Abstract
Background
Consistent findings on underlying brain features or specific structural atrophy patterns contributing to depression in multiple sclerosis (MS) are limited.
Objective:
To investigate how deep gray matter (DGM) features predict depressive symptom trajectories in MS patients.
Methods:
We used data from the MS Partners Advancing Technology and Health Solutions (MS PATHS) network in which standardized patient information and outcomes are collected. We performed whole brain segmentation using SLANT-CRUISE. We assessed if DGM structures were associated with elevated depressive symptoms over follow-up and with depressive symptom phenotypes.
Results:
We included 3844 participants (average age: 46.05±11.83y; 72.7% female) of whom 1905 (49.5%) experienced ≥1 periods of elevated depressive symptoms over 2.6±0.9y mean follow-up. Higher caudate, putamen, accumbens, ventral diencephalon, thalamus, and amygdala volumes were associated with lower odds of elevated depressive symptoms over follow-up (OR range per 1SD increase in volume: 0.88-0.94). For example, a 1 SD increase in accumbens or caudate volume was associated with 12% or 10% respective lower odds of having a period of elevated depressive symptoms over follow-up (for accumbens: OR: 0.88; 95% CI: 0.83-0.93; p<0.001; for caudate: OR: 0.90; 95% CI: 0.85-0.96; p=0.003).
Conclusions:
Lower DGM volumes were associated with depressive symptom trajectories in MS.
Keywords: multiple sclerosis, neuroimaging
I. Introduction
Multiple sclerosis (MS), an inflammatory and neurodegenerative disorder of the central nervous system (CNS), is the most common cause of progressive neurologic dysfunction in early to middle adulthood.[1] People with MS are at increased risk of psychiatric comorbidity. Lifetime prevalence of major depressive disorder (MDD) is estimated to be at least 50%; risk of MDD is 2 to 3 times higher in MS than in the general population, and almost twice that when depression risk in MS is compared with depression risk in other chronic conditions.[2] Depression also is linked with future increases in neurologic disability,[3] lower quality of life,[4] higher healthcare costs,[5] and enhanced fatigue, pain and cognitive impairment symptoms[6] in people with MS.
The specific mechanisms and risk factors driving the increased burden of depression in MS are not well understood. Structural neuroimaging studies may provide a critical window into underlying mechanisms governing how depressive symptoms in MS evolve and may serve as valuable markers of depression vulnerability prior to its onset. In MS, cortical thinning and deep GM degeneration, especially thalamic atrophy, are common, early features closely linked with long-term adverse outcomes, one of which may be depression.[7] For example, one early cross-sectional study comparing depressed vs. non-depressed MS patients who were frequency-matched for disability found brain structural differences (including indices of left anterior temporal lobe atrophy) accounted for 42% of variance in depression.[6],[8] Other cross-sectional and small longitudinal reports found reduced limbic structure volumes in depressed MS patients relative to the non-depressed.[9]-[11] The findings were consistent with large cross-sectional studies of MDD, which demonstrated differences in several deep gray matter volumes in MDD and potentially subtle, but non-trivial, differences in patterns of deep gray matter atrophy among subgroups of patients as predictors of MDD. For example, lower hippocampal volumes are associated with MDD which is hypothesized to be related to chronic hyperactivity of the hypothalamic-pituitary-adrenal axis may contribute to brain atrophy through remodeling and reductions in relevant growth factors including brain-derived neurotrophic factors.[12] Beyond the hippocampus, the nucleus accumbens is another brain region extensively studied in MDD given its role in reward circuitry and likely relevance to core depressive symptoms like anhedonia[13]; finding suggest structural alterations in accumbens volumes are linked to MDD with specific increases in anhedonic symptoms.[14] However, large scale studies linking detailed neuroimaging features with depressive symptom trajectories are sparse in people with MS, notable as previous studies suggest risk factors can differentially contribute to depressive symptom trajectories in MS when compared to individuals MDD without MS.[15] In addition, few studies have also evaluated whether there are specific profiles of regional atrophy that may predict depression worsening.
