Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder

Background

A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care.

Methods

Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology.

Results

We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05).

Conclusions

Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

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.

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.

Review: The use of functional magnetic resonance imaging (fMRI) in clinical trials and experimental research studies for depression

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that can be used to examine neural responses with and without the use of a functional task. Indeed, fMRI has been used in clinical trials and pharmacological research studies. In mental health, it has been used to identify brain areas linked to specific symptoms but also has the potential to help identify possible treatment targets. Despite fMRI's many advantages, such findings are rarely the primary outcome measure in clinical trials or research studies. This article reviews fMRI studies in depression that sought to assess the efficacy and mechanism of action of compounds with antidepressant effects. Our search results focused on selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed treatments for depression and ketamine, a fast-acting antidepressant treatment. Normalization of amygdala hyperactivity in response to negative emotional stimuli was found to underlie successful treatment response to SSRIs as well as ketamine, indicating a potential common pathway for both conventional and fast-acting antidepressants. Ketamine's rapid antidepressant effects make it a particularly useful compound for studying depression with fMRI; its effects on brain activity and connectivity trended toward normalizing the increases and decreases in brain activity and connectivity associated with depression. These findings highlight the considerable promise of fMRI as a tool for identifying treatment targets in depression. However, additional studies with improved methodology and study design are needed before fMRI findings can be translated into meaningful clinical trial outcomes.

Brain connectivity in major depressive disorder: a precision component of treatment modalities?

Major depressive disorder (MDD) is a very prevalent mental disorder that imposes an enormous burden on individuals, society, and health care systems. Most patients benefit from commonly used treatment methods such as pharmacotherapy, psychotherapy, electroconvulsive therapy (ECT), and repetitive transcranial magnetic stimulation (rTMS). However, the clinical decision on which treatment method to use remains generally informed and the individual clinical response is difficult to predict. Most likely, a combination of neural variability and heterogeneity in MDD still impedes a full understanding of the disorder, as well as influences treatment success in many cases. With the help of neuroimaging methods like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the brain can be understood as a modular set of functional and structural networks. In recent years, many studies have investigated baseline connectivity biomarkers of treatment response and the connectivity changes after successful treatment. Here, we systematically review the literature and summarize findings from longitudinal interventional studies investigating the functional and structural connectivity in MDD. By compiling and discussing these findings, we recommend the scientific and clinical community to deepen the systematization of findings to pave the way for future systems neuroscience roadmaps that include brain connectivity parameters as a possible precision component of the clinical evaluation and therapeutic decision.

A randomized controlled trial of desvenlafaxine-induced structural brain changes in the treatment of persistent depressive disorder

The anatomical changes that antidepressant medications induce in the brain and through which they exert their therapeutic effects remain largely unknown. We randomized 61 patients with Persistent Depressive Disorder (PDD) to receive either desvenlafaxine or placebo in a 12-week trial and acquired anatomical MRI scans in 42 of those patients at baseline before randomization and immediately at the end of the trial. We also acquired MRIs once in 39 age- and sex-matched healthy controls. We assessed whether the serotonin-norepinephrine reuptake inhibitor, desvenlafaxine, differentially changed cortical thickness during the trial compared with placebo. Patients relative to controls at baseline had thinner cortices across the brain. Although baseline thickness was not associated with symptom severity, thicker baseline cortices predicted greater reduction in symptom severity in those treated with desvenlafaxine but not placebo. We did not detect significant treatment-by-time effects on cortical thickness. These findings suggest that baseline thickness may serve as predictive biomarkers for treatment response to desvenlafaxine. The absence of treatment-by-time effects may be attributable either to use of insufficient desvenlafaxine dosing, a lack of desvenlafaxine efficacy in treating PDD, or the short trial duration.

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.

Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study

Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise therapy for patients with painful DPN. The aim of this study was to use deep learning to predict treatment response in patients with pDPN using resting state functional imaging (rs-fMRI). We divided 43 painful pDPN patients into responders and non-responders to lidocaine treatment (responders n = 29 and non-responders n = 14). We used rs-fMRI to extract functional connectivity features, using group independent component analysis (gICA), and performed automated treatment response deep learning classification with three-dimensional convolutional neural networks (3D-CNN). Using gICA we achieved an area under the receiver operating characteristic curve (AUC) of 96.60% and F1-Score of 95% in a ten-fold cross validation (CV) experiment using our described 3D-CNN algorithm. To our knowledge, this is the first study utilising deep learning methods to classify treatment response in pDPN.

Subclinical alterations of resting state functional brain network for adjunctive bright light therapy in nonseasonal major depressive disorder: A double blind randomized controlled trial

Introduction

The treatment effect of bright light therapy (BLT) on major depressive disorder (MDD) has been proven, but the underlying mechanism remains unclear. Neuroimaging biomarkers regarding disease alterations in MDD and treatment response are rarely focused on BLT. This study aimed to identify the modulatory mechanism of BLT in MDD using resting-state functional magnetic resonance imaging (rfMRI).

