Association between change in brain gray matter volume, cognition, and depression severity: Pre- and post- antidepressant pharmacotherapy for late-life depression

Cognitive Outcomes After Antidepressant Pharmacotherapy for Late-Life Depression: A Systematic Review and Meta-Analysis

OBJECTIVE

The authors evaluated whether treatment of late-life depression (LLD) with antidepressants leads to changes in cognitive function.

METHODS

A systematic review and meta-analysis of prospective studies of antidepressant pharmacotherapy for adults age 50 or older (or mean age of 65 or older) with LLD was conducted. MEDLINE, EMBASE, and PsycInfo were searched through December 31, 2022. The primary outcome was a change on cognitive test scores from baseline to after treatment. Secondary outcomes included the effects of specific medications and the associations between changes in depressive symptoms and cognitive test scores. Participants with bipolar disorder, psychotic depression, dementia, or neurological disease were excluded. Findings from all eligible studies were synthesized at a descriptive level, and a random-effects model was used to pool the results for meta-analysis.

RESULTS

Twenty-two studies were included. Thirteen of 19 studies showed an improvement on at least one cognitive test after antidepressant pharmacotherapy, with the most robust evidence for the memory and learning (nine of 16 studies) and processing speed (seven of 10 studies) domains and for sertraline (all five studies). Improvements in depressive symptoms were associated with improvement in cognitive test scores in six of seven relevant studies. The meta-analysis (eight studies; N=493) revealed a statistically significant overall improvement in memory and learning (five studies: effect size=0.254, 95% CI=0.103-0.404, SE=0.077); no statistically significant changes were seen in other cognitive domains. The evaluated risk of publication bias was low.

CONCLUSION

Antidepressant pharmacotherapy of LLD appears to improve certain domains of cognitive function, particularly memory and learning. This effect may be mediated by an improvement in depressive symptoms. Studies comparing individuals receiving pharmacotherapy with untreated control participants are needed.

Grey matter volume predicts improvement in geriatric depression in response to Tai Chi compared to Health Education

OBJECTIVES

Geriatric depression (GD) is associated with cognitive impairment and brain atrophy. Tai-Chi-Chih (TCC) is a promising adjunct treatment to antidepressants. We previously found beneficial effects of TCC on resting state connectivity in GD. We now tested the effect of TCC on gray matter volume (GMV) change and the association between baseline GMV and clinical outcome.

PARTICIPANTS

Forty-nine participants with GD (>=60 y) underwent antidepressant treatment (38 women).

INTERVENTION

Participants completed 3 months of TCC (N = 26) or health and wellness education control (HEW; N = 23).

MEASUREMENTS

Depression and anxiety symptoms and MRI scans were acquired at baseline and 3-month follow-up. General linear models (GLMs) tested group-by-time interactions on clinical scores. Freesurfer 6.0 was used to process T1-weighted images and to perform voxel-wise whole-brain GLMs of group on symmetrized percent GMV change, and on the baseline GMV and symptom change association, controlling for baseline symptom severity. Age and sex served as covariates in all models.

RESULTS

There were no group differences in baseline demographics or clinical scores, symptom change from baseline to follow-up, or treatment-related GMV change. However, whole-brain analysis revealed that lower baseline GMV in several clusters in the TCC, but not the HEW group, was associated with larger improvements in anxiety. This was similar for right precuneus GMV and depressive symptoms.

CONCLUSIONS

While we observed no effect on GMV due to the interventions, baseline regional GMV predicted symptom improvements with TCC but not HEW. Longer trials are needed to investigate the long-term effects of TCC on clinical symptoms and neuroplasticity.

Shared and specific neurobiology in bipolar disorder and unipolar disorder: Evidence based on the connectome gradient and a transcriptome-connectome association study

BACKGROUND

Distinguishing bipolar disorder (BD) and unipolar disorder (UD) remains challenging. To identify the common and diagnosis-specific neuropathological alterations and their potential molecular mechanisms in patients with UD and BD (with a current depressive episode).

