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We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49–89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06–1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76–3.61), p p = 0.001 for total 336 follow-up sample; 2.31 (1.44–3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43–4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

OBJECTIVE: The dysregulation of stress-related networks due to chronic symptoms such as severe worry and/or rumination is one of the putative pathways linking anxiety in late-life with cognitive decline and increased cardiovascular burden. Symptoms such as severe worry or rumination respond poorly to standard treatment and drive the morbidity associated with anxiety in older adults. We assessed if any of the neural networks anchored in the stressrelated regions of interest (ROIs) are associated with distinct anxiety phenotypes (worry, rumination and global anxiety). METHODS: We recruited older participants (over 50 years of age) with varying levels of worry (N=91) to undergo resting state fMRI. We computed seed-based connectivity for each ROI: the bed nucleus of the stria terminalis (BNST), the paraventricular nucleus of the hypothalamus (PVN), habenula, and amygdala. We limited our connectivity analyses to extracted regions for each seeded ROI-based network based on their canonical networks in 1000 participants (Neurosynth). Using connectivity and clinical factors, we fit cross-validated elastic net models to predict scores on Penn State Worry Questionnaire, Rumination Subscale Questionnaire, Hamilton Anxiety Rating Scale, and Perceived Stress Scale. RESULTS: We identified several distinct connectivity patterns that predict anxiety phenotypes’ severity. Greater worry was associated with greater PVN-subgenual anterior cingulate cortex, parahippocampal (PHC), and olfactory and amygdala-PHC connectivity. Greater global anxiety was associated with lower amygdala-superior temporal gyrus connectivity. Greater perceived stress was associated with lower amygdala-inferior temporal gyrus and amygdala-fusiform gyrus connectivity. CONCLUSIONS: Our study suggests that various late-life anxiety phenotypes (worry, global anxiety, rumination) may be associated with varying functional connectivity related to stress and emotion regulation. This may aid in the development of future targeted interventions.

Suicide is influenced by complex interactions among various psychopathological features. We aimed to examine the relationship between suicide risk and psychological risk factors such as defense mechanisms, personality, and anxiety.We established a psychiatric database by utilizing the Observational Medical Outcomes Partnership Common Data Model. We conducted a 1:1 propensity score matching with age, sex, and depression severity, and identified a sample (n = 258) with two groups: those with suicidal behavior and those with non-suicidal behavior. Using principal component analysis, we extracted nine psychological scales and applied network analysis to compare relationships among psychological factors between the two groups.Patients with non-suicidal behaviors showed associations between trait anxiety and defense mechanisms, while those with suicidal behaviors did not. For patients with suicidal ideation there was an association between somatization and trait anxiety. Patients with suicide attempts showed associations between paranoia and dissociation connected to trait anxiety.Longitudinal research is required to fully observe transitions from suicidal ideation to attempts and recurrent suicidal events. In addition, these networks may not generalize suicidal behaviors because the group participants are not homogeneous. Lastly, data from self-report questionnaires limits the reliability of responses.We presented important new insights on suicidal behavior by estimating psychological networks. Patients with non-suicidal behavior may exhibit discrete relationships between defense mechanisms and anxiety, compared to those with suicidal behavior. Patients with suicidal ideation and suicide attempts may show distinct associations between anxiety and psychopathological features.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

This Viewpoint discusses the updated US Preventive Services Task Force recommendations for anxiety screening in adults.

Objectives To investigate potential birth cohort effects in depression symptoms in older adults. Design Population-based prospective cohort. Setting Small-town communities in Pennsylvania. Participants Three thousand two hundred and twenty seven older adults (average baseline age = 71.6) born between 1902 and 1941. Measurements Four decade-long birth cohorts were the primary predictors in this study: 1902–1911, 1912–1921, 1922–1931, and 1932–1941. The outcome was symptoms of depression assessed at baseline and follow-up study visits using a modified Center for Epidemiologic Studies Depression Scale (mCES-D). The depression outcome was operationalized as: 1). A binary outcome of having greater than equal to 5 depression symptoms on the total mCES-D at any study visit, and 2). A continuous outcome of four factor-analyzed component scores of the mCES-D including depressed mood, anergia/hopelessness, withdrawal, and poor self-esteem. All analyses were jointly modeled with attrition and adjusted for age, sex, education, Mini Mental State Examination score, antidepressant medications, and total prescription medications. Results Participants from more recently born cohorts were significantly less likely to have a study visit in which they reported greater than or equal to 5 depression symptoms, controlling for attrition. Specifically, in comparison to the 1902–1911 referent cohort, the 1912–1921 birth cohort was 43% less likely (odds ratio [OR] = 0.566, 95% confidence interval [CI]: 0.341–0.939), the 1922–1931 birth cohort was 63% less likely (OR = 0.0369, 95% CI: 0.215–0.632), and the 1932–1941 cohort was 79% less likely (OR = 0.205, 95% CI: 0.106–0.399). The cohort effect was most evident in the depressed mood and anergia/hopelessness symptom composites. Conclusion Reduced rates of depression symptoms observed in successive birth cohorts of older adults may reflect compression of morbidity or other secular trends.