Materials and methods

This double-blind, randomized controlled clinical trial included a dim red light (dRL) control group and a BLT experimental group. All participants received light therapy for 30 min every morning for 4 weeks. The assessment of the Hamilton Depression Rating Scale-24 (HAMD-24) and brain MRI exam were performed at the baseline and the 4-week endpoint. The four networks in interest, including the default mode network (DMN), frontoparietal network (FPN), salience network (SN), and sensorimotor network (SMN), were analyzed. Between-group differences of the change in these four networks were evaluated.

Results

There were 22 and 21 participants in the BLT and dRL groups, respectively. Age, sex, years of education, baseline severity, and improvement in depressive symptoms were not significantly different between the two groups. The baseline rfMRI data did not show any significant functional connectivity differences within the DMN, FPN, SN, and SMN between the two groups. Compared with the dRL group, the BTL group showed significantly increased functional connectivity after treatment within the DMN, FPN, SN, and SMN. Graph analysis of the BLT group demonstrated an enhancement of betweenness centrality and global efficiency.

Conclusion

BLT can enhance intra-network functional connectivity in the DMN, FPN, SN, and SMN for MDD patients. Furthermore, BLT improves the information processing of the whole brain.

Clinical trial registration

The ClinicalTrials.gov identifier was NCT03941301.

Contrasting the amygdala activity and functional connectivity profile between antidepressant-free participants with major depressive disorder and healthy controls: A systematic review of comparative fMRI studies

Functional neuroimaging research suggests that the amygdala is implicated in the pathophysiology of major depressive disorder (MDD). This systematic review aimed to identify consistently reported amygdala activity and functional connectivity (FC) abnormalities in antidepressant-free participants with MDD as compared to healthy controls at baseline (i.e., before treatment initiation or experimental manipulation). A search for relevant published studies and registered clinical trials was conducted through OVID (MEDLINE, PsycINFO, and Embase) and ClinicalTrials.gov with an end date of March 7th, 2022. Fifty published studies and two registered clinical trials were included in this review. Participants with MDD frequently exhibited amygdala hyperactivity in response to negative stimuli, abnormal event-related amygdala-anterior cingulate cortex (ACC) FC, and abnormal resting-state amygdala FC with the insula and the prefrontal, temporal, and parietal cortices. Decreased resting-state FC was consistently found between the amygdala and the orbitofrontal cortex, striatum, cerebellum, and middle/inferior frontal gyri. Due to the limited number of studies examining resting-state amygdala activity and FC with specific subregions of interest, including those within the ACC, further investigation is warranted.

Combined fractional anisotropy and subcortical volumetric deficits in patients with mild-to-moderate depression: Evidence from the treatment of antidepressant traditional Chinese medicine

Numerous neuroimaging studies have demonstrated that diverse brain structural plasticity could occur in a human brain during a depressive episode. However, there is a lack of knowledge regarding the underlying mechanisms of mild-to-moderate depression (MMD), especially the changes of brain structural characteristics after treatment with the Shuganjieyu capsule (SG), a kind of traditional Chinese medicine that has been recommended for the specialized treatment of MMD. In this study, we investigated the structural brain plasticity in MMD that have been undergoing 8 weeks of SG treatment compared with age- and sex-matched healthy controls (HCs) and assessed the relationship between these brain structural alternations and clinical symptoms in MMD. At the baseline, we found that: (1) fractional anisotropy (FA) values in patients with MMD were found to be significantly increased in the regions of anterior limb of internal capsule (ALIC) [MNI coordinates: Peak (x/y/z) = 102, 126, 77; MMD FA_peak (Mean ± SD) = 0.621 ± 0.043; HCs FA_peak (Mean ± SD) = 0.524 ± 0.052; MMD > HCs, t = 9.625, p < 0.001] and posterior limb of internal capsule (PLIC) [MNI coordinates: Peak (x/y/z) = 109, 117, 87; MMD FA_peak (Mean ± SD) = 0.694 ± 0.042; HCs FA_peak (Mean ± SD) = 0.581 ± 0.041; MMD > HCs, t = 12.90, p < 0.001], and FA values were significantly positively correlated with HAMD scores in patients with MMD. (2) Patients with MMD showed smaller gray matter volume (GMV) of the dorsolateral prefrontal cortex (DLPFC), frontal cortex, occipital cortex, and precuneus, and the GMV of DLPFC was negatively correlated with HAMD scores. After SG treatment, we found that (1) the HAMD scores decreased; (2) FA values were significantly decreased in the regions of the ALIC and PLIC compared to those at baseline and TBSS revealed no significant differences in FA values between patients with MMD and HCs. (3) The structural characteristics of DLPFC in patients with MMD obtained at the 8th week were improved, e.g., no significant differences in GMV of DLPFC between the two groups. Taken together, our results provided neuroimaging evidence suggesting that SG is an effective treatment for patients with MMD. Moreover, alterations of GMV after 8 weeks of SG treatment indicated a potential modulation mechanism in brain structural plasticity within the DLPFC in patients with MMD.

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.

Emotional faces processing in major depressive disorder and prediction of antidepressant treatment response: A NeuroPharm study

BACKGROUND

Major depressive disorder (MDD) is a prevalent neuropsychiatric illness for which it is important to resolve underlying brain mechanisms. Current treatments are often unsuccessful, precipitating a need to identify predictive markers.

