Brain-based mechanisms of late-life depression: Implications for novel interventions

Brain Activation Associated With Response to Psychotherapies for Late-Life Depression: A Task-Based fMRI Study

BACKGROUND

The course of late-life depression is associated with functioning of multiple brain networks. Understanding the brain mechanisms associated with response to psychotherapy can inform treatment development and a personalized treatment approach. This study examined how activation of key regions of the salience network, default mode network and reward systems is associated with response to psychotherapies for late-life depression.

METHODS

Thirty-three older adults with major depressive disorder were randomized to 9 weeks of Engage or Problem-Solving Therapy for late-life depression. Participants completed a Probabilistic Reversal Learning task in the MRI at baseline and Week 6. We focused on focal activation in regions of interest selected a priori: the subgenual cingulate cortex (sgACC; DMN); the dorsal anterior cingulate cortex (dACC; salience network and reward system); and the nucleus accumbens (NAcc; reward system). We applied mixed-effects regression models to examine whether brain activation was associated with psychotherapy response.

RESULTS

We found that at baseline, low activation of the dACC and the sgACC was associated with lower depression severity over 6 weeks of psychotherapy. In addition, we observed significant time*activation interactions, such that after 6 weeks of psychotherapy, lower dACC activation and higher NAcc and sgACC activation were each associated with lower depression severity. Further, we found that baseline slower response to negative feedback and faster response to positive feedback was associated with lower depression severity over 6 weeks of psychotherapy.

CONCLUSIONS

Our findings suggest that activation of reward, salience, and DMN regions may serve as markers of response during psychotherapy for late-life depression. Engagement of these networks may be linked to treatment outcome. Personalized psychotherapies can target individuals' brain profiles to improve outcomes for older adults with major depression.

ARTICLE SUMMARY

This study examined whether activation of regions of the reward, salience and default mode networks is associated with response to psychotherapies for late-life depression. We found that baseline low activation of the dACC and the sgACC was associated with lower depression severity during psychotherapy. We also found that at week 6, lower dACC activation and higher NAcc and sgACC activation were linked with lower depression severity. These regions may represent promising brain mechanisms for future personalized interventions.

Change in Depression Severity Among Depressed Older Crime Victims

The crime rates in urban centers are rising. The psychological impact of crime can range from distress to longstanding mental health impairment. Despite the rising crime rates and psychological impact on victims, little research has been conducted on older adult crime victims' mental health. In this manuscript we describe the profile of older adult crime victims in New York City referred for mental health services, their rates of depression, and the preliminary outcome data from a brief therapy intervention (PROTECT) targeting depression among victims. Building on prior research demonstrating the efficacy of PROTECT for depressive symptom reduction in elder abuse victims, the present study hypothesized elder crime victims who completed PROTECT therapy would demonstrate clinically meaningful depression symptom reduction as measured by the Patient Health Questionnaire-9 (PHQ-9). Additionally, it was hypothesized that these clinically significant depression symptom reductions would be demonstrated regardless of sex, gender, victimization type, victimization frequency, or living status. The results demonstrate that 67.7% (21/31) of participants had a clinically significant reduction in PHQ-9 scores (≥5 points) upon completion of PROTECT. There were no significant group differences in clinically significant depression symptom reduction, suggesting that PROTECT is an effective intervention for a diverse sample of elder crime victims. Future research should focus on the mental health impact of crime against older adults and explore reductions in anxiety and PTSD symptoms among crime victims. Through our partnerships we aim to bridge the divide between victim service providers, and mental health service providers to assist victims to recover, heal, and thrive.