To better understand the contribution of deep gray matter atrophy to depression in MS, we link estimated volumes to longitudinal depressive symptom trajectories in a large, well-characterized international population of people with MS.
II. Methods
Study population and clinical information
Participants for this study included individuals from the MS Partners Advancing Technology and Health Solutions (MS PATHS) network. MS PATHS is a collaboration between 7 MS centers in the United States and 3 MS Centers in Europe. Institutional participation in MS PATHS required each center to (1) follow ≥500 patients; (2) have at least one Siemens 3T MRI; (3) be willing to standardize patient assessments; (4) share standardized data for research; and (5) offer universal enrollment to capture a representative sample. The individual sites are listed in Supplemental Table 1. MS PATHS is sponsored by Biogen. Institutional review boards at each institution approved the project. MS PATHS includes individuals[16] with confirmed MS who are ≥18 years of age. At each routine clinical follow-up visit, participants complete the MS performance test (MSPT), which is iPad-based electronic adaptation of the MS Functional Composite (MSFC) that also includes a patient health questionnaire and assessment of quality of life (QoL).[17],[18] Patients report demographic (e.g., age, sex, ethnicity, racial identity) and MS characteristics (age at symptom onset, disease subtype, disease modifying therapy. The MSPT also includes the patient determined disease steps (PDDS) to characterize participant disability. The PDDS is a patient-reported measure of disability that has excellent reproducibility and is strongly correlated with the Expanded Disability Status Scale (EDSS).[19] Components of the electronic adaptation of the MSFC have been similarly validated against the corresponding technician administered tests. Clinical information including assessment of height and weight (used to calculate body mass index [BMI] as kg/m2), systolic and diastolic blood pressure, and concomitant medications (including antidepressants), which were automatically abstracted from the electronic medical record (EMR).
Depression assessment
Participant QoL is assessed using the Quality of life in Neurological Disorders (Neuro-QoL) in the MSPT, which queries 12 domains including depression, anxiety, fatigue, cognitive function, sleep disturbance, ability to participate in social roles and activities, and satisfaction with social roles and activities, among others.[20] Raw Neuro-QoL raw scores are converted to T-scores that are calibrated to a reference population where a T score of 50 represents the mean score for a given subscale in the reference population and each T score has a standard deviation (SD) of 10. For depressive symptoms, the reference population used to calculate is the general population; higher Neuro-QoL T- scores denote being more depressed. The Neuro-QoL depression subscale has been further validated in people with MS and demonstrated concurrent validity, internal consistency and have high test-retest reliability. [21]
MRI analysis
Neuroimaging protocols are standardized across MS PATHS sites, and all images are obtained from 3T Siemens scanners. Participant MRIs are acquired as a part of clinical care at each institution and are generally obtained for monitoring purposes. Two 3-D sagittal, whole-brain sequences are used: 1) T2-weighted Fluid-attenuated inversion recovery (FLAIR; acquired resolution: 1x1x1mm; echo time [TE]: 392 ms; repetition time [TR]: 5,000 ms; inversion time [TI]: 1,800 ms) and 2) T1-weighted Magnetization-prepared rapid gradient-echo (MPRAGE; acquired resolution: 1x1x1mm; TE: 2.96 ms; TR: 2,300 ms; TI: 900ms). Images are reviewed by site neuroradiology leads to ensure correct sequence use, complete brain coverage and adequate image quality. Images that do not pass the quality review are not included in the MS PATHS MRI database. We performed whole brain segmentation using the SLANT-CRUISE analysis pipeline. Briefly, the images are co-registered and segmented using the SLANT deep learning algorithm.[22] Segmentations are then corrected to be consistent with cortical reconstruction using CRUISE. [23] Before the SLANT step, automated lesion segmentation is applied using a software prototype developed jointly by Biogen and Siemens, MSPie (MS PATHS Image Evaluation)[24] and the lesions are filled with values consistent with normal appearing white matter. The final segmentation reincorporates the MS lesions into the corrected substructure segmentation of the whole brain. Volumes were normalized by the subjects’ intracranial volume (ICV).[25] For this analysis, we included the following deep gray matter regions: caudate, putamen, globus pallidus, thalamus, ventral diencephalon (ventral DC), accumbens area, hippocampus, amygdala, and basal forebrain. To account for potential differences in volumes related to sites or scanner effects for images acquired longitudinally, we applied the longitudinal ComBat (denoted ‘combating batch effects when combining batches’) algorithm. ComBat models also adjusted for age as different sites had different underlying age distributions.[26] The harmonized deep gray matter volumes were derived and used for statistical analysis.
