Association of White Matter Integrity With Executive Function and Antidepressant Treatment Outcome in Patients With Late-Life Depression

Serum proteomic biomarker investigation of vascular depression using data-independent acquisition: a pilot study

Background

Vascular depression (VaD) is a depressive disorder closely associated with cerebrovascular disease and vascular risk factors. It remains underestimated owing to challenging diagnostics and limited information regarding the pathophysiological mechanisms of VaD. The purpose of this study was to analyze the proteomic signatures and identify the potential biomarkers with diagnostic significance in VaD.

Methods

Deep profiling of the serum proteome of 35 patients with VaD and 36 controls was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Functional enrichment analysis of the quantified proteins was based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Reactome databases. Machine learning algorithms were used to screen candidate proteins and develop a protein-based model to effectively distinguish patients with VaD.

Results

There were 29 up-regulated and 31 down-regulated proteins in the VaD group compared to the controls (|log2FC| ≥ 0.26, p ≤ 0.05). Enrichment pathways analyses showed that neurobiological processes related to synaptic vesicle cycle and axon guidance may be dysregulated in VaD. Extrinsic component of synaptic vesicle membrane was the most enriched term in the cellular components (CC) terms. 19 candidate proteins were filtered for further modeling. A nomogram was developed with the combination of HECT domain E3 ubiquitin protein ligase 3 (HECTD3), Nidogen-2 (NID2), FTO alpha-ketoglutarate-dependent dioxygenase (FTO), Golgi membrane protein 1 (GOLM1), and N-acetylneuraminate lyase (NPL), which could be used to predict VaD risk with favorable efficacy.

Conclusion

This study offers a comprehensive and integrated view of serum proteomics and contributes to a valuable proteomics-based diagnostic model for VaD.

Association Between White Matter Tract Integrity and Frontal-Executive Function in Non-Geriatric Adult Patients With Major Depressive Disorder

OBJECTIVE

This study investigated the association between white matter tract integrity and frontal executive function in adult non-geriatric patients with major depressive disorder (MDD) and healthy controls (HCs) using diffusion tensor imaging (DTI).

METHODS

In total, 57 patients with MDD and 115 HCs participated in this study. We calculated the integrity of the white matter tracts using the Tracts Constrained by Underlying Anatomy tool (TRACULA) from FreeSurfer. We performed cognitive function tests. Oneway analysis of covariance was used to investigate the DTI parameters as dependent variables; diagnosis of MDD as an independent variable; and age, sex, and education level as covariates. For correlation analysis between the DTI parameters and cognitive function tests, Pearson's partial correlation analyses were performed in the MDD and HC groups.

RESULTS

The patients with MDD showed significantly decreased axial diffusivity (AD) in forceps major (FMajor), left corticospinal tract (CST), left superior longitudinal fasciculus-parietal bundle (SLFP), right anterior thalamic radiation (ATR), right CST, right inferior longitudinal fasciculus (ILF) and right superior longitudinal fasciculus-temporal bundle (SLFT) and mean diffusivity (MD) in the left CST, right CST, and right SLFT compared to HCs. We found that non-geriatric patients with MDD showed a significant negative correlation between the response time in the Stroop task and the AD value of the FMajor.

CONCLUSION

Our findings suggest that impaired structural connectivity in the FMajor may be associated with cognitive dysfunction in non-geriatric patients with MDD.

Alteration of white matter microstructure in patients with sleep disorders after COVID-19 infection

BACKGROUND

The pathophysiology of coronasomnia remains unclear. This study aimed to investigate changes in white matter (WM) microstructure and inflammatory factors in patients with sleep disorders (SD) characterized by poor sleep quantity, quality, or timing following coronavirus disease 2019 (COVID-19) infection in the acute phase (within one month) and whether these changes could be recovered at 3-month follow-up.

METHODS

29 acute COVID-19 patients with SD (COVID_SD) and 27 acute COVID-19 patients without SD (COVID_NonSD) underwent diffusion tensor imaging (DTI), tested peripheral blood inflammatory cytokines level, and measured Pittsburgh Sleep Quality Index (PSQI), and matched 30 uninfected healthy controls. Analyzed WM abnormalities between groups in acute phase and explored its changes in COVID_SD at 3-month follow-up by using tract-based spatial statistics (TBSS). Correlations between DTI and clinical data were examined using Spearman partial correlation analysis.

