Intrinsic functional connectivity in late-life depression: trajectories over the course of pharmacotherapy in remitters and non-remitters

Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study

Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.

Structural Brain Connectivity and Treatment Improvement in Mood Disorder

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.

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

Background

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

Methods

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

Significance

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

Abnormal changes of dynamic topological characteristics in patients with major depressive disorder

BACKGROUND

Most studies have detected abnormalities of static topological characteristics in major depressive disorder (MDD). However, whether dynamic alternations in brain topology are influenced by MDD remains unknown.

METHODS

An approach was proposed to capture the dynamic topological characteristics with sliding-window and graph theory for a large data sample from the REST-meta-MDD project.

RESULTS

It was shown that patients with MDD were characterized by decreased nodal efficiency of the left orbitofrontal cortex. The temporal variability of topological characteristics was focused on the left opercular part of inferior frontal gyrus, and the right part of middle frontal gyrus, inferior parietal gyrus, precuneus and thalamus.

LIMITATIONS

Future studies need larger and diverse samples to explore the relationship between dynamic topological network characteristics and MDD symptoms.

CONCLUSIONS

The results support that the altered dynamic topology in cortex of frontal and parietal lobes and thalamus during resting-state activity may be involved in the neuropathological mechanism of MDD.

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.

Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder

Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a 'suicidality gradient'. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age (p < 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex (p < 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73-0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.

Hippocampal and parahippocampal volume and function predict antidepressant response in patients with major depression: A multimodal neuroimaging study

BACKGROUND

For many patients with major depressive disorder (MDD) adequate treatment remains elusive. Neuroimaging techniques received attention for their potential use in guiding and predicting response, but were rarely investigated in real-world psychiatric settings.

AIMS

To identify structural and functional Magnetic Resonance Imaging (MRI) biomarkers associated with antidepressant response in a real-world clinical sample.

METHODS

We studied 100 MDD inpatients admitted to our psychiatric ward, treated with various antidepressants upon clinical need. Hamilton Depression Rating Scale percentage decrease from admission to discharge was used as a measure of response. All patients underwent 3.0 T MRI scanning. Grey matter (GM) volumes were investigated both in a voxel-based morphometry (VBM), and in a regions of interest (ROI) analysis. In a subsample of patients, functional resting-state connectivity patterns were also explored.

RESULTS

In the VBM analysis, worse response was associated to lower GM volumes in two clusters, encompassing the left hippocampus and parahippocampal gyrus, and the right superior and middle temporal gyrus. Investigating ROIs, lower bilateral hippocampi and amygdalae volumes predicted worse treatment outcomes. Functional connectivity in the right temporal and parahippocampal gyrus was also associated to response.

CONCLUSION

Our results expand existing literature on the relationship between the structure and function of several brain regions and treatment response in MDD. While we are still far from routine use of MRI biomarkers in clinical practice, we confirm a possible role of these techniques in guiding treatment choices and predicting their efficacy.

Influences of resting-state intrinsic functional brain connectivity on the antidepressant treatment response in late-life depression

BACKGROUND

Late-life depression (LLD) is characterized by differences in resting state functional connectivity within and between intrinsic functional networks. This study examined whether clinical improvement to antidepressant medications is associated with pre-randomization functional connectivity in intrinsic brain networks.

METHODS

Participants were 95 elders aged 60 years or older with major depressive disorder. After clinical assessments and baseline MRI, participants were randomized to escitalopram or placebo with a two-to-one allocation for 8 weeks. Non-remitting participants subsequently entered an 8-week trial of open-label bupropion. The main clinical outcome was depression severity measured by MADRS. Resting state functional connectivity was measured between a priori key seeds in the default mode (DMN), cognitive control, and limbic networks.

RESULTS

In primary analyses of blinded data, lower post-treatment MADRS score was associated with higher resting connectivity between: (a) posterior cingulate cortex (PCC) and left medial prefrontal cortex; (b) PCC and subgenual anterior cingulate cortex (ACC); (c) right medial PFC and subgenual ACC; (d) right orbitofrontal cortex and left hippocampus. Lower post-treatment MADRS was further associated with lower connectivity between: (e) the right orbitofrontal cortex and left amygdala; and (f) left dorsolateral PFC and left dorsal ACC. Secondary analyses associated mood improvement on escitalopram with anterior DMN hub connectivity. Exploratory analyses of the bupropion open-label trial associated improvement with subgenual ACC, frontal, and amygdala connectivity.

CONCLUSIONS

Response to antidepressants in LLD is related to connectivity in the DMN, cognitive control and limbic networks. Future work should focus on clinical markers of network connectivity informing prognosis.

REGISTRATION

ClinicalTrials.gov NCT02332291.

Trait- and state-like co-activation pattern dynamics in current and remitted major depressive disorder

BACKGROUND

Distinguishing between trait- and state-like neural alternations in major depressive disorder (MDD) may advance our understanding of this recurring disorder. We aimed to investigate dynamic functional connectivity alternations in unmedicated individuals with current or past MDD using co-activation pattern analyses.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from individuals with first-episode current MDD (cMDD, n = 50), remitted MDD (rMDD, n = 44), and healthy controls (HCs, n = 64). Using a data-driven consensus clustering technique, four whole-brain states of spatial co-activation were identified and associated metrics (dominance, entries, transition frequency) were analyzed with respect to clinical characteristics.