OBJECTIVE Early experiences of having received maternal warmth predict responses to opportunities to connect with others later in life. However, the understanding of neurochemical mechanisms by which such relationships emerge remains incomplete. Endogenous opioids, involved in social connection in both animals and humans, may contribute to this link. Therefore, the current study examined a) relationships between early maternal warmth and brain and self-report responses to novel social targets (i.e., outcomes that may promote social connection) and b) the effect of the opioid antagonist, naltrexone, on such relationships. METHODS Eighty-two adult participants completed a retrospective report of early maternal warmth. On a second visit, participants were randomized to 50 mg of oral naltrexone (n = 42) or placebo (n = 40), followed by a magnetic resonance imaging scan where functional brain activity in response to images of novel social targets (strangers) was assessed. Approximately 24 hours later, participants reported on their feelings of social connection since leaving the scanner. RESULTS In the placebo condition, greater early maternal warmth was associated with less dorsal anterior cingulate cortex, anterior insula, ventral striatum, and amygdala activity in response to images of novel social targets (r values ≥ -0.360, p values ≤ .031), and greater feelings of social connection (r = 0.524, p < .001) outside of the laboratory. The same relationships, however, were not present in the naltrexone condition. CONCLUSIONS Results highlight relationships between early maternal warmth and responses to the social world at large and suggest that opioids might contribute to social connection by supporting the buffering effects of warm early life experiences on social connection later in life.Trial Registration: Clinical Trials NCT02818036.

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

HIGHLIGHTS • This study reports the mortality risk due to coronavirus disease 2019 (COVID-19) in elderly patients with mental disorders. • Comparing characteristics between two groups: patients with and without mental disorders suggested that the mental disorder group had more risk factors for COVID-19. • This study suggests that elderly patients with comorbid conditions and those taking psychiatric medications might be at a higher risk of COVID-19., Objective This study aimed to investigate the different clinical characteristics among elderly coronavirus disease 2019 (COVID-19) patients with and without mental disorders in South Korea and determine if these characteristics have an association with underlying mental disorders causing mortality. Method A population-based comparative cohort study was conducted using the national claims database. Individuals aged ≥65 years with confirmed COVID-19 between January 1, 2020 and April 10, 2020 were assessed. The endpoints for evaluating mortality for all participants were death, 21 days after diagnosis, or April 10, 2020. The risk of mortality associated with mental disorders was estimated using Cox hazards regression. Results We identified 814 elderly COVID-19 patients (255 [31.3%] with mental disorder and 559 [68.7%] with nonmental disorder). Individuals with mental disorders were found more likely to be older, taking antithrombotic agents, and had diabetes, hypertension, chronic obstructive lung disease, and urinary tract infections than those without mental disorders. After propensity score stratification, our study included 781 patients in each group (236 [30.2%] with mental disorder and 545 [69.8%] with nonmental disorder). The mental disorder group showed higher mortality rates than the nonmental disorder group (12.7% [30/236] versus 6.8% [37/545]). However, compared to patients without mental disorders, the hazard ratio (HR) for mortality in elderly COVID-19 patients with mental disorders was not statistically significant (HR: 1.57, 95%CI: 0.95–2.56). Conclusion Although the association between mental disorders in elderly individuals and mortality in COVID-19 is unclear, this study suggests that elderly patients with comorbid conditions and those taking psychiatric medications might be at a higher risk of COVID-19.