AIM

We evaluated (1) alterations in brain responses to an emotional faces functional magnetic resonance imaging (fMRI) paradigm in individuals with MDD, compared to controls, (2) whether pretreatment brain responses predicted antidepressant treatment response, and (3) pre-post change in brain responses following treatment.

METHODS

Eighty-nine medication-free, depressed individuals and 115 healthy controls completed the fMRI paradigm. Depressed individuals completed a nonrandomized, open-label, 8-week treatment with escitalopram, including the option to switch to duloxetine after 4 weeks. We examined patient-control group differences in regional fMRI responses at baseline, whether baseline fMRI responses predicted treatment response at 8 weeks, including early life stress moderating effects, and change in fMRI responses in 36 depressed individuals rescanned following 8 weeks of treatment.

RESULTS

Task reaction time was 5% slower in patients. Multiple brain regions showed significant task-related responses, but we observed no statistically significant patient-control group differences (Cohen's d < 0.35). Patient pretreatment brain responses did not predict antidepressant treatment response (area under the curve of the receiver operator characteristic (AUC-ROC) < 0.6) and brain responses were not statistically significantly changed after treatment (Cohen's d < 0.33).

CONCLUSION

This represents the largest prediction study to date examining emotional faces fMRI features as predictors of antidepressant treatment response. Brain response to this fMRI emotional faces paradigm did not distinguish depressed individuals from healthy controls, nor was it predictive of antidepressant treatment response.Clinical Trial Registration: Site: https://clinicaltrials.gov, Trial Number: NCT02869035, Trial Title: Treatment Outcome in Major Depressive Disorder.

Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection

Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method.

Role of Brain Imaging in Drug Development for Psychiatry

Background:

Over the last decades, many brain imaging studies have contributed to new insights in the pathogenesis of psychiatric disease. However, in spite of these developments, progress in the development of novel therapeutic drugs for prevalent psychiatric health conditions has been limited.

Objective:

In this review, we discuss translational, diagnostic and methodological issues that have hampered drug development in CNS disorders with a particular focus on psychiatry. The role of preclinical models is critically reviewed and opportunities for brain imaging in early stages of drug development using PET and fMRI are discussed. The role of PET and fMRI in drug development is reviewed emphasizing the need to engage in collaborations between industry, academia and phase I units.

Conclusion:

Brain imaging technology has revolutionized the study of psychiatric illnesses, and during the last decade, neuroimaging has provided valuable insights at different levels of analysis and brain organization, such as effective connectivity (anatomical), functional connectivity patterns and neurochemical information that may support both preclinical and clinical drug development. Since there is no unifying pathophysiological theory of individual psychiatric syndromes and since many symptoms cut across diagnostic boundaries, a new theoretical framework has been proposed that may help in defining new targets for treatment and thus enhance drug development in CNS diseases. In addition, it is argued that new proposals for data-mining and mathematical modelling as well as freely available databanks for neural network and neurochemical models of rodents combined with revised psychiatric classification will lead to new validated targets for drug development.

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.

Body-mind relaxation meditation modulates the thalamocortical functional connectivity in major depressive disorder: a preliminary resting-state fMRI study

Mindfulness-based interventions such as meditation have increasingly been utilized for the treatment of psychological disorders and have been shown to be effective in the treatment of depression and relapse prevention. However, it remains largely unclear the neural mechanism of the therapeutic effects of meditation among depressed individuals. In this study, we investigated how body-mind relaxation meditation (BMRM) can modulate the thalamocortical functional connectivity (FC) in major depressive disorder patients and healthy controls. In the present study, we recruited 21 medication-naive adolescents with major depressive disorder (MDDs) and 24 matched healthy controls (HCs). We designed an audio recording to induce body-mind relaxation meditation. Resting-state fMRI (rs-fMRI) scans were collected before and after the BMRM intervention in both groups. The thalamus subregions were defined according to the Human Brainnetome Atlas, and functional connectivity (FC) was measured and compared to find brain regions that were affected by the BMRM intervention. Before the BMRM intervention, MDDs showed reduced FC of the bilateral precuneus/post cingulate cortex with the left posterior parietal thalamus and left caudal temporal thalamus, as well as an increased FC of the left occipital thalamus with the left medial frontal cortex. Moreover, aberrant FCs in MDDs at baseline were normalized following the BMRM intervention. After the BMRM intervention, both MDDs and HCs showed decreased FC between the left rostral temporal thalamus and the left inferior occipital. Given the small sample used in this study, future studies are warranted to evaluate the generalizability of these findings. Our findings suggest that BMRM is associated with changes in thalamocortical functional connectivity in MDDs. BMRM may act by strengthening connections between the thalamus and the default mode network, which are involved in a variety of high-level functioning, such as attention and self-related processes.