Statistical analysis
Eligible participants for this study were individuals with a brain MRI taken within 6 months of completing a depressive symptom assessment and who had more than 2 depression assessments and were followed for at least 1 year. We used Neuro-QoL suggested T-score cut-points from recommended guidelines to identify participants with at least mild elevations in depressive symptoms occurring over follow-up as those individuals with T-score ≥ (mean T-score + 0.5 SD).[27] We defined a de novo elevation in depressive symptoms over follow-up as an increase in T scores at least 0.5 SD above the mean that is observed more than 12 months from the start of the previous elevation in depressive symptoms. Secondary analyses applied a more stringent cut-off for depression as those with T-score ≥ (mean T-score + 1 SD). We then assessed the association between gray matter volumes and neuroimaging features with risk of elevated depressive symptoms using generalized estimating equations (GEE) with a binomial link function. All models were adjusted for age, sex, race, antidepressant use, MS disease modifying therapy class, disability level using PDDS scores, MS subtype, disease duration, visit occurring during the COVID-19 pandemic (e.g., year of visit ≥2020), and obesity status (defined as body mass index [BMI] ≥ 30 kg/m2). We categorized DMT use “first line injectable” (interferon-beta and glatiramer acetate), oral (teriflunomide, sphingosine-1-phosphate inhibitors, and fumaric acid esters), “infusion or immune reconstitution”’ (anti-CD20 agents, natalizumab, alemtuzumab, and cladribine), “other”, and “untreated”. Rates of missingness were generally low and were accounted for using missing indicator variables. Secondary analyses also adjusted for T2 lesion volume or timed 25-foot walking speed (derived from the MSPT). Other analyses also adjusted for NeuroQoL anxiety and fatigue symptoms. In complementary analyses, we further classified individuals as those who were never depressed, intermittently depressed, and always depressed. We categorized individuals with n visits over follow-up as always depressed if they had elevated depressive symptoms (defined as having NeuroQoL T scores ≥0.5 SD above the mean) for all n visits. We categorized individuals with n visits over follow-up as intermittently depressed if they had elevated depressive symptoms (defined as having NeuroQoL T scores ≥0.5 SD above the mean) for ≥1 visits but < n visits. We then fit models evaluating the association between deep gray matter brain volumes and odds of depression symptom class (never, intermittent, always) using a multinomial model adjusted for a similar set of characteristics as GEE models.
Role of the funding source
The funding agencies had no role in the design or conduct of the study. Likewise, they had no roles in the collection, management, analysis or interpretation of the data nor in the preparation, review or approval of the manuscript, or the decision to submit the manuscript for publication.
Standard Protocol Approvals, Registrations and Patient Consents
The studies used in this manuscript were approved by all clinic or cohort-specific local Institutional Review Boards (IRB). All study participants provided written informed consent for participation in MS PATHS. This specific project followed all guidelines established by the outlined in the MS PATHS governance structure and was approved by the MS PATHS Data Use Committee.[16]
Data Availability
Requests for individual participant de-identified data may be available qualified investigators, based on information provided, including the proposed use and analysis plan.