Older adults with anxiety have lower gray matter brain volume—a component of accelerated aging. We have previously validated a machine learning model to predict brain age, an estimate of an individual's age based on voxel-wise gray matter images. We investigated associations between brain age and anxiety, depression, stress, and emotion regulation. We recruited 78 participants (≥50 years) along a wide range of worry severity. We collected imaging data and computed voxel-wise gray matter images, which were input into an existing machine learning model to estimate brain age. We conducted a multivariable linear regression between brain age and age, sex, race, education, worry, anxiety, depression, rumination, neuroticism, stress, reappraisal, and suppression. We found that greater brain age was significantly associated with greater age, male sex, greater worry, greater rumination, and lower suppression. Male sex, worry, and rumination are associated with accelerated aging in late life and expressive suppression may have a protective effect. These results provide evidence for the transdiagnostic model of negative repetitive thoughts, which are associated with cognitive decline, amyloid, and tau.

Recent data has linked anxiety and its disorders in late life to increased morbidity and mortality, especially related to a higher cardiovascular burden and an increased cognitive decline. Clinically, anxiety symptoms may be more difficult to elicit in older adults who are less accurate in identifying anxiety symptoms and tend to minimize symptoms and to attribute symptoms to physical illness. Although SSRIs have proven more effective than psychotherapy in late-life anxiety, many elderly anxious subjects prefer psychotherapeutic interventions. These interventions appear to work best when tailored for the needs, expectations, and cultural background of older anxious subjects.

Pharmacological treatment of major depressive disorder (MDD) typically involves a lengthy trial and error process to identify an effective intervention. This lengthy period prolongs suffering and worsens all-cause mortality, including from suicide, and is typically longer in late-life depression (LLD). Our group has recently demonstrated that during an open-label venlafaxine (serotonin-norepinephrine reuptake inhibitor) trial, significant changes in functional resting state connectivity occurred following a single dose of treatment, which persisted until the end of the trial. In this work, we propose an analysis framework to translate these perturbations in functional networks into predictors of clinical remission. Participants with LLD (N = 49) completed 12-weeks of treatment with venlafaxine and underwent functional magnetic resonance imaging (fMRI) at baseline and a day following a single dose of venlafaxine. Data was collected at rest as well as during an emotion reactivity task and an emotion regulation task. Remission was defined as a Montgomery-Asberg Depression Rating Scale (MADRS) ≤10 for two weeks. We computed eigenvector centrality (whole brain connectivity) and activation during the emotion regulation and emotion reactivity tasks. We employed principal components analysis, Tikhonov-regularized logistic classification, and least angle regression feature selection to predict remission by the end of the 12-week trial. We utilized ten-fold cross-validation and Receiver Operator Curves (ROC) curve analysis. To determine task-region pairs that significantly contributed to the algorithm's ability to predict remission, we used permutation testing. Using the fMRI data at both baseline and after the first dose of treatment yielded a sensitivity of 72% and a specificity of 68% (AUC = 0.77), a 15% increase in accuracy over baseline MADRS. In general, the accuracy at baseline was further improved by using the change in activation following a single dose. Activation of the frontal cortex, hippocampus, parahippocampus, caudate, thalamus, medial temporal cortex, middle cingulate, and visual cortex predicted treatment remission. Acute, dynamic trajectories of functional imaging metrics in response to a pharmacological intervention are a valuable tool for predicting treatment response in late-life depression and elucidating the mechanism of pharmacological therapies in the context of the brain's functional architecture., Highlights • Neural activation changes after a single dose of antidepressants have been observed. • Patients with late-life depression were treated with an antidepressant for 12 weeks. • Neuroimaging data was recorded pre-treatment and after a single dose. • Pre-treatment neuroimaging predicted remission at the end of the trial. • Neuroimaging after a single dose improved prediction and may guide treatment.

The significant public health burden associated with late-life depression (LLD) is magnified by the high rates of recurrence. In this session, 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 physiological changes in the HPA 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

We recently showed that late-life participants with generalized anxiety disorder (GAD), compared with non-anxious elderly, had lower cortical thickness in the dorsal anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC). In this follow-up study, we investigated the association between cortical thickness and clinical markers of treatment response in a subgroup from that same sample. Twelve GAD participants were randomly selected and were included in an open label twelve-week treatment with citalopram and pre-/post-treatment magnetic resonance imaging (MRI). Cortical thickness measurements were obtained with Freesurfer. Six regions were selected based on previous results: left dorsal ACC, left rostral ACC, left and right medial orbitofrontal, right pars triangularis and right pars opercularis. A two-tailed paired t-test (non-parametric Wilcoxon Signed Rank test) was used to compare pre- and post-treatment for all clinical variables. For each region of interest (ROI), the Pearson correlation coefficient was estimated between pre-treatment cortical thickness and change in Penn State Worry Questionnaire (PSWQ) from pre- to post-treatment. Our results indicate that greater left pre-treatment dorsal ACC cortical thickness was associated with improvement in worry severity post-treatment. In late-life GAD, greater treatment response is associated with greater cortical thickness in areas pivotal for emotion regulation.

