Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.

Assessing depression recurrence, cognitive burden, and neurobiological homeostasis in late life: Design and rationale of the REMBRANDT Study

Background

Late-life depression is characterized by disability, cognitive impairment and decline, and a high risk of recurrence following remission. Aside from past psychiatric history, prognostic neurobiological and clinical factors influencing recurrence risk are unclear. Moreover, it is unclear if cognitive impairment predisposes to recurrence, or whether recurrent episodes may accelerate brain aging and cognitive decline. The purpose of the REMBRANDT study (Recurrence markers, cognitive burden, and neurobiological homeostasis in late-life depression) is to better elucidate these relationships and identify phenotypic, cognitive, environmental, and neurobiological factors contributing to and predictive of depression recurrence.

Methods

Across three sites, REMBRANDT will enroll 300 depressed elders who will receive antidepressant treatment. The goal is to enroll 210 remitted depressed participants and 75 participants with no mental health history into a two-year longitudinal phase focusing on depression recurrence. Participants are evaluated every 2 months with deeper assessments occurring every 8 months, including structural and functional neuroimaging, environmental stress assessments, deep symptom phenotyping, and two weeks of 'burst' ecological momentary assessments to elucidate variability in symptoms and cognitive performance. A broad neuropsychological test battery is completed at the beginning and end of the longitudinal study.

Significance

REMBRANDT will improve our understanding of how alterations in neural circuits and cognition that persist during remission contribute to depression recurrence vulnerability. It will also elucidate how these processes may contribute to cognitive impairment and decline. This project will obtain deep phenotypic data that will help identify vulnerability and resilience factors that can help stratify individual clinical risk.

Predicting Age from White Matter Diffusivity with Residual Learning

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

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.

Sequential autoencoders for feature engineering and pretraining in major depressive disorder risk prediction

Objectives

We evaluated autoencoders as a feature engineering and pretraining technique to improve major depressive disorder (MDD) prognostic risk prediction. Autoencoders can represent temporal feature relationships not identified by aggregate features. The predictive performance of autoencoders of multiple sequential structures was evaluated as feature engineering and pretraining strategies on an array of prediction tasks and compared to a restricted Boltzmann machine (RBM) and random forests as a benchmark.

Materials and Methods

We study MDD patients from Vanderbilt University Medical Center. Autoencoder models with Attention and long-short-term memory (LSTM) layers were trained to create latent representations of the input data. Predictive performance was evaluated temporally by fitting random forest models to predict future outcomes with engineered features as input and using autoencoder weights to initialize neural network layers. We evaluated area under the precision-recall curve (AUPRC) trends and variation over the study population's treatment course.

Results

The pretrained LSTM model improved predictive performance over pretrained Attention models and benchmarks in 3 of 4 outcomes including self-harm/suicide attempt (AUPRCs, LSTM pretrained = 0.012, Attention pretrained = 0.010, RBM = 0.009, random forest = 0.005). The use of autoencoders for feature engineering had varied results, with benchmarks outperforming LSTM and Attention encodings on the self-harm/suicide attempt outcome (AUPRCs, LSTM encodings = 0.003, Attention encodings = 0.004, RBM = 0.009, random forest = 0.005).

Discussion

Improvement in prediction resulting from pretraining has the potential for increased clinical impact of MDD risk models. We did not find evidence that the use of temporal feature encodings was additive to predictive performance in the study population. This suggests that predictive information retained by model weights may be lost during encoding. LSTM pretrained model predictive performance is shown to be clinically useful and improves over state-of-the-art predictors in the MDD phenotype. LSTM model performance warrants consideration of use in future related studies.

Conclusion

LSTM models with pretrained weights from autoencoders were able to outperform the benchmark and a pretrained Attention model. Future researchers developing risk models in MDD may benefit from the use of LSTM autoencoder pretrained weights.

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.

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.

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.

Biological factors influencing depression in later life: role of aging processes and treatment implications

