions involved in GMV were based on the Automated Anatomical Labeling (AAL) atlas [33]. Cortical thickness and complexity were anatomically described using the Desikan-Killiany parcellation [34]. Statistical maps of cortical thickness and complexity differences were visualized using CAT12 tools, with results projected onto the cortical Surface. Only contiguous clusters with a minimum of 10 voxels were reported. Imaging findings were displayed using CAT12 and MRIcroGL software (https://www.nitrc.org/projects/mricrogl/).

Statistical analysis

Statistical analyses were performed using SPSS software (version 25.0; IBM Corp., Armonk, NY, USA). The significance level (α) was set at 0.05. Normality of continuous data was assessed with Shapiro–Wilk and Kolmogorov–Smirnov tests. Normally distributed data were presented as mean ± standard deviation, while non-normally distributed data were presented as medians with interquartile ranges. Depending on normality test results, two-sample t-tests or Mann–Whitney U tests were used to compare numerical variables between groups. Categorical data were expressed as proportions. Chi-square and Fisher's exact tests were used to compare categorical variables between groups. In the correlation analysis, Pearson correlation analysis was performed for normally distributed data, while Spearman correlation analysis was performed for non-normally distributed data.

For each participant, the average time series of voxels from each seed region was extracted. Imaging findings were correlated with the clinical assessment results. Given the exploratory nature of this study, no corrections for multiple comparisons were applied to these correlation results. Additionally, because the small number of participants in each group may have limited the statistical power, only a correlation analysis for all participants was performed. Statistical results were plotted using R software (version 4.2.3).

Results

Participant characteristics

A total of 69 participants successfully completed the study, with 29 (42.03%) in the PWH-DD group and 40 (57.97%) in the PWH control group. Demographic variables were well matched between the two groups. Demographic and clinical assessments are comprehensively outlined in Table 1.

Table 1.

The demographic and clinical characteristics of PWH-DD group and PWH control group

Demographic and clinical data	PWH-DD (N = 29)	PWH control (N = 40)	Statistic	P value
Age (years)	35.00 (30.50—42.50)	32.00 (29.00—38.50)	Z = −1.260	0.208a
Height (m)	1.74 ± 0.05	1.75 ± 0.05	t = −1.449	0.152b
Weight (kg)	68.00 (61.50—75.00)	67.00 (60.75—76.75)	Z = −0.152	0.879a
BMI (kg/m2)	23.08 ± 3.14	22.44 ± 2.64	t = 0.905	0.369b
Education (years)	16.00 (12.50—16.00)	16.00 (15.00—16.00)	Z = −1.001	0.317a
At the time of HIV diagnosis
CD4 at diagnosis (cells/μL)	357.66 (287.50—419.50)	324.92 (175.72—450.00)	Z = −1.155	0.248a
CD8 at diagnosis (cells/μL)	956.00 (850.00—1292.00)	946.43 (742.25—1084.75)	Z = −1.179	0.238a
CD4/CD8 ratio at diagnosis	0.35 (0.27—0.49)	0.35 (0.18—0.44)	Z = −0.700	0.484a
VL at diagnosis (log10 copies/mL)	4.03 (3.77—4.64)	4.01 (3.67—4.56)	Z = −0.651	0.515a
At the initiation of ART
CD4 at initiation of ART (cells/μL)	402.16 ± 181.64	321.65 ± 201.72	t = 1.705	0.093b
CD8 at initiation of ART (cells/μL)	1119.79 (779.94—1336.00)	921.14 (743.00—1103.13)	Z = −1.769	0.077a
CD4/CD8 ratio at initiation of ART	0.35 (0.26—0.56)	0.35 (0.21—0.44)	Z = −0.839	0.401a
VL at initiation of ART (log10 copies/mL)	3.99 (3.77—4.64)	4.05 (3.67—4.76)	Z = −0.116	0.908a
ART regimen at initiation (INSTI/Non-INSTI—based regimen)	5/24	8/32	χ2 = 0.084	0.772c
At the time of clinical and MRI assessment
Current CD4 (cells/μL)	633.14 ± 272.98	573.27 ± 260.63	t = 0.923	0.359b
Current CD8 (cells/μL)	967.00 (572.00—1322.00)	807.50 (652.00—1027.25)	Z = −0.474	0.635a
Current CD4/CD8 ratio	0.75 ± 0.42	0.67 ± 0.26	t = 0.917	0.364b
Current virus not detectable (yes/no)	29/0	40/0	NA	NA
Current ART regimen (INSTI/Non-INSTI—based regimen)	16/13	27/13	χ2 = 1.088	0.297c
Duration between diagnosis and initiation of ART (months)	0.70 (0.30—5.15)	0.50 (0.40—2.08)	Z = −0.628	0.530a
Duration of ART (months)	66.30 (22.70—95.10)	64.95 (43.15—87.98)	Z = −0.049	0.961a
Duration of HIV diagnosis (months)	76.20 (36.55—106.15)	69.80 (45.08—90.33)	Z = −0.371	0.711a