METHODS

Resting-state functional magnetic resonance imaging was obtained from 279 participants (95 BD patients, 107 UD patients and 77 health controls). Connectome gradients analysis was performed to explore the shared and diagnosis-specific gradient alterations in BD and UD. The Allen Human Brain Atlas data was used to explore the potential gene mechanisms of the gradient alterations.

RESULTS

BD and UD had shared hierarchical disorganisation, including downgrading and contraction from the unimodal sensory networks (vision and sensorimotor) to the transmodal cognitive networks (limbic, frontoparietal, dorsal attention, and default) (all P < 0.05, FDR corrected) in gradient 1 and gradient 2. The BD patients had specific connectome gradient dysfunction in the subcortical network. Moreover, the hierarchical disorganisation was closely correlated with profiles of gene expression specific to the neuroglial cells in the prefrontal cortex in BD and UD, while the most correlated gene ontology biological processes and function were concentrated in synaptic signalling, calcium ion binding, and transmembrane transporter activity.

CONCLUSION

These findings reveal the shared and diagnosis-specific neurobiological mechanism underlying BD and UD patients, which advances our understanding of the neuromechanisms of these disorders.

Brain-cognition relationships in late-life depression: a systematic review of structural magnetic resonance imaging studies

BACKGROUND

Most patients with late-life depression (LLD) have cognitive impairment, and at least one-third meet diagnostic criteria for mild cognitive impairment (MCI), a prodrome to Alzheimer's dementia (AD) and other neurodegenerative diseases. However, the mechanisms linking LLD and MCI, and brain alterations underlying impaired cognition in LLD and LLD + MCI remain poorly understood.

METHODS

To address this knowledge gap, we conducted a systematic review of studies of brain-cognition relationships in LLD or LLD + MCI to identify circuits underlying impaired cognition in LLD or LLD + MCI. We searched MEDLINE, PsycINFO, EMBASE, and Web of Science databases from inception through February 13, 2023. We included studies that assessed cognition in patients with LLD or LLD + MCI and acquired: (1) T1-weighted imaging (T1) measuring gray matter volumes or thickness; or (2) diffusion-weighted imaging (DWI) assessing white matter integrity. Due to the heterogeneity in studies, we only conducted a descriptive synthesis.

RESULTS

Our search identified 51 articles, resulting in 33 T1 studies, 17 DWI studies, and 1 study analyzing both T1 and DWI. Despite limitations, reviewed studies suggest that lower thickness or volume in the frontal and temporal regions and widespread lower white matter integrity are associated with impaired cognition in LLD. Lower white matter integrity in the posterior cingulate region (precuneus and corpus callosum sub-regions) was more associated with impairment executive function and processing speed than with memory.

CONCLUSION

Future studies should analyze larger samples of participants with various degrees of cognitive impairment and go beyond univariate statistical models to assess reliable brain-cognition relationships in LLD.

The heterogeneity of late-life depression and its pathobiology: a brain network dysfunction disorder

Depression is frequent in older individuals and is often associated with cognitive impairment and increasing risk of subsequent dementia. Late-life depression (LLD) has a negative impact on quality of life, yet the underlying pathobiology is still poorly understood. It is characterized by considerable heterogeneity in clinical manifestation, genetics, brain morphology, and function. Although its diagnosis is based on standard criteria, due to overlap with other age-related pathologies, the relationship between depression and dementia and the relevant structural and functional cerebral lesions are still controversial. LLD has been related to a variety of pathogenic mechanisms associated with the underlying age-related neurodegenerative and cerebrovascular processes. In addition to biochemical abnormalities, involving serotonergic and GABAergic systems, widespread disturbances of cortico-limbic, cortico-subcortical, and other essential brain networks, with disruption in the topological organization of mood- and cognition-related or other global connections are involved. Most recent lesion mapping has identified an altered network architecture with "depressive circuits" and "resilience tracts", thus confirming that depression is a brain network dysfunction disorder. Further pathogenic mechanisms including neuroinflammation, neuroimmune dysregulation, oxidative stress, neurotrophic and other pathogenic factors, such as β-amyloid (and tau) deposition are in discussion. Antidepressant therapies induce various changes in brain structure and function. Better insights into the complex pathobiology of LLD and new biomarkers will allow earlier and better diagnosis of this frequent and disabling psychopathological disorder, and further elucidation of its complex pathobiological basis is warranted in order to provide better prevention and treatment of depression in older individuals.