RESULTS

Relative to rMDD and HC, cMDD showed increased dominance and entries of state 1 (primarily involving default mode network (DMN)), and decreased dominance of state 4 (mostly involving frontal-parietal network (FPN)). Among cMDD, state 1 entries correlated positively with trait rumination. Conversely, relative to cMDD and HC, individuals with rMDD were characterized by increased state 4 entries. Relative to HC, both MDD groups showed increased state 4-to-1 (FPN to DMN) transition frequency but reduction in state 3 (spanning visual attention, somatosensory, limbic networks), with the former metric specifically related to trait rumination.

LIMITATIONS

Further confirmation with longitudinal studies are required.

CONCLUSIONS

Regardless of symptoms, MDD was characterized by increased FPN-to-DMN transitions and reduced dominance of a hybrid network. State-related effect emerged in regions critically implicated in repetitive introspection and cognitive control. Asymptomatic individuals with past MDD were uniquely linked to increased FPN entries. Our findings identify trait-like brain network dynamics that might increase vulnerability to future MDD.

Changes in cerebral connectivity and brain tissue pulsations with the antidepressant response to an equimolar mixture of oxygen and nitrous oxide: an MRI and ultrasound study

Nitrous oxide (N2O) has recently emerged as a potential fast-acting antidepressant but the cerebral mechanisms involved in this effect remain speculative. We hypothesized that the antidepressant response to an Equimolar Mixture of Oxygen and Nitrous Oxide (EMONO) would be associated with changes in cerebral connectivity and brain tissue pulsations (BTP). Thirty participants (20 with a major depressive episode resistant to at least one antidepressant and 10 healthy controls-HC, aged 25-50, only females) were exposed to a 1-h single session of EMONO and followed for 1 week. We defined response as a reduction of at least 50% in the MADRS score 1 week after exposure. Cerebral connectivity of the Anterior Cingulate Cortex (ACC), using ROI-based resting state fMRI, and BTP, using ultrasound Tissue Pulsatility Imaging, were compared before and rapidly after exposure (as well as during exposure for BTP) among HC, non-responders and responders. We conducted analyses to compare group × time, group, and time effects. Nine (45%) depressed participants were considered responders and eleven (55%) non-responders. In responders, we observed a significant reduction in the connectivity of the subgenual ACC with the precuneus. Connectivity of the supracallosal ACC with the mid-cingulate also significantly decreased after exposure in HC and in non-responders. BTP significantly increased in the three groups between baseline and gas exposure, but the increase in BTP within the first 10 min was only significant in responders. We found that a single session of EMONO can rapidly modify the functional connectivity in the subgenual ACC-precuneus, nodes within the default mode network, in depressed participants responders to EMONO. In addition, larger increases in BTP, associated with a significant rise in cerebral blood flow, appear to promote the antidepressant response, possibly by facilitating optimal drug delivery to the brain. Our study identified potential cerebral mechanisms related to the antidepressant response of N2O, as well as potential markers for treatment response with this fast-acting antidepressant.

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.

The heterogeneity of late-life depression and its pathobiology: a brain network dysfunction disorder

Depression is frequent in older individuals and is often associated with cognitive impairment and increasing risk of subsequent dementia. Late-life depression (LLD) has a negative impact on quality of life, yet the underlying pathobiology is still poorly understood. It is characterized by considerable heterogeneity in clinical manifestation, genetics, brain morphology, and function. Although its diagnosis is based on standard criteria, due to overlap with other age-related pathologies, the relationship between depression and dementia and the relevant structural and functional cerebral lesions are still controversial. LLD has been related to a variety of pathogenic mechanisms associated with the underlying age-related neurodegenerative and cerebrovascular processes. In addition to biochemical abnormalities, involving serotonergic and GABAergic systems, widespread disturbances of cortico-limbic, cortico-subcortical, and other essential brain networks, with disruption in the topological organization of mood- and cognition-related or other global connections are involved. Most recent lesion mapping has identified an altered network architecture with "depressive circuits" and "resilience tracts", thus confirming that depression is a brain network dysfunction disorder. Further pathogenic mechanisms including neuroinflammation, neuroimmune dysregulation, oxidative stress, neurotrophic and other pathogenic factors, such as β-amyloid (and tau) deposition are in discussion. Antidepressant therapies induce various changes in brain structure and function. Better insights into the complex pathobiology of LLD and new biomarkers will allow earlier and better diagnosis of this frequent and disabling psychopathological disorder, and further elucidation of its complex pathobiological basis is warranted in order to provide better prevention and treatment of depression in older individuals.

Antidepressant treatment-related brain activity changes in remitted major depressive disorder

Recent evidence has shown that some brain regions are core hubs and play a key role in the treatment of depression. Twenty-five unmedicated patients with major depressive disorder (MDD) were included, and telephone follow-up was performed at 8, 24, and 48 weeks after enrollment. After reaching clinical remission, they were scheduled for a second magnetic resonance imaging scan and clinical evaluation. Thirty-one healthy controls were also investigated. The intrinsic functional connectivity (degree centrality) of each participant was mapped using a computationally efficient approach. Then, functional connectivity of patients was calculated between the identified regions of interest by degree centrality analysis and every voxel. Later, linear regression analysis was used to identify potential variables predictive of an improvement in disease severity. The prominent hubs identified by degree centrality analysis included the cerebellum, inferior temporal gyrus, lingual gyrus, dorsal medial prefrontal cortex (DMPFC), and dorsal lateral prefrontal cortex. We also found that the increased degree centrality of DMPFC was associated with improvement in depressive symptoms. The brain activity associated with antidepressant effects, especially brain connectivity changes in the left DMPFC, can potentially be used to monitor treatment response and predict treatment outcomes.