III. Results
Characteristics of eligible MS PATHS participants
The characteristics of the study population are listed in Table 1. Patients were aged on average 46.1 years (SD: 11.9 years), were predominately female (72.7%), White (83.7%), and had CIS/RRMS (64.0%). Of the 3844 participants included, 1939 (50.4%) participants reported never being depressed, 1413 (36.8%) participants were intermittently depressed, and 492 (12.7%) patients were always depressed. On average, individuals with intermittent depression reported at least mild depressive symptoms on 44.1% (SD: 23.5%) of visits. Patients who were always depressed were more likely to report moderate-to-severe disability and had lower total brain and overall gray matter volumes and higher lesion volumes. Participant were followed for an average of 2.6 years (SD: 0.9 years) and depressive symptoms were queried a mean 5.5 (SD: 2.3) times. Follow-up time and number of depressive assessments were similar across the three depressive symptom categories.
Table 1.
Characteristics of included MS PATHS participants by depressive symptom category
| Depressive symptom class | ||||
|---|---|---|---|---|
| Overall | Never | Intermittent | Always | |
| N | 3844 | 1939 | 1413 | 492 |
| Number of follow-up visits, mean (SD) | 5.5 (2.3) | 5.3 (2.1) | 5.9 (2.5) | 5.2 (2.3) |
| Age at MRI scan, years, mean (SD) | 46.05 (11.83) | 45.40 (11.82) | 46.38 (11.96) | 47.63 (11.33) |
| Male sex, n (%) | 1050 (27.3) | 543 (28.0) | 360 (25.5) | 147 (29.9) |
| Race, n (%) | ||||
| White | 3219 (83.7) | 1659 (85.6) | 1152 (81.5) | 408 (82.9) |
| Black | 263 (6.8) | 128 (6.6) | 102 (7.2) | 33 (6.7) |
| Other | 224 (5.8) | 95 (4.9) | 98 (6.9) | 31 (6.3) |
| Unknown | 138 (3.6) | 57 (2.9) | 61 (4.3) | 20 (4.1) |
| Ethnicity | ||||
| Hispanic or Latino | ||||
| Not Hispanic or Latino | 1997 (52.0) | 1063 (54.8) | 689 (48.8) | 245 (49.8) |
| Unknown | 87 (2.3) | 32 (1.7) | 42 (3.0) | 13 (2.6) |
| DMT class, n (%) | 1760 (45.8) | 844 (43.5) | 682 (48.3) | 234 (47.6) |
| Infusion | 1029 (26.8) | 492 (25.4) | 397 (28.1) | 140 (28.5) |
| Injectable | 706 (18.4) | 407 (21.0) | 236 (16.7) | 63 (12.8) |
| No therapy | 893 (23.2) | 368 (19.0) | 364 (25.8) | 161 (32.7) |
| Oral | 1189 (30.9) | 659 (34.0) | 403 (28.5) | 127 (25.8) |
| Other therapy | 9 (0.2) | 5 (0.3) | 4(0.3) | 0 (0.0) |
| Unknown | 18 (0.5) | 8 (0.4) | 9 (0.6) | 1 (0.2) |
| Disability, n (%) | ||||
| Mild | 2270 (59.1) | 1430 (73.7) | 701 (49.6) | 139 (28.3) |
| Moderate | 1312 (34.1) | 425 (21.9) | 600 (42.5) | 287 (58.3) |
| Severe | 234 (6.1) | 70 (3.6) | 101 (7.1) | 63 (12.8) |
| Unknown | 28 (0.7) | 14 (0.7) | 11 (0.8) | 3 (0.6) |
| Obesity1, n (%) | 702 (18.3) | 341 (17.6) | 260 (18.4) | 101 (20.5) |
| Age at diagnosis, year, mean (SD) | 32.18 (11.04) | 31.97 (10.80) | 32.49 (11.24) | 32.13 (11.39) |
| Disease duration quartiles, n (%) | ||||
| Q1 | 998 (26.0) | 508 (26.2) | 355 (25.1) | 135 (27.4) |
| Q2 | 1045 (27.2) | 550 (28.4) | 370 (26.2) | 125 (25.4) |
| Q3 | 885 (23.0) | 438 (22.6) | 330 (23.4) | 117 (23.8) |
| Q4 | 822 (21.4) | 402 (20.7) | 317 (22.4) | 103 (20.9) |
| Missing disease duration | 94 (2.4) | 41 (2.1) | 41 (2.9) | 12 (2.4) |
| Antidepressant use, n (%) | 840 (21.9) | 293 (15.1) | 377 (26.7) | 170 (34.6) |
| MS Subtype, n (%) | ||||
| CIS/RRMS | 2461 (64.0) | 1421 (73.3) | 831 (58.8) | 209 (42.5) |