Background Late-life generalized anxiety disorder (GAD) is one of the most common anxiety disorders in older adults. However, its neural markers have received relatively little attention. In this study, we explored the association between worry severity and limbic-prefrontal connectivity during emotional reactivity in late-life GAD. Methods We recruited 16 anxious (GAD) and 20 non-anxious (HC) older adults to perform the faces/shapes emotional reactivity task during functional magnetic resonance imaging (fMRI). We investigated the functional connectivity of both the amygdala and the bed nucleus of stria terminalis (BNST) with the prefrontal cortex (PFC) using generalized psychophysiological interaction (gPPI) analysis. We tested for (1) group differences in connectivity, (2) association between worry severity and connectivity, and (3) interaction between group and worry severity and its association with connectivity. Results Amygdala-PFC and BNST-PFC functional connectivity were associated with worry severity in an inverse U-shape, and was independent of depression severity, global anxiety, neuroticism, and general cognitive function. Limitations Our limitations include slightly skewed PSWQ distributions, lack of non-anxious individuals with high worry, small sample size, and low depression comorbidity in a sample of late-life GAD that may not generalize to GAD in younger populations. Conclusions This suggests that moderate worry is associated with maximum engagement of the limbic-PFC connectivity, while severe worry is associated with failure of the limbic-PFC emotional regulation circuit. This may explain the aberrant and exaggerated responses to negative stimuli observed in participants with pathological worry.

The following case describes the utilization of bitemporal ECT as a treatment of last resort in a 47-year-old woman with profoundly treatment-resistant behavioral disturbance poststroke. The use of ECT led to improvement in symptoms sufficient for discharge from an inpatient psychiatric unit to the nursing home. Neuropsychiatric sequelae of stroke include poststroke depression, anxiety, mania, psychosis, apathy, pathological laughter and crying, catastrophic reaction, and mild and major vascular neurocognitive disorders. Behavioral disturbance is common and may pose diagnostic and therapeutic difficulty in the poststroke patient. In most cases, first-line treatment includes pharmacologic intervention tailored to the most likely underlying syndrome. Frequent use of sedating medications is a more drastic option when behaviors prove recalcitrant to first-line approaches and markedly affect quality of life and patient safety. ECT is generally safe, is well tolerated, and may be effective in improving symptoms in treatment-resistant behavioral disturbance secondary to stroke with major neurocognitive impairment, as suggested in this case.

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.

Objective Generalized anxiety disorder (GAD) in older adults is associated with persistent deficits in emotion reactivity (ER) and regulation, yet the neural basis of these deficits has not been explored. This study focuses on the neural basis of ER deficits in late-life GAD and the association with cerebrovascular burden. Methods Twenty elderly nonanxious participants and 17 late-life GAD participants were included. The faces-shapes functional magnetic resonance imaging task was used to assess ER; the Hamilton Anxiety Rating Scale and the Penn State Worry Questionnaire to measure global anxiety and worry, respectively; linear regression models to examine the association between ER and global anxiety severity and between ER and worry severity; and mediation analysis to explore the effect of ER on the relationship between global anxiety/worry severity and cerebrovascular burden. Results A positive association was found between ER and global anxiety in the left parahippocampus, left and right precuneus, and right superior occipital gyrus. A negative association was found between ER and worry severity in the left and right precuneus. The association between cerebrovascular burden and anxiety/worry severity was indirectly mediated by increased ER in limbic and paralimbic areas and by decreased ER in prefrontal regulatory regions. Conclusion These results indicate that ER is associated with different neural activation patterns for worry and global anxiety and that ER-related functional connectivity indirectly mediates the relationship between cerebrovascular burden and late-life GAD. This latter result supports a yet-unexplored cerebrovascular pathway involved in the pathophysiology of late-life anxiety.

