Association of disease course and brain structural alterations in major depressive disorder

Association between depressive duration and cognitive decline in middle-aged and older adults: Evidence from the Health and Retirement Study 2010-2018

BACKGROUND

Depression has been found to be associated with cognitive decline, but whether longer depressive durations lead to more severe cognitive declines has not been investigated. We aimed to estimate the association between depressive duration and cognitive decline in middle-aged and older Americans based on a large-scale representative population study.

METHODS

We included 27,886 participants from the Health and Retirement Study (HRS) in 2010-2018. Four datasets with 2-, 4-, 6-, and 8-year consecutive interviews were further derived which involving persistent depressed and persistent depression-free individuals. Multiple linear regressions were constructed to estimate the effects of each depressive duration on the decline in global cognition, memory and mental status. Meta-regressions were performed to test the linear trends and to explore the heterogeneity between sex, age and baseline cognitive function along with subgroup analyses.

RESULTS

Depressive durations of 2, 4, 6, and 8 years were associated with reductions in global cognitive scores of 0.62 points (95% CI: 0.51-0.73), 0.77 points (95% CI: 0.60-0.94), 0.83 points (95% CI: 0.55-1.10), and 1.09 points (95% CI: 0.63-1.55), respectively, indicating a linear trend (P = 0.016). More pronounced associations were observed in middle-aged adults and females. Similar patterns were found in the associations between depressive duration and two subdomains, i.e., memory and mental health.

LIMITATIONS

This study is essentially a cross-sectional study and therefore cannot provide causal associations.

CONCLUSIONS

Longer depressive durations were linearly related to more severe cognitive declines. Timely intervention for depression targeted middle-aged adults can more effectively alleviate cognition-related burdens.

Gray matter volumetric correlates of the polygenic risk of depression: A study of the Human Connectome Project data

Genetic factors confer risks for depression. Understanding the neural endophenotypes, including brain morphometrics, of genetic predisposition to depression would help in unraveling the pathophysiology of depression. We employed voxel-based morphometry (VBM) to examine how gray matter volumes (GMVs) were correlated with the polygenic risk score (PRS) for depression in 993 young adults of the Human Connectome Project. The phenotype of depression was quantified with a DSM-oriented scale of the Achenbach Adult Self-Report. The PRS for depression was computed for each subject using the Psychiatric Genomics Association Study as the base sample. In multiple regression with age, sex, race, drinking severity, and total intracranial volume as covariates, regional GMVs in positive correlation with the PRS were observed in bilateral hippocampi and right gyrus rectus. Regional GMVs in negative correlation with the PRS were observed in a wide swath of brain regions, including bilateral frontal and temporal lobes, anterior cingulate cortex, thalamus, lingual gyri, cerebellum, and the left postcentral gyrus, cuneus, and parahippocampal gyrus. We also found sex difference in anterior cingulate volumes in manifesting the genetic risk of depression. In addition, the GMV of the right cerebellum crus I partially mediated the link from PRS to depression severity. These findings add to the literature by highlighting 1) a more diverse pattern of the volumetric markers of depression, with most regions showing lower but others higher GMVs in association with the genetic risks of depression, and 2) the cerebellar GMV as a genetically informed neural phenotype of depression, in neurotypical individuals.

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

White and gray matter alterations in bipolar I and bipolar II disorder subtypes compared with healthy controls - exploring associations with disease course and polygenic risk

Patients with bipolar disorder (BD) show alterations in both gray matter volume (GMV) and white matter (WM) integrity compared with healthy controls (HC). However, it remains unclear whether the phenotypically distinct BD subtypes (BD-I and BD-II) also exhibit brain structural differences. This study investigated GMV and WM differences between HC, BD-I, and BD-II, along with clinical and genetic associations. N = 73 BD-I, n = 63 BD-II patients and n = 136 matched HC were included. Using voxel-based morphometry and tract-based spatial statistics, main effects of group in GMV and fractional anisotropy (FA) were analyzed. Associations between clinical and genetic features and GMV or FA were calculated using regression models. For FA but not GMV, we found significant differences between groups. BD-I patients showed lower FA compared with BD-II patients (ptfce-FWE = 0.006), primarily in the anterior corpus callosum. Compared with HC, BD-I patients exhibited lower FA in widespread clusters (ptfce-FWE < 0.001), including almost all major projection, association, and commissural fiber tracts. BD-II patients also demonstrated lower FA compared with HC, although less pronounced (ptfce-FWE = 0.049). The results remained unchanged after controlling for clinical and genetic features, for which no independent associations with FA or GMV emerged. Our findings suggest that, at a neurobiological level, BD subtypes may reflect distinct degrees of disease expression, with increasing WM microstructure disruption from BD-II to BD-I. This differential magnitude of microstructural alterations was not clearly linked to clinical and genetic variables. These findings should be considered when discussing the classification of BD subtypes within the spectrum of affective disorders.