Eight-week antidepressant treatment reduces functional connectivity in first-episode drug-naïve patients with major depressive disorder

Previous neuroimaging studies have revealed abnormal functional connectivity of brain networks in patients with major depressive disorder (MDD), but findings have been inconsistent. A recent big‐data study found abnormal intrinsic functional connectivity within the default mode network in patients with recurrent MDD but not in first‐episode drug‐naïve patients with MDD. This study also provided evidence for reduced default mode network functional connectivity in medicated MDD patients, raising the question of whether previously observed abnormalities may be attributable to antidepressant effects. The present study (ClinicalTrials.gov identifier: NCT03294525) aimed to disentangle the effects of antidepressant treatment from the pathophysiology of MDD and test the medication normalization hypothesis. Forty‐one first‐episode drug‐naïve MDD patients were administrated antidepressant medication (escitalopram or duloxetine) for 8 weeks, with resting‐state functional connectivity compared between posttreatment and baseline. To assess the replicability of the big‐data finding, we also conducted a cross‐sectional comparison of resting‐state functional connectivity between the MDD patients and 92 matched healthy controls. Both Network‐Based Statistic analyses and large‐scale network analyses revealed intrinsic functional connectivity decreases in extensive brain networks after treatment, indicating considerable antidepressant effects. Neither Network‐Based Statistic analyses nor large‐scale network analyses detected significant functional connectivity differences between treatment‐naïve patients and healthy controls. In short, antidepressant effects are widespread across most brain networks and need to be accounted for when considering functional connectivity abnormalities in MDD.

Efficacy and Safety of tDCS and tACS in Treatment of Major Depressive Disorder: A Randomized, Double-Blind, Factorial Placebo-Controlled Study Design

BACKGROUND

Transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) are regarded as promising antidepressant treatments.

OBJECTIVE

To compare the efficacy and safety of tDCS, tACS, escitalopram, and placebo/sham stimulation controls.

DESIGN

Randomized, parallel, double-blind, placebo-controlled study.

METHODS

Sample sizes were calculated based on data from previous similar studies. Eligible non-treatment-resistant-depressive outpatient subjects with moderate-to-severe depression (HRDS ≥17) are randomized to receive (1) tDCS + placebo; (2) tACS + placebo; (3) escitalopram + placebo; or (4) sham stimulation + placebo. The intensity of electricity is 2 mA, lasting for 30 minutes over two consecutive working days (10 sessions in total). The medication lasts for 6 weeks. The primary outcome measure was the response rates within 6 weeks (week 6 is also the endpoint of the study), and secondary outcome measures included changes in other clinical measurements. Safety and acceptability are measured by adverse event rates and dropout rates. Exploring outcome consist of the performance of cognitive battery as well as neurophysiology results.

CONCLUSION

To the best of our knowledge, the present study is the first double-blind controlled study comparing tDCS, tACS, and clinically used antidepressants, which will provide further evidence for their efficacy and safety in possible clinical applications.

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.

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.

Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45-0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.

Discovery and replication of cerebral blood flow differences in major depressive disorder

Major depressive disorder (MDD) is a serious, heterogeneous disorder accompanied by brain-related changes, many of which are still to be discovered or refined. Arterial spin labeling (ASL) is a neuroimaging technique used to measure cerebral blood flow (CBF; perfusion) to understand brain function and detect differences among groups. CBF differences have been detected in MDD, and may reveal biosignatures of disease-state. The current work aimed to discover and replicate differences in CBF between MDD participants and healthy controls (HC) as part of the EMBARC study. Participants underwent neuroimaging at baseline, prior to starting study medication, to investigate biosignatures in MDD. Relative CBF (rCBF) was calculated and compared between 106 MDD and 36 HC EMBARC participants (whole-brain Discovery); and 58 MDD EMBARC participants and 58 HC from the DLBS study (region-of-interest Replication). Both analyses revealed reduced rCBF in the right parahippocampus, thalamus, fusiform and middle temporal gyri, as well as the left and right insula, for those with MDD relative to HC. Both samples also revealed increased rCBF in MDD relative to HC in both the left and right inferior parietal lobule, including the supramarginal and angular gyri. Cingulate and prefrontal regions did not fully replicate. Lastly, significant associations were detected between rCBF in replicated regions and clinical measures of MDD chronicity. These results (1) provide reliable evidence for ASL in detecting differences in perfusion for multiple brain regions thought to be important in MDD, and (2) highlight the potential role of using perfusion as a biosignature of MDD.

Longitudinal brain changes in MDD during emotional encoding: effects of presence and persistence of symptomatology

BACKGROUND

The importance of the hippocampus and amygdala for disrupted emotional memory formation in depression is well-recognized, but it remains unclear whether functional abnormalities are state-dependent and whether they are affected by the persistence of depressive symptoms.

METHODS

Thirty-nine patients with major depressive disorder and 28 healthy controls were included from the longitudinal functional magnetic resonance imaging (fMRI) sub-study of the Netherlands Study of Depression and Anxiety. Participants performed an emotional word-encoding and -recognition task during fMRI at baseline and 2-year follow-up measurement. At baseline, all patients were in a depressed state. We investigated state-dependency by relating changes in brain activation over time to changes in symptom severity. Furthermore, the effect of time spent with depressive symptoms in the 2-year interval was investigated.