Late-life depression occurring in older adults is common, recurrent, and malignant. It is characterized by affective symptoms, but also cognitive decline, medical comorbidity, and physical disability. This behavioral and cognitive presentation results from altered function of discrete functional brain networks and circuits. A wide range of factors across the lifespan contributes to fragility and vulnerability of those networks to dysfunction. In many cases, these factors occur earlier in life and contribute to adolescent or earlier adulthood depressive episodes, where the onset was related to adverse childhood events, maladaptive personality traits, reproductive events, or other factors. Other individuals exhibit a later-life onset characterized by medical comorbidity, pro-inflammatory processes, cerebrovascular disease, or developing neurodegenerative processes. These later-life processes may not only lead to vulnerability to the affective symptoms, but also contribute to the comorbid cognitive and physical symptoms. Importantly, repeated depressive episodes themselves may accelerate the aging process by shifting allostatic processes to dysfunctional states and increasing allostatic load through the hypothalamic-pituitary-adrenal axis and inflammatory processes. Over time, this may accelerate the path of biological aging, leading to greater brain atrophy, cognitive decline, and the development of physical decline and frailty. It is unclear whether successful treatment of depression and avoidance of recurrent episodes would shift biological aging processes back towards a more normative trajectory. However, current antidepressant treatments exhibit good efficacy for older adults, including pharmacotherapy, neuromodulation, and psychotherapy, with recent work in these areas providing new guidance on optimal treatment approaches. Moreover, there is a host of nonpharmacological treatment approaches being examined that take advantage of resiliency factors and decrease vulnerability to depression. Thus, while late-life depression is a recurrent yet highly heterogeneous disorder, better phenotypic characterization provides opportunities to better utilize a range of nonspecific and targeted interventions that can promote recovery, resilience, and maintenance of remission.

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.

Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection

BACKGROUND

Anterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence between pre- and post-surgical surfaces. Yet, most cortical surface registration methods are designed for normal neuroanatomy. Surgical changes can introduce wide ranging artifacts in correspondence, for which conventional surface registration methods may not work as intended.

METHODS

In this paper, we propose a novel particle method for one-to-one dense shape correspondence between pre- and post-surgical surfaces with temporal lobe resection. The proposed method can handle partial structural abnormality involving non-rigid changes. Unlike existing particle methods using implicit particle adjacency, we consider explicit particle adjacency to establish a smooth correspondence. Moreover, we propose hierarchical optimization of particles rather than full optimization of all particles at once to avoid trappings of locally optimal particle update.

RESULTS

We evaluate the proposed method on 25 pairs of T1-MRI with pre- and post-simulated resection on the anterior temporal lobe and 25 pairs of patients with actual resection. We show improved accuracy over several cortical regions in terms of ROI boundary Hausdorff distance with 4.29 mm and Dice similarity coefficients with average value 0.841, compared to existing surface registration methods on simulated data. In 25 patients with actual resection of the anterior temporal lobe, our method shows an improved shape correspondence in qualitative and quantitative evaluation on parcellation-off ratio with average value 0.061 and cortical thickness changes. We also show better smoothness of the correspondence without self-intersection, compared with point-wise matching methods which show various degrees of self-intersection.

CONCLUSION

The proposed method establishes a promising one-to-one dense shape correspondence for temporal lobe resection. The resulting correspondence is smooth without self-intersection. The proposed hierarchical optimization strategy could accelerate optimization and improve the optimization accuracy. According to the results on the paired surfaces with temporal lobe resection, the proposed method outperforms the compared methods and is more reliable to capture cortical thickness changes.

Use of focused computerized cognitive training (Neuroflex) to improve symptoms in women with persistent chemotherapy-related cognitive impairment

Purpose

Chemotherapy-related cognitive impairment (CRCI) is a distressing and increasingly recognized long-term sequela reported by breast cancer patients following cancer treatment. There is an urgent but unmet clinical need for treatments that improve CRCI. In this context, we proposed the use of a novel cognitive enhancement strategy called Neuroflex to target CRCI experienced by breast cancer survivors.

Methods

The primary aim of this pilot study was to evaluate the feasibility and acceptability of Neuroflex, a novel digital cognitive enhancement strategy, in breast and gynecologic cancer survivors with CRCI. Secondary analyses focused on whether improvements in performance on Neuroflex were associated with improvement in subjective cognitive complaints and objective cognitive performance measures.

Results

Participants (N = 21) completed an average of 7.42 hours of Neuroflex training per week, an average of 44.5 (±1.01) hours total, and had a 100% completion rate. Participants exhibited significant improvement in self-reported cognitive function as well as significant improvement on tasks of verbal learning and memory and auditory working memory. Participants also exhibited improvement in mood, as well as improvement on a disability assessment.

Conclusions

Results demonstrate feasibility and that breast cancer survivors are capable of completing a lengthy and challenging cognitive training program. Secondly, Neuroflex may confer specific cognitive benefits to both self-reported and objective performance. Results strongly support further investigation of Neuroflex in a larger controlled trial to establish efficacy for CRCI symptoms. Further studies may also result in optimization of this digital intervention for women with CRCI.

Association of 24-Hour Activity Pattern Phenotypes With Depression Symptoms and Cognitive Performance in Aging

Importance

Evidence regarding the nature and prevalence of 24-hour activity pattern phenotypes in older adults, especially those related to depression symptoms and cognition, is needed to guide the development of targeted mechanism research and behavioral interventions.

Objectives

To identify subgroups of older adults with similar 24-hour activity rhythm characteristics and characterize associated depression symptoms and cognitive performance.

