Default-mode network connectivity and white matter burden in late-life depression

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.

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.

Common and distinct functional brain network abnormalities in adolescent, early-middle adult, and late adult major depressive disorders

BACKGROUND

The age-related heterogeneity in major depressive disorder (MDD) has received significant attention. However, the neural mechanisms underlying such heterogeneity still need further investigation. This study aimed to explore the common and distinct functional brain abnormalities across different age groups of MDD patients from a large-sample, multicenter analysis.

METHODS

The analyzed sample consisted of a total of 1238 individuals including 617 MDD patients (108 adolescents, 12-17 years old; 411 early-middle adults, 18-54 years old; and 98 late adults, > = 55 years old) and 621 demographically matched healthy controls (60 adolescents, 449 early-middle adults, and 112 late adults). MDD-related abnormalities in brain functional connectivity (FC) patterns were investigated in each age group separately and using the whole pooled sample, respectively.

RESULTS

We found shared FC reductions among the sensorimotor, visual, and auditory networks across all three age groups of MDD patients. Furthermore, adolescent patients uniquely exhibited increased sensorimotor-subcortical FC; early-middle adult patients uniquely exhibited decreased visual-subcortical FC; and late adult patients uniquely exhibited wide FC reductions within the subcortical, default-mode, cingulo-opercular, and attention networks. Analysis of covariance models using the whole pooled sample further revealed: (1) significant main effects of age group on FCs within most brain networks, suggesting that they are decreased with aging; and (2) a significant age group × MDD diagnosis interaction on FC within the default-mode network, which may be reflective of an accelerated aging-related decline in default-mode FCs.

CONCLUSIONS

To summarize, these findings may deepen our understanding of the age-related biological and clinical heterogeneity in MDD.

Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.

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.

Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder

BACKGROUND

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD.

METHODS

Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD.

RESULTS

Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up.

CONCLUSION

Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

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.

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.

Disrupted intrinsic functional brain network in patients with late-life depression: Evidence from a multi-site dataset

BACKGROUND

Late-life depression (LLD) is a common and serious mental disorder, whose neural mechanisms are not yet fully understood. In this study, we aimed to characterize LLD-related changes in intrinsic functional brain networks using a large, multi-site sample.

METHODS

Using resting-state functional magnetic resonance imaging, the edge-based functional connectivity (FC) as well as multiple topological brain network metrics at both global and nodal levels were compared between 206 LLD patients and 210 normal controls (NCs).

RESULTS

Compared with NCs, the LLD patients had extensive alterations in the intrinsic brain FCs, especially significant decreases in FCs within the default mode network (DMN) and within the somatomotor network (SMN). The LLD patients also showed alterations in several global brain network metrics compared with NCs, including significant decreases in global efficiency, local efficiency, clustering coefficient, and small-worldness, as well as a significantly increased characteristic path length. Moreover, significant alterations in nodal network metrics (increased nodal betweenness and decreased nodal efficiency) were found in patients with LLD, which mainly involved the DMN and SMN. Post-hoc subgroup analyses indicated that the above changes in FC strengths were present in both first-episode, drug-naïve (FEDN) and non-FEDN patients, and were correlated with depression severity in the FEDN patients. Moreover, changes in FC strengths were found in both the early/late-onset (depression starts before/after the age of 50) patients, while altered topological metrics were found in only the late-onset patients.

CONCLUSIONS

These results may help to strengthen our understanding of the underlying neural mechanisms and biological heterogeneity in LLD.

Association of functional connectivity of the executive control network or default mode network with cognitive impairment in older adults with remitted major depressive disorder or mild cognitive impairment

Major depressive disorder (MDD) is associated with an increased risk of developing dementia. The present study aimed to better understand this risk by comparing resting state functional connectivity (rsFC) in the executive control network (ECN) and the default mode network (DMN) in older adults with MDD or mild cognitive impairment (MCI). Additionally, we examined the association between rsFC in the ECN or DMN and cognitive impairment transdiagnostically. We assessed rsFC alterations in ECN and DMN in 383 participants from five groups at-risk for dementia-remitted MDD with normal cognition (MDD-NC), non-amnestic mild cognitive impairment (naMCI), remitted MDD + naMCI, amnestic MCI (aMCI), and remitted MDD + aMCI-and from healthy controls (HC) or individuals with Alzheimer's dementia (AD). Subject-specific whole-brain functional connectivity maps were generated for each network and group differences in rsFC were calculated. We hypothesized that alteration of rsFC in the ECN and DMN would be progressively larger among our seven groups, ranked from low to high according to their risk for dementia as HC, MDD-NC, naMCI, MDD + naMCI, aMCI, MDD + aMCI, and AD. We also regressed scores of six cognitive domains (executive functioning, processing speed, language, visuospatial memory, verbal memory, and working memory) on the ECN and DMN connectivity maps. We found a significant alteration in the rsFC of the ECN, with post hoc testing showing differences between the AD group and the HC, MDD-NC, or naMCI groups, but no significant alterations in rsFC of the DMN. Alterations in rsFC of the ECN and DMN were significantly associated with several cognitive domain scores transdiagnostically. Our findings suggest that a diagnosis of remitted MDD may not confer functional brain risk for dementia. However, given the association of rs-FC with cognitive performance (i.e., transdiagnostically), rs-FC may help in stratifying this risk among people with MDD and varying degrees of cognitive impairment.

The implication of a diversity of non-neuronal cells in disorders affecting brain networks

In the central nervous system (CNS) neurons are classically considered the functional unit of the brain. Analysis of the physical connections and co-activation of neurons, referred to as structural and functional connectivity, respectively, is a metric used to understand their interplay at a higher level. A myriad of glial cell types throughout the brain composed of microglia, astrocytes and oligodendrocytes are key players in the maintenance and regulation of neuronal network dynamics. Microglia are the central immune cells of the CNS, able to affect neuronal populations in number and connectivity, allowing for maturation and plasticity of the CNS. Microglia and astrocytes are part of the neurovascular unit, and together they are essential to protect and supply nutrients to the CNS. Oligodendrocytes are known for their canonical role in axonal myelination, but also contribute, with microglia and astrocytes, to CNS energy metabolism. Glial cells can achieve this variety of roles because of their heterogeneous populations comprised of different states. The neuroglial relationship can be compromised in various manners in case of pathologies affecting development and plasticity of the CNS, but also consciousness and mood. This review covers structural and functional connectivity alterations in schizophrenia, major depressive disorder, and disorder of consciousness, as well as their correlation with vascular connectivity. These networks are further explored at the cellular scale by integrating the role of glial cell diversity across the CNS to explain how these networks are affected in pathology.