RESULTS

Symptom change was linearly associated with higher activation over time of the left anterior hippocampus extending to the amygdala during positive and negative word-encoding. Especially during positive word encoding, this effect was driven by symptomatic improvement. There was no effect of time spent with depression in the 2-year interval on change in brain activation. Results were independent of medication- and psychotherapy-use.

CONCLUSION

Using a longitudinal within-subjects design, we showed that hippocampal-amygdalar activation during emotional memory formation is related to depressive symptom severity but not persistence (i.e. time spent with depression or 'load'), suggesting functional activation patterns in depression are not subject to functional 'scarring' although this hypothesis awaits future replication.

Brain-derived neurotrophic factor association with amygdala response in major depressive disorder

BACKGROUND

Brain-derived neurotrophic factor (BDNF) has an essential role in synaptic plasticity and neurogenesis. BDNF mediates amygdala-dependent learning for both aversive and appetitive emotional memories. The expression of BDNF in limbic regions is posited to contribute the development of depression, and amygdala responsivity is a potential marker of depressive state.

METHODS

The present study examined the relationship between platelet BDNF levels and amygdala volume and function in major depressive disorder (MDD). Participants were 23 MDD (mean age 38.9 years) and 23 healthy controls (mean age 38.8 years). All participants were recruited from the community. MDD participants were in a current depressive episode of moderate severity and medication-free. Amygdala responses were acquired during a functional MRI task of implicit emotional processing with sad facial expressions.

RESULTS

Significant correlation was observed between platelet BDNF levels and left amygdala responses, but no significant correlations were found with right amygdala responses or with amygdala volumes.

LIMITATIONS

Interactions with neuroprotective as well as neurotoxic metabolites in the kyneurenine pathway were not examined.

CONCLUSIONS

Relationship between BDNF levels and amygdala responsivity to emotionally salient stimuli in MDD could reflect the importance of BDNF in amygdala-dependent learning with clinical implications for potential pathways for treatment.

Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder

The acute treatment duration for major depressive disorder (MDD) is 8 weeks or more. Treatment of patients with MDD without predictors of treatment response and future recurrence presents challenges and clinical problems to patients and physicians. Recently, many neuroimaging studies have been published on biomarkers for treatment response and recurrence of MDD using various methods such as brain volumetric magnetic resonance imaging (MRI), functional MRI (resting-state and affective tasks), diffusion tensor imaging, magnetic resonance spectroscopy, near-infrared spectroscopy, and molecular imaging (i.e., positron emission tomography and single photon emission computed tomography). The results have been inconsistent, and we hypothesize that this could be due to small sample size; different study design, including eligibility criteria; and differences in the imaging and analysis techniques. In the future, we suggest a more sophisticated research design, larger sample size, and a more comprehensive integration including genetics to establish biomarkers for the prediction of treatment response and recurrence of MDD.

Widespread Morphometric Abnormalities in Major Depression: Neuroplasticity and Potential for Biomarker Development

Major depression is common and debilitating. Identifying neurobiological subtypes that comprise the disorder and predict clinical outcome are key challenges. Genetic and environmental factors leading to major depression are expressed in neural structure and function. Volumetric decreases in gray matter have been demonstrated in corticolimbic circuits involved in emotion regulation. MR imaging observable abnormalities reflect cytoarchitectonic alterations within a local neuroendocrine milieu with systemic effects. Multivariate pattern analysis offers the potential to identify the neurobiological subtypes and predictors of clinical outcome. It is essential to characterize disease heterogeneity by incorporating data-driven inductive and symptom-based deductive approaches in an iterative process.

An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report

Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10-20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2-10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=-29,9,54.5,-7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks.

Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies

Antidepressive pharmacotherapy (AD), electroconvulsive therapy (ECT) and cognitive behavioural therapy (CBT) are effective treatments for major depressive disorder. With our review, we aim to provide a systematic overview of neuroimaging studies that investigate the effects of AD, ECT and CBT on brain grey matter volume (GMV) and biomarkers associated with response. After a systematic database research on PubMed, we included 50 studies using magnetic resonance imaging and investigating (1) changes in GMV, (2) pre-treatment GMV biomarkers associated with response, or (3) the accuracy of predictions of response to AD, ECT or CBT based on baseline GMV data. The strongest evidence for brain structural changes was found for ECT, showing volume increases within the temporal lobe and subcortical structures - such as the hippocampus-amygdala complex, anterior cingulate cortex (ACC) and striatum. For AD, the evidence is heterogeneous as only 4 of 11 studies reported significant changes in GMV. The results are not sufficient in order to draw conclusions about the structural brain effects of CBT. The findings show consistently that higher pre-treatment ACC volume is associated with response to AD, ECT and CBT. An association of higher pre-treatment hippocampal volume and response has only been reported for AD. Machine learning approaches based on pre-treatment whole brain patterns reach accuracies of 64-90% for predictions of AD or ECT response on the individual patient level. The findings underline the potential of brain biomarkers for the implementation in clinical practice as an additive feature within the process of treatment selection.