A scoping review of functional near-infrared spectroscopy biomarkers in late-life depression: Depressive symptoms, cognitive functioning, and social functioning

Late-life depression is one of the most damaging mental illnesses, disrupting the normal lives of older people by causing chronic illness and cognitive impairment. Patients with late-life depression, accompanied by changes in appetite, insomnia, fatigue and guilt, are more likely to experience irritability, anxiety and somatic symptoms. It increases the risk of suicide and dementia and is a major challenge for the public health systems. The current clinical assessment, identification and effectiveness assessment of late-life depression are primarily based on history taking, mental status examination and scale scoring, which lack subjectivity and precision. Functional near-infrared spectroscopy is a rapidly developing optical imaging technology that objectively reflects the oxygenation of hemoglobin in different cerebral regions during different tasks and assesses the functional status of the cerebral cortex. This article presents a comprehensive review of the assessment of functional near-infrared spectroscopy technology in assessing depressive symptoms, social functioning, and cognitive functioning in patients with late-life depression. The use of functional near-infrared spectroscopy provides greater insight into the neurobiological mechanisms underlying depression and helps to assess these three aspects of functionality in depressed patients. In addition, the study discusses the limitations of previous research and explores potential advances in the field.

Changes in brain structure and function during early aging in patients with chronic low back pain

Objective

To explore the structural and functional changes in cognition-related brain regions in patients with chronic low back pain (CLBP) at earlier ages, and explore the impact of the interaction between CLBP and age on the brain.

Methods

Seventy-six patients with CLBP were recruited and divided into "younger" age group (20-29 years, YA), "middle" age group (30-39 years, MA), and "older" age group (40-49 years, OA). All patients underwent functional magnetic resonance imaging (fMRI) as well as clinical psychological and pain-related symptoms assessments.

Results

Structural analysis showed that patients in OA group had lower gray matter (GM) volumes in the orbitofrontal cortex (OFC) bilaterally and the right superior frontal gyrus (SFG) compared to YA group. The resting-state brain activity analysis showed that amplitude of low-frequency fluctuation (ALFF) values in the bilateral postcentral gyrus and left ventral medial prefrontal cortex (mPFC) were significantly different in the OA group. The functional connectivity (FC) in the right ventral dorsolateral prefrontal cortex (DLPFC) and the right insula was significantly decreased in the OA group compared to the YA and MA groups. Likewise, the FC in the left caudal parahippocampal gyrus (PHG) and left inferior parietal lobule (IPL) were significantly lower in the MA and OA groups compared to the YA group. In addition, both the structural properties and the FC values of these brain regions were significantly correlated with age.

Conclusion

This preliminary study concludes that CLBP affects the aging process. The synergistic effects of CLBP and aging accelerate the functional and structural decline of certain areas of the brain, which not only affects pain processing, but are also may be associated with cognitive declines.

Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression

Background

Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear.

Methods

Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed.

Results

Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items.

Conclusion

These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.

Individualized prediction of cognitive test scores from functional brain connectome in patients with first-episode late-life depression

BACKGROUND

In the realm of cognitive screening, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are widely utilized for detecting cognitive deficits in patients with late-life depression (LLD), However, the interindividual variability in neuroimaging biomarkers contributing to individual-specific symptom severity remains poorly understood. In this study, we used a connectome-based predictive model (CPM) approach on resting-state functional magnetic resonance imaging data from patients with LLD to establish individualized prediction models for the MoCA and the MMSE scores.

METHODS

We recruited 135 individuals diagnosed with first-episode LLD for this research. Participants underwent the MMSE and MoCA tests, along with resting-state functional magnetic resonance imaging scans. Functional connectivity matrices derived from these scans were utilized in CPM models to predict MMSE or MoCA scores. Predictive precision was assessed by correlating predicted and observed scores, with the significance of prediction performance evaluated through a permutation test.

RESULTS

The negative model of the CPM procedure demonstrated a significant capacity to predict MoCA scores (r = -0.309, p = 0.002). Similarly, the CPM procedure could predict MMSE scores (r = -0.236, p = 0.016). The predictive models for cognitive test scores in LLD primarily involved the visual network, somatomotor network, dorsal attention network, and ventral attention network.

CONCLUSIONS

Brain functional connectivity emerges as a promising predictor of personalized cognitive test scores in LLD, suggesting that functional connectomes are potential neurobiological markers for cognitive performance in patients with LLD.