Charting the dorsal-medial functional gradient of the default mode network in major depressive disorder

Major depressive disorder (MDD) is a common and disabling psychiatric condition associated with aberrant functional activity of the default mode network (DMN). However, it is unclear how the DMN dysfunction in MDD patients is characterized by functional connectivity diversity or gradient and whether antidepressant therapy causes the abnormal functional gradient of the DMN to change toward normalization. In current work, we estimated the functional gradient of the DMN derived from resting state functional magnetic resonance imaging in MDD patients (n = 70) and matching healthy controls (n = 43) and identified MDD-related functional connectivity diversity of the DMN. The longitudinal changes of the DMN functional gradient in 36 MDD patients were assessed before and after 12-week antidepressant treatment. Compared to the healthy controls, the functional gradient of the DMN exhibited relatively relative compression along the dorsal-medial axis in MDD patients at baseline and antidepressant treatment could normalize these DMN gradient abnormalities. A regularized least-squares regression model based on DMN gradient features at baseline significantly predicted the change of Hamilton Depression Rating (HAMD) Scale scores after antidepressant treatment. The medial prefrontal cortex gradient had a more contribution to prediction of antidepressant efficacy. Our findings provided a novel insight into the neurobiological mechanism underlying MDD from the perspective of the DMN functional gradient.

MRI predictors of pharmacotherapy response in major depressive disorder

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

Depression history modulates effects of subthalamic nucleus topography on neuropsychological outcomes of deep brain stimulation for Parkinson's disease

Patients with psychiatric symptoms, such as depression, anxiety, and visual hallucinations, may be at increased risk for adverse effects following deep brain stimulation of the subthalamic nucleus for Parkinson's disease, but there have been relatively few studies of associations between locations of chronic stimulation and neuropsychological outcomes. We sought to determine whether psychiatric history modulates associations between stimulation location within the subthalamic nucleus and postoperative affective and cognitive changes. We retrospectively identified 42 patients with Parkinson's disease who received bilateral subthalamic nucleus deep brain stimulation and who completed both pre- and postoperative neuropsychological testing. Active stimulation contacts were localized in MNI space using Lead-DBS software. Linear discriminant analysis identified vectors maximizing variance in postoperative neuropsychological changes, and Pearson's correlations were used to assess for linear relationships. Stimulation location was associated with postoperative change for only 3 of the 18 neuropsychological measures. Variation along the superioinferior (z) axis was most influential. Constraining the analysis to patients with a history of depression revealed 10 measures significantly associated with active contact location, primarily related to location along the anterioposterior (y) axis and with worse outcomes associated with more anterior stimulation. Analysis of patients with a history of anxiety revealed 5 measures with location-associated changes without a predominant axis. History of visual hallucinations was not associated with significant findings. Our results suggest that a history of depression may influence the relationship between active contact location and neuropsychological outcomes following subthalamic nucleus deep brain stimulation. These patients may be more sensitive to off-target (nonmotor) stimulation.

Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease

Background

White matter hyperintensity (WMH) is one of the typical neuroimaging manifestations of cerebral small vessel disease (CSVD), and the WMH correlates closely to cognitive impairment (CI). CSVD patients with WMH own altered topological properties of brain functional network, which is a possible mechanism that leads to CI. This study aims to identify differences in the characteristics of some brain functional network among patients with different grades of WMH and estimates the correlations between these different brain functional network characteristics and cognitive assessment scores.

Methods

110 CSVD patients underwent 3.0 T Magnetic resonance imaging scans and neuropsychological cognitive assessments. WMH of each participant was graded on the basis of Fazekas grade scale and was divided into two groups: (A) WMH score of 1–2 points (n = 64), (B) WMH score of 3–6 points (n = 46). Topological indexes of brain functional network were analyzed using graph-theoretical method. T-test and Mann–Whitney U test was used to compare the differences in topological properties of brain functional network between groups. Partial correlation analysis was applied to explore the relationship between different topological properties of brain functional networks and overall cognitive function.

Results

Patients with high WMH scores exhibited decreased clustering coefficient values, global and local network efficiency along with increased shortest path length on whole brain level as well as decreased nodal efficiency in some brain regions on nodal level (p < 0.05). Nodal efficiency in the left lingual gyrus was significantly positively correlated with patients' total Montreal Cognitive Assessment (MoCA) scores (p < 0.05). No significant difference was found between two groups on the aspect of total MoCA and Mini-mental State Examination (MMSE) scores (p > 0.05).

Conclusion

Therefore, we come to conclusions that patients with high WMH scores showed less optimized small-world networks compared to patients with low WMH scores. Global and local network efficiency on the whole-brain level, as well as nodal efficiency in certain brain regions on the nodal level, can be viewed as markers to reflect the course of WMH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00769-7.

Default mode network dissociation linking cerebral beta amyloid retention and depression in cognitively normal older adults

Cerebral beta amyloid (Aβ) deposition and late-life depression (LLD) are known to be associated with the trajectory of Alzheimer's disease (AD). However, their neurobiological link is not clear. Previous studies showed aberrant functional connectivity (FC) changes in the default mode network (DMN) in early Aβ deposition and LLD, but its mediating role has not been elucidated. This study was performed to investigate the distinctive association pattern of DMN FC linking LLD and Aβ retention in cognitively normal older adults. A total of 235 cognitively normal older adults with (n = 118) and without depression (n = 117) underwent resting-state functional magnetic resonance imaging and ¹⁸F-flutemetamol positron emission tomography to investigate the associations between Aβ burden, depression, and DMN FC. Independent component analysis showed increased anterior DMN FC and decreased posterior DMN FC in the depression group compared with the no depression group. Global cerebral Aβ retention was positively correlated with anterior and negatively correlated with posterior DMN FC. Anterior DMN FC was positively correlated with severity of depression, whereas posterior DMN FC was negatively correlated with cognitive function. In addition, the effects of global cerebral Aβ retention on severity of depression were mediated by subgenual anterior cingulate FC. Our results of anterior and posterior DMN FC dissociation pattern may be pivotal in linking cerebral Aβ pathology and LLD in the course of AD progression. Further longitudinal studies are needed to confirm the causal relationships between cerebral Aβ retention and LLD.