SSRI Treatment Response Prediction in Depression Based on Brain Activation by Emotional Stimuli

Introduction: The prediction of antidepressant treatment response may improve outcome. Functional magnetic resonance imaging (fMRI) of emotion processing in major depressive disorder (MDD) may reveal regional brain function serving as predictors of response to treatment with selective serotonin reuptake inhibitor (SSRI). Methods: We examined the association between pre-treatment neural activity by means of fMRI during the perception of emotional stimuli in 22 patients with MDD and the treatment outcome after 6 weeks' medication with an SSRI. A whole brain correlation analysis with Beck Depression Inventory (BDI) change between pre- to post-treatment was conducted to identify neural regions associated with treatment response. Results: During the perception of positive stimuli, responders were characterized by more activation in posterior cingulate cortex (PCC), medial prefrontal cortex, and thalamus as well as middle temporal gyrus. During perception of negative stimuli, PCC, and pregenual anterior cingulate cortex showed the highest correlation with treatment response. Furthermore, responders exhibited higher activation to emotional stimuli than to neutral stimuli in all the above-mentioned regions, while non-responders demonstrated an attenuated neural response to emotional compared to neutral stimuli. Conclusion: Our data suggest that the activity of distinct brain regions is correlated with SSRI treatment outcome and may serve as treatment response predictor. While some regions, in which activity was correlated with treatment response, can be assigned to networks that have been implied in the pathophysiology of depression, most of our regions of interest could also be matched to the default mode network (DMN). Higher DMN activity has been associated with increased rumination as well as negative self-referential processing in previous studies. This may suggest our responders to SSRI to be characterized by such dysregulations and that SSRIs might modify the function associated with this network.

Cortical thickening in remitters compared to non-remitters with major depressive disorder following 8-week antidepressant treatment

OBJECTIVE

Little is known about the relationship between antidepressant treatment outcomes and underlying neurobiological mechanisms in patients with major depressive disorder (MDD). In this prospective study, we aimed to investigate how cortical thickness and subcortical volumes differed between remitter and non-remitter patients with MDD.

METHODS

Fifty-eight patients with MDD with a score of at least 17 on the 17-item Hamilton Depression Rating Scale and free of medication for at least 2 months and 41 healthy controls underwent structural magnetic resonance imaging. At the baseline, patients with MDD started on either selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, or vortioxetine. After 8-week antidepressant treatment, patients with MDD were scanned using the same MRI protocol. Structural images were analyzed using the FreeSurfer software package (version 6.0).

RESULTS

Longitudinal analyses showed remitter patients with MDD had significantly greater right cerebral cortex thickening in six significant clusters, including superior temporal cortex, precuneus, rostral middle frontal cortex, pars opercularis (although the cluster extends into the insula), inferior parietal cortex, and supramarginal cortex than in non-remitter patients with MDD.

CONCLUSION

Our results suggest that distinct antidepressant treatment-related structural alterations in brain regions implicated in cognition, emotion regulation, and rumination might be associated with treatment outcome.

Addressing heterogeneity (and homogeneity) in treatment mechanisms in depression and the potential to develop diagnostic and predictive biomarkers

It has been 10 years since machine learning was first applied to neuroimaging data in psychiatric disorders to identify diagnostic and prognostic markers at the level of the individual. Proof of concept findings in major depression have since been extended in international samples and are beginning to include hundreds of samples from multisite data. Neuroimaging provides the unique capability to detect an acute depressive state in major depression, while we would not expect perfect classification with current diagnostic criteria which are based solely on clinical features. We review developments and the potential impact of heterogeneity, as well as homogeneity, on classification for diagnosis and prediction of clinical outcome. It is likely that there are distinct biotypes which comprise the disorder and which predict clinical outcome. Neuroimaging-based biotypes could aid in identifying the illness in individuals who are unable to recognise their illness and perhaps to identify the treatment resistant form early in the course of the illness. We propose that heterogeneous symptom profiles can arise from a limited number of neural biotypes and that apparently heterogeneous clinical outcomes include a common baseline predictor and common mechanism of treatment. Baseline predictors of clinical outcome reflect factors which indicate the general likelihood of response as well as those which are selective for a particular form of treatment. Irrespective of the mechanism, the capacity for response will moderate the outcome, which includes inherent models of interpersonal relationships that could be associated with genetic risk load and represented by patterns of functional and structural neural correlates as a predictive biomarker. We propose that methods which directly address heterogeneity are essential and that a synergistic combination could bring together data-driven inductive and symptom-based deductive approaches. Through this iterative process, major depression can develop from being syndrome characterized by a collection of symptoms to a disease with an identifiable pathophysiology.