| Progressive | 1101 (28.6) | 373 (19.2) | 482 (34.1) | 246 (50.0) |
| Unknown | 282 (7.3) | 145 (7.5) | 100 (7.1) | 37 (7.5) |
| BPF, mean (SD) | 0.79 (0.04) | 0.79 (0.04) | 0.79 (0.04) | 0.78 (0.04) |
| Gray matter fraction, mean (SD) | 0.79 (0.04) | 0.79 (0.04) | 0.79 (0.04) | 0.78 (0.04) |
| Cortical gray matter fraction, mean (SD) | 0.38 (0.02) | 0.38 (0.02) | 0.38 (0.02) | 0.38 (0.02) |
| Deep gray matter fraction, mean (SD) | 0.03 (0.00) | 0.03 (0.00) | 0.03 (0.00) | 0.03 (0.00) |
| White matter fraction, mean (SD) | 0.28 (0.02) | 0.28 (0.02) | 0.28 (0.02) | 0.27 (0.02) |
| Log(lesion volume)2, mean (SD) | 8.31 (1.31) | 8.17 (1.29) | 8.42 (1.30) | 8.57 (1.35) |
Defined as body mass index (BMI) in kg/m2 of ≥30
Lesion volume was log-transformed as it was highly skewed.
Gray matter volumes and odds of having elevated depressive symptoms over follow-up.
A total of 2950 (16.5%) periods of elevated depressive symptoms occurred at an annualized rate of 0.18 periods per year; 1905 individuals experienced at least one period of elevated depressive symptoms. Higher overall lesion volume was associated with an increased odds of having a period of elevated depressive symptoms (OR per 1 SD increase in log[lesion volume]: 1.12; 95% CI: 1.06, 1.19; p<0.001). Likewise, higher total gray matter volume was associated with a lower-odds of having elevated depressive symptoms (OR per 1 SD increase in total gray matter volume: 0.91; 95% CI: 0.84, 0.97; p=0.008). Higher caudate, putamen, accumbens area, ventral DC, thalamus and amygdala volumes are associated with lower odds of experiencing elevations in depressive symptoms over follow-up with ORs ranging from 0.88 (accumbens area) to 0.94 (amygdala) in models adjusting for potential confounders including disability status. (Figure 1). Results were consistent when we additionally adjusted for fatigue and anxiety or walking speed (Supplemental Tables 2 and 3). Results were also attenuated but qualitatively similar when we applied a more stringent cut off for a to classify a period of elevated depressive symptoms aligned with moderate-to-severe elevations in depressive symptoms (e.g., T scores that are at least 1 SD above the mean depressive symptom status (Supplemental Table 4). For this analysis, 1620 periods of elevated depressive symptoms occurred with 1097 individuals experiencing at least one period of elevated symptoms.
Figure 1. Deep gray matter volumes1 and odds of having a period of elevated depressive symptoms 2 over follow-up.
1OR displayed are for 1 SD increase in deep gray matter structure volume; higher volumes were generally associated with reduced risk of a having a period of elevated depressive symptoms over follow-up. OR are adjusted for age, sex, race, ethnicity, antidepressant use, MS disease modifying therapy class (none, injectable, oral, infusion), disability level using PDDS scores, MS subtype, disease duration and obesity status.
2A period of elevated depressive symptoms is defined as a T score at least 0.5SD above the mean T scores.
3FDR-adjusted p-values for ORs are as following: caudate (p=0.003), putamen (p=0.05), globus pallidus (p=0.22), accumbens area (p<0.001), ventral DC (p=0.01), thalamus (p=0.008), basal forebrain (p=0.31), hippocampus (p=0.05), and amygdala (p=0.05).