White Noise-Is Anxiety in Late-Life Associated With White Matter Hyperintensity Burden?

OBJECTIVE

We investigated the relationship between anxiety phenotypes (global anxiety, worry, and rumination) and white matter hyperintensities (WMH), with special consideration for the roles of age and executive function (EF). Our hypotheses were 1) anxiety phenotypes would be associated with WMH and 2) EF would moderate this relationship.

DESIGN

Cross-sectional.

SETTING

Participants were recruited from the local community (Pittsburgh, PA).

PARTICIPANTS

We recruited 110 older adults (age ≥ 50) with varying worry severity and clinical comorbidity.

INTERVENTIONS

Not applicable.

MEASUREMENTS

Demographics (age, sex, race, education), clinical measures (cumulative illness burden, global anxiety, worry, and rumination), EF, and WMH quantified with magnetic resonance imaging.

RESULTS

Lower global anxiety and worry severity were significantly correlated with higher WMH volume, though the global anxiety relationship was not significant after controlling for age. Rumination as not associated with WMH burden. EF was not correlated with either global anxiety, worry, rumination, or WMH. However, in those with advanced age and/or greater WMH burden, there was an association between worry and EF as well as EF and WMH.

CONCLUSION

Longitudinal studies are needed in order to clarify the complex interactions between anxiety phenotypes, WMH, and EF.

Sex matters: acute functional connectivity changes as markers of remission in late-life depression differ by sex

The efficacy of antidepressant treatment in late-life is modest, a problem magnified by an aging population and increased prevalence of depression. Understanding the neurobiological mechanisms of treatment response in late-life depression (LLD) is imperative. Despite established sex differences in depression and neural circuits, sex differences associated with fMRI markers of antidepressant treatment response are underexplored. In this analysis, we assess the role of sex on the relationship of acute functional connectivity changes with treatment response in LLD. Resting state fMRI scans were collected at baseline and day one of SSRI/SNRI treatment for 80 LLD participants. One-day changes in functional connectivity (differential connectivity) were related to remission status after 12 weeks. Sex differences in differential connectivity profiles that distinguished remitters from non-remitters were assessed. A random forest classifier was used to predict the remission status with models containing various combinations of demographic, clinical, symptomatological, and connectivity measures. Model performance was assessed with area under the curve, and variable importance was assessed with permutation importance. The differential connectivity profile associated with remission status differed significantly by sex. We observed evidence for a difference in one-day connectivity changes between remitters and non-remitters in males but not females. Additionally, prediction of remission was significantly improved in male-only and female-only models over pooled models. Predictions of treatment outcome based on early changes in functional connectivity show marked differences between sexes and should be considered in future MR-based treatment decision-making algorithms.

Lower Dorsal Lateral Prefrontal Cortex Functional Connectivity in Late-Life Depression With Suicidal Ideation

OBJECTIVE

The dorsal lateral prefrontal cortex (DLPFC) has been identified as a neuromodulation target for alleviating suicidal ideation. Dysfunctional DLPFC has been implicated in suicidality in depression. This study aimed to investigate the functional connectivity (FC) of the DLPFC in late-life depression (LLD) with suicidal ideation.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data from 32 LLD patients with suicidal ideation (LLD-S), 41 LLD patients without suicidal ideation (LLD-NS), and 54 healthy older adults (HOA) were analyzed using DLPFC seed-based FC analyses. Group differences in FC were examined, and machine learning was applied to explore the potential of DLPFC-FC for classifying LLD-S from LLD-NS.

RESULTS

Abnormal DLPFC-FC patterns were observed in LLD-S, characterized by lower connectivity with the angular gyrus, precuneus, and superior frontal gyrus compared to LLD-NS and healthy controls. A classification model based on the identified DLPFC-FC achieved an accuracy of 75%.