Handedness and depression: A meta-analysis across 87 studies

Alterations in functional brain lateralization, often indicated by an increased prevalence of left- and/or mixed-handedness, have been demonstrated in several psychiatric and neurodevelopmental disorders like schizophrenia or autism spectrum disorder. For depression, however, this relationship is largely unclear. While a few studies found evidence that handedness and depression are associated, both the effect size and the direction of this association remain elusive. Here, we collected data from 87 studies totaling 35,501 individuals to provide a precise estimate of differences in left-, mixed- and non-right-handedness between depressed and healthy samples and computed odds ratios (ORs) between these groups. Here, an OR > 1 signifies higher rates of atypical handedness in depressed compared to healthy samples. We found no differences in left- (OR = 1.04, 95% CI = [0.95, 1.15], p = .384), mixed- (OR = 1.64, 95% CI = [0.98, 2.74], p = .060) or non-right-handedness (OR = 1.05, 95% CI = [0.96, 1.15], p = .309) between the two groups. We could thus find no link between handedness and depression on the meta-analytical level.

The Association of Metabolic Dysfunction and Mood Across Lifespan Interacts With the Default Mode Network Functional Connectivity

Background: Numerous studies suggest a relationship between depression and metabolic syndrome, which is likely influenced by age. Interestingly, functional imaging analysis has shown an association between functional connectivity in the default mode network (DMN-FC) and components of metabolic syndrome, which is explored in this study.

Methods: From a larger longitudinal cohort study on healthy aging, 943 individuals were extensively characterized for mood and cognition. Among these, 120 individuals who were selected for displaying extreme cognitive performance within the normal range (good and poor performers) were further studied. Here, in a cross-sectional design, using confirmatory factor analysis (CFA), the association between metabolic dysfunction and depressive mood as a function of age and its relationship with DMN-FC was studied.

Results: Metabolic dysfunction was modeled as a second-order latent variable using CFA. First-order latent variables were obesity, glucose dysmetabolism, lipids imbalance, and blood pressure. Using multiple linear regression models, this study observed that metabolic dysfunction, glucose dysmetabolism, and lipids imbalance were linearly associated with depressive mood, and the association with obesity was U-shaped. The association of metabolic dysfunction, obesity, and glucose dysmetabolism with depressive mood is positive for the younger individuals in our sample and vanishes with aging. The FC of the right superior temporal gyrus with the DMN correlated with both obesity and depressive mood. In participants with higher obesity scores, FC increased with higher GDS scores, while in those with lower GDS scores, FC decreased. Age and blood pressure were associated with a more complex pattern of association between FC of the right supramarginal gyrus and GDS score.

Conclusion: The association of metabolic dysfunction with depressive mood is influenced by age and relates with differential patterns of DMN-FC. The combination of the effects of age, mood, and metabolic dysfunction is likely to explain the heterogeneity of DMN-FC, which deserves further investigation with larger and longitudinal studies.

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

Late-life depression (LLD) is a particularly debilitating illness. Older adults suffering from depression commonly experience poor outcomes in response to antidepressant treatments, medical comorbidities, and declines in daily functioning. This review aims to further our understanding of the brain network dysfunctions underlying LLD that contribute to disrupted cognitive and affective processes and corresponding clinical manifestations. We provide an overview of a network model of LLD that integrates the salience network, the default mode network (DMN) and the executive control network (ECN). We discuss the brain-based structural and functional mechanisms of LLD with an emphasis on their link to clinical subtypes that often fail to respond to available treatments. Understanding the brain networks that underlie these disrupted processes can inform the development of targeted interventions for LLD. We propose behavioral, cognitive, or computational approaches to identifying novel, personalized interventions that may more effectively target the key cognitive and affective symptoms of LLD.

Exploring brain connectivity changes in major depressive disorder using functional-structural data fusion: A CAN-BIND-1 study

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT‐awFC). The novel FATCAT‐awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN‐BIND‐1) study. Large‐scale resting‐state networks were assessed. We found statistically significant anatomically‐weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region‐pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.

Risk factors for suicidal behavior in late-life depression: A retrospective preliminary clinical study

AIM

To explore the risk factors associated with suicidal behavior (SB) using logistic regression analysis and the propensity score matching (PSM) method among Chinese patients suffering from late-life depression (LLD).

METHOD

Patient information sheets were retrieved with the International Classification of Diseases, Tenth Revision (ICD-10) code from an electronic database that comprised patient medical information. Herein, we set SB as a dependent variable, and gender, marital status, monthly income, quality of interpersonal relationships, hobbies, impulsivity, severity of depression, psychiatric symptoms or not, and having histories of smoking, drinking, major mental trauma as independent variables according to clinical experience and previous findings. For uncertain independent risk factors associated with SB generated by logistic regression analysis, PSM was performed for further verification.

RESULTS

The differences between the SB group and non-SB group for marital status, severity of depression, a history of drinking, and a history of major mental trauma were found to be statistically significant in univariate comparisons (P < 0.05); binary logistic regression analysis and PSM analysis showed that the severity of depression, a history of drinking, and a history of major mental trauma were independent risk factors associated with SB of patients with LLD with an odds ratio greater than one.

CONCLUSION

The severity of depression, a history of drinking, and a history of major mental trauma were independently associated with the occurrence of SB of patients with LLD in China. Further longitudinal and prospective studies are warranted to examine the dynamic changes of confounding risk factors. Geriatr Gerontol Int 2021; 21: 849-854.

Mechanisms and Treatment of Late-Life Depression

(Appeared originally in Translational Psychiatry 2019; 9:188).

Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature

BACKGROUND

Cerebral small vessel disease (CSVD) is a common neurological disease present in the ageing population that is associated with an increased risk of dementia and stroke. Damage to white matter tracts compromises the substrate for interneuronal connectivity. Analysing resting-state functional magnetic resonance imaging (fMRI) can reveal dysfunctional patterns of brain connectivity and contribute to explaining the pathophysiology of clinical phenotypes in CSVD.

MATERIALS AND METHODS

This systematic review provides an overview of methods and results of recent resting-state functional MRI studies in patients with CSVD. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, a systematic search of the literature was performed.

