The relationship of mental health symptoms to chemotherapy toxicity risk in older adults with cancer: Results from the geriatric assessment-driven intervention study.

BACKGROUND: Depression and anxiety are prevalent in older adults with cancer but are often undertreated. Older adults are also at increased risk of chemotherapy toxicity (CT). This study evaluated the impact of depression and anxiety symptoms on severe CT risk in older adults with cancer. METHODS: This is a secondary analysis of a randomized trial (2:1) evaluating geriatric assessment-driven intervention (GAIN) versus standard of care (SOC) to reduce grade 3+ CT in older adults with cancer. Mental health was assessed via the Mental Health Inventory 13. CT was graded by National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0. RESULTS: A total of 605 patients enrolled (402 GAIN; 203 SOC). Overall, 35% were depressed and 47% were anxious. Patients with depression had increased CT in the SOC arm (70.7% vs. 54.3%; p = .02) but not in the GAIN arm (54.3% vs. 48.5%; p = .27). CT was more likely in SOC patients with depression (odds ratio [OR], 2.03; 95% CI, 1.10-3.72). This association persisted after adjusting for Cancer and Aging Research Group toxicity score (OR, 1.98; 95% CI, 1.07-3.65) and for demographic, disease, and treatment factors (OR, 2.00; 95% CI, 1.03-3.85). Depression and CT were not associated in the GAIN arm (OR, 1.26; 95% CI, 0.84-1.91). Anxiety and CT were not associated in either arm. CONCLUSIONS: Elevated depression symptoms are associated with increased risk of severe CT in older adults with cancer, which was mitigated with GAIN. This suggests that treating depression symptoms may lower toxicity risk. Future studies are needed to confirm and investigate the impact of depression-specific interventions on outcomes.

Influences of resting-state intrinsic functional brain connectivity on the antidepressant treatment response in late-life depression.

BACKGROUND: Late-life depression (LLD) is characterized by differences in resting state functional connectivity within and between intrinsic functional networks. This study examined whether clinical improvement to antidepressant medications is associated with pre-randomization functional connectivity in intrinsic brain networks. METHODS: Participants were 95 elders aged 60 years or older with major depressive disorder. After clinical assessments and baseline MRI, participants were randomized to escitalopram or placebo with a two-to-one allocation for 8 weeks. Non-remitting participants subsequently entered an 8-week trial of open-label bupropion. The main clinical outcome was depression severity measured by MADRS. Resting state functional connectivity was measured between a priori key seeds in the default mode (DMN), cognitive control, and limbic networks. RESULTS: In primary analyses of blinded data, lower post-treatment MADRS score was associated with higher resting connectivity between: (a) posterior cingulate cortex (PCC) and left medial prefrontal cortex; (b) PCC and subgenual anterior cingulate cortex (ACC); (c) right medial PFC and subgenual ACC; (d) right orbitofrontal cortex and left hippocampus. Lower post-treatment MADRS was further associated with lower connectivity between: (e) the right orbitofrontal cortex and left amygdala; and (f) left dorsolateral PFC and left dorsal ACC. Secondary analyses associated mood improvement on escitalopram with anterior DMN hub connectivity. Exploratory analyses of the bupropion open-label trial associated improvement with subgenual ACC, frontal, and amygdala connectivity. CONCLUSIONS: Response to antidepressants in LLD is related to connectivity in the DMN, cognitive control and limbic networks. Future work should focus on clinical markers of network connectivity informing prognosis. REGISTRATION: ClinicalTrials.gov NCT02332291.

Influences of dopaminergic system dysfunction on late-life depression.

Deficits in cognition, reward processing, and motor function are clinical features relevant to both aging and depression. Individuals with late-life depression often show impairment across these domains, all of which are moderated by the functioning of dopaminergic circuits. As dopaminergic function declines with normal aging and increased inflammatory burden, the role of dopamine may be particularly salient for late-life depression. We review the literature examining the role of dopamine in the pathogenesis of depression, as well as how dopamine function changes with aging and is influenced by inflammation. Applying a Research Domain Criteria (RDoC) Initiative perspective, we then review work examining how dopaminergic signaling affects these domains, specifically focusing on Cognitive, Positive Valence, and Sensorimotor Systems. We propose a unified model incorporating the effects of aging and low-grade inflammation on dopaminergic functioning, with a resulting negative effect on cognition, reward processing, and motor function. Interplay between these systems may influence development of a depressive phenotype, with an initial deficit in one domain reinforcing decline in others. This model extends RDoC concepts into late-life depression while also providing opportunities for novel and personalized interventions.