The continuous data were presented as mean ± standard deviation or median (interquartile range), and the categorical data were expressed as numbers. Two-sample t-tests were used for continuous data with a normal distribution, while Mann–Whitney U-tests were applied to continuous data that did not obey a normal distribution. Chi-square and Fisher’s exact tests were used to compare categorical variables

Abbreviations: PWH people with HIV, PWH-DD people with HIV with comorbid depressive disorders, NA not available, BMI body mass index, CD4 CD4⁺ T cell count, CD8 CD8⁺ T cell count, VL viral load, ART antiretroviral therapy, INSTIs integrase strand transfer inhibitors, MRI magnetic resonance imaging

^aMann-Whitney U test

^btwo-sample t-test

^cchi-square test

No significant differences were observed between the two groups in HIV-related clinical parameters, including CD4⁺ T cell count, CD8⁺ T cell count, viral load, treatment duration, and ART regimen (Table 1).

Participants in the PWH-DD group exhibited higher SAS scores (P < 0.001), SDS scores (P < 0.001), PSQI scores (P < 0.001), CTQ scores (P = 0.022), and SCL-90 scores (P < 0.001) compared to those in the PWH control group. No significant differences were found between the two groups in AUQ, VAS for alcohol craving, or MoCA (Table 2). Further analysis of the MoCA subdomain scores revealed that the PWH-DD group had significantly lower scores in the attention domain compared to the PWH control group (Z = −2.239, P = 0.025), while no statistically significant differences were observed in other subdomains such as naming and memory (Supplementary Table 1 and Supplementary Fig. 2).

Table 2.

The neuropsychiatric assessment differences between the PWH-DD group and PWH control group

Clinical assessment	PWH-DD (N = 29)	PWH control (N = 40)	Statistic	P value
SAS	41.00 (36.00—47.50)	29.00 (24.00—33.75)	Z = −5.079	< 0.001a
SDS	43.00 (35.50—49.00)	30.00 (25.25—35.00)	Z = −4.387	< 0.001a
PSQI	7.00 (4.50—11.50)	3.00 (2.00—6.75)	Z = −3.874	< 0.001a
CTQ	61.00 (54.50—64.50)	57.00 (49.00—61.00)	Z = −2.295	0.022a
SCL-90	168.00 (145.50—226.00)	109.00 (98.25—139.75)	Z = −4.694	< 0.001a
Somatization	1.75 (1.29—2.29)	1.17 (1.00—1.42)	Z = −4.173	< 0.001a
Obsessive compulsive	2.30 (1.90—2.95)	1.40 (1.13—1.90)	Z = −4.846	< 0.001a
Interpersonal sensitivity	1.89 (1.50—2.33)	1.22 (1.00—1.84)	Z = −3.759	< 0.001a
Depression	2.38 (1.77—2.85)	1.31 (1.08—1.75)	Z = −4.385	< 0.001a
Anxiety	1.90 (1.55—2.60)	1.10 (1.00—1.48)	Z = −4.951	< 0.001a
Hostility	1.83 (1.42—2.33)	1.17 (1.00—1.50)	Z = −4.537	< 0.001a
Phobic anxiety	1.29 (1.14—1.86)	1.00 (1.00—1.29)	Z = −3.262	0.001a
Paranoid ideation	1.67 (1.33—2.00)	1.17 (1.00—1.63)	Z = −3.342	0.001a
Psychoticism	1.80 (1.45—2.15)	1.15 (1.00—1.55)	Z = −4.349	< 0.001a
Other	2.00 (1.43—2.57)	1.29 (1.00—1.68)	Z = −4.264	< 0.001a
Satisfied the SCL‐90 sum > 160 (yes/no)	19/10	6/34	χ2 = 18.569	< 0.001b
SCL-90 positive items > 43 (yes/no)	21/8	9/31	χ2 = 17.044	< 0.001b
At least one factor score > 2 (yes/no)	24/5	9/31	χ2 = 24.464	< 0.001b
A positive screen of SCL-90 (yes/no)	24/5	10/30	χ2 = 22.438	< 0.001b
AUQ	9.00 (8.00—19.00)	10.00 (8.00—14.00)	Z = −0.101	0.919a
VAS	2.00 (1.00—4.00)	2.00 (1.00—3.00)	Z = −0.102	0.919a
MoCA	27.00 (24.50—28.00)	27.00 (25.00—28.00)	Z = −0.321	0.748a