Design, Setting, and Participants

From January to March 2022, a cross-sectional analysis of the 2011-2014 National Health and Nutrition Examination and Survey (NHANES) accelerometer study was conducted. The NHANES used a multistage probability sample that was designed to be representative of noninstitutionalized adults in the US. The main analysis included participants 65 years or older who had accelerometer and depression measures weighted to represent approximately 32 million older adults.

Exposures

Latent profile analysis identified subgroups with similar 24-hour activity pattern characteristics as measured using extended-cosine and nonparametric methods.

Main Outcomes and Measures

Covariate-adjusted sample-weighted regressions assessed associations of subgroup membership with (1) depression symptoms defined as 9-Item Patient Health Questionnaire (PHQ-9) scores of 10 or greater (PHQ-9) and (2) having at least psychometric mild cognitive impairment (p-MCI) defined as scoring less than 1 SD below the mean on a composite cognitive performance score.

Results

The actual clustering sample size was 1800 (weighted: mean [SD] age, 72.9 [7.3] years; 57% female participants). Clustering identified 4 subgroups: (1) 677 earlier rising/robust (37.6%), (2) 587 shorter active period/less modelable (32.6%), (3) 177 shorter active period/very weak (9.8%), and (4) 359 later settling/very weak (20.0%). The prevalence of a PHQ-9 score of 10 or greater differed significantly across groups (cluster 1, 3.5%; cluster 2, 4.7%; cluster 3, 7.5%; cluster 4, 9.0%; χ2 P = .004). The prevalence of having at least p-MCI differed significantly across groups (cluster 1, 7.2%; cluster 2, 12.0%; cluster 3, 21.0%; cluster 4, 18.0%; χ2 P < .001). Five of 9 depression symptoms differed significantly across subgroups.

Conclusions and Relevance

In this cross-sectional study, findings indicate that approximately 1 in 5 older adults in the US may be classified in a subgroup with weak activity patterns and later settling, and approximately 1 in 10 may be classified in a subgroup with weak patterns and shorter active duration. Future research is needed to investigate the biologic processes related to these behavioral phenotypes, including why earlier and robust activity patterns appear protective, and whether modifying disrupted patterns improves outcomes.

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.

MRI predictors of pharmacotherapy response in major depressive disorder

Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.

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.

Applying A Biopsychosocial Framework to Achieve Durable Behavior Change in Kidney Disease

Chronic disease self-management is the establishment and maintenance of behaviors needed to be an active participant in one's health care and experience the best health outcomes. Kidney disease self-management behaviors to slow disease progression include engaging in exercise or physical activity; adhering to a diet low in sodium, potassium, and phosphorus; monitoring laboratory parameters; managing complex medication regimens; coping with disease-related emotional distress; and communicating effectively with providers. Durable behavior change has been difficult to achieve in kidney disease, in part because of an incomplete understanding of the multilevel factors determining chronic disease self-management in this patient group. The biopsychosocial model of chronic illness care posits that an individual's health outcomes result from biological, psychological, social, and environmental factors as part of a multilevel systems hierarchy. Although this theoretical model has been used to comprehensively identify factors driving self-management in other chronic conditions, it has been applied infrequently to behavioral interventions in kidney disease. In this scoping review, we apply the biopsychosocial model of health to identify individual, interpersonal, and systems-level drivers of kidney disease self-management behaviors. We further highlight factors that may serve as novel, impactful targets of theory-based behavioral interventions to understand and sustain behavior change in kidney disease.

Differential effects of estradiol on neural and emotional stress response in postmenopausal women with remitted Major Depressive Disorder

BACKGROUND

Estrogen fluctuations throughout the lifespan may contribute to major depressive disorder (MDD) risk in women through effects on brain networks important in stress responding, and mood regulation. Although there is evidence to support ovarian hormone treatment for peri-menopausal depression, postmenopausal use has not been well examined. The objective of this study was to investigate whether estrogen modulation of the neural and emotional cognitive responses to stress differs between postmenopausal women with and without MDD history.

METHODS

60 postmenopausal women completed an fMRI psychosocial stress task, after receiving no drug or 3 months of daily estradiol (E2). fMRI activity and subjective mood response were examined.

RESULTS

In women without a history of MDD, E2 was associated with a more negative mood response to stress and less activity in emotional regulation regions. In women with a history of MDD, E2 was associated with a less negative mood response to stress and less activity in emotion perception regions.

LIMITATIONS

This study was limited by open-label estradiol administration and inclusion of participants using antidepressants.

CONCLUSIONS

These results support a differential effect of estrogen on emotional and neural responses to psychosocial stress in postmenopausal women with MDD history and may reflect a shift in brain activity patterns related to emotion processing following menopause.

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.

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.

Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different automated state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

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.

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).