Shared and Unique Changes in Brain Connectivity Among Depressed Patients After Remission With Pharmacotherapy Versus Psychotherapy

OBJECTIVE

The authors sought to determine the shared and unique changes in brain resting-state functional connectivity (rsFC) between patients with major depressive disorder who achieved remission with cognitive-behavioral therapy (CBT) or with antidepressant medication.

METHODS

The Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) trial randomized adults with treatment-naive major depressive disorder to 12 weeks of treatment with CBT (16 1-hour sessions) or medication (duloxetine 30-60 mg/day or escitalopram 10-20 mg/day). Resting-state functional MRI scans were performed at baseline and at week 12. The primary outcome was change in the whole-brain rsFC of four seeded brain networks among participants who achieved remission.

RESULTS

Of the 131 completers with usable MRI data (74 female; mean age, 39.8 years), remission was achieved by 19 of 40 CBT-treated and 45 of 91 medication-treated patients. Three patterns of connectivity changes were observed. First, those who remitted with either treatment shared a pattern of reduction in rsFC between the subcallosal cingulate cortex and the motor cortex. Second, reciprocal rsFC changes were observed across multiple networks, primarily increases in CBT remitters and decreases in medication remitters. And third, in CBT remitters only, rsFC increased within the executive control network and between the executive control network and parietal attention regions.

CONCLUSIONS

Remission from major depression via treatment with CBT or medication is associated with changes in rsFC that are mostly specific to the treatment modality, providing biological support for the clinical practice of switching between or combining these treatment approaches. Medication is associated with broadly inhibitory effects. In CBT remitters, the increase in rsFC strength between networks involved in cognitive control and attention provides biological support for the theorized mechanism of CBT. Reducing affective network connectivity with motor systems is a shared process important for remission with both CBT and medication.

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.

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Abnormal dynamics of resting-state functional activity and couplings in postpartum depression with and without anxiety

Postpartum depression (PPD) and PPD comorbid with anxiety (PPD-A) are highly prevalent and severe mental health problems in postnatal women. PPD and PPD-A share similar pathopsychological features, leading to ongoing debates regarding the diagnostic and neurobiological uniqueness. This paper aims to delineate common and disorder-specific neural underpinnings and potential treatment targets for PPD and PPD-A by characterizing functional dynamics with resting-state functional magnetic resonance imaging in 138 participants (45 first-episode, treatment-naïve PPD; 31 PDD-A patients; and 62 healthy postnatal women [HPW]). PPD-A group showed specifically increased dynamic amplitude of low-frequency fluctuation in the subgenual anterior cingulate cortex (sgACC) and increased dynamic functional connectivity (dFC) between the sgACC and superior temporal sulcus. PPD group exhibited specifically increased static FC (sFC) between the sgACC and ventral anterior insula. Common disrupted sFC between the sgACC and middle temporal gyrus was found in both PPD and PPD-A patients. Interestingly, dynamic changes in dFC between the sgACC and superior temporal gyrus could differentiate PPD, PPD-A, and HPW. Our study presents initial evidence on specifically abnormal functional dynamics of limbic, emotion regulation, and social cognition systems in patients with PDD and PPD-A, which may facilitate understanding neurophysiological mechanisms, diagnosis, and treatment for PPD and PPD-A.

Individual variation in brain network topology is linked to course of illness in major depressive disorder

Major depressive disorder (MDD) is a chronic and highly recurrent disorder. The functional connectivity in depression is affected by the cumulative effect of course of illness. However, previous neuroimaging studies on abnormal functional connection have not mainly focused on the disease duration, which is seen as a secondary factor. Here, we used a data-driven analysis (multivariate distance matrix regression) to examine the relationship between the course of illness and resting-state functional dysconnectivity in MDD. This method identified a region in the anterior cingulate cortex, which is most linked to course of illness. Specifically, follow-up seed analyses show this phenomenon resulted from the individual differences in the topological distribution of three networks. In individuals with short-duration MDD, the connection to the default mode network was strong. By contrast, individuals with long-duration MDD showed hyperconnectivity to the ventral attention network and the frontoparietal network. These results emphasized the centrality of the anterior cingulate cortex in the pathophysiology of the increased course of illness and implied critical links between network topography and pathological duration. Thus, dissociable patterns of connectivity of the anterior cingulate cortex is an important dimension feature of the disease process of depression.

Structural network alterations in patients with nasopharyngeal carcinoma after radiotherapy: A 1-year longitudinal study

This longitudinal study explored the changed patterns of structural brain network after radiotherapy (RT) in patients with nasopharyngeal carcinoma (NPC). Diffusion tensor imaging (DTI) data were gathered from 35 patients with NPC at four time points: before RT (baseline), 0∼3 (acute), 6 (early delayed), and 12 months (late-delayed) after RT. The graph theory was used to characterize the dynamic topological properties after RT and the significant changes were detected over time at the global, regional and modular levels. Significantly altered regional metrics (nodal efficiency and degree centrality) were distributed in the prefrontal, temporal, parietal, frontal, and subcortical regions. The module, that exhibited a significantly altered within-module connectivity, had a high overlap with the default mode network (DMN). In addition, the global, regional and modular metrics showed a tendency of progressive decrease at the acute and early delayed stages, and a partial/full recovery at the late-delayed stage. This changed pattern illustrated that the radiation-induced brain damage began at the acute reaction stage and were aggravated at the early-delayed stage, and then partially recovered at the late-delayed stage. Furthermore, the spearman’s correlations between the abnormal nodal metrics and temporal dose were calculated and high correlations were found at the temporal (MTG.R and HES.L), subcortical (INS.R), prefrontal (ORBinf.L and ACG.L), and parietal (IPL.R) indicating that these regions were more sensitive to dose and should be mainly considered in radiotherapy treatment plan.

