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.

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.

Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis.

A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.

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.

Towards Portable Large-Scale Image Processing with High-Performance Computing.

High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called "spiders." The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software development and expansion, and (3) scalable spider deployment compatible with HPC clusters and local workstations.

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.

Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment.

The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has developed a database built on XNAT housing over a quarter of a million scans. The database provides framework for (1) rapid prototyping, (2) large scale batch processing of images and (3) scalable project management. The system uses the web-based interfaces of XNAT and REDCap to allow for graphical interaction. A python middleware layer, the Distributed Automation for XNAT (DAX) package, distributes computation across the Vanderbilt Advanced Computing Center for Research and Education high performance computing center. All software are made available in open source for use in combining portable batch scripting (PBS) grids and XNAT servers.

Posterior structural brain volumes differ in maltreated youth with and without chronic posttraumatic stress disorder.

Magnetic resonance imaging studies of maltreated children with posttraumatic stress disorder (PTSD) suggest that maltreatment-related PTSD is associated with adverse brain development. Maltreated youth resilient to chronic PTSD were not previously investigated and may elucidate neuromechanisms of the stress diathesis that leads to resilience to chronic PTSD. In this cross-sectional study, anatomical volumetric and corpus callosum diffusion tensor imaging measures were examined using magnetic resonance imaging in maltreated youth with chronic PTSD (N = 38), without PTSD (N = 35), and nonmaltreated participants (n = 59). Groups were sociodemographically similar. Participants underwent assessments for strict inclusion/exclusion criteria and psychopathology. Maltreated youth with PTSD were psychobiologically different from maltreated youth without PTSD and nonmaltreated controls. Maltreated youth with PTSD had smaller posterior cerebral and cerebellar gray matter volumes than did maltreated youth without PTSD and nonmaltreated participants. Cerebral and cerebellar gray matter volumes inversely correlated with PTSD symptoms. Posterior corpus callosum microstructure in pediatric maltreatment-related PTSD differed compared to maltreated youth without PTSD and controls. The group differences remained significant when controlling for psychopathology, numbers of Axis I disorders, and trauma load. Alterations of these posterior brain structures may result from a shared trauma-related mechanism or an inherent vulnerability that mediates the pathway from chronic PTSD to comorbidity.

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.

EPI susceptibility correction introduces significant differences far from local areas of high distortion.

PURPOSE: In echo-planar diffusion-weighted imaging, correcting for susceptibility-induced artifacts typically requires acquiring pairs of images, known as blip-up blip-down acquisitions, to create an undistorted volume as a target to correct distortions that are often focal where regions with differences in magnetic susceptibility interface, such as the frontal and temporal areas. However, blip-up blip-down acquisitions are not always available, and distortion effects may not be specifically localized to such areas, with subtle effects potentially extending throughout the brain. Here, we apply a deep learning technique to generate an undistorted volume to correct susceptibility-induced artifacts and demonstrate implications for image fidelity and diffusion-based inference outside of areas where high focal distortion is present. METHODS: To demonstrate differences due to susceptibility artifact correction, uncorrected baseline images were compared to identical images where correction was performed using an undistorted target volume produced by the deep learning tool "PreQual". Widespread geometric distortion was assessed visually by referencing diffusion-weighted images to T1-weighted images. Tract-based spatial statistics (TBSS) were utilized to perform whole brain analysis of fractional anisotropy (FA) values to assess differences between subject groups (depressed vs. non-depressed) via permutation-based, voxel-wise testing. Multivariate regression models were then used to contrast TBSS results between corrected and non-corrected diffusion images. RESULTS: Susceptibility artifact correction resulted in visible, widespread improvement in image fidelity when referenced to T1-weighted images. TBSS results were dependent on susceptibility artifact correction with correction resulting in widespread structural alterations of the mean FA skeleton, changes in skeletal FA, and additional positive tests of significance of regression coefficients in subsequent regression models. CONCLUSION: Our results indicated that EPI distortion effects are not purely focal, and that reducing distortion can result in significant differences in the interpretation of diffusion data, even in areas remote from high distortion.

Reduction of dorsolateral prefrontal cortex gray matter in late-life depression.

Postmortem studies have documented abnormalities in the dorsolateral prefrontal cortex (dlPFC) in depressed subjects. In this study we used magnetic resonance imaging to test for dlPFC volume differences between older depressed and non-depressed individuals. Eighty-eight subjects meeting DSM IV criteria for major depressive disorder and thirty-five control subjects completed clinical evaluations and cranial 3T magnetic resonance imaging. After tissue types were identified using an automated segmentation process, the dlPFC was measured in both hemispheres using manual delineation based on anatomical landmarks. Depressed subjects had significantly lower gray matter in the left and right dorsolateral prefrontal cortex (standardized to cerebral parenchyma) after controlling for age and sex. Our study confirmed the reduction of dorsolateral prefrontal cortex in elderly depressed subjects, especially in the gray matter. These regional abnormalities may be associated with psychopathological changes in late-life depression.

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.

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.

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.

Cognitive complaints are associated with smaller right medial temporal gray-matter volume in younger postmenopausal women.

OBJECTIVE: Menopause is associated with increasing cognitive complaints and older women are at increased risk of developing Alzheimer disease compared to men. However, there is difficulty in early markers of risk using objective performance measures. We investigated the impact of subjective cognitive complaints on the cortical structure in a sample of younger postmenopausal women. METHODS: Data for this cross-sectional study were drawn from the baseline visit of a longer double-blind study examining estrogen-cholinergic interactions in normal postmenopausal women. Structural Magnetic Resonance Imaging was acquired on 44 women, aged 50-60 years and gray-matter volume was defined by voxel-based morphometry. Subjective measures of cognitive complaints and postmenopausal symptoms were obtained as well as tests of verbal episodic and working memory performance. RESULTS: Increased levels of cognitive complaints were associated with lower gray-matter volume in the right medial temporal lobe (r = -0.445, P < 0.002, R = 0.2). Increased depressive symptoms and somatic complaints were also related to increased cognitive complaints and smaller medial temporal volumes but did not mediate the effect of cognitive complaints. In contrast, there was no association between performance on the memory tasks and subjective cognitive ratings, or medial temporal lobe volume. CONCLUSIONS: The findings of the present study indicate that the level of reported cognitive complaints in postmenopausal women may be associated with reduced gray-matter volume which may be associated with cortical changes that may increase risk of future cognitive decline. : Video Summary:http://links.lww.com/MENO/A626.

Video Summary:http://links.lww.com/MENO/A626.

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').