Validation of Group-wise Registration for Surface-based Functional MRI Analysis

Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities and functions of the brain. Conventionally, rsfMRIs are often registered to structural images in the Euclidean space without considering cortical surface geometry. Meanwhile, a surface-based representation offers a relaxed coordinate chart, but this still requires surface registration for group-wise data analysis. In this work, we investigate the performance of two existing surface registration methods in a surface-based rsfMRI analysis framework: FreeSurfer and Hierarchical Spherical Deformation (HSD). To minimize registration bias, we establish shape correspondence using both methods in a group-wise manner that estimates the unbiased average of a given cohort. To evaluate their performance, we focus on neuroanatomical alignment as well as the amount of distortion that can potentially bias surface tessellation for secondary level rsfMRI data analyses. In the pilot analysis, we examine a single timepoint of imaging data from 100 subjects out of an aging cohort. Overall, HSD establishes improved shape correspondence with reduced mean curvature deviation (10.94% less on average per subject, paired t-test: p <10-10) and reduced registration distortion (FreeSurfer: average 41.91% distortion per subject, HSD: 18.63%, paired t-test: p <10-10). Furthermore, HSD introduces less distortion than FreeSurfer in the areas identified in the individual components that were extracted by surface-based independent component analysis (ICA) after spatial smoothing and time series normalization. Consequently, we show that FreeSurfer capture individual components with globally similar but locally different patterns in ICA in visual inspection.

Establishing Surface Correspondence for Post-surgical Cortical Thickness Changes in Temporal Lobe Epilepsy

In pre- and post-surgical surface shape analysis, establishing shape correspondence is necessary to investigate the postoperative surface changes. However, structural absence after the operation accompanies focal non-rigid changes, which leads to challenges in existing surface registration methods. In this paper, we present a fully automatic particle-based method to establish surface correspondence that can handle partial structural abnormality in the temporal lobe resection. Our method optimizes the coordinates of points which are modeled as particles on surfaces in a hierarchical way to reduce a chance of being trapped in a local minimum during the optimization. In the experiments, we evaluate the effectiveness of our method in comparison with conventional spherical registration (FreeSurfer) on two scenarios: cortical thickness changes in healthy controls within a short scan-rescan time window and patients with temporal lobe resection. The post-surgical scan is acquired at least 1 year after the presurgical scan. In region of interest-wise (ROI-wise) analysis, no changes on cortical thickness are found in both methods on the healthy control group. In patients, since there is no ground truth available, we instead investigated the disagreement between our method and FreeSurfer. We see poorly matched ROIs and large cortical thickness changes using FreeSurfer. On the contrary, our method shows well-matched ROIs and subtle cortical thickness changes. This suggests that the proposed method can establish a stable shape correspondence, which is not fully captured in a conventional spherical registration.

EEG as a Functional Marker of Nicotine Activity: Evidence From a Pilot Study of Adults With Late-Life Depression

Late-life depression (LLD) is a debilitating condition that is associated with poor response to antidepressant medications and deficits in cognitive performance. Nicotinic cholinergic stimulation has emerged as a potentially effective candidate to improve cognitive performance in patients with cognitive impairment. Previous studies of nicotinic stimulation in animal models and human populations with cognitive impairment led to examining potential cognitive and mood effects of nicotinic stimulation in older adults with LLD. We report results from a pilot study of transdermal nicotine in LLD testing whether nicotine treatment would enhance cognitive performance and mood. The study used electroencephalography (EEG) recordings as a tool to test for potential mechanisms underlying the effect of nicotine. Eight non-smoking participants with LLD completed EEG recordings at baseline and after 12 weeks of transdermal nicotine treatment (NCT02816138). Nicotine augmentation treatment was associated with improved performance on an auditory oddball task. Analysis of event-related oscillations showed that nicotine treatment was associated with reduced beta desynchronization at week 12 for both standard and target trials. The change in beta power on standard trials was also correlated with improvement in mood symptoms. This pilot study provides preliminary evidence for the impact of nicotine in modulating cortical activity and improving mood in depressed older adults and shows the utility of using EEG as a marker of functional engagement in nicotinic interventions in clinical geriatric patients.

Medial temporal lobe volumes in late-life depression: effects of age and vascular risk factors