Impact of Tai Chi as an adjunct treatment on brain connectivity in geriatric depression

BACKGROUND

As an adjunct to antidepressant treatment, Tai Chi Chih (TCC) is superior to health education and wellness (HEW) training in improving the general health of patients with geriatric depression (GD). This study investigated the brain connectivity changes associated with TCC and HEW in combination with antidepressant treatment in patients with GD.

METHODS

Forty patients with GD under stable antidepressant treatment underwent TCC training (n = 21) or HEW training (n = 19) for 12 weeks, and completed baseline and 3-month follow-up resting state magnetic resonance imaging scans. Within-group and between-group differences in parcel-to-parcel connectivity changes with intervention were evaluated by general linear modeling. Relationships between significant connectivity changes and symptom/resilience improvement were evaluated by partial least squares correlation analysis.

RESULTS

Significantly greater increases in connectivity with TCC than with HEW (FDR-corrected p < .05) were observed for 167 pairwise connections, most frequently involving the default mode network (DMN). In both groups, increased connectivity involving largely DMN regions was significantly and positively correlated with improvement in symptoms/resilience.

LIMITATIONS

The sample size was relatively small, mainly due to neuroimaging contraindications (e.g., implants). Additionally, the standard antidepressant treatment varied greatly among patients, adding heterogeneity.

CONCLUSIONS

Non-pharmacological adjuncts, such as TCC, may enhance DMN connectivity changes associated with improved depressive symptoms and psychological resilience in the treatment of GD.

Resting-state functional connectivity of the amygdala in major depressive disorder with suicidal ideation

Suicide is a common issue among major depressive disorder (MDD) patients and suicidal ideation (SI) is the first step toward it. There are no definitive objective biomarkers of SI relative to MDD. In this study, a seed-based correlation analysis was performed among 36 MDD patients with SI, 66 MDD patients without SI (NSI), and 57 healthy controls (HCs) using amygdala resting-state functional connectivity (RSFC). Furthermore, the correlation between amygdala RSFC and clinical features was examined in the SI group. When compared to the NSI group, SI group exhibited increased RSFC between the left amygdala seed and left medial superior frontal gyrus (SFGmed) as well as left middle frontal gyrus (MFG). In turn, a decreased RSFC was observed between the left amygdala seed and the following brain regions including the left inferior parietal lobule (IPL), right precentral gyrus (PrCG), and left superior parietal lobule (SPL) in SI group compared to NSI group. Moreover, the SI group exhibited increased RSFC of the right amygdala with left middle temporal gyrus (MTG); In addition, the RSFC of the left amygdala with left MFG was negatively associated with learning and memory (VSM), speed of processing (SOP). The RSFC of the amygdala is distinct between MDD patients with SI and without SI. Our findings reveal the neurobiological characteristics of MDD with respect to SI and provide new clues regarding vulnerability to mental illness. It is necessary to carry out repeated and more longitudinal researches using multimodal approaches on SI in the future.

Postpartum depression and major depressive disorder: the same or not? Evidence from resting-state functional MRI

Background

Although postpartum depression (PPD) and non-peripartum major depressive disorder (MDD) occurring within and outside the postpartum period share many clinical characteristics, whether PPD and MDD are the same or not remains controversial.

Methods

The current study was devoted to identify the shared and different neural circuits between PPD and MDD by resting-state functional magnetic resonance imaging data from 77 participants (22 first-episodic drug-naïve MDD, 26 drug-naïve PPD, and 29 healthy controls (HC)).

Results

Both the PPD and MDD groups exhibited higher fractional amplitude of low-frequency fluctuation (fALFF) in left temporal pole relative to the HC group; the MDD group showed specifically increased degree centrality in the right cerebellum while PPD showed specifically decreased fALFF in the left supplementary motor area and posterior middle temporal gyrus (pMTG_L), and specifically decreased functional connectivities between pMTG and precuneus and between left subgeneual anterior cingulate cortex (sgACC_L) and right sgACC. Moreover, sgACC and left thalamus showed abnormal regional homogeneity of functional activities between any pair of HC, MDD, and PPD.

Conclusions

These results provide initial evidence that PPD and MDD have common and distinct neural circuits, which may facilitate understanding the neurophysiological basis and precision treatment for PPD.

Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression

OBJECTIVE

Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response.

DESIGN AND INTERVENTIONS

Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion.

PARTICIPANTS

Ninety-five individuals with LLD.

MEASUREMENTS

A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials.

RESULTS

In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a change in depression severity over time across both arms in the blinded phase or in the subsequent open-label bupropion phase. We similarly did not observe effects of WMH volume or hippocampal volume on change in depression severity over time.

CONCLUSION

sMRI markers of accelerated brain aging were not associated with treatment response in this sequential antidepressant trial.

MRI predictors of pharmacotherapy response in major depressive disorder

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

EEG microstate in first-episode drug-naive adolescents with depression

BACKGROUND

A growing number of studies have revealed significant abnormalities in EEG microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far.

METHODS

Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train a SVM for classification of patients with depression.

RESULTS

Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained.

CONCLUSION

Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.

Default mode network connectivity and treatment response in geriatric depression

OBJECTIVES

Default mode network (DMN) connectivity is altered in depression. We evaluated the relationship between changes in within-network DMN connectivity and improvement in depression in a subsample of our parent clinical trial comparing escitalopram/memantine (ESC/MEM) to escitalopram/placebo (ESC/PBO) in older depressed adults (NCT01902004).