The continuous data were presented as mean ± standard deviation or median (interquartile range), and the categorical data were expressed as numbers. Two-sample t-tests were used for continuous data with a normal distribution, while Mann–Whitney U-tests were applied to continuous data that did not obey a normal distribution. Chi-square and Fisher’s exact tests were used to compare categorical variables

AbbreviationsPWH people with HIV, PWH-DD people with HIV with comorbid depressive disorders, SAS Self-rating Anxiety Scale, SDS Self-rating Depression Scale, PSQI Pittsburgh Sleep Quality Index, CTQ Childhood Trauma Questionnaire, SCL-90 Symptom Checklist 90, AUQ Alcohol Urge Questionnaire, VAS Visual Analogue Scale for alcohol craving, MoCA Montreal Cognitive Assessment

^aMann-Whitney U test

^bchi-square test

Comparisons of brain structural MRI metrics

GMV

The PWH-DD group exhibited lower GMV in the left middle frontal gyrus compared to the PWH control group (GRF correction, cluster level P < 0.05; Fig. 1 and Supplementary Table 2).

Fig. 1.

Participants in the PWH-DD group displayed lower gray matter volume in the left middle frontal gyrus compared to those in the PWH control group. Coordinates (X, Y, Z) refer to the peak MNI coordinates of brain regions with peak intensity (Gaussian Random Field correction, cluster level P < 0.05). Abbreviations: PWH-DD, people with HIV with comorbid depressive disorders, MNI, Montreal Neurological Institute

Cortical thickness/gyrification index

No significant differences were found between the two groups in cortical thickness or gyrification index.

Sulcal depth

The sulcal depth in participants from the PWH-DD group was found to be shallower than that in the PWH control group, especially in the left supramarginal gyrus, left superior parietal gyrus, and right superior parietal gyrus (Fig. 2 and Supplementary Table 3).

Fig. 2.

Participants in the PWH-DD group exhibited shallower sulcal depth compared to those in the PWH control group, particularly in the left supramarginal gyrus, left superior parietal gyrus, and right superior parietal gyrus. Statistical significance was set at P < 0.001 (uncorrected). Abbreviations: PWH-DD, people with HIV with comorbid depressive disorders

Fractal dimension

Compared to participants in the PWH control group, those in the PWH-DD group had lower fractal dimension in the left caudal middle frontal gyrus, right rostral middle frontal gyrus, right frontal pole, and left middle temporal gyrus (Fig. 3 and Supplementary Table 4).

Fig. 3.

Participants in the PWH-DD group exhibited lower fractal dimensions in the left caudal middle frontal gyrus, right rostral middle frontal gyrus, right frontal pole, and left middle temporal gyrus compared to those in the PWH control group. Statistical significance was set at P < 0.001 (uncorrected). Abbreviations: PWH-DD, people with HIV with comorbid depressive disorders

In the analysis of all participants, correlations of the imaging results with the clinical assessment results

In terms of GMV, the left middle frontal gyrus was observed to have a negative correlation with the scores of the SAS, SDS, CTQ, and the SCL-90. Regarding sulcal depth, the left supramarginal, left superior parietal, and right superior parietal regions were found to have a negative correlation with the SAS score. For fractal dimension, the left caudal middle frontal region exhibited a negative correlation with both the CTQ and the SCL-90 scores; the right rostral middle frontal and right frontal pole areas showed negative correlations with the SDS, CTQ, and SCL-90 scores; and the left middle temporal region was negatively correlated with the SAS, SDS, and SCL-90 scores (Fig. 4). Further correlation analysis across all participants revealed that the fractal dimension of the left middle temporal region was positively correlated with visuospatial/executive function scores (Supplementary Table 5).