CONCLUSION

The lower FC of DLPFC networks may contribute to the neurobiological mechanism of suicidal ideation in late-life depression. These findings may facilitate suicide prevention for LLD by providing potential neuroimaging markers and network-based neuromodulation targets. However, further confirmation with larger sample sizes and experimental designs is warranted.

Task-based functional connectivity reveals aberrance with the salience network during emotional interference in late-life depression

OBJECTIVES

Late-life depression (LLD) is a common and debilitating disorder. Previously, resting-state studies have revealed abnormal functional connectivity (FC) of brain networks in LLD. Since LLD is associated with emotional-cognitive control deficits, the aim of this study was to compare FC of large-scale brain networks in older adults with and without a history of LLD during a cognitive control task with emotional stimuli.

METHODS

Cross-sectional case-control study. Twenty participants diagnosed with LLD and 37 never-depressed adults 60-88 years of age underwent functional magnetic resonance imaging during an emotional Stroop task. Network-region-to-region FC was assessed with seed regions in the default mode, the frontoparietal, the dorsal attention, and the salience networks.

RESULTS

FC between salience and sensorimotor network regions and between salience and dorsal attention network regions were reduced in LLD patients compared to controls during the processing of incongruent emotional stimuli. The normally positive FC between these networks were negative in LLD patients and inversely correlated with vascular risk and white matter hyperintensities.

CONCLUSIONS

Emotional-cognitive control in LLD is associated with aberrant functional coupling between salience and other networks. This expands on the network-based LLD model and proposes the salience network as a target for future interventions.

Late life depression and concepts of aging: an emerging paradigm

Late life depression (LLD) is an emerging challenge, and recognized as a significant barrier to long-term healthy aging. Viewed within the context of the medical/biological model, advances in brain sciences over the last several decades have led to a deeper understanding of the biology of LLD. These advances in current knowledge include the description of aging brain pathophysiology; the biology and biochemistry of neurotransmitters; the correspondence between changes in neurological structure, function, and neural network; the description of neural, hormonal and inflammatory biomarkers; and identification of typical phenotypic subtypes of LLD. Despite these advances, current treatment of LLD, which remains largely pharmacological with accompanying cognitive and behavioral interventions, has poor success rate for long-term remission among older people. A wider perspective, in keeping with several emerging aging concepts, is suggested as an alternative framework within which to view LLD. A growing body of research supports the important role in LLD of frailty, resilience, intrinsic capacity, and functional integrity. Similarly, important social determinants need to be addressed in the etiology of LLD, rooted largely in negative stereotypes of aging, with consequent repercussions of reduced participation and inclusion, growing social isolation, with loss of identity, meaning and hope. This perspective suggests the importance of a wider integrative conceptualization of depression, set against a background of emerging aging concepts.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model

Objectives

Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD).

Participants

Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study.

Design

Cross-sectional.

Measurements

Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes.

Results

Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex.

Conclusions

We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

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.

Multiple examinations indicated associations between abnormal regional homogeneity and cognitive dysfunction in major depressive disorder

Background

This study aimed to investigate the relationships between regional neural activity and multiple related indicators in patients with major depressive disorder (MDD).

Methods

Forty-two patients and 42 healthy controls (HCs) were enrolled. Pearson/Spearman correlation analyses were applied to examine the associations between abnormal regional homogeneity (ReHo) and different indicators in the patients.

Results

Compared with HCs, patients with MDD had increased ReHo in the left inferior temporal gyrus (ITG) and decreased ReHo values in the left putamen, anterior cingulate cortex (ACC), and precentral gyrus. The ReHo of the left putamen was positively correlated with the PR interval, Repeatable Battery for the Assessment of Neuropsychological Status 4A, and Discriminant analysis (D), and negatively correlated with Ae (block) and Ae (total) in the patients. The ReHo value of the left ACC was positively correlated with the severity of depression, Stroop Color Word Test of C - 2B + 100 in reaction time, and negatively correlated with Ce (Missay) and Perseverative Responses in the patients. The ReHo of the left ITG was positively correlated with the Neuroticism scores and negatively correlated with the Lie scores in the patients.