The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols

Studies of clinical populations that combine MRI data generated at multiple sites are increasingly common. The Canadian Biomarker Integration Network in Depression (CAN-BIND; www.canbind.ca) is a national depression research program that includes multimodal neuroimaging collected at several sites across Canada. The purpose of the current paper is to provide detailed information on the imaging protocols used in a number of CAN-BIND studies. The CAN-BIND program implemented a series of platform-specific MRI protocols, including a suite of prescribed structural and functional MRI sequences supported by real-time monitoring for adherence and quality control. The imaging data are retained in an established informatics and databasing platform. Approximately 1300 participants are being recruited, including almost 1000 with depression. These include participants treated with antidepressant medications, transcranial magnetic stimulation, cognitive behavioural therapy and cognitive remediation therapy. Our ability to analyze the large number of imaging variables available may be limited by the sample size of the substudies. The CAN-BIND program includes a multimodal imaging database supported by extensive clinical, demographic, neuropsychological and biological data from people with major depression. It is a resource for Canadian investigators who are interested in understanding whether aspects of neuroimaging — alone or in combination with other variables — can predict the outcomes of various treatment modalities.

Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI

Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system.

Association of antidepressants with brain morphology in early stages of psychosis: an imaging genomics approach

Depressive symptoms in subjects at Clinical High Risk for Psychosis (CHR-P) or at first-episode psychosis (FEP) are often treated with antidepressants. Our cross-sectional study investigated whether brain morphology is altered by antidepressant medication. High-resolution T₁-weighted structural MRI scans of 33 CHR-P and FEP subjects treated with antidepressants, 102 CHR-P and FEP individuals without antidepressant treatment and 55 controls, were automatically segmented using Freesurfer 6.0. Linear mixed-effects modelling was applied to assess the differences in subcortical volume, surface area and cortical thickness in treated, non-treated and healthy subjects, taking into account converted dosages of antidepressants. Increasing antidepressant dose was associated with larger volume of the pallidum and the putamen, and larger surface of the left inferior temporal gyrus. In a pilot subsample of separately studied subjects of known genomic risk loci, we found that in the right postcentral gyrus, the left paracentral lobule and the precentral gyrus antidepressant dose-associated surface increase depended on polygenic schizophrenia-related-risk score. As the reported regions are linked to the symptoms of psychosis, our findings reflect the possible beneficial effects of antidepressant treatment on an emerging psychosis.

Topologically state-independent and dependent functional connectivity patterns in current and remitted depression

OBJECTIVE

Identification of state-independent and -dependent neural biomarkers may provide insight into the pathophysiology and effective treatment of major depressive disorder (MDD), therefore we aimed to investigate the state-independent and -dependent topological alterations of MDD.

METHOD

Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 59 patients with unmedicated first episode current MDD (cMDD), 48 patients with remitted MDD (rMDD) and 60 demographically matched healthy controls (HCs). Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the global and nodal level.

RESULTS

At a global level, both patient groups showed decreased normalized clustering coefficient in relative to HCs. On a nodal level, both patient groups showed decreased nodal centrality, predominantly in cortex-mood-regulation brain regions including the dorsolateral prefrontal cortex, posterior parietal cortex and posterior cingulate cortex. By comparison to cMDD patients, rMDD group had a higher nodal centrality in right parahippocampal gyrus.

LIMITATIONS

The present study, an exploratory analysis, may require further confirmation with task-based and experimental studies.

CONCLUSIONS

Deficits in the topological organization of the whole brain and cortex-mood-regulation brain regions in both rMDD and cMDD represent state-independent biomarkers.

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.

A systematic review of the impact of social cognitive deficits on psychosocial functioning in major depressive disorder and opportunities for therapeutic intervention

Social cognition is the ability to identify, perceive and interpret socially relevant information from the external world. It is an important adaptive trait, but is frequently affected in major depressive disorder by a mood-congruent interpretive bias. The present review examined the existing body of literature to determine (i) the impact social cognitive deficits in depression have on psychosocial functioning; and (ii) the utility of psychotropic, psychological and procedural interventions employed to target these deficits. A total of 107 studies met inclusion criteria for review. Social cognitive performance was found to adversely impact depressed patients' psychosocial functioning across the key domains of general cognitive functioning and quality of life. Secondly, many current therapies were found to have a normalising effect on the social cognitive abilities of subjects with major depressive disorder, both at a neural and functional level. In particular, certain anti-depressant medications corrected facial affect recognition deficits, while several psychotherapeutic approaches improved impairments in theory of mind and negative interpretive bias.

Segregation of salience network predicts treatment response of depression to repetitive transcranial magnetic stimulation

Background

The present study tested the hypothesis that network segregation, a graph theoretic measure of functional organization of the brain, is correlated with treatment response in patients with major depressive disorder (MDD) undergoing repetitive transcranial magnetic stimulation (rTMS).

Methods

Network segregation, calculated from resting state functional magnetic resonance imaging scans, was measured in 32 patients with MDD who entered a sham-controlled, double-blinded, randomized trial of rTMS to the left dorsolateral prefrontal cortex, and a cohort of 20 healthy controls (HCs). Half of the MDD patients received sham treatment in the blinded phase, followed by active rTMS in the open-label phase. The analyses focused on segregation of the following networks: default mode (DMN), salience (SN), fronto-parietal (FPN), cingulo-opercular (CON), and memory retrieval (MRN).

Results

There was no differential change in network segregation comparing sham to active treatment. However, in the combined group of patients who completed active rTMS treatment (in the blinded plus open-label phases), higher baseline segregation of SN significantly predicted more symptom improvement after rTMS. Compared to HCs at baseline, MDD patients showed decreased segregation in DMN, and trend-level decreases in SN and MRN.