Cognitive, Disability, and Treatment Outcome Implications of Symptom-Based Phenotyping in Late-Life Depression.

OBJECTIVE: Late-life depression is associated with substantial heterogeneity in clinical presentation, disability, and response to antidepressant treatment. We examined whether self-report of severity of common symptoms, including anhedonia, apathy, rumination, worry, insomnia, and fatigue were associated with differences in presentation and response to treatment. We also examined whether these symptoms improved during treatment with escitalopram. DESIGN: Eighty-nine older adults completed baseline assessments, neuropsychological testing and providing self-reported symptom and disability scales. They then entered an 8-week, placebo-controlled randomized trial of escitalopram, and self-report scales were repeated at the trial's end. Raw symptom scale scores were combined into three standardized symptom phenotypes and models examined how symptom phenotype severity was associated with baseline measures and depression improvement over the trial. RESULTS: While rumination/worry appeared independent, severity of apathy/anhedonia and fatigue/insomnia were associated with one another and with greater self-reported disability. Greater fatigue/insomnia was also associated with slower processing speed, while rumination/worry was associated with poorer episodic memory. No symptom phenotype severity score predicted a poorer overall response to escitalopram. In secondary analyses, escitalopram did not improve most phenotypic symptoms more than placebo, aside for greater reductions in worry and total rumination severity. CONCLUSION: Deeper symptom phenotype characterization may highlight differences in the clinical presentation of late-life depression. However, when compared to placebo, escitalopram did not improve many of the symptoms assessed. Further work is needed to determine whether symptom phenotypes inform longer-term course of illness, and which treatments may best benefit specific symptoms.

Advanced Research Institute (ARI): Supporting the Geriatric Mental Health Research Pipeline.

The Advanced Research Institute (ARI) in Mental Health and Aging is a NIMH-funded mentoring network to help transition early-career faculty to independent investigators and scientific leaders. Since 2004, ARI has enrolled 184 Scholars from 61 institutions across 34 states. We describe the ARI components and assess the impact and outcomes of ARI on research careers of participants. Outcomes of ARI graduates (n = 165) came from NIH Reporter, brief surveys, and CVs: 87.3% remained active researchers, 83.6% performed scientific service, and 80.6% obtained federal grants. A population-based analysis examined NIMH mentored K awardees initially funded from 2002-2018 (n = 1160): in this group, 77.1% (47/61) of ARI participants versus 49.5% (544/1099) of nonparticipants obtained an R01. Controlling for time, ARI participants were 3.2 times more likely to achieve R01 funding than nonparticipants. Given the struggle to reduce attrition from the research career pipeline, the effectiveness of ARI model could be relevant to other fields.

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.

Delirium, depression, and long-term cognition.

OBJECTIVES: We examined whether preadmission history of depression is associated with less delirium/coma-free (DCF) days, worse 1-year depression severity and cognitive impairment. DESIGN AND MEASUREMENTS: A health proxy reported history of depression. Separate models examined the effect of preadmission history of depression on: (a) intensive care unit (ICU) course, measured as DCF days; (b) depression symptom severity at 3 and 12 months, measured by the Beck Depression Inventory-II (BDI-II); and (c) cognitive performance at 3 and 12 months, measured by the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) global score. SETTING AND PARTICIPANTS: Patients admitted to the medical/surgical ICU services were eligible. RESULTS: Of 821 subjects eligible at enrollment, 261 (33%) had preadmission history of depression. After adjusting for covariates, preadmission history of depression was not associated with less DCF days (OR 0.78, 95% CI, 0.59-1.03 p = 0.077). A prior history of depression was associated with higher BDI-II scores at 3 and 12 months (3 months OR 2.15, 95% CI, 1.42-3.24 p = <0.001; 12 months OR 1.89, 95% CI, 1.24-2.87 p = 0.003). We did not observe an association between preadmission history of depression and cognitive performance at either 3 or 12 months (3 months beta coefficient -0.04, 95% CI, -2.70-2.62 p = 0.97; 12 months 1.5, 95% CI, -1.26-4.26 p = 0.28). CONCLUSION: Patients with a depression history prior to ICU stay exhibit a greater severity of depressive symptoms in the year after hospitalization.