Conclusion

These results suggested that the decreased ReHo of the salience network might be the underpinning of cognitive impairments in patients with MDD.

Diagnosis of late-life depression using structural equation modeling and dynamic effective connectivity during resting fMRI

BACKGROUND

Late-life depression (LLD) is characterized by cognitive and social impairments. Determining neurobiological alterations in connectivity in LLD by means of fMRI may lead to a better understanding of the neural basis underlying this disorder and more precise diagnostic markers. The primary objective of this paper is to identify a structural model that best explains the dynamic effective connectivity (EC) of the default mode network (DMN) in LLD patients compared to controls.

METHODS

Twenty-seven patients and 29 healthy controls underwent resting-state fMRI during a period of eight minutes. In both groups, jackknife correlation matrices were generated with six ROIs of the DMN that constitute the posterior DMN (pDMN). The different correlation matrices were used as input to estimate each structural equation model (SEM) for each subject in both groups incorporating dynamic effects.

RESULTS

The results show that the proposed LLD diagnosis algorithm achieves perfect accuracy in classifying LLD patients and controls. This differentiation is based on three aspects: the importance of ROIs 4 and 6, which seem to be the most distinctive among the subnetworks; the shape that the specific connections adopt in their networks, or in other words, the directed connections that are established among the ROIs in the pDMN for each group; and the number of dynamic effects that seem to be greater throughout the six ROIs studied [t = 54.346; df = 54; p < .001; 95 % CI difference = 5.486-5.906].

LIMITATIONS

The sample size was moderate, and the participants continued their current medications.

CONCLUSIONS

The network models that we developed describe a pattern of dynamic activation in the pDMN that may be considered a possible biomarker for LLD, which may allow early diagnosis of this disorder.

Comparison of Functional and Structural Neural Network Features in Older Adults With Depression With vs Without Apathy and Association With Response to Escitalopram: Secondary Analysis of a Nonrandomized Clinical Trial

IMPORTANCE

Apathy is prevalent among individuals with late-life depression and is associated with poor response to pharmacotherapy, including chronicity and disability. Elucidating brain networks associated with apathy and poor treatment outcomes can inform intervention development.

OBJECTIVES

To assess the brain network features of apathy among individuals with late-life depression and identify brain network abnormalities associated with poor antidepressant response.

DESIGN, SETTING, AND PARTICIPANTS

This secondary analysis of a single-group, open-label nonrandomized clinical trial of escitalopram conducted at an outpatient geriatric psychiatry clinic enrolled 40 adults aged 59 to 85 years with major depressive disorder from July 1, 2012, to July 31, 2019.

INTERVENTIONS

After a 2-week washout period, participants received escitalopram titrated to a target of 20 mg/d for 12 weeks.

MAIN OUTCOMES AND MEASURES

Baseline and posttreatment magnetic resonance imaging (MRI), clinical, and cognitive assessments were conducted. Functional MRI was used to map group differences in resting state functional connectivity (rsFC) of the salience network, and diffusion MRI connectometry was performed to evaluate pathway-level disruptions in structural connectivity. The Apathy Evaluation Scale was used to quantify apathy, and the Hamilton Depression Rating Scale (HAM-D) was used to quantify the primary outcome of depression severity.

RESULTS

Forty participants (26 women [65%]; mean [SD] age, 70.0 [6.6] years [range, 59-85 years]) with depression were included; 20 participants (50%) also had apathy. Relative to nonapathetic participants with depression, those with depression and apathy had lower rsFC of salience network seeds with the dorsolateral prefrontal cortex (DLPFC), premotor cortex, midcingulate cortex, and paracentral lobule and greater rsFC with the lateral temporal cortex and temporal pole (z score >2.7; Bonferroni-corrected threshold of P < .0125). Compared with participants without apathy, those with apathy had lower structural connectivity in the splenium, cingulum, and fronto-occipital fasciculus (t score >2.5; false discovery rate-corrected P = .02). Twenty-seven participants completed escitalopram treatment; 16 (59%) achieved remission (HAM-D score <10). Lower insula-DLPFC/midcingulate cortex rsFC was associated with less symptomatic improvement (HAM-D % change) (β [df] = 0.588 [26]; P = .001) and a higher likelihood of nonremission (odds ratio, 1.041 [95% CI, 1.003-1.081]; P = .04) after treatment and, in regression models, was a mediator of the association between baseline apathy and persistence of depression. Lower dorsal anterior cingulate-DLPFC/paracentral rsFC was associated with residual cognitive difficulties on measures of attention (β [df] = 0.445 [26]; P = .04) and executive function (β [df] = 0.384 [26]; P = .04).