Increased Risk of Developing Depression in Disability after Stroke: A Korean Nationwide Study

Stroke is a leading cause of mortality and a major cause of disability worldwide. A significant number of stroke survivors suffer from depression, impeding the activities of daily living and rehabilitation. Here, we examined the risk of depression among stroke survivors according to the severity of disabilities and compared its incidence with a matched control group. We included data from the Korean National Health Insurance Service of 207,678 stroke survivors. Cox proportional hazard models were used to calculate the risk of depression among stroke survivors. Stroke survivors had a greater risk of developing depression than the matched control group with an adjusted hazard ratio of 2.12 (95% confidence interval 2.09-2.15). Stroke survivors with more severe disabilities were associated with a higher risk of depression than those with mild disabilities. The risk of developing depression was prominently high within the first year after a stroke. Males and younger people (<65 years) were independent risk factors for depression in stroke survivors. This study demonstrated an increased risk of developing depression in stroke survivors compared to control subjects, and a higher risk of depression was associated with a more severe degree of disability. Clinicians should be aware of the risk of depression developing in stroke survivors, especially those with disabilities.

Combination of spontaneous regional brain activity and HTR1A/1B DNA methylation to predict early responses to antidepressant treatments in MDD

BACKGROUND

Antidepressant medications are suggested as the first-line treatment in patients with major depressive disorder (MDD). However, the drug therapy outcomes vary from person to person. The functional activity of the brain and DNA methylation levels correlate with the antidepressant efficacy. To predict the early antidepressant responses in MDD and establish the prediction framework, we aimed to apply multidimensional data based on the resting-state activity of the brain and HTR1A/1B methylation.

METHODS

The values of Amplitude of Low-Frequency Fluctuations (ALFF) and regional homogeneity (ReHo) were measured as variables in 116 brain regions along with 181 CpG sites in the promoter region of HTR1A/1B and 11 clinical characteristics. After performing the feature reduction step using the least absolute shrinkage and selection operator (LASSO) method, the selected variables were put into Support Vector Machines (SVM), Random Forest (RF), Naïve Bayes (NB), and logistic regression (LR), consecutively, to construct the prediction models. The models' performance was evaluated by the Leave-One-Out Cross-Validation.

RESULTS

The LR model composed of the selected multidimensional features reached a maximum performance of 78.57% accuracy and 0.8340 area under the ROC curve (AUC). The prediction accuracies based on multidimensional datasets were found to be higher than those obtained from the data based only on fMRI or methylation.

LIMITATIONS

A relatively small sample size potentially restricted the usage of our prediction framework in clinical applications.

CONCLUSION

Our study revealed that combining the data of brain imaging and DNA methylation could provide a complementary effect in predicting early-stage antidepressant outcomes.

Gut Microbiome Diversity and Abundance Correlate with Gray Matter Volume (GMV) in Older Adults with Depression

Growing evidence supports the concept that bidirectional brain–gut microbiome interactions play an important mechanistic role in aging, as well as in various neuropsychiatric conditions including depression. Gray matter volume (GMV) deficits in limbic regions are widely observed in geriatric depression (GD). We therefore aimed to explore correlations between gut microbial measures and GMV within these regions in GD. Sixteen older adults (>60 years) with GD (37.5% female; mean age, 70.6 (SD = 5.7) years) were included in the study and underwent high-resolution T1-weighted structural MRI scanning and stool sample collection. GMV was extracted from bilateral regions of interest (ROI: hippocampus, amygdala, nucleus accumbens) and a control region (pericalcarine). Fecal microbiota composition and diversity were assessed by 16S ribosomal RNA gene sequencing. There were significant positive associations between alpha diversity measures and GMV in both hippocampus and nucleus accumbens. Additionally, significant positive associations were present between hippocampal GMV and the abundance of genera Family_XIII_AD3011_group, unclassified Ruminococcaceae, and Oscillibacter, as well as between amygdala GMV and the genera Lachnospiraceae_NK4A136_group and Oscillibacter. Gut microbiome may reflect brain health in geriatric depression. Future studies with larger samples and the experimental manipulation of gut microbiome may clarify the relationship between microbiome measures and neuroplasticity.