Apathy as a Within-Person Mediator of Depressive Symptoms and Cognition in Parkinson's Disease: Longitudinal Mediation Analyses.

OBJECTIVE: Greater depressive symptoms are associated with worse cognitive functions in Parkinson's disease (PD); however, it is unclear what underlying factors drive this association. Apathy commonly develops in PD and may be a pathway through which depressive symptoms negatively influence cognition. Prior research examining depressive symptoms, apathy, and cognition in PD is limited by being predominantly cross-sectional. This study examined the role of apathy as a within- and between-person mediator for the longitudinal relationships between depression severity and cognitive functioning in patients with early PD. METHODS: Participants included 487 individuals newly diagnosed with PD followed annually for up to 5 years by the Parkinson's Progression Marker Initiative. At each visit, participants completed depressive symptom measures, apathy ratings, and cognitive tests. Multi-level structural equation models examined both the within- and between-person effects of depressive symptoms on cognition through apathy, controlling for demographics and motor severity. RESULTS: At the within-person level, apathy mediated the association between depressive symptoms and select cognitive functions (global cognition, attention/working memory, visuospatial functions, and immediate verbal memory; indirect effects, bootstrap p's <0.05). Significant between-person direct effects were found for depressive symptoms predicting apathy (boostrap p <0.001) and lower scores on most cognitive tests (bootstrap p's <0.05). However, the indirect effects did not reach significance, suggesting between-person mediation did not occur. CONCLUSION: Findings suggest worsening of depressive symptoms over time in patients with PD may be a risk factor for increased apathy and subsequent decline in specific cognitive functions.

Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression.

OBJECTIVE: Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response. DESIGN AND INTERVENTIONS: Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion. PARTICIPANTS: Ninety-five individuals with LLD. MEASUREMENTS: A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials. RESULTS: In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a change in depression severity over time across both arms in the blinded phase or in the subsequent open-label bupropion phase. We similarly did not observe effects of WMH volume or hippocampal volume on change in depression severity over time. CONCLUSION: sMRI markers of accelerated brain aging were not associated with treatment response in this sequential antidepressant trial.

Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training.

The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling).

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images.

PURPOSE: Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. METHODS: The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses. RESULTS: Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets. CONCLUSIONS: The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.

Preliminary Evidence That Cortical Amyloid Burden Predicts Poor Response to Antidepressant Medication Treatment in Cognitively Intact Individuals With Late-Life Depression.

OBJECTIVE: Amyloid accumulation, the pathological hallmark of Alzheimer's disease, may predispose some older adults to depression and cognitive decline. Deposition of amyloid also occurs prior to the development of cognitive decline. It is unclear whether amyloid influences antidepressant outcomes in cognitively intact depressed elders. DESIGN: A pharmacoimaging trial utilizing florbetapir (18F) PET scanning followed by 2 sequential 8-week antidepressant medication trials. PARTICIPANTS: Twenty-seven depressed elders who were cognitively intact on screening. MEASUREMENTS AND INTERVENTIONS: After screening, diagnostic testing, assessment of depression severity and neuropsychological assessment, participants completed florbetapir (18F) PET scanning. They were then randomized to receive escitalopram or placebo for 8 weeks in a double-blinded two-to-one allocation rate. Individuals who did not respond to initial treatment transitioned to a second open-label trial of bupropion for another 8 weeks. RESULTS: Compared with 22 amyloid-negative participants, 5 amyloid-positive participants exhibited significantly less change in depression severity and a lower likelihood of remission. In the initial blinded trial, 4 of 5 amyloid-positive participants were nonremitters (80%), while only 18% (4 of 22) of amyloid-negative participants did not remit (p = 0.017; Fisher's Exact test). In separate models adjusting for key covariates, both positive amyloid status (t = 3.07, 21 df, p = 0.003) and higher cortical amyloid binding by standard uptake value ratio (t = 2.62, 21 df, p = 0.010) were associated with less improvement in depression severity. Similar findings were observed when examining change in depression status across both antidepressant trials. CONCLUSIONS: In this preliminary study, amyloid status predicted poor antidepressant response to sequential antidepressant treatment. Alternative treatment approaches may be needed for amyloid-positive depressed elders.