Objective This study investigated neural substrate changes in affective processing among late-life depression (LLD) patients undergoing antidepressant treatment and determined if these changes correlated with remission status. Methods Thirty-three LLD patients were enrolled in a 12-week venlafaxine treatment course. During treatment functional magnetic resonance imaging (fMRI) scans, paired with an affective task that assessed emotional reactivity and regulation, were performed on days 1, 2, 3, and 7 and at week 12. Following treatment patients were classified as remitters or non-remitters. A voxel-wise two-way repeated-measures ANOVA was performed to assess the fMRI data at a significance level of α = 0.05, corrected. Results The emotional reactivity contrast demonstrated a significant interaction between remission status and scan time in the right middle temporal gyrus (MTG) (F = 24.1, df = 1,112, k = 102). Further analysis showed increased emotional reactivity–induced activity among non-remitters, and decreased activity among remitters, which significantly differed from baseline at day 7 (95% CI: 0.027, 0.540; Cohen's d = −1.35) and week 12 (95% CI: −0.171, −0.052; Cohen's d = 0.68), respectively. No significant interaction was observed with the emotional regulation contrast, but multiple regions had significant main effects of scan time, including the cuneus, occipital lobe, insula, lingual gyrus, posterior cingulate cortex, and MTG. Conclusions During treatment of LLD patients , affective processing–induced activity in the right MTG shows changes based on remission status. This alteration becomes evident early during the course of treatment, suggesting that antidepressant pharmacotherapy may acutely affect the neural basis of emotional reactivity in a differential manner that is relevant to illness remission.

Automated segmentation of the brain is challenging in the presence of brain pathologies such as white matter hyperintensities (WMH). A late-life depression population was used to demonstrate the effect of WMH on brain segmentation and normalization. We used an automated algorithm to detect WMH, and either filled them with normal-appearing white-matter (NAWM) intensities or performed a multi-spectral segmentation, and finally compared the standard approach to the WMH filling or multi-spectral segmentation approach using intra-class correlation coefficients (ICC). The presence of WMH affected segmentations for both approaches suggesting that studies investigating structural differences in populations with high WMH should account for WMH. We also investigated how functional data contrasts are affected using normalization between the standard compared to fill and multi-spectral approach. We found that the functional data was not affected. While replication with a larger sample is needed, this study shows that WMH can significantly affect the results of segmentation and these areas are not limited to those affected by WMH. It is clear that to study gray matter differences that some correction should be made to account for WMH. Future studies should investigate which methods for accounting for WMH are most effective.

Previous studies in late-life depression (LLD) have found that patients have altered intrinsic functional connectivity in the dorsal default mode network (DMN) and executive control network (ECN). We aimed to detect connectivity differences across a treatment trial among LLD patients as a function of remission status. LLD patients (N=37) were enrolled into a 12-week trial of venlafaxine and underwent five functional magnetic resonance imaging resting state scans during treatment. Patients had no history of drug abuse, psychosis, dementia/neurodegenerative diseases or medical conditions with known effects on mood. We investigated whether there were differences in three networks: DMN, ECN and anterior salience network connectivity, as well as a whole brain centrality measure (eigenvector centrality). We found that remitters showed increases in ECN connectivity in the right precentral gyrus and decreases in DMN connectivity in the right inferior frontal gyrus and supramarginal gyrus. The ECN and DMN had regions (middle temporal gyrus and bilateral middle/inferior temporal/fusiform gyrus, respectively) that showed reversed effects (decreased ECN and increased DMN, respectively). Early changes in functional connectivity can occur after initial medication exposure. This study offers new data, indicating that functional connectivity changes differ depending on treatment response and can occur shortly after exposure to antidepressant medication.

Socially warm experiences, when one feels connected to others, have been linked with physical warmth. Opioids, hypothesized to support social bonding with close others and, separately, physical warmth, may underlie both experiences. In order to test this hypothesis, 80 participants were randomly assigned to the opioid antagonist, naltrexone or placebo before neural and emotional responses to social and physical warmth were collected. Social and physical warmth led to similar increases in ventral striatum (VS) and middle-insula (MI) activity. Further, feelings of social connection were positively related to neural activity to social warmth. However, naltrexone (vs placebo) disrupted these effects by (i) reducing VS and MI activity to social and physical warmth, (ii) erasing the subjective experience–brain association to social warmth and (iii) disrupting the neural overlap between social and physical warmth. Results provide additional support for the theory that social and physical warmth share neurobiological, opioid receptor-dependent mechanisms and suggest multiple routes by which social connections may be maintained.