Conclusion

The results highlight the importance of network segregation in MDD, particularly in the SN, where more normal baseline segregation of SN may predict better treatment response to rTMS in depression.

•

We examined network segregation in a cohort of MDD patients receiving rTMS treatment.

•

More normal segregation of SN predicted better response of depression to rTMS.

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Patients with MDD had decreased network segregation in DMN.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.

Neuroimaging biomarkers as predictors of treatment outcome in Major Depressive Disorder

BACKGROUND

Current practice for selecting pharmacological and non-pharmacological antidepressant treatments has yielded low response and remission rates in Major Depressive Disorder (MDD). Neuroimaging biomarkers of brain structure and function may be useful in guiding treatment selection by predicting response vs. non-response outcomes.

METHODS

In this review, we summarize data from studies examining predictors of treatment response using structural and functional neuroimaging modalities, as they pertain to pharmacotherapy, psychotherapy, and stimulation treatment strategies. A literature search was conducted in OVID Medline, EMBASE, and PsycINFO databases with coverage from January 1990 to January 2017.

RESULTS

Several imaging biomarkers of therapeutic response in MDD emerged: frontolimbic regions, including the prefrontal cortex, anterior cingulate cortex, hippocampus, amygdala, and insula were regions of interest. Since these sub-regions are implicated in the etiology of MDD, their association with response outcomes may be the result of treatments having a normalizing effect on structural or activation abnormalities.

LIMITATIONS

The direction of findings is inconsistent in studies examining these biomarkers, and variation across 'biotypes' within MDD may account for this. Limitations in sample size and differences in methodology likely also contribute.

CONCLUSIONS

The identification of accurate, reliable neuroimaging biomarkers of treatment response holds promise toward improving treatment outcomes and reducing burden of illness for patients with MDD. However, before these biomarkers can be translated into clinical practice, they will need to be replicated and validated in large, independent samples, and integrated with data from other biological systems.

Shared facial emotion processing functional network findings in medication-naïve major depressive disorder and healthy individuals: detection by sICA

BACKGROUND

The fundamental mechanism underlying emotional processing in major depressive disorder (MDD) remains unclear. To better understand the neural correlates of emotional processing in MDD, we investigated the role of multiple functional networks (FNs) during emotional stimuli processing.

METHODS

Thirty-two medication-naïve subjects with MDD and 36 healthy controls (HCs) underwent an emotional faces fMRI task that included neutral, happy and fearful expressions. Spatial independent component analysis (sICA) and general linear model (GLM) were conducted to examine the main effect of task condition and group, and two-way interactions of group and task conditions.

RESULTS

In sICA analysis, MDD patients and HCs together showed significant differences in task-related modulations in five FNs across task conditions. One FN mainly involving the ventral medial prefrontal cortex showed lower activation during fearful relative to happy condition. Two FNs mainly involving the bilateral inferior frontal gyrus and temporal cortex, showed opposing modulation relative to the ventral medial prefrontal cortex FN, i.e., greater activation during fearful relative to happy condition. Two remaining FNs involving the fronto-parietal and occipital cortices, showed reduced activation during both fearful and happy conditions relative to the neutral condition. However, MDD and HCs did not show significant differences in expression-related modulations in any FNs in this sample.

CONCLUSIONS

SICA revealed differing functional activation patterns than typical GLM-based analyses. The sICA findings demonstrated unique FNs involved in processing happy and fearful facial expressions. Potential differences between MDD and HCs in expression-related FN modulation should be investigated further.

Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers

Default mode network (DMN) dysfunction (particularly within the anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC)) has been implicated in major depressive disorder (MDD); however, its contribution to treatment outcome has not been clearly established. Here we tested the role of DMN functional connectivity as a general and differential biomarker for predicting treatment outcomes in a large, unmedicated adult sample with MDD. Seventy-five MDD outpatients completed fMRI scans before and 8 weeks after randomization to escitalopram, sertraline, or venlafaxine-XR. A whole-brain voxel-wise t-test identified profiles of pretreatment intrinsic functional connectivity that distinguished patients who were subsequently classified as remitters or non-remitters at follow-up. Connectivity was seeded in the PCC, an important node of the DMN. We further characterized differences between remitters, non-remitters, and 31 healthy controls and characterized changes pretreatment to posttreatment. Remitters were distinguished from non-remitters by relatively intact connectivity between the PCC and ACC/mPFC, not distinguishable from healthy controls, while non-remitters showed relative hypo-connectivity. In validation analyses, we demonstrate that PCC-ACC/mPFC connectivity predicts remission status with >80% cross-validated accuracy. In analyses testing whether intrinsic connectivity differentially relates to outcomes for a specific type of antidepressant, interaction models did not survive the corrected threshold. Our findings demonstrate that the overall capacity to remit on commonly used antidepressants may depend on intact organization of intrinsic functional connectivity between PCC and ACC/mPFC prior to treatment. The findings highlight the potential utility of functional scans for advancing a more precise approach to tailoring antidepressant treatment choices.