Double-wavelet transform for multi-subject resting state functional magnetic resonance imaging data.

Conventional regions of interest (ROIs)-level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double-wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting-state fMRI data with reasonable computational burden. Another advantage of our double-wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting-state fMRI data to study the difference in resting-state functional connectivity between healthy subjects and patients with major depressive disorder.

Disruption of Neural Homeostasis as a Model of Relapse and Recurrence in Late-Life Depression.

The significant public health burden associated with late-life depression (LLD) is magnified by the high rates of recurrence. In this manuscript, we review what is known about recurrence risk factors, conceptualize recurrence within a model of homeostatic disequilibrium, and discuss the potential significance and challenges of new research into LLD recurrence. The proposed model is anchored in the allostatic load theory of stress. We review the allostatic response characterized by neural changes in network function and connectivity and physiologic changes in the hypothalamic-pituitary-adrenal axis, autonomic nervous system, immune system, and circadian rhythm. We discuss the role of neural networks' instability following treatment response as a source of downstream disequilibrium, triggering and/or amplifying abnormal stress response, cognitive dysfunction and behavioral changes, ultimately precipitating a full-blown recurrent episode of depression. We propose strategies to identify and capture early change points that signal recurrence risk through mobile technology to collect ecologically measured symptoms, accompanied by automated algorithms that monitor for state shifts (persistent worsening) and variance shifts (increased variability) relative to a patient's baseline. Identifying such change points in relevant sensor data could potentially provide an automated tool that could alert clinicians to at-risk individuals or relevant symptom changes even in a large practice.

Harmonization of cortical thickness measurements across scanners and sites.

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as "scanner effects", can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.

Cognitive performance in antidepressant-free recurrent major depressive disorder.

BACKGROUND: Cognitive complaints are common in depression, and cognition may be an important treatment target as cognitive problems often remain during remission and may contribute to recurrence risk. Previous studies of cognitive performance in depression have mainly examined late-life depression, with a focus on older adults, or assessed performance in specific cognitive tasks rather than cognitive domains. METHODS: This study examined cognitive performance across multiple cognitive domains in antidepressant-free depressed adults with early onset recurrent depression compared to never-depressed controls. Domain scores were calculated for episodic memory, executive function, processing speed, and working memory, and the effect of depression diagnosis, depression severity, and depression duration on each domain score was examined, including interactions with age, sex, and education. RESULTS: Currently depressed adults (n = 91) exhibited poorer performance in the processing speed domain compared with never-depressed adults (n = 105). Additionally, there was an interactive effect of depression duration and age on processing speed and executive function domain performance, such that performance was worse with older age and longer duration of depression. There were no effects of depression severity on performance across the cognitive domains. CONCLUSIONS: These findings support that processing speed deficits appear in young adults with early onset depression that may not be related to current mood. Additionally, the effects of cumulative depressive episodes may interact with aging such that cognitive performance deficits worsen with recurrence over the lifespan.

Predictors of recurrence in remitted late-life depression.