Abnormal stability of spontaneous neuronal activity as a predictor of diagnosis conversion from major depressive disorder to bipolar disorder

OBJECTIVE

Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage, which may lead to inappropriate treatment. This study aimed to characterize the alterations of spontaneous neuronal activity in patients with depressive episodes whose diagnosis transferred from MDD to BD.

METHODS

532 patients with MDD and 132 healthy controls (HCs) were recruited over 10 years. During the follow-up period, 75 participants with MDD transferred to BD (tBD), and 157 participants remained with the diagnosis of unipolar depression (UD). After excluding participants with poor image quality and excessive head movement, 68 participants with the diagnosis of tBD, 150 participants with the diagnosis of UD, and 130 HCs were finally included in the analysis. The dynamic amplitude of low-frequency fluctuations (dALFF) of spontaneous neuronal activity was evaluated in tBD, UD and HC using functional magnetic resonance imaging at study inclusion. Receiver operating characteristic (ROC) analysis was performed to evaluate sensitivity and specificity of the conversion prediction from MDD to BD based on dALFF.

RESULTS

Compared to HC, tBD exhibited elevated dALFF at left premotor cortex (PMC_L), right lateral temporal cortex (LTC_R) and right early auditory cortex (EAC_R), and UD showed reduced dALFF at PMC_L, left paracentral lobule (PCL_L), bilateral medial prefrontal cortex (mPFC), right orbital frontal cortex (OFC_R), right dorsolateral prefrontal cortex (DLPFC_R), right posterior cingulate cortex (PCC_R) and elevated dALFF at LTC_R. Furthermore, tBD exhibited elevated dALFF at PMC_L, PCL_L, bilateral mPFC, bilateral OFC, DLPFC_R, PCC_R and LTC_R than UD. In addition, ROC analysis based on dALFF in differential areas obtained an area under the curve (AUC) of 72.7%.

CONCLUSIONS

The study demonstrated the temporal dynamic abnormalities of tBD and UD in the critical regions of the somatomotor network (SMN), default mode network (DMN), and central executive network (CEN). The differential abnormal patterns of temporal dynamics between the two diseases have the potential to predict the diagnosis transition from MDD to BD.

Association of hospitalization with structural brain alterations in patients with affective disorders over nine years

Repeated hospitalizations are a characteristic of severe disease courses in patients with affective disorders (PAD). To elucidate how a hospitalization during a nine-year follow-up in PAD affects brain structure, a longitudinal case-control study (mean [SD] follow-up period 8.98 [2.20] years) was conducted using structural neuroimaging. We investigated PAD (N = 38) and healthy controls (N = 37) at two sites (University of Münster, Germany, Trinity College Dublin, Ireland). PAD were divided into two groups based on the experience of in-patient psychiatric treatment during follow-up. Since the Dublin-patients were outpatients at baseline, the re-hospitalization analysis was limited to the Münster site (N = 52). Voxel-based morphometry was employed to examine hippocampus, insula, dorsolateral prefrontal cortex and whole-brain gray matter in two models: (1) group (patients/controls)×time (baseline/follow-up) interaction; (2) group (hospitalized patients/not-hospitalized patients/controls)×time interaction. Patients lost significantly more whole-brain gray matter volume of superior temporal gyrus and temporal pole compared to HC (p_FWE = 0.008). Patients hospitalized during follow-up lost significantly more insular volume than healthy controls (p_FWE = 0.025) and more volume in their hippocampus compared to not-hospitalized patients (p_FWE = 0.023), while patients without re-hospitalization did not differ from controls. These effects of hospitalization remained stable in a smaller sample excluding patients with bipolar disorder. PAD show gray matter volume decline in temporo-limbic regions over nine years. A hospitalization during follow-up comes with intensified gray matter volume decline in the insula and hippocampus. Since hospitalizations are a correlate of severity, this finding corroborates and extends the hypothesis that a severe course of disease has detrimental long-term effects on temporo-limbic brain structure in PAD.