OBJECTIVE Studies have identified longitudinally that there exists an association between depression, cerebral blood flow (CBF), and white matter hyperintensities that are thought to be due to vascular pathologies in the brain. However, the changes in CBF, a measure that reflects cerebrovascular integrity, following pharmacotherapy are not well understood. In this study, we investigated the dynamic CBF changes over the course of antidepressant treatment and the association of these changes with depressive symptoms. METHODS We used pseudocontinuous arterial spin labeling to investigate CBF changes in a sample of older patients (≥ 50 years of age; N = 46; 29 female) with a DSM-IV diagnosis of major depressive disorder. Participants had 5 magnetic resonance imaging scans (at baseline, the day after receiving a placebo, the day after receiving a first dose of venlafaxine, a week after starting venlafaxine treatment, and at the end of trial [12 weeks]). Montgomery-Asberg Depression Rating Scale (MADRS) was used to evaluate depression severity and treatment outcome. We investigated the association between changes in depression severity with changes in voxel-wise CBF while adjusting for potential confounding factors. RESULTS Increased CBF in the middle and posterior cingulate between baseline and end of treatment was significantly associated with percent decrease in MADRS score, independent of sex and Mini-Mental State Examination score (5,000 permutations, cluster forming threshold P < .005, family-wise error P < .05). No significant effects were detected between baseline and other scans (ie, placebo, acute [single dose], or subacute [after a week]). CONCLUSIONS Regional CBF increases were associated with decreases in depressive symptoms. This observation is consistent with the vascular depression hypothesis in late-life depression. TRIAL REGISTRATION ClinicalTrials.gov identifiers: NCT00892047 and NCT01124188.

Introduction Many older adults with depressive symptoms have adverse health outcomes and reduced quality of life, but do not meet the clinical threshold for Major Depressive Disorder (MDD). Studies suggest that prevalence of MDD and depressive symptoms is increasing in children and younger adults, but little is known about population trends in depressive symptoms in older adults over age 65. Methods We pooled data from two large prospective community-based epidemiological studies of older adults in southwestern Pennsylvania between 1987-Present. We identified four birth cohorts of sufficient sample size: 1902-1911 (n=305), 1912-1921 (n=1202), 1922-1931 (n=1051), 1932-1941 (n=669). In both studies, a modified Center for Epidemiological Studies Depression Scale (mCESD) was used to determine presence of symptomatic depression (≥5 symptoms) at each wave of examination. The percentage of participants in each birth cohort who had at least one study visit with symptomatic depression varied by age but ranged between 22.9-23.1% for the 1902-1911 cohort, 19.5-26.0% for the 1912-1921 cohort, 9.8-17.0%% for the 1922-1931 cohort, and 12.2-15.4% for the 1932-1941 cohort. To minimize potential bias due to the association between depression and attrition, we fit a shared parameter model that jointly modeled depressive symptoms and attrition. Results The 1922-1931 and 1932-1941 cohorts were significantly less likely to report ≥5 depressive symptoms than the 1902-1911 cohort (p Conclusions Understanding trends in older adult depression will help identify relevant subgroups and factors which will improve ability to plan better mental health services for our aging population. This research was funded by This work was supported by the National Institute on Aging at the National Institutes of Health (R01 AG023651, U01 AG06782, R01 AG07562, P30 AG053760, P30 AG008017 and T32 AG000181).

Objective Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Methods Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. Results A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Conclusions Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures—rather than region-based differences—are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley & Sons, Ltd.

Late-life Generalized Anxiety Disorder (GAD) is relatively understudied and the underlying structural and functional neuroanatomy has received little attention. In this study, we compare the brain structural characteristics in white and gray matter in 31 non-anxious older adults and 28 late-life GAD participants. Gray matter indices (cortical thickness and volume) were measured using FreeSurfer parcellation and segmentation, and mean diffusivity was obtained through Diffusion Tensor Imaging (DTI). We assessed both macroscopic white matter changes [using white matter hyperintensity (WMH) burden] and microscopic white matter integrity [using fractional anisotropy (FA)]. No differences in macro- or microscopic white matter integrity were found between GAD and non-anxious controls (HC). GAD participants had lower cortical thickness in the orbitofrontal cortex (OFC), inferior frontal gyrus, and pregenual anterior cingulate cortex (ACC). Higher worry severity was associated with gray matter changes in OFC, ACC and the putamen. The results did not survive the multiple comparison correction, but the effect sizes indicate a moderate effect. The study suggests that late-life GAD is associated with gray matter changes in areas involved in emotion regulation, more so than with white matter changes. We conclude that anxiety-related chronic hypercortisolemia may have a dissociative effect on gray and white matter integrity.