BACKGROUND: Late-life depression (LLD) is associated with a fragile antidepressant response and high recurrence risk. This study examined what measures predict recurrence in remitted LLD. METHODS: Individuals of age 60 years or older with a Diagnostic and Statistical Manual - IV (DSM-IV) diagnosis of major depressive disorder were enrolled in the neurocognitive outcomes of depression in the elderly study. Participants received manualized antidepressant treatment and were followed longitudinally for an average of 5 years. Study analyses included participants who remitted. Measures included demographic and clinical measures, medical comorbidity, disability, life stress, social support, and neuropsychological testing. A subset underwent structural magnetic resonance imaging (MRI). RESULTS: Of 241 remitted elders, approximately over 4 years, 137 (56.8%) experienced recurrence and 104 (43.2%) maintained remission. In the final model, greater recurrence risk was associated with female sex (hazard ratio [HR] = 1.536; confidence interval [CI] = 1.027-2.297), younger age of onset (HR = 0.990; CI = 0.981-0.999), higher perceived stress (HR = 1.121; CI = 1.022-1.229), disability (HR = 1.060; CI = 1.005-1.119), and less support with activities (HR = 0.885; CI = 0.812-0.963). Recurrence risk was also associated with higher Montgomery-Asberg Depression Rating Scale (MADRS) scores prior to censoring (HR = 1.081; CI = 1.033-1.131) and baseline symptoms of suicidal thoughts by MADRS (HR = 1.175; CI = 1.002-1.377) and sadness by Center for Epidemiologic Studies-Depression (HR = 1.302; CI, 1.080-1.569). Sex, age of onset, and suicidal thoughts were no longer associated with recurrence in a model incorporating report of multiple prior episodes (HR = 2.107; CI = 1.252-3.548). Neither neuropsychological test performance nor MRI measures of aging pathology were associated with recurrence. CONCLUSIONS: Over half of the depressed elders who remitted experienced recurrence, mostly within 2 years. Multiple clinical and environmental measures predict recurrence risk. Work is needed to develop instruments that stratify risk.

Longitudinal Cognitive Outcomes of Clinical Phenotypes of Late-Life Depression.

OBJECTIVE: Late-life depression is associated with cognitive deficits and increased risk for cognitive decline. The purpose of the study was to determine whether clinical characteristics could serve as phenotypes informative of subsequent cognitive decline. Age at depression onset and antidepressant remission at 3 months (acute response) and 12 months (chronic response) were examined. METHODS: In a longitudinal study of late-life depression in an academic center, 273 depressed and 164 never-depressed community-dwelling elders aged 60 years or older were followed on average for over 5 years. Participants completed annual neuropsychological testing. Neuropsychological measures were converted to z-scores derived from the baseline performance of all participants. Cognitive domain scores at each time were then created by averaging z-scores across tests, grouped into domains of episodic memory, attention-working memory, verbal fluency, and executive function. RESULTS: Depressed participants exhibited poorer performance at baseline and greater subsequent decline in all domains. Early-onset depressed individuals exhibited a greater decline in all domains than late-onset or nondepressed groups. For remission, remitters and nonremitters at both 3 and 12 month exhibited greater decline in episodic memory and attention-working memory than nondepressed subjects. Three-month remitters also exhibited a greater decline in verbal fluency and executive function, whereas 12-month nonremitters exhibited greater decline in executive function than other groups. CONCLUSION: Consistent with past studies, depressed elders exhibit greater cognitive decline than nondepressed subjects, particularly individuals with early depression onset, supporting the theory that repeated depressive episodes may contribute to decline. Clinical remission is not associated with less cognitive decline.

CADASIL as a Useful Medical Model and Genetic Form of Vascular Depression.

OBJECTIVE: The main magnetic resonance imaging (MRI) findings of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) are white matter hyperintensities (WMHs), lacunar infarctions, and cerebral microbleeds (CMBs). The purpose of this study was to investigate the effects of these three neuroimaging markers of CADASIL on depression to determine whether CADASIL is a useful medical model supporting the vascular depression hypothesis. METHODS: Eighty-four subjects with CADASIL, aged 34-86 years, participated in this study. They underwent comprehensive clinical evaluation, including 3T MRI and genotyping of NOTCH3. The effects of WMH, lacunar infarctions, and CMBs were analyzed by path analyses and multivariate logistic regression analyses. RESULTS: Patients with CADASIL exhibited frequencies of 17.9% for major depressive disorder (MDD) and 10.7% for minor depressive disorder. The frequency of MDD increased from 5.0% to 46.2% as WMH volume increased from first quartile to fourth quartile. WMH volume (OR: 1.03, 95% CI: 1.003-1.06) in patients with CADASIL was associated with the current depressive disorder. Path analyses demonstrated that only WMH volume was associated with the Korean version of the short form Geriatric Depression Scale score, Center for Epidemiologic Studies Depression Scale score, and 17-item Hamilton depression scale score. The effects of lacunar infarctions and CMBs on depression were not significant in path analyses and multivariate logistic regression analyses. CONCLUSIONS: This study demonstrates that WMHs are closely associated with depression in patients with CADASIL. This supports that CADASIL might be a useful medical model and genetic form of vascular depression.