Substantial work associates late-life depression with hippocampal pathology. However, there is less information about differences in hippocampal subfields and other connected temporal lobe regions and how these regions may be influenced by vascular factors. Individuals aged 60 years or older with and without a DSM-IV diagnosis of Major Depressive Disorder completed clinical assessments and 3 T cranial MRI using a protocol allowing for automated measurement of medial temporal lobe subfield volumes. A subset also completed pseudo-continuous arterial spin labeling, allowing for the measurement of hippocampal cerebral blood flow. In 59 depressed and 21 never-depressed elders (mean age = 66.4 years, SD = 5.8y, range 60-86y), the depressed group did not exhibit statistically significant volumetric differences for the total hippocampus or hippocampal subfields but did exhibit significantly smaller volumes of the perirhinal cortex, specifically in the BA36 region. Additionally, age had a greater effect in the depressed group on volumes of the cornu ammonis, entorhinal cortex, and BA36 region. Finally, both clinical and radiological markers of vascular risk were associated with smaller BA36 volumes, while reduced hippocampal blood flow was associated with smaller hippocampal and cornu ammonis volumes. In conclusion, while we did not observe group differences in hippocampal regions, we observed group differences and an effect of vascular pathology on the BA36 region, part of the perirhinal cortex. This is a critical region exhibiting atrophy in prodromal Alzheimer's disease. Moreover, the observed greater effect of age in the depressed groups is concordant with past longitudinal studies reporting greater hippocampal atrophy in late-life depression.

Mental Health Treatment for Front-Line Clinicians During and After the Coronavirus Disease 2019 (COVID-19) Pandemic: A Plea to the Medical Community

The COVID-19 pandemic has placed front-line health care professionals—who were already at higher risk for negative effects of chronic stress before the pandemic—at even greater risk for depression and anxiety. This article reminds us of the importance of mutual support and caring for our own mental health, including seeking help from our mental health colleagues when needed.

A bayesian approach to examining default mode network functional connectivity and cognitive performance in major depressive disorder

To reconcile the inconsistency of the association between the resting-state functional connectivity (RSFC) and cognitive performance in healthy and depressed groups due to high variance of both measures, we proposed a Bayesian spatio-temporal model to precisely and accurately estimate the RSFC in depressed and nondepressed participants. This model was employed to estimate spatially-adjusted functional connectivity (saFC) in the extended default mode network (DMN) that was hypothesized to correlate with cognitive performance in both depressed and nondepressed. Multiple linear regression models were used to study the relationship between DMN saFC and cognitive performance scores measured in the following four cognitive domains while adjusting for age, sex, and education. In ROI pairs including the posterior cingulate (PCC) and anterior cingulate (ACC) cortex regions, the relationship between connectivity and cognition was found only with the Bayesian approach. Moreover, only the Bayesian approach was able to detect a significant diagnostic difference in the association in ROI pairs, including both PCC and ACC regions, due to smaller variance for the saFC estimator. The results confirm that a reliable and precise saFC estimator, based on the Bayesian model, can foster scientific discovery that may not be feasible with the conventional ROI-based FC estimator (denoted as 'AVG-FC').

In vivo network models identify sex differences in the spread of tau pathology across the brain

Introduction

We examined networks of tau connectivity between brain regions based on correlations of their [¹⁸F]flortaucipir positron emission tomography (PET) uptake to evaluate sex‐specific differences in brain‐wide tau propagation.

Methods

PET data of clinically normal and mild cognitive impairment (MCI) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to examine differences in network architectures across the groups.

Results

The tau‐based network architecture resembled progression of tauopathy from Braak stage I to VI regions. Compared to men, women had higher network density and an increased number of direct regional connections in co‐occurrence with increased brain‐wide tau burden, particularly at MCI. Several regions, including superior parietal lobe and parahippocampus served as connecting bridges between communities at different Braak stages.

Discussion

Network characteristics in women may favor an accelerated brain‐wide tau spread leading to a higher tau burden in women than men with MCI with implications for the greater female preponderance in Alzheimer's disease diagnosis.

Persistent Intrinsic Functional Network Connectivity Alterations in Middle-Aged and Older Women With Remitted Depression

BACKGROUND

In younger adults, residual alterations in functional neural networks persist during remitted depression. However, there are fewer data for midlife and older adults at risk of recurrence. Such residual network alterations may contribute to vulnerability to recurrence. This study examined intrinsic network functional connectivity in midlife and older women with remitted depression.

METHODS

A total of 69 women (24 with a history of depression, 45 with no psychiatric history) over 50 years of age completed 3T fMRI with resting-state acquisition. Participants with remitted depression met DSM-IV-TR criteria for an episode in the last 10 years but not the prior year. Whole-brain seed-to-voxel resting-state functional connectivity analyses examined the default mode network (DMN), executive control network (ECN), and salience network (SN), plus bilateral hippocampal seeds. All analyses were adjusted for age and used cluster-level correction for multiple comparisons with FDR < 0.05 and a height threshold of p < 0.001, uncorrected.

RESULTS

Women with a history of depression exhibited decreased functional connectivity between the SN (right insula seed) and ECN regions, specifically the left superior frontal gyrus. They also exhibited increased functional connectivity between the left hippocampus and the left postcentral gyrus. We did not observe any group differences in functional connectivity for DMN or ECN seeds.