METHODS

Twenty-six participants with major depression (age > 60 years) and subjective memory complaints underwent treatment with ESC/MEM (n = 13) or ESC/PBO (n = 13), and completed baseline and 3-month follow-up resting state magnetic resonance imaging scans. Multi-block partial least squares correlation analysis was used to evaluate the impact of treatment on within-network DMN connectivity changes and their relationship with symptom improvement at 3 months (controlling for age and sex).

RESULTS

A significant latent variable was identified, reflecting within-network DMN connectivity changes correlated with symptom improvement (p = .01). Specifically, although overall group differences in within-network DMN connectivity changes failed to reach significance, increased within-network connectivity of posterior/lateral DMN regions (precuneus, angular gyrus, superior/middle temporal cortex) was more strongly and positively correlated with symptom improvement in the ESC/MEM group (r = 0.97, 95% confidence interval: 0.86-0.98) than in the ESC/PBO group (r = 0.36, 95% confidence interval: 0.13-0.72).

CONCLUSIONS

Increased within-network connectivity of core DMN nodes was more strongly correlated with depressive symptom improvement with ESC/MEM than with ESC/PBO, supporting an improved engagement of brain circuitry implicated in the amelioration of depressive symptoms with combined ESC/MEM treatment in older adults with depression and subjective memory complaints.

Disrupted default mode network and executive control network are associated with depression severity on structural network

OBJECTIVE

Major depressive disorder (MDD) is a psychiatric disorder with a relatively limited response to treatment. It is necessary to better understand the neuroanatomical mechanisms of structural networks.

METHODS

The current study recruited 181 first-onset, untreated adult MDD patients: slight MDD (SD, N = 23), moderate MDD (MD, N = 77), Heavy MDD (HD, N = 81) groups; along with a healthy control group (HC, N = 81) with matched general clinical data. FreeSurfer was used to preprocess T1 images for gray matter volume (GMV), and the default mode network (DMN) and the execution control network (ECN) were analyzed by structural covariance network (SCN).

RESULTS

Present study found that the GMV of brain regions reduced with the severity of the disease. Specifically, the GMV of the left anterior cingulate gyrus (ACC.L) is negatively correlated with MDD severity. In addition, the SCN connectivity of the whole-brain network increases with the increase of severity in MDD. ACC.L is a key brain region with increased connectivity between the left orbitofrontal in DMN and between the right orbitofrontal in ECN, which leads to damage to the balance of neural circuits.

CONCLUSIONS

Patients with smaller GMV of ACC.L are more likely to develop severe MDD, and as a key region in both networks which have distinct structural network models in DMN and ECN. MDD patients with different severity have different neuroimaging changes in DMN and ECN.

An examination of the relationships between attention/deficit hyperactivity disorder symptoms and functional connectivity over time

Previous cross-sectional work has demonstrated resting-state connectivity abnormalities in children and adolescents with attention/deficit hyperactivity disorder (ADHD) relative to typically developing controls. However, it is unclear to what extent these neural abnormalities confer risk for later symptoms of the disorder, or represent the downstream effects of symptoms on functional connectivity. Here, we studied 167 children and adolescents (mean age at baseline = 10.74 years (SD = 2.54); mean age at follow-up = 13.3 years (SD = 2.48); 56 females) with varying levels of ADHD symptoms, all of whom underwent resting-state functional magnetic resonance imaging and ADHD symptom assessments on two occasions during development. Resting-state functional connectivity was quantified using eigenvector centrality mapping. Using voxelwise cross-lag modeling, we found that less connectivity at baseline within right inferior frontal gyrus was associated with more follow-up symptoms of inattention (significant at an uncorrected cluster-forming threshold of p ≤ 0.001 and a cluster-level familywise error corrected threshold of p < 0.05). Findings suggest that previously reported cross-sectional abnormalities in functional connectivity within inferior frontal gyrus in patients with ADHD may represent a longitudinal risk factor for the disorder, in line with efforts to target this region with novel therapeutic methods.

Prediction of Antidepressant Efficacy by Cognitive Function in First-Episode Late-Life Depression: A Pilot Study

The response rate of treatment for late-life depression (LLD) is only 25-60%. The cognitive impairment associated with LLD often affects the effectiveness of antidepressants and may has the potential ability to predict response. This study seeks a biomarker for baseline cognitive function to predict efficacy of antidepressants. Sixty patients diagnosed with LLD received escitalopram or sertraline treatment for 8 weeks. Clinical symptom was measured using Hamilton Depression Rating Scale-17 (HAMD-17) and cognitive function was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Trail Making Test (TMT) before and after 8-week treatment. Patients were divided into treatment effective group (TE) and treatment ineffective group (TI) according to reduction rate in scores of HAMD-17 after treatment. Thirty-eight matched healthy controls (HC) were assessed using RBANS and TMT. There was significant decrease of score of RBANS and increase of score of TMT in patients with LLD compared with HC. Regression analysis revealed that change in HAMD-17 score was significantly positively associated with baseline score of picture naming, figure copy, digit span, and delayed memory. The preliminary findings suggested that working memory, attention, visuospatial, language function, and delayed memory should be examined further as a means of providing the useful objective biomarkers of treatment response.

CLINICAL TRIALS REGISTRATION

[www.ClinicalTrials.gov], identifier [ChiCTR2100042370].

Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder

Antidepressants are often the first-line medications prescribed for patients with major depressive disorder (MDD). Given the critical role of the default mode network (DMN) in the physiopathology of MDD, the current study aimed to investigate the effects of antidepressants on the resting-state functional connectivity (rsFC) within and between the DMN subsystems. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 36 unmedicated MDD patients at baseline and after escitalopram treatment for 12 weeks. The rs-fMRI data were also collected from 61 matched healthy controls at the time point with the same interval. Then, we decomposed the DMN into three subsystems based on a template from previous studies and computed the rsFC within and between the three subsystems. Finally, repeated measures analysis of covariance was conducted to identify the main effect of group and time and their interaction effect. We found that the significantly reduced within-subsystem rsFC in the DMN core subsystem in patients with MDD at baseline was increased after escitalopram treatment and became comparable with that in the healthy controls, whereas the reduced within-subsystem rsFC persisted in the DMN dorsal medial prefrontal cortex (dMPFC) and medial temporal subsystems in patients with MDD following escitalopram treatment. In addition, the reduced between-subsystem rsFC between the core and dMPFC subsystem showed a similar trend of change after treatment in patients with MDD. Moreover, our main results were confirmed using the DMN regions from another brain atlas. In the current study, we found different effects of escitalopram on the rsFC of the DMN subsystems. These findings deepened our understanding of the neuronal basis of antidepressants' effect on brain function in patients with MDD. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.

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.

Classification of Adolescent Major Depressive Disorder Via Static and Dynamic Connectivity

This paper introduces an approach for classifying adolescents suffering from MDD using resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients and their parents, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), behavioral observation as well as the experience of a clinician. Discovering predictive biomarkers for diagnosing MDD patients using functional magnetic resonance imaging (fMRI) scans can assist the clinicians in their diagnostic assessments. This paper investigates various static and dynamic connectivity measures extracted from resting-state fMRI for assisting with MDD diagnosis. First, absolute Pearson correlation matrices from 85 brain regions are computed and they are used to calculate static features for predicting MDD. A predictive sub-network extracted using sub-graph entropy classifies adolescent MDD vs. typical healthy controls with high accuracy, sensitivity and specificity. Next, approaches utilizing dynamic connectivity are employed to extract tensor based, independent component based and principal component based subject specific attributes. Finally, features from static and dynamic approaches are combined to create a feature vector for classification. A leave-one-out cross-validation method is used for the final predictor performance. Out of 49 adolescents with MDD and 33 matched healthy controls, a support vector machine (SVM) classifier using a radial basis function (RBF) kernel using differential sub-graph entropy combined with dynamic connectivity features classifies MDD vs. healthy controls with an accuracy of 0.82 for leave-one-out cross-validation. This classifier has specificity and sensitivity of 0.79 and 0.84, respectively.

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.

Preliminary Evidence That Cortical Amyloid Burden Predicts Poor Response to Antidepressant Medication Treatment in Cognitively Intact Individuals With Late-Life Depression

OBJECTIVE

Amyloid accumulation, the pathological hallmark of Alzheimer's disease, may predispose some older adults to depression and cognitive decline. Deposition of amyloid also occurs prior to the development of cognitive decline. It is unclear whether amyloid influences antidepressant outcomes in cognitively intact depressed elders.

DESIGN

A pharmacoimaging trial utilizing florbetapir (18F) PET scanning followed by 2 sequential 8-week antidepressant medication trials.

PARTICIPANTS

Twenty-seven depressed elders who were cognitively intact on screening.

MEASUREMENTS AND INTERVENTIONS

After screening, diagnostic testing, assessment of depression severity and neuropsychological assessment, participants completed florbetapir (18F) PET scanning. They were then randomized to receive escitalopram or placebo for 8 weeks in a double-blinded two-to-one allocation rate. Individuals who did not respond to initial treatment transitioned to a second open-label trial of bupropion for another 8 weeks.

RESULTS

Compared with 22 amyloid-negative participants, 5 amyloid-positive participants exhibited significantly less change in depression severity and a lower likelihood of remission. In the initial blinded trial, 4 of 5 amyloid-positive participants were nonremitters (80%), while only 18% (4 of 22) of amyloid-negative participants did not remit (p = 0.017; Fisher's Exact test). In separate models adjusting for key covariates, both positive amyloid status (t = 3.07, 21 df, p = 0.003) and higher cortical amyloid binding by standard uptake value ratio (t = 2.62, 21 df, p = 0.010) were associated with less improvement in depression severity. Similar findings were observed when examining change in depression status across both antidepressant trials.

CONCLUSIONS

In this preliminary study, amyloid status predicted poor antidepressant response to sequential antidepressant treatment. Alternative treatment approaches may be needed for amyloid-positive depressed elders.

Disrupted hemispheric connectivity specialization in patients with major depressive disorder: Evidence from the REST-meta-MDD Project

BACKGROUND

Functional specialization is a feature of human brain for understanding the pathophysiology of major depressive disorder (MDD). The degree of human specialization refers to within and cross hemispheric interactions. However, most previous studies only focused on interhemispheric connectivity in MDD, and the results varied across studies. Hence, brain functional connectivity asymmetry in MDD should be further studied.

METHODS

Resting-state fMRI data of 753 patients with MDD and 451 healthy controls were provided by REST-meta-MDD Project. Twenty-five project contributors preprocessed their data locally with the Data Processing Assistant State fMRI software and shared final indices. The parameter of asymmetry (PAS), a novel voxel-based whole-brain quantitative measure that reflects inter- and intrahemispheric asymmetry, was reported. We also examined the effects of age, sex and clinical variables (including symptom severity, illness duration and three depressive phenotypes).