Volumetric alterations in subregions of the amygdala in adults with major depressive disorder

BACKGROUND

Although major depressive disorder (MDD) has been associated with volumetric abnormalities in the amygdala, studies investigating the association between structural alterations of the amygdala and depression have yielded varying results. Since the amygdala comprises several subregions, it is difficult to detect subtle regional changes by measuring the total amygdala volume. This study aimed to examine the volume in each amygdala subregion in adults with and without a diagnosis of MDD.

METHODS

A total of 147 participants with a current history of major depression and 144 healthy participants ranging in age from 19 to 64 years underwent 3T magnetic resonance imaging scanning. Automatic segmentation of the nine nuclei of the amygdala was performed using FreeSurfer. One-way analysis of covariance, with individual volumes as dependent variables, and age, sex, and total intracranial volume as covariates, was performed to analyze volume differences.

RESULTS

Patients with MDD had significantly lower volumes of the entire amygdala and subregions, including the lateral nucleus and anterior amygdaloid area, than healthy volunteers (HCs). There were no significant associations between subregion volumes and antidepressant use, illness duration, or depression severity.

LIMITATIONS

Our cross-sectional design cannot provide a causal relationship between the volume change in the amygdala subregion and the risk of MDD.

CONCLUSION

Our findings suggest that specific amygdala subregions are more susceptible to volumetric alterations in patients with MDD than in HCs. These findings may advance our understanding of the neuroanatomic basis on MDD.

Cerebral amyloid accumulation is associated with distinct structural and functional alterations in the brain of depressed elders with mild cognitive impairment

BACKGROUND

Elderly patients with late-life depression (LLD) often report mild cognitive impairment (MCI), so Alzheimer's disease (AD) is hard to identify in these patients. We aimed to identify the structural and functional differences between prodromal AD and LLD-related MCI.

METHODS

We performed voxel-based morphometry and functional connectivity (FC) analyses in elderly patients with both LLD and MCI to compare alterations between those with cerebral amyloidopathy and those without. We subdivided patients into subthreshold depression (STD) and major depressive disorder (MDD) groups. Using florbetaben positron emission tomography (PET), we compared volume and connectivity between healthy controls and four STD and MDD groups with or without amyloid deposition(A): STD-MCI-A(+), MDD-MCI-A(+), STD-MCI-A(-), and MDD-MCI-A(-).

RESULTS

Subjects with MDD or amyloid deposition showed greater volume reduction in the left middle temporal gyrus. MDD groups had lower FC than STD groups in the frontal, cortical, and limbic areas. The STD-MCI-A(+) group showed greater FC reduction than the MDD-MCI-A(-) and STD-MCI-A(-) groups, particularly in the hippocampus, parahippocampus, and frontal and temporal cortices. The functional differences associated with amyloid plaques were more evident in the STD group than in the MDD group.

LIMITATIONS

Limitations include disproportional sex ratios, inability to determine the longitudinal effects of amyloidopathy in large populations.

CONCLUSIONS

Regional gray matter loss and alterations in brain networks may reflect impairments caused by amyloid deposition and depression. Such changes may facilitate the detection of prodromal AD in elderly patients with both depression and cognitive dysfunction, allowing earlier intervention and more appropriate treatment.

Machine Learning Prediction of Treatment Outcome in Late-Life Depression

Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.

Combined treatment with escitalopram and memantine increases gray matter volume and cortical thickness compared to escitalopram and placebo in a pilot study of geriatric depression

BACKGROUND

Geriatric depression with subjective cognitive complaints increases the risk of Alzheimer's Disease (AD). Memantine is a cognitive enhancer used to treat AD. In a 6-month double-blind randomized placebo-controlled trial of escitalopram and memantine (ESC/MEM), ESC/MEM improved cognition at 12 month in geriatric depression (NCT01902004). We now investigated structural neuroplastic changes at 3 months.