CONCLUSIONS

Remitted depression in women is associated with connectivity differences between the SN and ECN and between the hippocampus and the postcentral gyrus, a region involved in interoception. Further work is needed to determine whether these findings are related to functional alterations or are predictive of recurrence.

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.

Nicotinic treatment of post-chemotherapy subjective cognitive impairment: a pilot study

PURPOSE

Persistent chemotherapy-related cognitive impairment (pCRCI) is commonly reported following cancer treatment and negatively affects quality of life; however, there is currently no pharmacological treatment indicated for pCRCI. This pilot study obtained preliminary data regarding the use of transdermal nicotine patches as a therapeutic strategy for women with pCRCI to (1) reduce subjective cognitive complaints and (2) enhance objective cognitive performance in breast, colon, lymphoma, or ovarian cancer survivors with pCRCI.

METHODS

Participants were randomized to either placebo (n = 11) or transdermal nicotine (n = 11) for 6 weeks, followed by 2 weeks of treatment withdrawal for a total of 8 weeks. Participants were assessed using both subjective and objective measures of cognitive functioning at five visits before, during, and after treatment.

RESULTS

Over the course of the study, women in both groups improved substantially in severity of self-reported cognitive complaints measured by Functional Assessment of Cancer Therapy-Cognitive Function Perceived Cognitive Impairments regardless of treatment arm. Additionally, objective cognitive performance measures improved in both groups; however, there was no significant difference in improvement between groups.

CONCLUSIONS

Due to a large placebo response, we were unable to determine if a drug effect was present. However, we did observe substantial improvement in self-reported cognitive symptoms, likely resulting from factors related to participation in the trial rather than specific drug treatment effects.

TRIAL REGISTRATION

The study was registered with clinicaltrials.gov (trial registration: NCT02312943).

IMPLICATIONS FOR CANCER SURVIVORS

These results suggest that women with pCRCI can exhibit improvement in subjective cognition, with attention paid to symptoms and close follow-up over a short period of time.

Brain network functional connectivity and cognitive performance in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is one of the most prevalent and debilitating psychiatric disorders. Cognitive complaints are commonly reported in MDD and cognitive impairment is a criterion item for MDD diagnosis. As cognitive processes are increasingly understood as the consequences of distributed interactions between brain regions, a network-based approach may provide novel information about the neurobiological basis of cognitive deficits in MDD.

METHODS

51 Depressed (MDD, n = 23) and non-depressed (control, n = 28) adult participants completed neuropsychological testing and resting-state fMRI (rsfMRI). Cognitive domain scores (processing speed, working memory, episodic memory, and executive function) were calculated. Anatomical regions of interests were entered as seeds for functional connectivity analyses in: default mode (DMN), salience, and executive control (ECN) networks. Partial correlations controlling for age and sex were conducted for cognitive domain scores and functional connectivity in clusters with significant differences between groups.

RESULTS

Significant rsfMRI differences between groups were identified in multiple clusters in the DMN and ECN. Greater positive connectivity within the ECN and between ECN and DMN regions was associated with poorer episodic memory performance in the Non-Depressed group but better performance in the MDD group. Greater connectivity within the DMN was associated with better episodic and working memory performance in the Non-Depressed group but worse performance in the MDD group.

CONCLUSIONS

These results provide evidence that cognitive performance in MDD may be associated with aberrant functional connectivity in cognitive networks and suggest patterns of alternate brain function that may support cognitive processes in MDD.

Perspectives on the Management of Vascular Depression

In the next few years, the youngest of the baby boomers will reach age 65, resulting in the greatest proportion of elderly adults in US history. Concurrent with this demographic change is the growing number of adults living with chronic conditions that increase risk of vascular disease, including hypertension, obesity, hypercholesterolemia, insulin resistance and diabetes mellitus. We address how these conditions contribute to age-related cerebrovascular changes and lead to subsequent effects on mood and cognitive function, with an emphasis on the role of “vascular depression” as a focus of treatment. The case of an elderly gentleman with vascular disease associated with psychiatric symptoms and cognitive changes is presented. We discuss vascular depression in the context of suicide in late life and provide perspectives on treatment that focus not merely on pharmacologic and psychotherapeutic management of depressive symptoms but also emphasize the importance of sleep and health maintenance strategies. Guidelines are offered to help reduce the burden of disability associated with this condition among our older population.

Intrinsic Functional Network Connectivity Is Associated With Clinical Symptoms and Cognition in Late-Life Depression

BACKGROUND

Late-life depression (LLD) has been associated with alterations in intrinsic functional networks, best characterized in the default mode network (DMN), cognitive control network (CCN), and salience network. However, these findings often derive from small samples, and it is not well understood how network findings relate to clinical and cognitive symptomatology.