RESULTS

Compared with healthy controls, patients with MDD showed increased PAS scores (decreased hemispheric specialization) in most of the areas of default mode network, control network, attention network and some regions in the cerebellum and visual cortex. Demographic characteristics and clinical variables have significant effects on these abnormalities.

LIMITATIONS

Although a large sample size could improve statistical power, future independent efforts are needed to confirm our results.

CONCLUSIONS

Our results highlight the idea that many brain networks contribute to broad clinical pathophysiology of MDD, and indicate that a lateralized, efficient and economical brain information processing system is disrupted in MDD. These findings may help comprehensively clarify the pathophysiology of MDD in a new hemispheric specialization perspective.

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.

Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder

BACKGROUND

Alterations in gray matter (GM) have been recognized as playing an important role in the neurobiological mechanism underlying major depressive disorder (MDD) and antidepressant responses. However, little is known about white matter (WM) connectivity in MDD, leaving an incomplete understanding of the pathophysiology of the disorder.

PURPOSE

To examine the functional connectivity (FC) of WM, GM, and WM-GM in MDD patients and explore the relationship between FC and antidepressant response.

STUDY TYPE

Longitudinal study.

SUBJECTS

In all, 129 MDD patients and 89 healthy controls (HC).

FIELD STRENGTH/SEQUENCE

Whole-brain blood oxygen level-dependent (BOLD) single-shot echo planar imaging was acquired at 3.0T.

ASSESSMENT

At baseline, all participants received Hamilton depression rating scale (HAMD) assessment and an fMRI scan. After 2- and 8-week antidepressant treatment, patients completed the HAMD again. The HAMD reductive rate of 2- and 8-weeks were calculated.

STATISTICAL TESTS

The comparisons of age, education, HAMD scores, and FC values (false discovery rate correction) between patients and controls were calculated with a two-sample t-test. The chi-square test was employed to compare the differences of gender between these two groups. Correlations between FC and HAMD, as well as the reductive rate of HAMD, were analyzed with Pearson or Spearman correlation. Receiver operator curve analysis was performed to predict the antidepressant response.

RESULTS

Compared to HC, MDD patients exhibited widespread decreases in FC of WM-GM. Furthermore, 28 GM regions and 11 WM bundles had lower connectivity in MDD patients. At baseline, four FC of WM-GM showed negative correlations with the HAMD scores. Six FC of WM-GM correlated with the 2-week reductive rate of HAMD. Moreover, FC in GM, WM, and WM-GM also exhibited significantly positive correlations with an 8-week reductive rate of HAMD.

DATA CONCLUSION

The FC of WM-GM was decreased in MDD and may play a role in its pathophysiology and antidepressant responses.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

2.

Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model

AIMS

Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population.

METHODS

The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period.

RESULTS

AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2  = .17, P < .001) at baseline in the combined high-risk population (N = 72).

CONCLUSIONS

The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.

Antidepressants normalize brain flexibility associated with multi-dimensional symptoms in major depressive patients

BACKGROUND

The fundamental pathophysiology of major depressive disorder (MDD) could be characterized by functional brain networks which tightly and dynamically connect into groups as communities, making the flexible brain possible to external multifarious demands. We aim to scrutinize what brain dynamics go awry in MDD and antidepressants effects on multi-dimensional symptoms.

METHODS

Thirty-five patients and thirty-five controls underwent resting-state functional magnetic resonance imaging (MRI). Patients were scanned before and after 8 or 12 weeks of pharmacotherapy. Group independent component analysis decomposed resting-state images to instinct networks and networks' integrated flexibility was calculated. Network flexibility between patients at baseline and after therapy were compared.

RESULTS

All patients completed the clinical trial and MRI scans. Following antidepressants treatment, we found significant normalization of reduced network flexibility in default mode network (DMN) and cognitive control network (CCN) of MDD patients. Selectively significant correlations between network flexibility and multi-dimensional symptoms such as anxiety/somatization and hysteresis factor were also found.

CONCLUSIONS

"Hypoflexible" CCN may involve in anxiety syndrome. Low flexibility in DMN may be indicative of hysteresis. These suggest an important pathophysiology of depressive manifestation of MDD. The antidepressant-induced normalization of the "hypoflexibility" suggests a selective pathway through which antidepressants may alleviate symptoms in depression.

Depression Affects Intrinsic Brain Activity in Patients With Mild Cognitive Impairment

Numerous observational studies have shown that depressive symptoms are common in individuals with mild cognitive impairment (MCI) who have a higher rate of progress to dementia. However, it is still uncertain whether there are any differences between MCI patients with and without depression symptom in their brain function activities. Here we have identified the brain function activity differences in two groups of MCI patients (with depression or without depression) using the resting state MRI (rsfMRI) measurements. 76 right-handed MCI subjects have been recruited in this study, including 27 MCI patients with depression symptom (MCID), 49 MCI patients without depression symptom (MCIND). Analyses based on 7 rsfMRI measurements, including four static measurements (ALFF, fALFF, PerAF, and ReHo) and three dynamic measurements (dALFF, dfALFF, and dReHo) have been used to explore the temporal variability of intrinsic brain activity. No significant differences in ALFF and dALFF between the two group were found. In the MCID group, fALFF decreased in temporal gyrus, frontal gyrus, inferior occipital gyrus, middle frontal gyrus and cerebellum, but increased in cuneus, calcarine, lingual; while PerAF increased in left parahippocampus. The differences of ReHo in the two groups was only found in cerebellum. Compared to MCIND group, dfALFF in MCID decreased in cuneus, occipital gyrus and calcarine, while dReHo in MCID increased in bilateral temporal gyrus, frontal gyrus, superior parietal gyrus, inferior parietal gyrus and precuneus. Our results may provide a better understanding in the relationship between the depressive symptoms and memory deficits.