Childhood sexual abuse related to brain activity abnormalities in right inferior temporal gyrus among major depressive disorder

Regional brain activity abnormalities have been reported in major depressive disorder (MDD) with childhood trauma (CT). However, these findings were variable and equivocal. The present study aims to explore further the correlation between CT and the amplitude of low-frequency fluctuations (ALFF) alteration in MDD. In this study, we recruited 60 healthy controls (HCs) and 66 MDD patients to complete resting-state fMRI scans. All MDD patients were evaluated clinical symptoms and childhood trauma experience using the Hamilton Depression Rating Scale-17 (HDRS-17), the Hamilton Anxiety Scale (HAMA), and the Childhood Trauma Questionnaire (CTQ). Compared to HCs, MDD patients demonstrated significantly altered ALFF in the right middle occipital gyrus (MOG), bilateral inferior temporal gyrus (ITG), bilateral cerebellum posterior lobe, bilateral anterior cingulate gyrus (ACC), and bilateral superior frontal gyrus (SFG). More importantly, we found negative correlation between childhood sexual abuse (CSA) scores and ALFF value appeared mainly in the right ITG among MDD patients. After adjusting for covariates (age, gender, mean framewise displacement (FD), HRDS-17, and HAMA scores), this correlation remained significant. Meanwhile, ALFF in the right ITG could distinguish MDD patients with or without CSA (area under the curve (AUC) = 0.713). Our findings revealed that the regional brain activity abnormalities in the right ITG might be a potential biomarker for MDD patients with CSA.

Brain structural correlates of recurrence following the first episode in patients with major depressive disorder

Former prospective studies showed that the occurrence of relapse in Major Depressive Disorder (MDD) is associated with volume loss in the insula, hippocampus and dorsolateral prefrontal cortex (DLPFC). However, these studies were confounded by the patient's lifetime disease history, as the number of previous episodes predict future recurrence. In order to analyze neural correlates of recurrence irrespective of prior disease course, this study prospectively examined changes in brain structure in patients with first-episode depression (FED) over 2 years. N = 63 FED patients and n = 63 healthy controls (HC) underwent structural magnetic resonance imaging at baseline and after 2 years. According to their disease course during the follow-up interval, patients were grouped into n = 21 FED patients with recurrence (FEDrec) during follow-up and n = 42 FED patients with stable remission (FEDrem). Gray matter volume changes were analysed using group by time interaction analyses of covariance for the DLPFC, hippocampus and insula. Significant group by time interactions in the DLPFC and insula emerged. Pairwise comparisons showed that FEDrec had greater volume decline in the DLPFC and insula from baseline to follow-up compared with FEDrem and HC. No group by time interactions in the hippocampus were found. Cross-sectional analyses at baseline and follow-up revealed no differences between groups. This longitudinal study provides evidence for neural alterations in the DLPFC and insula related to a detrimental course in MDD. These effects of recurrence are already detectable at initial stages of MDD and seem to occur without any prior disease history, emphasizing the importance of early interventions preventing depressive recurrence.

Lateralized subgenual ACC metabolic connectivity patterns in refractory melancholic depression: does it matter?

Although treatment resistance to antidepressant pharmacotherapy is quite common, the phenomenon of refractory major depressive disorder (rMDD) is not well understood. Nevertheless, the metabolic activity of the subgenual anterior cingulate cortex (sgACC) has been put forward as a possible metabolic biomarker of clinical prediction and response, albeit sgACC lateralization differences in functional connectivity have not yet been extensively examined. Also not in the refractory depressed state. To examine sgACC lateralization differences in metabolic connectivity, we recruited 43 right-handed antidepressant-free unipolar melancholic rMDD patients and 32 right-handed healthy controls to participate in this 18FDG PET study and developed a searchlight-based interregional covariance connectivity approach. Compared to non-depressed individuals, sgACC covariance analysis showed stronger metabolic connections with frontolimbic brain regions known to be affected in the depressed state. Furthermore, whereas the left sgACC showed stronger metabolic connections with ventromedial prefrontal cortical regions, implicated in anhedonia, suicidal ideation, and self-referential processes, the right sgACC showed significantly stronger metabolic connections with posterior hippocampal and cerebellar regions, respectively specialized in memory and social processing. Overall, our results substantiate earlier research that the sgACC is a metabolic key player when clinically depressed and that distinct lateralized sgACC metabolic connectivity patterns are present.

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

OBJECTIVE

This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies' publications in the PubMed database to understand the historical growth, current state, and potential expansion trend.

METHODS

All MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analysis. The generative statistical algorithm "Latent Dirichlet allocation" (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus.

RESULTS

A total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, "Symptoms and treatment," "Brain structure and imaging," and "Comorbidities research."

CONCLUSION

Latent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are "Symptoms and sleep," "Brain structure study," and "functional connectivity." New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.