METHODS

We studied 100 older adults (n = 79 with LLD, n = 21 nondepressed) and collected resting-state functional magnetic resonance imaging, clinical measures of depression, and performance on cognitive tests. We selected canonical network regions for each intrinsic functional network (DMN, CCN, and salience network) as seeds in seed-to-voxel analysis. We compared connectivity between the depressed and nondepressed groups and correlated connectivity with depression severity among depressed subjects. We then investigated whether the observed connectivity findings were associated with greater severity of common neuropsychiatric symptoms or poorer cognitive performance.

RESULTS

LLD was characterized by decreased DMN connectivity to the frontal pole, a CCN region (Wald χ²₁ = 22.33, p < .001). No significant group differences in connectivity were found for the CCN or salience network. However, in the LLD group, increased CCN connectivity was associated with increased depression severity (Wald χ²₁ > 20.14, p < .001), greater anhedonia (Wald χ²₁ = 7.02, p = .008) and fatigue (Wald χ²₁ = 6.31, p = .012), and poorer performance on tests of episodic memory (Wald χ²₁ > 4.65, p < .031), executive function (Wald χ²₁ = 7.18, p = .007), and working memory (Wald χ²₁ > 4.29, p < .038).

CONCLUSIONS

LLD is characterized by differences in DMN connectivity, while CCN connectivity is associated with LLD symptomology, including poorer performance in several cognitive domains.

Depression Plays a Moderating Role in the Cognitive Decline Associated With Changes of Brain White Matter Hyperintensities

OBJECTIVE

In the elderly, depression and white matter hyperintensities (WMH) are common and associated with cognitive impairment. This study investigated the possible interactions between depression and WMH in their influences on cognition of the elderly.

METHODS

Using multiple neuropsychological tests, we evaluated the cognitive function of 122 community-dwelling elders with depression at baseline between November 2008 and February 2009. Major depressive disorder, dysthymic disorder, and minor depressive disorder were diagnosed according to DSM-IV criteria. Subsyndromal depressive disorder was operationally defined using a modification of DSM-IV criteria. We visually rated WMH severity according to the modified Fazekas scale and calculated WMH volume using an automated method. We defined WMH (+) as having a score of 2 or higher on the modified Fazekas scale. In the 3-year follow-up study, baseline participants were reassessed between November 2011 and February 2013 with the same methodology.

RESULTS

Baseline depression was associated with a decline over 3 years in the Categorical Verbal Fluency Test (VFT) (P = .001), Word List Memory Test (WLMT) (P = .019), Trail Making Test A (TMT-A) (P = .018), and Mini-Mental State Examination (MMSE) (P = .017), while baseline WMH (+) was associated with a decline in WLMT (P = .039) only. An increase of WMH volume over 3 years was associated with a decline in the performances of VFT (P = .044), WLMT (P = .044), Word List Recall Test (P = .040), Word List Recognition Test (P = .036), and TMT-A (P = .001) over the same period only in the subjects with depression at baseline.

CONCLUSIONS

Depressive disorder and WMH are interactively associated with the poor performance of multiple cognitive functions. Depressive disorder may moderate the cognitive decline associated with the changes of brain WMH.

Transdermal Nicotine for the Treatment of Mood and Cognitive Symptoms in Nonsmokers With Late-Life Depression

OBJECTIVE

Late-life depression (LLD) is characterized by poor antidepressant response and cognitive dysfunction. This study examined whether transdermal nicotine benefits mood symptoms and cognitive performance in LLD.

METHODS

In a 12-week open-label outpatient study conducted between November 2016 and August 2017, transdermal nicotine was given to 15 nonsmoking older adults (≥ 60 years of age). Eligible participants met DSM-IV-TR criteria for major depressive disorder with ≥ 15 on the Montgomery-Asberg Depression Rating scale (MADRS) and endorsed subjective cognitive impairment. Transdermal nicotine patches were applied daily and titrated in a rigid dose escalation strategy to a maximum dose of 21.0 mg/d, allowing dose reductions for tolerability. The primary mood outcome was MADRS change measured every 3 weeks, with response defined as ≥ 50% improvement from baseline and remission as MADRS score ≤ 8. The primary cognitive outcome was the Conners Continuous Performance Test (CPT), a test of attention.

RESULTS

Robust rates of response (86.7%; 13/15 subjects) and remission (53.3%; 8/15 subjects) were observed. There was a significant decrease in MADRS scores over the study (β = -1.51, P < .001), with improvement seen as early as 3 weeks (Bonferroni-adjusted P value = .004). We also observed improvement in apathy and rumination. We did not observe improvement on the CPT but did observe improvement in subjective cognitive performance and signals of potential drug effects on secondary cognitive measures of working memory, episodic memory, and self-referential emotional processing. Overall, transdermal nicotine was well tolerated, although 6 participants could not reach the maximum targeted dose.