CONCLUSIONS AND RELEVANCE

This study suggests that disturbances in connectivity between the salience network and other large-scale networks that support goal-directed behavior may give rise to apathy and may be associated with poor response of late-life depression to antidepressant pharmacotherapy. These network disturbances may serve as targets for novel interventions.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01728194.

The enigma of vascular depression in old age: a critical update

Depression is common in older individuals and is associated with high disability and increased mortality, yet the factors predicting late-life depression (LLD) are poorly understood. The relationship between of depressive disorder, age- and disease-related processes have generated pathogenic hypotheses and provided new treatment options. LLD syndrome is often related to a variety of vascular mechanisms, in particular hypertension, cerebral small vessel disease, white matter lesions, subcortical vascular impairment, and other processes (e.g., inflammation, neuroimmune regulatory dysmechanisms, neurodegenerative changes, amyloid accumulation) that may represent etiological factors by affecting frontolimbic and other neuronal networks predisposing to depression. The "vascular depression" hypothesis suggests that cerebrovascular disease (CVD) and vascular risk factors may predispose, induce or perpetuate geriatric depressive disorders. It is based on the presence of various cerebrovascular risk factors in many patients with LLD, its co-morbidity with cerebrovascular lesions, and the frequent development of depression after stroke. Other findings related to vascular depression are atrophy of the medial temporal cortex or generalized cortical atrophy that are usually associated with cognitive impairment. Other pathogenetic hypotheses of LLD, such as metabolic or inflammatory ones, are briefly discussed. Treatment planning should consider there may be a modest response to antidepressants, but several evidence-based and novel treatment options for LLD exist, such as electroconvulsive therapy, transcranial magnetic stimulation, neurobiology-based psychotherapy, as well as antihypertension and antiinflammatory drugs. However, their effectiveness needs further investigation, and new methodologies for prevention and treatment of depression in older individuals should be developed.

Estimated Regional White Matter Hyperintensity Burden, Resting State Functional Connectivity, and Cognitive Functions in Older Adults

OBJECTIVE

White matter hyperintensities (WMH) are linked to deficits in cognitive functioning, including cognitive control and memory; however, the structural, and functional mechanisms are largely unknown. We investigated the relationship between estimated regional disruptions to white matter fiber tracts from WMH, resting state functional connectivity (RSFC), and cognitive functions in older adults.

DESIGN

Cross-sectional study.

SETTING

Community.

PARTICIPANTS

Fifty-eight cognitively-healthy older adults.

MEASUREMENTS

Tasks of cognitive control and memory, structural MRI, and resting state fMRI. We estimated the disruption to white matter fiber tracts from WMH and its impact on gray matter regions in the cortical and subcortical frontoparietal network, default mode network, and ventral attention network by overlaying each subject's WMH mask on a normative tractogram dataset. We calculated RSFC between nodes in those same networks. We evaluated the interaction of regional WMH burden and RSFC in predicting cognitive control and memory.

RESULTS

The interaction of estimated regional WMH burden and RSFC in cortico-striatal regions of the default mode network and frontoparietal network was associated with delayed recall. Models predicting working memory, cognitive inhibition, and set-shifting were not significant.

CONCLUSION

Findings highlight the role of network-level structural and functional alterations in resting state networks that are related to WMH and impact memory in older adults.