RESULTS

Of 493 studies that were screened, 44 reports were identified that investigated resting-state fMRI connectivity in the context of cerebral small vessel disease. The risk of bias and heterogeneity of results were moderate to high. Patterns associated with CSVD included disturbed connectivity within and between intrinsic brain networks, in particular the default mode, dorsal attention, frontoparietal control, and salience networks; decoupling of neuronal activity along an anterior-posterior axis; and increases in functional connectivity in the early stage of the disease.

CONCLUSION

The recent literature provides further evidence for a functional disconnection model of cognitive impairment in CSVD. We suggest that the salience network might play a hitherto underappreciated role in this model. Low quality of evidence and the lack of preregistered multi-centre studies remain challenges to be overcome in the future.

Crosstalk between Existential Phenomenological Psychotherapy and Neurological Sciences in Mood and Anxiety Disorders

Psychotherapy is a comprehensive biological treatment modifying complex underlying cognitive, emotional, behavioral, and regulatory responses in the brain, leading patients with mental illness to a new interpretation of the sense of self and others. Psychotherapy is an art of science integrated with psychology and/or philosophy. Neurological sciences study the neurological basis of cognition, memory, and behavior as well as the impact of neurological damage and disease on these functions, and their treatment. Both psychotherapy and neurological sciences deal with the brain; nevertheless, they continue to stay polarized. Existential phenomenological psychotherapy (EPP) has been in the forefront of meaning-centered counseling for almost a century. The phenomenological approach in psychotherapy originated in the works of Martin Heidegger, Ludwig Binswanger, Medard Boss, and Viktor Frankl, and it has been committed to accounting for the existential possibilities and limitations of one's life. EPP provides philosophically rich interpretations and empowers counseling techniques to assist mentally suffering individuals by finding meaning and purpose to life. The approach has proven to be effective in treating mood and anxiety disorders. This narrative review article demonstrates the development of EPP, the therapeutic methodology, evidence-based accounts of its curative techniques, current understanding of mood and anxiety disorders in neurological sciences, and a possible converging path to translate and integrate meaning-centered psychotherapy and neuroscience, concluding that the EPP may potentially play a synergistic role with the currently prevailing medication-based approaches for the treatment of mood and anxiety disorders.

Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning

Alzheimer’s disease (AD) is the most common type of dementia, and depression is a risk factor for developing AD. Epidemiological studies provide a clinical correlation between late-life depression (LLD) and AD. Depression patients generally remit with no residual symptoms, but LLD patients demonstrate residual cognitive impairment. Due to the lack of effective treatments, understanding how risk factors affect the course of AD is essential to manage AD. Advances in neuroimaging, including resting-state functional MRI (fMRI), have been used to address neural systems that contribute to clinical symptoms and functional changes across various psychiatric disorders. Resting-state fMRI studies have contributed to understanding each of the two diseases, but the link between LLD and AD has not been fully elucidated. This review focuses on three crucial and well-established networks in AD and LLD and discusses the impacts on cognitive decline, clinical symptoms, and prognosis. Three networks are the (1) default mode network, (2) executive control network, and (3) salience network. The multiple properties emphasized here, relevant for the hypothesis of the linkage between LLD and AD, will be further developed by ongoing future studies.

Neural substrates for late-life depression: A selective review of structural neuroimaging studies

Recent neuroimaging studies have characterized the pathophysiology of late-life depression (LLD) as a dysfunction of the brain networks involved in the regulation of emotion, motivational behavior, cognitive control, executive function, and self-referential thinking. In this article, we reviewed LLD-associated structural neuroimaging markers such as white matter hyperintensity (WMH), white matter integrity measured by diffusion tensor imaging, cortical and subcortical volumes, and cortical thickness, which may provide a structural basis for brain network dysfunction in LLD. LLD was associated with greater severity or volumes of deep, periventricular, or overall WMH and with decreased white matter integrity in the brain regions belonging to the fronto-striatal-limbic circuits and reduced white matter tract integrity which connects these circuits, such as the cingulum, corpus callosum, or uncinate fasciculus. Decreased volumes or cortical thickness in the prefrontal cortex, orbitofrontal cortex, anterior and posterior cingulate cortex, several temporal and parietal regions, hippocampus, amygdala, striatum, thalamus, and the insula were associated with LLD. These structural neuroimaging findings were also associated with cognitive dysfunction, which is a prominent clinical feature in LLD. Several structural neuroimaging markers including the WMH burden, white matter integrity, and cortical and subcortical volumes predicted antidepressant response in LLD. These structural neuroimaging findings support the hypothesis that disruption of the brain networks involved in emotion regulation and cognitive processing by impaired structural connectivity is strongly associated with the pathophysiology of LLD.

Prediction on treatment improvement in depression with resting state connectivity: A coordinate-based meta-analysis

BACKGROUND

Previous neuroimaging studies revealed abnormal resting-state functional connectivity between distributed brain areas in patients with major depressive disorder. Those abnormalities were normalized after treatment. Moreover, the functional connectivity could predict clinical response to those treatments. However, there has currently been no meta-analysis to verify these findings.

METHODS

The current study aimed to investigate how the resting-state connectivity patterns predict antidepressant response to various treatments across depressive studies by using coordinate-based meta-analysis named activation likelihood estimation. The relevant articles were obtained by searching on PubMed and Web of Science.

RESULTS

Following exclusion criteria of inappropriate studies, seventeen papers with 392 individual depressive patients were included. Those articles contained repetitive transcranial magnetic stimulation (rTMS) treatment, pharmacotherapy, cognitive behavioral therapy (CBT), electroconvulsive therapy (ECT) and transcutaneous vagus nerve stimulation in patients with depression. Meta-analysis revealed that clinical response to all treatments could be predicted by baseline default mode network connectivity in patients with depression. The rTMS treatment had larger effect size compared to other treatment strategies. Furthermore, subgroup meta-analysis showed that the baseline connectivity of perigenual anterior cingulate cortex (pgACC) and ventral medial prefrontal cortex could predict symptoms improvement of rTMS treatment.

LIMITATIONS

More resting-state connectivity studies of CBT and ECT treatment are needed.

CONCLUSIONS

This study highlighted crucial role of DMN, especially the pgACC, in understanding the underlying treatment mechanism of depression.

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').