METHODS

Forty-one older depressed adults (mean age=70.43, SD=7.33, 26 female) were randomized to receive ESC/MEM or ESC/PBO. Mood scores (Hamilton Depression Rating Scale, HAMD) and high-resolution structural T1-weighted images were acquired at baseline and 3 months. Freesurfer 6.0 for image processing and General Linear Models was used to examine group differences in symmetrized percent change gray matter volume (GMV) and cortical thickness, controlling for age and intracranial volume. Nonparametric tests were used to investigate group differences in mood and subcortical volume change.

RESULTS

Among 27 completers (ESC/MEM n = 13; ESC/PBO n = 14), 62% achieved remission (HAMD≤6) with ESC/MEM and 43% with ESC/PBO (Fisher's exact p=.45). Change in HAMD did not differ between groups (F(1,23)=0.14, p=.7). GMV and thickness increased more with ESC/MEM than with ESC/PBO in the left middle and inferior temporal lobe, right medial, and lateral orbito-frontal cortex (OFC).

LIMITATIONS

included small sample size, dropout, and the lack of cognitive data at 3 months.

CONCLUSIONS

Although significant group differences in mood improvement were not observed, ESC/MEM resulted in increased GMV and cortical thickness in several brain regions compared to placebo. Larger longitudinal clinical trials can further examine the neuroprotective effect of memantine in geriatric depression.

Altered gray matter volumes in post-stroke depressive patients after subcortical stroke

Stroke survivors are known to suffer from post-stroke depression (PSD). However, the likelihood of structural changes in the brains of PSD patients has not been explored. This study aims to extract changes in the gray matter of these patients and test how these changes account for the PSD symptoms. High-resolution T1 weighted images were collected from 23 PSD patients diagnosed with subcortical stroke. Voxel-based morphometry and support vector machine analyses were used to analyze the data. The results were compared with those collected from 33 non-PSD patients. PSD group showed decreased gray matter volume (GMV) in the left middle frontal gyrus (MFG) when compared to the non-PSD patients. Together with the clinical and demographic variables, the MFG's GMV predictive model was able to distinguish PSD from the non-PSD patients (0•70 sensitivity and 0•88 specificity). The changes in the left inferior frontal gyrus (61%) and dorsolateral prefrontal cortex (39%) suggest that the somatic/affective symptoms in PSD is likely to be due to patients' problems with understanding and appraising negative emotional stimuli. The impact brought by the reduced prefrontal to limbic system connectivity needs further exploration. These findings indicate possible systemic involvement of the frontolimbic network resulting in PSD after brain lesions which is likely to be independent from the location of the lesion. The results inform specific clinical interventions to be provided for treating depressive symptoms in post-stroke patients.

Medications for alcohol use disorders: An overview

Patients who suffer from alcohol use disorders (AUDs) usually go through various socio-behavioral and pathophysiological changes that take place in the brain and other organs. Recently, consumption of unhealthy food and excess alcohol along with a sedentary lifestyle has become a norm in both developed and developing countries. Despite the beneficial effects of moderate alcohol consumption, chronic and/or excessive alcohol intake is reported to negatively affect the brain, liver and other organs, resulting in cell death, organ damage/failure and death. The most effective therapy for alcoholism and alcohol related comorbidities is alcohol abstinence, however, chronic alcoholic patients cannot stop drinking alcohol. Therefore, targeted therapies are urgently needed to treat such populations. Patients who suffer from alcoholism and/or alcohol abuse experience harmful effects and changes that occur in the brain and other organs. Upon stopping alcohol consumption, alcoholic patients experience acute withdrawal symptoms followed by a protracted abstinence syndrome resulting in the risk of relapse to heavy drinking. For the past few decades, several drugs have been available for the treatment of AUDs. These drugs include medications to reduce or stop severe alcohol withdrawal symptoms during alcohol detoxification as well as recovery medications to reduce alcohol craving and support abstinence. However, there is no drug that completely antagonizes the adverse effects of excessive amounts of alcohol. This review summarizes the drugs which are available and approved by the FDA and their mechanisms of action as well as the medications that are under various phases of preclinical and clinical trials. In addition, the repurposing of the FDA approved drugs, such as anticonvulsants, antipsychotics, antidepressants and other medications, to prevent alcoholism and treat AUDs and their potential target mechanisms are summarized.