Gray matter volumes and depressive symptom trajectory
We assessed whether deep gray matter structure volumes may relate to categories of depressive symptom trajectories over follow-up. The distributions of brain region volumes varied across depressive symptom class (Figure 2) in which participants never reporting being depressed appeared to have the highest overall brain and mean deep gray matter volumes and the lowest mean lesion volume. In contrast, patients who were consistently depressed showed on average the lowest brain and substructure and highest lesion volumes. Adjusted multinomial regression models suggested that specific deep gray matter volumes contributed to depressive symptom trajectory (Table 2). Higher volumes of the caudate (OR per 1SD increase: 0.81; 95% CI: 0.72-0.91; p<0.001), putamen (OR: 0.87; 95% CI: 0.78-0.98; p=0.02), accumbens area (OR: 0.77; 95% CI: 0.69-0.87; p<0.001), ventral DC (OR: 0.86; 95% CI: 0.77-0.97; p=0.01), thalamus (OR: 0.86; 95% CI: 0.77-0.97; p=0.01), hippocampus (OR: 0.89; 95% CI: 0.79-0.99; p=0.03) and amygdala (OR: 0.90; 95% CI: 0.81-1.00; p=0.05) were associated with a lower risk of always being depressed relative to never being depressed. Results were generally consistent when we adjusted for walking speed (as a proxy for overall MS burden) as well as fatigue and anxiety (Supplemental Tables 5 and 6).
Figure 2. Distributions of Deep Gray Matter Volume Z-scores by depressive symptom status.
Average brain volumes across each of the subgroups. Brain volumes were transformed to Z scores with mean 0 and SD of 1 to allow to allow all regions to be plotted on the same scale. For example, individuals who were never depressed had greater than the mean for each of the brain regions considered whereas those in the always depressed group had below the mean for each of the regions considered.
Table 2.
Deep gray matter volumes and odds of depressive symptom class
| Deep Gray Matter Region |
Age, sex and race-adjusted model | Fully adjusted model2 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Intermittently Depressed3 | Always Depressed | Intermittently Depressed | Always Depressed | |||||||||
| OR (95% CI) | P value | FDR- adjusted P |
OR (95% CI) | P value | FDR- adjusted P |
OR (95% CI) | P value | FDR- adjusted P |
OR (95% CI) | P value | FDR- adjusted P |
|
| Caudate4 | 0.88 (0.82, 0.95) | 0.001 | 0.004 | 0.77 (0.69, 0.86) | <0.001 | <0.001 | 0.90 (0.83, 0.98) | 0.01 | 0.04 | 0.82 (0.72, 0.92) | <0.001 | 0.004 |
| Putamen | 0.97 (0.90, 1.04) | 0.4 | 0.4 | 0.83 (0.75, 0.93) | <0.001 | 0.001 | 0.98 (0.91, 1.07) | 0.69 | 0.71 | 0.88 (0.78, 0.98) | 0.02 | 0.04 |
| Globus pallidus | 0.94 (0.88, 1.02) | 0.12 | 0.26 | 0.83 (0.75, 0.93) | <0.001 | 0.001 | 0.98 (0.91, 1.07) | 0.71 | 0.71 | 0.93 (0.83, 1.04) | 0.22 | 0.25 |
| Accumbens Area | 0.87 (0.80, 0.93) | <0.001 | 0.002 | 0.71 (0.63, 0.79) | <0.001 | <0.001 | 0.90 (0.83, 0.98) | 0.01 | 0.04 | 0.78 (0.69, 0.88) | <0.001 | <0.001 |
| Ventral DC | 0.95 (0.88, 1.02) | 0.14 | 0.26 | 0.81 (0.73, 0.90) | <0.001 | <0.001 | 0.96 (0.89, 1.04) | 0.36 | 0.53 | 0.86 (0.77, 0.97) | 0.01 | 0.04 |
| Thalamus | 0.87 (0.81, 0.94) | <0.001 | 0.002 | 0.78 (0.70, 0.87) | <0.001 | <0.001 | 0.90 (0.83, 0.98) | 0.01 | 0.04 | 0.87 (0.77, 0.98) | 0.02 | 0.04 |
| Basal forebrain | 0.95 (0.89, 1.03) | 0.2 | 0.26 | 0.91 (0.82, 1.02) | 0.09 | 0.09 | 0.97 (0.90, 1.04) | 0.37 | 0.53 | 0.94 (0.84, 1.06) | 0.31 | 0.31 |