Vascular depression for radiology: A review of the construct, methodology, and diagnosis

Vascular depression (VD) as defined by magnetic resonance imaging (MRI) has been proposed as a unique subtype of late-life depression. The VD hypothesis posits that cerebrovascular disease, as characterized by the presence of MRI-defined white matter hyperintensities, contributes to and increases the risk for depression in older adults. VD is also accompanied by cognitive impairment and poor antidepressant treatment response. The VD diagnosis relies on MRI findings and yet this clinical entity is largely unfamiliar to neuroradiologists and is rarely, if ever, discussed in radiology journals. The primary purpose of this review is to introduce the MRI-defined VD construct to the neuroradiology community. Case reports are highlighted in order to illustrate the profile of VD in terms of radiological, clinical, and neuropsychological findings. A secondary purpose is to elucidate and elaborate on the measurement of cerebrovascular disease through visual rating scales and semi- and fully-automated volumetric methods. These methods are crucial for determining whether lesion burden or lesion severity is the dominant pathological contributor to VD. Additionally, these rating methods have implications for the growing field of computer assisted diagnosis. Since VD has been found to have a profile that is distinct from other types of late-life depression, neuroradiologists, in conjunction with psychiatrists and psychologists, should consider VD in diagnosis and treatment planning.

Integrity of Neurocognitive Networks in Dementing Disorders as Measured with Simultaneous PET/Functional MRI

Functional MRI (fMRI) studies have reported altered integrity of large-scale neurocognitive networks (NCNs) in dementing disorders. However, findings on the specificity of these alterations in patients with Alzheimer disease (AD) and behavioral-variant frontotemporal dementia (bvFTD) are still limited. Recently, NCNs have been successfully captured using PET with ¹⁸F-FDG. Methods: Network integrity was measured in 72 individuals (38 male) with mild AD or bvFTD, and in healthy controls, using a simultaneous resting-state fMRI and ¹⁸F-FDG PET. Indices of network integrity were calculated for each subject, network, and imaging modality. Results: In either modality, independent-component analysis revealed 4 major NCNs: anterior default-mode network (DMN), posterior DMN, salience network, and right central executive network (CEN). In fMRI data, the integrity of the posterior DMN was found to be significantly reduced in both patient groups relative to controls. In the AD group the anterior DMN and CEN appeared to be additionally affected. In PET data, only the integrity of the posterior DMN in patients with AD was reduced, whereas 3 remaining networks appeared to be affected only in patients with bvFTD. In a logistic regression analysis, the integrity of the anterior DMN as measured with PET alone accurately differentiated between the patient groups. A correlation between indices of 2 imaging modalities was low overall. Conclusion: FMRI and ¹⁸F-FDG PET capture partly different aspects of network integrity. A higher disease specificity for NCNs as derived from PET data supports metabolic connectivity imaging as a promising diagnostic tool.

Evidence for Structural and Functional Alterations of Frontal-Executive and Corticolimbic Circuits in Late-Life Depression and Relationship to Mild Cognitive Impairment and Dementia: A Systematic Review

Depression is a risk factor for developing Alzheimer's disease and Related Dementia (ADRD). We conducted a systematic review between 2008 and October 2018, to evaluate the evidence for a conceptual mechanistic model linking depression and ADRD, focusing on frontal-executive and corticolimbic circuits. We focused on two neuroimaging modalities: diffusion-weighted imaging measuring white matter tract disruptions and resting-state functional MRI measuring alterations in network dynamics in late-life depression (LLD), mild cognitive impairment (MCI), and LLD+MCI vs. healthy control (HC) individuals. Our data synthesis revealed that in some but not all studies, impairment of both frontal-executive and corticolimbic circuits, as well as impairment of global brain topology was present in LLD, MCI, and LLD+MCI vs. HC groups. Further, posterior midline regions (posterior cingulate cortex and precuneus) appeared to have the most structural and functional alterations in all patient groups. Future cohort and longitudinal studies are required to address the heterogeneity of findings, and to clarify which subgroups of people with LLD are at highest risk for developing MCI and ADRD.

Default mode network alterations after intermittent theta burst stimulation in healthy subjects

Understanding the mechanisms by which intermittent theta burst stimulation (iTBS) protocols exert changes in the default-mode network (DMN) is paramount to develop therapeutically more effective approaches in the future. While a full session (3000 pulses) of 10 Hz repetitive transcranial magnetic stimulation (HF-rTMS) reduces the functional connectivity (FC) of the DMN and the subgenual anterior cingulate cortex, the current understanding of the effects of a single session of iTBS on the DMN in healthy subjects is limited. Here, we use a previously validated target selection approach for an unprecedented investigation into the effects of a single session (1800 pulses) of iTBS over the DMN in healthy controls. Twenty-six healthy subjects participated in a double-blind, crossover, sham-controlled study. After iTBS to the personalized left dorsolateral prefrontal cortex (DLPFC) targets, we investigated the time lapse of effects in the DMN and its relationship to the harm avoidance (HA) personality trait measure (Temperament and Character Inventory/TCI). Approximately 25-30 min after stimulation, we observed reduced FC between the DMN and the rostral and dorsal anterior cingulate cortex (dACC). About 45 min after stimulation the FC of rostral and dACC strongly decreased further, as did the FC of right anterior insula (AI) with the DMN. Also, we report a positive correlation between the FC decrease in the rostral ACC and the HA domain of TCI, indicating that the HA scores can potentially predict iTBS response. Overall, our results show the time lapse by which iTBS at left-DLPFC targets reduces the FC between DMN and the dACC and right AI, regions typically described as nodes of the salience network.

Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder

Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.

A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants

More than six decades have passed since the discovery of monoaminergic antidepressants. Yet, it remains a mystery why these drugs take weeks to months to achieve therapeutic effects, although their monoaminergic actions are present rapidly after treatment. In an attempt to solve this mystery, rather than studying the acute neurochemical effects of antidepressants, here we propose focusing on the early changes in the brain functional connectome using traditional statistics and machine learning approaches. Capitalizing on three independent datasets (n = 1,261) and recent developments in data and network science, we identified a specific connectome fingerprint that predates and predicts response to monoaminergic antidepressants. The discovered fingerprint appears to generalize to antidepressants with differing mechanism of action. We also established a consensus whole-brain hierarchical connectivity architecture and provided a set of model-based features engineering approaches suitable for identifying connectomic signatures of brain function in health and disease.