CONCLUSIONS

Nicotine may be a promising therapy for depressed mood and cognitive performance in LLD. A definitive placebo-controlled trial and establishment of longer-term safety are necessary before clinical usage.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02816138​.

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.

Evaluating the consistency of scales used in adult attention deficit hyperactivity disorder assessment of college-aged adults

OBJECTIVE

Neurocognitive evaluations are commonly integrated with clinical assessment to evaluate adult Attention Deficit Hyperactivity Disorder (ADHD). Study goal is to identify measures most strongly related to ADHD diagnosis and to determine their utility in screening processes.

PARTICIPANTS

230 students who were evaluated at the Vanderbilt University Psychological and Counseling Center between July 2013 and October 2015.

METHODS

We retrospectively examined charts, including clinical diagnosis, family history, childhood parental reported and current self-reported ADHD symptoms, psychiatric comorbidities, and continuous performance test (CPT).

RESULT

Positive report of childhood and current ADHD symptoms, and lack of comorbid psychiatric symptoms were strongly associated with clinical diagnosis. CPT results were not associated with an ADHD diagnosis. The absence of reported childhood and current ADHD symptoms may serve as a contradictory marker for ADHD diagnosis.

CONCLUSION

Clinical assessment of ADHD symptoms and ADHD childhood history, but not CPT, contributes to an accurate diagnosis of ADHD in college-aged adults.

Anterior-posterior gradient differences in lobar and cingulate cortex cerebral blood flow in late-life depression

Vascular pathology is common in late-life depression, contributing to changes in cerebral function. We examined whether late-life depression was associated with differences in cerebral blood flow (CBF) and whether such differences were related to vascular risk and cerebrovascular pathology, specifically white matter hyperintensity (WMH) volumes. Twenty-three depressed elders and 20 age- and sex-matched elders with no psychiatric history completed cranial 3T MRI. MRI procedures included a pseudo-continuous Arterial Spin Labeling (pcASL) acquisition obtained while on room air and during a hypercapnia challenge allowing for calculation of cerebrovascular reactivity (CVR). Brain segmentation identified frontal, temporal, parietal and cingulate sub-regions in which CBF and CVR were calculated. The depressed group exhibited an anterior-posterior gradient in CBF, with lower CBF throughout the frontal lobe but higher CBF in the parietal lobe, temporal lobe, thalamus and hippocampus. A similar anterior to posterior gradient was observed in the cingulate cortex, with anterior regions exhibiting lower CBF and posterior regions exhibiting higher CBF. We did not observe any group differences in CVR measures. We did not observe significant relationships between CBF and CVR with vascular risk or WMH volumes, aside from an isolated finding associating higher WMH volumes with lower CBF in the rostral anterior cingulate cortex. Decreased anterior CBF in depressed elders might reflect decreased metabolic activity in these regions, while increased posterior CBF may represent either compensatory processes or different activity of posterior intrinsic functional networks. Future work should examine how these findings are related to compensatory changes with aging.

APOE ε4 associated with preserved executive function performance and maintenance of temporal and cingulate brain volumes in younger adults

The APOE ε4 allele is associated with cognitive deficits and brain atrophy in older adults, but studies in younger adults are mixed. We examined APOE genotype effects on cognition and brain structure in younger adults and whether genotype effects differed by age and with presence of depression. 157 adults (32 % ε4 carriers, 46 % depressed) between 20 and 50 years of age completed neuropsychological testing, 131 of which also completed 3 T cranial MRI. We did not observe a direct effect of APOE genotype on cognitive performance or structural MRI measures. A significant genotype by age interaction was observed for executive function, where age had less of an effect on executive function in ε4 carriers. Similar interactions were observed for the entorhinal cortex, rostral and caudal anterior cingulate cortex and parahippocampal gyrus, where the effect of age on regional volumes was reduced in ε4 carriers. There were no significant interactions between APOE genotype and depression diagnosis. The ε4 allele benefits younger adults by allowing them to maintain executive function performance and volumes of cingulate and temporal cortex regions with aging, at least through age fifty years.

An excess of massive stars in the local 30 Doradus starburst

The 30 Doradus star-forming region in the Large Magellanic Cloud is a nearby analog of large star-formation events in the distant universe. We determined the recent formation history and the initial mass function (IMF) of massive stars in 30 Doradus on the basis of spectroscopic observations of 247 stars more massive than 15 solar masses ([Formula: see text]). The main episode of massive star formation began about 8 million years (My) ago, and the star-formation rate seems to have declined in the last 1 My. The IMF is densely sampled up to 200 [Formula: see text] and contains 32 ± 12% more stars above 30 [Formula: see text] than predicted by a standard Salpeter IMF. In the mass range of 15 to 200 [Formula: see text], the IMF power-law exponent is [Formula: see text], shallower than the Salpeter value of 2.35.