| Hippocampus | 0.96 (0.90, 1.04) | 0.31 | 0.35 | 0.87 (0.78, 0.96) | 0.007 | 0.009 | 0.97 (0.90, 1.04) | 0.41 | 0.53 | 0.88 (0.79, 0.99) | 0.03 | 0.05 |
| Amygdala | 0.95 (0.89, 1.02) | 0.19 | 0.26 | 0.90 (0.82, 1.00) | 0.05 | 0.05 | 0.95 (0.88, 1.02) | 0.15 | 0.33 | 0.90 (0.81, 1.00) | 0.05 | 0.07 |
Always depressed are individuals with n visits who had elevated depressive symptoms (defined as having NeuroQoL T scores ≥0.5 SD above the mean) for all n visits. Intermittently depressed are individuals with n visits who had elevated depressive symptoms (defined as having NeuroQoL T scores ≥0.5 SD above the mean) for ≥1 visit but < n visits.
Adjusted for age, sex, race, ethnicity, antidepressant use, MS disease modifying therapy class (none, injectable, oral, infusion), disability level using PDDS scores, MS subtype, disease duration and obesity status
The referent category for each model is never depressed.
The odds ratios presented denote the odds of being depressed intermittently or always for a 1 SD increase in substructure volume.
IV. Discussion
In this study, we explored the associations between deep gray matter volume and depressive symptom phenotypes in a large population of people with MS. We found that lower deep gray matter volumes were generally associated with increased odds of a having a period of elevated depressive symptoms and a higher risk of always being depressed.
Results implicate the volume of structures of the basal ganglia including the striatum (caudate and putamen) and accumbens area as being individual regions most strongly associated with depressive symptom burden in people with MS. Reductions in the volume of these substructures have been linked with MDD in previous studies as well as with some characteristic features of MDD, including anhedonia. These regions are heavily involved in reward processing, in which alterations are thought to drive anhedonia phenotypes in depression. For example, the accumbens is extensively connected to other limbic regions and the prefrontal cortex and has a complex set of shared interactions with dopaminergic, serotoninergic and glutamatergic systems.[28],[29] Functional MRI studies have also noted decreased accumbens connectivity within core subdivsions was observed in people with MDD and correlated with the severity of anhedonic symptoms specifically.[14] Likewise, reduced volumes of the caudate and the accumens are observed in MDD and, in some cases, have been linked with anhedonia symptoms in clinical and non-clinical populations.[30],[31] Intriguingly, our previous work has demonstrated a potential link between MS and anhedonia severity using mendelian randomization and detailed genetic correlation analyses.[32] An important next step will be to determine the association between gray matter regions and specific subtypes of depressive symptoms in people with MS. This framework of connecting specific symptoms to well-defined characteristics of the brain is motivated by the Research Domain Criteria model [33]. This model hypothesizes individual symptoms in MDD are more likely to be linked with specific biological components than diagnostic categories, and, importantly, translates more succinctly to a precision medicine approach. Thus, exploration of contributors to specific symptoms, including those derived from neuroimaging, may provide insight into downstream candidate interventions or therapies allowing for the improved management of depression in people with MS.