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Machine learning methods were used to fully investigate the brain connectome

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Network-informed features engineering approaches were proposed

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A cortical-subcortical hierarchical brain atlas was established

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A specific connectome signature was found to predict response to antidepressants

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

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

Functional connectivity predictors of acute depression treatment outcome

Few studies have examined functional connectivity (FC) patterns using functional magnetic resonance imaging (fMRI) to predict outcomes in late-life depression. We hypothesized that FC within and between frontal and limbic regions would be associated with 12-week depression outcome in older depressed adults. Seventy-one subjects with major depression were enrolled in the study. A study geriatric psychiatrist performed a clinical interview and completed a Montgomery-Åsberg Depression Rating Scale (MADRS). All study participants were free of medication at baseline and had a brain fMRI scan. Using a regions of interest (ROI) atlas (including 164 ROIs), we conducted ROI-to-ROI resting-state FC analyses for each participant. In terms of treatment participants were offered sertraline initially, although in this naturalistic study, other medications were also prescribed. Subjects were evaluated every 2 weeks up to 12 weeks by the study psychiatrist, who followed a flexible, clinically based medication dosing schedule. Multivariate regression analysis was used to examine correlation between change of MADRS score over 12 weeks and baseline FC between brain regions, controlling for age, gender, mean head motion, and baseline MADRS. We found greater FC between the left inferior frontal gyrus pars triangularis and the left frontal eye field and FC of these two regions with a number of brain regions related to reward, salience, and sensorimortor function were correlated with change in MADRS score over 12 weeks. Our results highlight the important role of between inner speech-reward, attention-salience, and attention-sensorimotor network synchronization in predicting acute treatment response in late-life depression.

Mechanisms and treatment of late-life depression

Depression predisposes to medical illnesses and advances biological aging indicated by shorter telomere length, accelerated brain aging and advanced epigenetic aging. Medical illnesses also increase the risk of late-life depression. The reciprocal relationships of depression with aging-related and disease-related processes have generated pathogenetic hypotheses and provided treatment targets. Targeting risk factors of vascular disease in mid-life is a logical approach in prevention of vascular depression. The depression-executive dysfunction and the vascular depression syndromes have clinical presentations and neuroimaging findings consistent with frontostriatal abnormalities. Dopamine D_2/3 agonists are effective in depression of Parkinson's disease and their efficacy needs to be assessed in these two syndromes. Computerized cognitive remediation targeting functions of the cognitive control network may improve both executive functions and depressive symptoms of late-life major depression. Significant progress has been made in neurostimulation treatments in depressed younger adults. TMS targeting deep structures responsible for mood regulation is well tolerated by older adults and its efficacy in syndromes of late-life depression needs to be studied. Efficacious psychotherapies for late-life depression exist, but are underutilized in part because of their complexity. Streamlined, stepped psychotherapies targeting behaviors assumed to result from dysfunction of brain networks implicated in late-life depression can be easy to learn and have potential for dissemination. However, their effectiveness needs further investigation. Depression increases the risk of dementing disorders. Antidepressants are rather ineffective in treating depression of demented patients, but long-term use of antidepressants may reduce the risk of dementia. However, confirmation studies are needed.

The impact of white matter hyperintensities on the structural connectome in late-life depression: Relationship to executive functions

BACKGROUND

White matter hyperintensities (WMH) represent ischemic white matter damage in late-life depression (LLD) and are associated with cognitive control dysfunction. Understanding the impact of WMH on the structural connectivity of gray matter and the cognitive control correlates of WMH-related structural dysconnectivity can provide insight into the pathophysiology of LLD.

METHODS

We compared WMH burden and performance on clinical measures of cognitive control in patients with LLD (N = 44) and a control group of non-depressed older adults (N = 59). We used the Network Modification (NeMo) Tool to investigate the impact of WMH on structural dysconnectivity in specific gray matter regions, and how such connectivity was related to cognitive control functions.

RESULTS

Compared to the control group, LLD participants had greater WMH burden, poorer performance on Trail Making Test (TMT) A & B, and greater self-reported dysexecutive behavior on the Frosntal Systems Behavior Scale-Executive Function subscale (FrSBe-EF). Within the LLD group, disrupted connectivity in the left supramarginal gyrus, paracentral lobule, thalamus, and pallidum was associated with psychomotor slowing (TMT-A). Altered connectivity in the left supramarginal gyrus, paracentral lobule, precentral gyrus, postcentral gyrus, thalamus, and pallidum was associated with poor attentional set-shifting (TMT-B). A follow-up analysis that isolated set-shifting ability (TMT-B/A ratio) confirmed the association with dysconnectivity in the bilateral paracentral lobule, right thalamus, left precentral gyrus, postcentral gyrus, and pallidum; additionally, it revealed associations with dysconnectivity in the right posterior cingulate, and left anterior cingulate, middle frontal cortex, and putamen.

CONCLUSIONS

In LLD, WMH are associated with region-specific disruptions in cortical and subcortical gray matter areas involved in attentional aspects of cognitive control systems and sensorimotor processing, which in turn are associated with slower processing speed, and reduced attentional set-shifting.

CLINICAL TRIALS REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT01728194.

Personalized repetitive transcranial magnetic stimulation temporarily alters default mode network in healthy subjects

High frequency repetitive transcranial magnetic stimulation (HF-rTMS) delivered to the left dorsolateral prefrontal cortex (DLPFC) is an effective treatment option for treatment resistant depression. However, the underlying mechanisms of a full session of HF-rTMS in healthy volunteers have not yet been described. Here we investigated, with a personalized selection of DLPFC stimulation sites, the effects driven by HF-rTMS in healthy volunteers (n = 23) over the default mode network (DMN) in multiple time windows. After a complete 10 Hz rTMS (3000 pulses) session, we observe a decrease of functional connectivity between the DMN and the subgenual Anterior Cingulate Cortex (sgACC), as well as the ventral striatum (vStr). A negative correlation between the magnitude of this decrease in the right sgACC and the harm avoidance domain measure from the Temperament and Character Inventory was observed. Moreover, we identify that coupling strength of right vStr with the DMN post-stimulation was proportional to a decrease in self-reports of negative mood from the Positive and Negative Affect Schedule. This shows HF-rTMS attenuates perception of negative mood in healthy recipients in agreement with the expected effects in patients. Our study, by using a personalized selection of DLPFC stimulation sites, contributes understanding the effects of a full session of rTMS approved for clinical use in depression over related brain regions in healthy volunteers.