The rise and fall of MRI studies in major depressive disorder

Structural and functional brain alterations are common in patients with major depressive disorder (MDD). In this review, we assessed the recent literature (1995-2018) on the structural and functional magnetic resonance imaging (MRI) studies of MDD. Despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop "bridges" to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. From our findings, we proposed that the "bridges" may be built using multidisciplinary technologies, such as artificial intelligence, multimodality imaging, and nanotheranostics, allowing for the further study of MDD at the biological level. In return, the "bridges" will aid in the development of future diagnostics for MDD and other mental disorders.

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.

Recent advances in the use of imaging in psychiatry: functional magnetic resonance imaging of large-scale brain networks in late-life depression

Advances in neuroimaging have identified neural systems that contribute to clinical symptoms that occur across various psychiatric disorders. This transdiagnostic approach to understanding psychiatric illnesses may serve as a precise guide to identifying disease mechanisms and informing successful treatments. While this work is ongoing across multiple psychiatric disorders, in this article we emphasize recent findings pertaining to major depression in the elderly, or late-life depression (LLD), a common and debilitating neuropsychiatric illness. We discuss how neural functioning of three networks is linked to symptom presentation, illness course, and cognitive decline in LLD. These networks are (1) an executive control network responsible for complex cognitive processing, (2) a default mode network normally deactivated during cognitive demanding when individuals are at rest, and a (3) salience network relevant to attending to internal and external emotional and physiological sensations. We discuss how dysfunction in multiple networks contributes to common behavioral syndromes, and we present an overview of the cognitive control, default mode, and salience networks observed in LLD.

Increased brain entropy of resting-state fMRI mediates the relationship between depression severity and mental health-related quality of life in late-life depressed elderly

BACKGROUND

Entropy analysis is a computational method used to quantify the complexity in a system, and loss of brain complexity is hypothesized to be related to mental disorders. Here, we applied entropy analysis to the resting-state functional magnetic resonance imaging (rs-fMRI) signal in subjects with late-life depression (LLD), an illness combined with emotion dysregulation and aging effect.

METHODS

A total of 35 unremitted depressed elderly and 22 control subjects were recruited. Multiscale entropy (MSE) analysis was performed in the entire brain, 90 automated anatomical labeling-parcellated ROIs, and five resting networks in each study participant.

LIMITATIONS

Due to ethical concerns, all the participants were under medication during the study.

RESULTS

Regionally, subjects with LLD showed decreased entropy only in the right posterior cingulate gyrus but had universally increased entropy in affective processing (putamen and thalamus), sensory, motor, and temporal nodes across different time scales. We also found higher entropy in the left frontoparietal network (FPN), which partially mediated the negative correlation between depression severity and mental components of the quality of life, reflecting the possible neural compensation during depression treatment.

CONCLUSION

MSE provides a novel and complementary approach in rs-fMRI analysis. The temporal-spatial complexity in the resting brain may provide the adaptive variability beneficial for the elderly with depression.

Functional MRI findings, pharmacological treatment in major depression and clinical response

Major depressive disorders are common conditions with relatively limited response to treatment. In order to improve response to treatment, a better understanding of functional neuroanatomy is necessary to improve treatment targets at brain level. This work summarises the literature of longitudinal functional magnetic resonance imaging studies in major depression to identify brain regions where aberrant neural activity normalises after clinical response following treatment with pharmacological compounds with known antidepressant properties. Hyperactivity in regions such as the amygdala and the ventral components of the anterior cingulate cortex were some of the most replicated findings of functional MRI studies in major depression and normalisation of aberrant activity one of the best predictive biomarkers of treatment response.

Low-dose augmentation with buprenorphine increases emotional reactivity but not reward activity in treatment resistant mid- and late-life depression

Buprenorphine is currently being studied for treatment-resistant depression because of its rapid effect, relative safety, and unique pharmacodynamics. To understand the neural impact of buprenorphine in depression, we examined acute limbic and reward circuit changes during an intervention with low-dose buprenorphine augmentation pharmacotherapy. Mid and late-life adults with major depression (N = 31) who did not completely respond to an adequate trial of venlafaxine were randomized to augmentation with low-dose buprenorphine or matching placebo. We investigated early neural changes using functional magnetic resonance imaging (fMRI) from pre-randomization to 3 weeks using both an emotional reactivity task and a gambling task. We tested if: 1) there were significant neural changes acutely per intervention group, and 2) if acute neural changes were associated with depressive symptom change over 8 weeks using both the total score and the dysphoria subscale of the Montgomery Asberg Depression Rating Scale. Participants in both the buprenorphine and placebo groups showed similar changes in depressive symptoms. Neither the emotional reactivity nor gambling task resulted in significant neural activation changes from pre-randomization to 3-weeks. In both groups, increases in rostral anterior cingulate (rACC) and ventromedial prefrontal cortex (vmPFC) activation during the emotional reactivity task were associated with overall symptom improvement. In the buprenorphine but not the placebo group, increased activation in left anterior insula (aINS) and bilateral middle frontal gyrus (MFG) was associated with improvement on the dysphoria subscale. Activation changes in the reward task were not associated with buprenorphine. This is the first study to show an association between acute neural changes during emotion reactivity and changes in depression severity with buprenorphine treatment.

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.