RESULTS

Both COVID_SD and COVID_NonSD exhibited widespread WM microstructure abnormalities. The COVID_SD group showed specific WM microstructure changes in right inferior fronto-occipital fasciculus (IFOF) (lower fractional anisotropy [FA]/axial diffusivity [AD] and higher radial diffusivity [RD]) and left corticospinal tract (CST) (higher FA and lower RD) and higher interleukin-1β (IL-1β) compared with COVID_NonSD group. These WM abnormalities and IL-1β levels were correlated PSQI score. After 3 months, the IFOF integrity and IL-1β levels tended to return to normal accompanied by symptom improvement in the COVID_SD relative to baseline.

CONCLUSION

Abnormalities in right IFOF and left CST and elevated IL-1β levels were important neurophenotypes correlated with COVID_SD, which might provide new insights into the pathogenesis of neuroinflammation in SD patients induced by COVID-19.

Brain-Cognition Associations in Older Patients With Remitted Major Depressive Disorder or Mild Cognitive Impairment: A Multivariate Analysis of Gray and White Matter Integrity

BACKGROUND

Almost half of older patients with major depressive disorder (MDD) present with cognitive impairment, and one-third meet diagnostic criteria for mild cognitive impairment (MCI). However, mechanisms linking MDD and MCI remain unclear. We investigated multivariate associations between brain structural alterations and cognition in 3 groups of older patients at risk for dementia, remitted MDD (rMDD), MCI, and rMDD+MCI, as well as cognitively healthy nondepressed control participants.

METHODS

We analyzed magnetic resonance imaging data and cognitive domain scores in participants from the PACt-MD (Prevention of Alzheimer's Disease With Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression) study. Following quality control, we measured cortical thickness and subcortical volumes of selected regions from 283 T1-weighted scans and fractional anisotropy of white matter tracts from 226 diffusion-weighted scans. We assessed brain-cognition associations using partial least squares regressions in the whole sample and in each subgroup.

RESULTS

In the entire sample, atrophy in the medial temporal lobe and subregions of the motor and prefrontal cortex was associated with deficits in verbal and visuospatial memory, language skills, and, to a lesser extent, processing speed (p < .0001; multivariate r = 0.30, 0.34, 0.26, and 0.18, respectively). Widespread reduced white matter integrity was associated with deficits in executive functioning, working memory, and processing speed (p = .008; multivariate r = 0.21, 0.26, 0.35, respectively). Overall, associations remained significant in the MCI and rMDD+MCI groups, but not the rMDD or healthy control groups.

CONCLUSIONS

We confirm findings of brain-cognition associations previously reported in MCI and extend them to rMDD+MCI, but similar associations in rMDD are not supported. Early-onset and treated MDD might not contribute to structural alterations associated with cognitive impairment.

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

BACKGROUND

Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.

PURPOSE

To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.

STUDY TYPE

Prospective.

SUBJECTS

A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging.

ASSESSMENT

Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.

STATISTICAL TESTS

The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).

DATA CONCLUSION

The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 2.

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.

Cognitive phenotypes in late-life depression

OBJECTIVE

To identify cognitive phenotypes in late-life depression (LLD) and describe relationships with sociodemographic and clinical characteristics.

DESIGN

Observational cohort study.

SETTING

Baseline data from participants recruited via clinical referrals and community advertisements who enrolled in two separate studies.

PARTICIPANTS

Non-demented adults with LLD (n = 120; mean age = 66.73 ± 5.35 years) and non-depressed elders (n = 56; mean age = 67.95 ± 6.34 years).

MEASUREMENTS

All completed a neuropsychological battery, and individual cognitive test scores were standardized across the entire sample without correcting for demographics. Five empirically derived cognitive domain composites were created, and cluster analytic approaches (hierarchical, k-means) were independently conducted to classify cognitive patterns in the depressed cohort only. Baseline sociodemographic and clinical characteristics were then compared across groups.

RESULTS

A three-cluster solution best reflected the data, including "High Normal" (n = 47), "Reduced Normal" (n = 35), and "Low Executive Function" (n = 37) groups. The "High Normal" group was younger, more educated, predominantly Caucasian, and had fewer vascular risk factors and higher Mini-Mental Status Examination compared to "Low Executive Function" group. No differences were observed on other sociodemographic or clinical characteristics. Exploration of the "High Normal" group found two subgroups that only differed in attention/working memory performance and length of the current depressive episode.