White matter abnormalities predict residual negative self-referential thinking following treatment of late-life depression with escitalopram: A preliminary study

BACKGROUND

Negative self-referential thinking is a common symptom of depression associated with poor treatment response. In late-life depression, white matter abnormalities may contribute to negative self-referential thoughts following antidepressant treatment. We investigated the association of fractional anisotropy (FA) in select regions of the negative valence system (NVS) with residual negative self-referential thoughts following treatment with escitalopram for late-life depression.

METHODS

The participants were older adults with major depression and psychiatrically normal controls. Depressed participants received 12 weeks of treatment with escitalopram. To assess self-referential thinking, participants completed a Trait Adjective Task at baseline and at week 12. Baseline MRI scans included a diffusion imaging sequence for FA analyses.

RESULTS

Participants with late-life depression differed from controls on all performance measures of the Trait Adjective Task at baseline and at 12 weeks. Depressed participants endorsed fewer negative personality traits and more positive personality traits at week 12 compared to baseline. Lower FA in the dorsal anterior cingulate and in the uncinate fasciculus in depressed participants was correlated with residual negative self-referential thinking (e.g., more endorsed negative adjectives, fewer rejected negative adjectives) at treatment end.

LIMITATIONS

The sample size is modest so the findings are preliminary. FA analyses were restricted to predetermined regions.

CONCLUSIONS

Negative self-referential thinking improved in depressed older adults following 12 weeks of treatment with escitalopram. Baseline FA in select white matter regions of the NVS was associated with residual negative self-referential thinking. These findings may help identify treatment targets for residual negative self-referential thoughts.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Risk factors associated with cognitions for late-onset depression based on anterior and posterior default mode sub-networks

BACKGROUND

Abnormal functional connectivity (FC) in the default mode network (DMN) plays an important role in late-onset depression (LOD) patients. In this study, the risk predictors of LOD based on anterior and posterior DMN are explored.

METHODS

A total of 27 LOD patients and 40 healthy controls (HC) underwent resting-state functional magnetic resonance imaging and cognitive assessments. Firstly, FCs within DMN sub-networks were determined by placing seeds in the ventral medial prefrontal cortex (vmPFC) and posterior cingulate cortex (PCC). Secondly, multivariable logistic regression was used to identify risk factors for LOD patients. Finally, correlation analysis was performed to investigate the relationship between risk factors and the cognitive value.

RESULTS

Multivariable logistic regression showed that the FCs between the vmPFC and right middle temporal gyrus (MTG) (vmPFC-MTG_R), FCs between the vmPFC and left precuneus (PCu), and FCs between the PCC and left PCu (PCC-PCu_L) were the risk factors for LOD. Furthermore, FCs of the vmPFC-MTG_R and PCC-PCu_L correlated with processing speed (R = 0.35, P = 0.002; R = 0.32, P = 0.009), and FCs of the vmPFC-MTG_R correlated with semantic memory (R = 0.41, P = 0.001).

LIMITATIONS

The study was a cross-sectional study. The results may be potentially biased because of a small sample.

CONCLUSIONS

In this study, we confirmed that LOD patients mainly present cognitive deficits in processing speed and semantic memory. Moreover, our findings further suggested that FCs within DMN sub-networks associated with cognitions were risk factors, which may be used for the prediction of LOD.

Modes of Resting Functional Brain Organization Differentiate Suicidal Thoughts and Actions: A Preliminary Study

OBJECTIVE

A major target in suicide prevention is interrupting the progression from suicidal thoughts to action. Use of complex algorithms in large samples has identified individuals at very high risk for suicide. We tested the ability of data-driven pattern classification analysis of brain functional connectivity to differentiate recent suicide attempters from patients with suicidal ideation.

METHODS

We performed a cross-sectional study using resting-state functional magnetic resonance imaging in depressed inpatients and outpatients of both sexes recruited from a university hospital between March 2014 and June 2016: recent suicide Attempters within 3 days of an attempt (n = 10), Suicidal Ideators (n = 9), Depressed Non-Suicidal Controls (n = 17), and Healthy Controls (n = 18). All depressed patients fulfilled DSM-IV-TR criteria for major depressive episode and either major depressive disorder, bipolar disorder, or depression not otherwise specified. A subset of suicide attempters (n = 7) were rescanned within 7 days. We used a support vector machine data-driven neural pattern classification analysis of resting-state functional connectivity to characterize recent suicide attempters and then tested the classifier's specificity.

RESULTS

A binary classifier trained to discriminate patterns of resting-state functional connectivity robustly differentiated Suicide Attempters from Suicidal Ideators (mean accuracy = 0.788, signed rank test: P = .002; null hypothesis: area under the curve = 0.5), with distinct functional connectivity between the default mode and the limbic, salience, and central executive networks. The classifier did not discriminate stable Suicide Attempters from Suicidal Ideators (mean accuracy = 0.58, P = .33) or presence from absence of lifetime suicidal behavior (mean accuracy = 0.543, P = .348) and was not improved by modeling clinical variables (mean accuracy = 0.736, P = .002).

CONCLUSIONS

Measures of intrinsic brain organization may have practical value as objective measures of suicide risk and its underlying mechanisms. Further incorporation of serum or cognitive markers and use of a prospective study design are needed to validate and refine the clinical relevance of this candidate biomarker of suicide risk.

A brain network model for depression: From symptom understanding to disease intervention

Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal-striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top-down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks-and corresponding symptom improvement-following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression.

Acute trajectories of neural activation predict remission to pharmacotherapy in late-life depression

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.

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Neural activation changes after a single dose of antidepressants have been observed.

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Patients with late-life depression were treated with an antidepressant for 12 weeks.

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Neuroimaging data was recorded pre-treatment and after a single dose.

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Pre-treatment neuroimaging predicted remission at the end of the trial.

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Neuroimaging after a single dose improved prediction and may guide treatment.