Our results suggesting a possible association between hippocampal volume and depression are consistent with large neuroimaging studies of people with MDD. For example, one large study of 1728 individuals with MDD and 7199 controls found a strong inverse association between hippocampal volume and MDD risk.[34]
In people with MS, previous neuroimaging studies have largely been small and cross-sectional. Our study is consistent with some previous reports linking lesion volume with depressive symptoms or other studies suggesting a connection between neuroinflammation and depression. Beyond neuroinflammation, one early cross-sectional study comparing depressed vs. non-depressed MS patients who were frequency-matched for disability found brain structural differences (including indices of left anterior temporal lobe atrophy) accounted for 42% of variance in depression.[8] Other cross-sectional and small longitudinal reports found reduced limbic structure volumes in depressed MS patients relative to the non-depressed.[8]-[11]
Strengths of our study include the large size, standardized collection of clinical and imaging characteristics. Specifically, all MRIs were collected using the same protocol and were obtained on a Siemens scanner. The included a large population of people with MS encompassing a broad array of clinical, demographic, and functional characteristics. We also included an average of approximately 5 depressive symptom assessments over follow-up, which may more accurately capture participant symptom trajectories when compared to a single assessment.
Limitations of this study include that symptom of depression and our definition of elevated depressive symptoms relied on self-report using the NeuroQoL scale rather than formal diagnoses from a psychiatrist or other clinicians. While our MRI protocol was standardized across centers and all were obtained on Siemens scanners, underlying scanner heterogeneity likely contributes extraneous variation in our estimates of deep gray matter volumes. We expect this bias to be non-differential with respect to depressive symptom status resulting in the observed odds ratios being attenuated towards the null. Generalizability is also a concern; it is possible that patients agreeing to be a part of MS PATHS participants and those eligible to be a part of this particular study are not representative of the overall MS population. We also used a relatively relaxed cut-off for defining elevated depressive symptoms; however, results were consistent when we applied a more stringent cut-off. Our analysis also relied on the collection of depressive symptoms in the context of clinical follow-up visits in which the probability of having a clinical encounter (and outcome assessment in this case) or the completion of NeuroQoL assessments could be related to underlying patient and depressive symptoms characteristics. We do note that the average number of assessments by depressive symptom trajectory group was relatively consistent suggesting visit frequency was not a strong determinant of trajectory group. We also adjusted for disability using self-reported PDDS instead of clinician adjudicated disability in primary analysis, though results were consistent when we additionally adjusted for objective assessments of walking speed. We also did not account for changes in disability occurring over follow-up, as this could potentially partially mediate the association between deep gray matter volumes and depressive symptom burden. We also explored whether deep gray volumes are associated with overall depressive symptom levels when it’s possible that certain combinations of depressive symptoms (e.g., anhedonia versus others) may be more strongly associated with different patterns of atrophy. Lastly, we considered a single MRI time-point, future studies will be important to evaluate if within-person change in imaging markers are particularly predictive of change in depressive symptoms.
In conclusion, this study explored the contributions of deep gray matter brain alterations to the burden of depression in a large population of people with MS. We found particularly strong associations between reduced striatum volumes and depression symptom burden. Future studies incorporating multiple MRI time points or other structural imaging modalities may provide further insight into how structural changes in the brain may contribute to psychiatric symptoms in people with MS.
Supplementary Material
Funding statement
This study was supported in part by the NIH (1K01MH121582-01 to KCF), the National Multiple Sclerosis Society (TA-1805-31136 to KCF, FG-2008-36966 to BED).
Abbreviations:
- MS
Multiple Sclerosis
- MDD
Major Depressive Disorder
- MS PATHS
MS Partners Advancing Technology and Health Solutions
- MRI
Magnetic Resonance Imaging
- MSFC
Multiple Sclerosis Functional Composite
- MSPT
Multiple Sclerosis Performance Test
- EDSS
Expanded Disability Status Scale
- Neuro-QoL
Quality of life in Neurological Disorders
- GEE
Generalized estimating equations
- EMR
electronic medical record
- BMI
Body Mass Index
Footnotes
Disclosures
Ms. Hu, Dr. Dewey and Dr. Fitzgerald have no disclosures. Dr. Mowry reports research support from Biogen, Teva and Genentech, and royalties for editorial duties from UpToDate. Dr. Mowry also serves as the Editor for Topical Reviews for Multiple Sclerosis Journal.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Requests for individual participant de-identified data may be available qualified investigators, based on information provided, including the proposed use and analysis plan.