Fig. 4.

In the analysis of all participants, correlation between structural neuroimaging findings and the clinical assessment results. Abbreviations: GMV, gray matter volume; SD, sulcal depth; FD, fractal dimension; L, left; R, right; SAS, Self-rating Anxiety Scale; SDS, Self-rating Depression Scale; PSQI, Pittsburgh Sleep Quality Index; CTQ, Childhood Trauma Questionnaire; SCL-90, Symptom Checklist 90; AUQ, Alcohol Urge Questionnaire; VAS, Visual Analogue Scale for alcohol craving; MoCA, Montreal Cognitive Assessment. *P < 0.05; **P < 0.01

Discussion

In this study, we examined the clinical and brain structural neuroimaging differences between PWH with DD and those without neuropsychiatric disorders. Our results reveal that PWH-DD exhibit poorer mental health, alongside gray matter atrophy, shallower sulcal depth, and lower fractal dimension in specific brain regions, including the left middle frontal gyrus, left supramarginal gyrus, and left caudal middle frontal gyrus. The correlation analyses further suggested a negative relationship between depression and brain structural alterations. Thus, DD are not only a prevalent mental health issue among HIV-positive MSM, but may also be potentially associated with structural changes in specific brain areas.

The middle frontal gyrus is located in the central region of the frontal lobe, adjacent to the prefrontal cortex, and is part of the frontoparietal control network, commonly associated with memory, attention, and cognitive control [35]. More importantly, the middle frontal gyrus is also involved in emotional regulation; injury to this region can make individuals more sensitive to negative emotional stimuli and decrease emotional regulation capacity, core characteristics of depression [36, 37]. Multiple studies have also confirmed the strong association between the middle frontal gyrus and depression, with some reporting reduced gray matter volume in this region in individuals with depression [38, 39]. In our VBM analysis, gray matter atrophy was observed in the left middle frontal gyrus among the PWH-DD group, consistent with the aforementioned studies. Additionally, a significant negative correlation was found between the volume of the left middle frontal gyrus and depression scores. This finding further validates the strong association between depression and volume deficits in the middle frontal gyrus, even within the specific population of PWH.

The supramarginal gyrus, part of the inferior parietal lobule within the default mode network, facilitates language perception and processing [40], and is thought to play a critical role in attention, written language processing, and working memory for emotional stimuli [41]. Moreover, the supramarginal gyrus is essential for social functions, particularly in empathy and emotional processing [42]. The superior parietal lobule belongs to a functional network, where it is crucial in attention shifting and orientation [43, 44]. It is noteworthy that the parietal lobe is integral to the mirror neuron system, which is composed of neural circuits extensively involved in the regulation of action observation, perception, and emotional responses [45, 46]. Relevant research indicates that shrinkage in the parietal lobe tissue is associated with greater psychopathic tendencies in adolescents [47]. A fundamental feature of psychopathy is empathy deficiency, manifesting as impaired recognition and response to sadness, deficits in observational learning, and insensitivity to the effects of one’s actions on others [48, 49]. In our morphological analysis, we found shallower sulcal depth in the left supramarginal gyrus and bilateral superior parietal gyrus in the PWH-DD group. Generally, sulci tend to widen and become shallower with aging, and such changes are often a result of atrophy in regions and adjacent gyri [50]. The shallower sulci observed in these areas usually reflect structural changes in the cortex, potentially impacting emotional resonance and language comprehension, reducing emotional perception and regulation abilities, and possibly leading to difficulties in handling complex social situations, executing spatial perception tasks, and regulating emotional responses, which may elevate the risk of depressive and anxiety disorders. Future studies should employ additional imaging techniques to further analyze brain surface area, enabling a more comprehensive understanding of the mechanisms underlying sulcal depth changes and their functional implications.