Indices of functional connectivity in the default mode network (DMN) are promising neural markers of treatment response in late-life depression. We examined the differences in DMN functional connectivity between treatment-responsive and treatment-resistant depressed older adults. Forty-seven depressed older adults underwent MRI scanning pre- and post- pharmacotherapy. Forty-six never depressed older adults underwent MR scanning as comparison subjects. Treatment response was defined as achieving a Hamilton Depression Rating Scale of 10 or less post-treatment. We analyzed resting state functional connectivity using the posterior cingulate cortex as the seed region-of-interest. The resulting correlation maps were employed to investigate between-group differences. Additionally we examined the association between white matter hyperintensity burden and functional connectivity results. Comparison of pre- and post-treatment scans of depressed participants revealed greater post-treatment functional connectivity in the frontal precentral gyrus. Relative to treatment-responsive participants, treatment-resistant participants had increased functional connectivity in the left striatum. When adjusting for white matter hyperintensity burden, the observed differences lost significance for the PCC-prefrontal functional connectivity, but not for the PCC-striatum functional connectivity. The post-treatment “frontalization” of the DMN connectivity suggests a normalizing effect of antidepressant treatment. Moreover, our study confirms the central role of white matter lesions in disrupting brain functional connectivity.

Objectives Generalized anxiety disorder (GAD) is one of the most prevalent anxiety disorders, but its neural basis is relatively understudied. This study aims to characterize the functional connectivity in the default mode network (DMN) in GAD across the lifespan. Design and settings Functional and structural magnetic resonance imaging data were collected with subjects at rest. We analyzed the resting state functional connectivity patterns in the DMN for 27 GAD participants and 39 non-anxious comparison participants. Using a two-way analysis of variance, we explored the interaction between age and GAD status on functional connectivity. In GAD participants, we analyzed the correlation of functional connectivity indices with the duration of illness and worry severity. Results The age-by-anxiety interaction showed a greater anxiety effect on the functional connectivity between the posterior cingulate seed and the medial prefrontal cortex for the older group relative to the younger participants. Longer duration of illness was positively correlated with greater functional connectivity between the posterior cingulate cortex and the insula. Worry severity was inversely correlated with the functional connectivity between the posterior cingulate cortex seed and the medial prefrontal cortex. Conclusion The presence of GAD, longer duration of illness, and more severe worry exacerbate the effects of age on the functional connectivity in the DMN. These results support the need for tailored research and interventions in late-life anxiety. Copyright © 2013 John Wiley & Sons, Ltd.

OBJECTIVE Generalized anxiety disorder is common among older adults and leads to diminished health and cognitive functioning. Although antidepressant medications are efficacious, many elderly individuals require augmentation treatment. Furthermore, little is known about maintenance strategies for older people. The authors examined whether sequenced treatment combining pharmacotherapy and cognitive-behavioral therapy (CBT) boosts response and prevents relapse in older adults with generalized anxiety disorder. METHOD Participants were individuals at least 60 years of age with generalized anxiety disorder (N=73) who were recruited from outpatient clinics at three sites. Participants received 12 weeks of open-label escitalopram and were then randomly assigned to one of four conditions: 16 weeks of escitalopram (10-20 mg/day) plus modular CBT, followed by 28 weeks of maintenance escitalopram; escitalopram alone, followed by maintenance escitalopram; escitalopram plus CBT, followed by pill placebo; and escitalopram alone, followed by placebo. RESULTS Escitalopram augmented with CBT increased response rates on the Penn State Worry Questionnaire but not on the Hamilton Anxiety Rating Scale compared with escitalopram alone. Both escitalopram and CBT prevented relapse compared with placebo. CONCLUSIONS This study demonstrates effective strategies for treatment of generalized anxiety disorder in older adults. The sequence of antidepressant medication augmented with CBT leads to worry reduction in the short-term. Continued medication prevents relapse, but for many individuals, CBT would allow sustained remission without requiring long-term pharmacotherapy.