A supplementary functional connectivity microstate attached to the default mode network in depression revealed by resting-state magnetoencephalography

Default mode network (DMN) has discernable involvement in the representation of negative, self-referential information in depression. Both increased and decreased resting-state functional connectivity between the anterior and posterior DMN have been observed in depression. These conflicting connectivity differences necessitated further exploration of the resting-state DMN dysfunction in depression. Hence, we investigated the time-varying dynamic interactions within the DMN via functional connectivity microstates in a sub-second level. 25 patients with depression and 25 matched healthy controls were enrolled in the MEG analysis. Spherical K-means algorithms embedded within an iterative optimization frame were applied to sliding windowed correlation matrices, resulting in sub-second alternations of two functional connectivity microstates for groups and highlighting the presence of functional variability. In the power dominant state, depressed patients showed a transient decreased pattern that reflected inter/intra-subnetwork deregulation. A supplementary negatively correlated state simultaneously presented with increased connectivity between the ventromedial prefrontal cortex (vmPFC) and the posterior cingulate cortex (PCC), two core nodes for the anterior and posterior DMN respectively. Additionally, depressed patients stayed longer in the supplementary microstate compared to healthy controls. During the time spent in the supplementary microstate, an attempt to compensate for the aberrant effect of vmPFC on PCC across DMN subnetworks was possibly made to balance the self-related processes disturbed by the dominant pattern. The functional compensation mechanism of the supplementary microstate attached to the dominant disrupted one provided a possible explanation to the existing inconsistent findings between the anterior and posterior DMN in depression.

Default mode network modifications in Fabry disease: A resting-state fMRI study with structural correlations

Aim of the study was to evaluate the presence of Default Mode Network (DMN) modifications in Fabry Disease (FD), and their possible correlations with structural alterations and neuropsychological scores. Thirty-two FD patients with a genetically confirmed diagnosis of classical FD (12 males, mean age 43.3 ± 12.2) were enrolled, along with 35 healthy controls (HC) of comparable age and sex (14 males, mean age 42.1 ± 14.5). Resting-State fMRI data were analyzed using a seed-based approach, with six different seeds sampling the main hubs of the DMN. Structural modifications were assessed by means of Voxel-Based Morphometry (VBM) and Tract-Based Spatial Statistics analyses. Between-group differences and correlations with neuropsychological variables were probed voxelwise over the whole brain. Possible correlations between FC modifications and global measures of microstructural alteration were also tested in FD patients with a partial correlation analysis. In the FD group, clusters of increased functional connectivity involving both supratentorial and infratentorial regions emerged, partially correlated to the widespread white matter (WM) damage found in these patients. No gray matter volume differences were found at VBM between the two groups. The connectivity between right inferior frontal gyrus and precuneus was significantly correlated with the Corsi block-tapping test results (p = .0001). Widespread DMN changes are present in FD patients that correlate with WM alterations and cognitive performance. Our results confirm the current view of a cerebral involvement in FD patients not simply associated to major cerebrovascular events, but also related to significant and diffuse microstructural and functional changes.

Current sleep disturbance in older people with a lifetime history of depression is associated with increased connectivity in the Default Mode Network

BACKGROUND

The present study investigated Default Mode Network (DMN) functional connectivity in subjects with a lifetime history of major depression, comparing those with and without current sleep disturbance. Controls were included to assess DMN abnormalities specific to depression.

METHODS

A total of 93 adults aged 50 years and over were recruited from the Healthy Brain Ageing Clinic at the Brain and Mind Centre, Sydney, Australia. The sample comprised two groups, including 22 controls and 71 participants with a lifetime history of DSM-IV major depression (with depressive episode current or remitted). 52 of those with a lifetime history of depression also met criteria for Mild Cognitive Impairment (MCI). Participants underwent resting-state fMRI along with comprehensive psychiatric, neuropsychological, and medical assessment. Subjective sleep quality was assessed via the Pittsburgh Sleep Quality Index (PSQI). Sleep disturbance was defined as a PSQI score > 5. A total of 68% (n = 48) of cases with a lifetime history of depression met criteria for sleep-disturbance. DMN functional connectivity was assessed via ROI-to-ROI analyses.

RESULTS

Relative to controls, those with lifetime major depression demonstrated significantly increased functional connectivity between the ventromedial prefrontal cortex and the temporal pole. Within the depression group (n = 48), those with current sleep disturbance had significantly increased connectivity between the anterior medial prefrontal cortex and both the parahippocampal cortex and the hippocampal formation, relative to those without sleep disturbance (n = 23). These results were present after controlling for MCI diagnosis.

CONCLUSIONS

Current sleep disturbance together with depression is associated with distinct abnormalities in DMN functioning incorporating regions responsible for self-reflection and declarative memory processes. Impaired sleep is associated with increased connectivity between these regions. Future studies may augment these findings with complementary imaging techniques including cortical thickness and diffusion tensor imaging, as well as high density electroencephalogram recording.

Altered Connectivity of the Anterior Cingulate and the Posterior Superior Temporal Gyrus in a Longitudinal Study of Later-life Depression

Patients with later-life depression (LLD) show abnormal gray matter (GM) volume, white matter (WM) integrity and functional connectivity in the anterior cingulate cortex (ACC) and posterior superior temporal gyrus (pSTG), but it remains unclear whether these abnormalities persist over time. We examined whether structural and functional abnormalities in these two regions are present within the same subjects during depressed vs. remitted phases. Sixteen patients with LLD and 30 healthy subjects were studied over a period of 1.5 years. Brain images obtained with a 3-Tesla magnetic resonance imaging (MRI) system were analyzed by voxel-based morphometry of the GM volume, and diffusion tensor imaging (DTI) and resting-state functional MRI were used to assess ACC–pSTG connectivity. Patients with LLD in the depressed and remitted phases showed significantly smaller GM volume in the left ACC and left pSTG than healthy subjects. Both patients with LLD in the depressed and remitted phases had significantly higher diffusivities in the WM tract of the left ACC–pSTG than healthy subjects. Remitted patients with LLD showed lower functional ACC–pSTG connectivity compared to healthy subjects. No difference was found in the two regions between depressed and remitted patients in GM volume, structural or functional connectivity. Functional ACC–pSTG connectivity was positively correlated with lower global function during remission. Our preliminary data show that structural and functional abnormalities of the ACC and pSTG occur during LLD remission. Our findings tentatively reveal the brain pathophysiology involved in LLD and may aid in developing neuroanatomical biomarkers for this condition.