In the morphological analysis of fractal dimension, we observed that the PWH-DD group exhibited lower fractal dimension values in the left caudal middle frontal gyrus, right rostral middle frontal gyrus, right frontal pole, and left middle temporal gyrus. The caudal middle frontal gyrus, rostral middle frontal gyrus, and frontal pole are essential components of the prefrontal cortex [51], which is involved in numerous higher-order functions, especially in working memory, attention, and emotion regulation [52], and is implicated in several neuropsychiatric conditions such as depression, schizophrenia, and autism [18, 53, 54]. The left middle temporal gyrus participates in integrating various sensory inputs, including somatosensory, visual, and auditory signals, and also plays a role in short-term verbal memory [55, 56]. Fractal dimension provides a more comprehensive metric for cortical morphological complexity than gyrification and may serve as a more sensitive marker for neuronal damage than cortical thickness and volume measurements [57, 58]. It has been widely applied in neuroimaging analyses of various psychiatric disorders. For example, studies on chronic psychiatric conditions, such as anorexia nervosa, obsessive–compulsive disorder, and schizophrenia, have demonstrated decreased fractal dimension, indicating lower cortical complexity [59, 60]. Reductions in cortical complexity have also been observed in certain advanced neurological diseases with psychiatric symptoms, including frontal lobe epilepsy [61] and Alzheimer's disease [62]. In this study, we noted a decline in fractal dimension within the frontal and temporal lobes of PWH-DD, suggesting a reduction in cortical structure and complexity, which may affect emotional and mental health functions, such as attention control, executive ability, emotion regulation, memory, and higher-order thinking. Correlation analyses revealed a significant association between fractal dimension in the above brain regions and both depression as well as mental health status, further supporting the notion that PWH-DD may experience more pronounced brain injury compared to PWH controls.

Previous studies have reported that, compared with healthy controls, PWH exhibit GMV reductions in the thalamus, anterior prefrontal cortex, precuneus, posterior parietal cortex, and occipital lobes, as well as decreased FD in the four lobes (frontal, parietal, temporal, and occipital) and the caudate nucleus [58, 63]. Consistent with these studies, the PWH-DD group in our study showed reduced GMV in the left middle frontal gyrus and decreased FD in the frontal and temporal lobes; however, we discovered for the first time that this cohort exhibited a reduction in sulcal depth in the left supramarginal region and bilateral superior parietal lobules, a phenomenon not previously reported in the PWH control group. This discrepancy may stem from depressive disorders-through mechanisms such as chronic immune activation, inflammatory cytokine upregulation, and HPA-axis dysregulation-superimposed on HIV-related neurodegeneration, resulting in complex structural injury at molecular and macro-morphological levels distinct from those in the PWH control group [64]. This finding suggests that future neuroprotective and mental health interventions for PWH with DD should address the interplay of these dual pathological mechanisms. In the general population, DD are associated with gray matter atrophy in brain regions such as the striatum, hippocampus, and thalamus [65, 66]. However, in our PWH-DD cohort, these alterations exhibited distinct features, suggesting that the context of HIV infection may exacerbate structural changes in frontal and temporal regions. These distinctive morphological patterns may be related to chronic immune activation and persistent elevation of inflammatory cytokines, resulting in cumulative structural injury within the central nervous system. Multiple investigations have shown that, under psychosocial stress and adverse behavioral conditions, PWH exhibit significantly higher anxiety and depression scores than the general population [67–69]. In this study, the PWH control group still exhibited notable emotional or depressive symptoms, indicating that even in the absence of a formal depression diagnosis, PWH manifest subclinical affective disturbances. This suggests a substantial affective burden in the context of HIV infection and underscores the importance of early emotional health interventions in HIV management alongside virological and immunological monitoring.

The present study has the following advantages over previous investigations. First, in contrast to most HIV-related neuropsychiatric studies that rely solely on scale-based tools, this study utilizes professional diagnoses based on DSM-5 criteria made by psychiatric experts. Neuroimaging studies that rely on scale tools can only describe the depressive symptomatology in PWH, whereas our study, based on professional diagnoses, provides neuroimaging data on DD in PWH, supplementing the existing literature in this field. Secondly, this study employed more refined brain morphological analysis methods, further enriching the data on brain morphological indicators of HIV with comorbid depression. While brain morphological indicators such as fractal dimension are widely used in the general population, relevant data in the HIV-positive population remain limited.