CONCLUSIONS

Three cognitive phenotypes in LLD were identified that slightly differed in sociodemographic and disease-specific variables, but not in the quality of specific symptoms reported. Future work on these cognitive phenotypes will examine relationships to treatment response, vulnerability to cognitive decline, and neuroimaging markers to help disentangle the heterogeneity seen in this patient population.

Interleukin-6-white matter network differences explained the susceptibility to depression after stressful life events

BACKGROUND

Stressful life events (SLEs) are well-established proximal predictors of the onset of depression. However, the fundamental causes of interindividual differences in depression outcomes are poorly understood. This study addressed this depression susceptibility mechanism using a well-powered sample of adults living in China.

METHODS

Healthy participants with SLEs (n = 185; mean = 47.51 years, 49.73% female), drawn from a longitudinal study on the development of depression, underwent diffusion tensor imaging, interleukin-6 (IL-6) level measurement, and trimonthly standardized clinical and scale evaluations within a two-year period.

RESULTS

Receiver operating characteristic analyses indicated that reduced feeder connection and HIP.R nodal efficiency improved the predictive accuracy of post-SLEs depression (OR_feeder = 0.623, AUC = 0.869, P < 0.001; OR_HIP = 0.459, AUC = 0.855, P < 0.001). The successfully established path analysis model confirmed the significant partial effect of SLEs-IL-6-white matter (WM) network differences-depression (onset and severity) (x²/8 = 1.453, goodness-of-fit [GFI] = 0.935, standard root-mean-square error of approximation [SRMR] = 0.024). Females, individuals with lower exercise frequency (EF) or annual household income (AHI) were more likely to have higher IL-6 level after SLEs (β_{int-female⁎SLEs} = -0.420, P < 0.001; β_{int-exercise⁎SLEs} = -0.412, P < 0.001; β_{int-income⁎SLEs} = -0.302, P = 0.005).

LIMITATIONS

The sample size was restricted due to the limited incidence rate and prospective follow-up design.

CONCLUSIONS

Our results suggested that among healthy adults after SLEs, those who exhibited abnormal IL-6-WM differences were susceptible to developing depression. Females, lower AHI or EF might account for an increased risk of developing these abnormal IL-6-WM differences.

Hearing Rehabilitative Treatment for Older Adults With Comorbid Hearing Loss and Depression: Effects on Depressive Symptoms and Executive Function

OBJECTIVES

Recent research has revealed important neural and psychiatric consequences of hearing loss (HL) in older adults. This pilot study examined the neural effects of HL and the impact of hearing aids on neuropsychiatric outcomes in major depressive disorder (MDD).

DESIGN

Twelve-week, double-blind, randomized controlled trial.

PARTICIPANTS/INTERVENTION

N = 25 (≥60 years) with MDD and moderate-profound HL were randomized to receive hearing aids (100% gain targets) or sham hearing aids (flat 30 dB HL) in addition to psychiatric treatment-as-usual.

MEASUREMENTS

Depressive symptoms (Hamilton Rating Scale for Depression [HRSD]), executive functioning (NIH Toolbox Flanker), integrity of auditory brain areas (structural MRI, diffusion tensor imaging).

RESULTS

At baseline, worse speech discrimination was associated with auditory cortical thinning (Left anterior transverse temporal gyrus: r = 0.755, p = 0.012) and lower integrity of the superior longitudinal fasciculus (FA: Left r = 0.772, p = 0.025, Right r = 0.782, p = 0.022). After 12-weeks, hearing aids were effective at improving hearing functioning (Hearing Handicap for the Elderly: active -12.47 versus sham -4.19, t = -2.64, df = 18, p = 0.016) and immediate memory (active +14.9 versus sham +5.7, t = 2.28, df = 16, p = 0.037). Moderate improvement was observed for hearing aids on executive functioning but did not reach statistical significance (Flanker: active +4.8 versus sham -2.4, t = 1.95, df = 15, p = 0.071). No significant effect on depression was found (HRSD: active -5.50 versus sham -7.32, t = 0.75, df = 19, p = 0.46).

CONCLUSIONS

HL can affect brain regions important for auditory and cognitive processing, and hearing remediation may have beneficial effects on executive functioning in MDD. Future studies may evaluate whether impairment in cognitive control consequent to HL may be an important risk mechanism for MDD.