Nevertheless, this study has several potential limitations that warrant consideration. First, it was a cross-sectional study from just a single time point and lacked longitudinal follow-up data, leaving the exact causal relationship between DD and brain structure in PWH yet to be investigated. Second, this study focused exclusively on HIV-positive MSM, without considering variations in sexual orientation and gender, so the findings may not fully represent the entire HIV-infected population. Third, the MRI scanner used operates at 1.5 T, which lowers the resolution of structural data acquisition, potentially leading to low sensitivity in the imaging results. Fourth, given the limited sample size in each subgroup, this study did not perform separate correlation analyses between clinical scale scores and brain structural measures in the PWH-DD and PWH control groups, nor did it apply multiple-comparison correction to the correlation results. The absence of correction may elevate the risk of false positives. Future studies should replicate these associations in larger samples under a more stringent correction framework and delineate the independent and interactive effects of HIV infection and depressive status on brain structure. Fifth, this study did not include individuals who had ever received or were currently undergoing antidepressant treatment, and thus may not have captured distinct brain structural patterns resulting from medication induced neuroplastic changes.

Conclusions

The study demonstrated that PWH-DD displayed gray matter atrophy and poorer morphological brain alterations. The correlation analysis further highlighted a significant association between depression and structural brain defects. This study demonstrates a potential association between DD and brain structural injury in HIV-positive MSM, emphasizing the importance of intervening in neuropsychiatric conditions among PWH. In the future, more comprehensive analyses on brain morphology are expected to offer deeper neuroscientific insights for PWH-DD. However, longitudinal studies are still needed to further investigate the precise causal link between DD and brain alterations among the HIV-positive population.

Abbreviations

DD

Depressive disorders

PWH

People with HIV

PWH-DD

PWH with comorbid DD

ART

Antiretroviral therapy

VBM

Voxel-based morphometry

SBM

Surface-based morphometry

GMV

Gray matter volume

MSM

Men who have sex with men

MoCA

Montreal Cognitive Assessment

SAS

Self-Rating Anxiety Scale

SDS

Self-Rating Depression Scale

SCL-90

Symptom Checklist 90

PSQI

Pittsburgh Sleep Quality Index

CTQ

Childhood Trauma Questionnaire

AUQ

Alcohol Urge Questionnaire

VAS

Visual Analogue Scale

CAT

Computational Anatomy Toolbox

SPM

Statistical Parametric Mapping

FWHM

Full-width at half-maximum

GRF

Gaussian Random Field

AAL

Automated Anatomical Labeling

Authors’ contributions

YH: Investigation, Data curation, Formal analysis, Writing—original draft. WW: Investigation, Data curation, Formal analysis, Writing—original draft. PW: Data curation, Formal analysis, Funding acquisition, Writing—original draft. GS: Data curation, Formal analysis. YM: Data curation, Formal analysis. MC: Data curation, Formal analysis. LL: Data curation, Formal analysis. YL: Supervision, Data curation, Formal analysis, Funding acquisition. LW: Supervision, Formal analysis, Conceptualization. YZ: Supervision, Formal analysis, Conceptualization, Funding acquisition. CW: Supervision, Formal analysis, Conceptualization, Writing—review & editing. TZ: Supervision, Methodology, Formal analysis, Funding acquisition, Conceptualization, Writing—review & editing. YZ: Supervision, Funding acquisition, Methodology, Formal analysis, Conceptualization, Writing—review & editing.

Funding

This research was funded by the National Key Research and Development Program of China (2022YFC2305004 to Y.L.), the Beijing Research Ward Excellence Program (BRWEP2024W042180103 to Y.Z., BRWEP2024W042180106 to W.W., BRWEP2024W042180101 to Y.L.Z.), the Beijing Hospital Management Center Phase III “Sailing” Program: Clinical Technology Innovation Project (ZLRK202532 to T.Z.), Young Investigator Grant in Beijing You’an Hospital Affiliated to Capital Medical University (BJYAYY-YN2024-24 to Y.Z.), the National Natural Science Foundation of China (82241072 to T.Z.), the Beijing Hospitals Authority Innovation Studio of Young Staff Funding Support (2021037 to Y.Z.), the Research and Translational Application of Clinical Characteristic Diagnostic and Treatment Techniques in Capital City (Z221100007422055 to T.Z.), the National Key Research and Development Program of China (2021YFC2501402 to P.W.), and the Beijing Research Center for Respiratory Infectious Diseases (BJRID2024-001 to Y.L.Z.).

Data availability

Due to data privacy regulations regarding individual data, raw data cannot be publicly shared. The datasets used and analyzed in the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the Institutional Review Boards of Beijing Youan Hospital, Capital Medical University (JYKL 2023–057). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 2013 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants prior to their participation in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.