Pretreatment and early-treatment cortical thickness is associated with SSRI treatment response in major depressive disorder

Antidepressant effects of esketamine via the BDNF/AKT/mTOR pathway in mice with postpartum depression and their offspring

Postpartum depression (PPD) is a serious mental health problem that can negatively affect future generations. BDNF/AKT/mTOR signaling in the frontal lobe and hippocampus in mice is associated with depression, but its role in mice with PPD and their offspring is unknown. This study was aimed at investigating the effects of esketamine (ESK), a drug approved for treatment of refractory depression, on the BDNF/AKT/mTOR pathway in mice with PPD and their offspring. A model of chronic unpredictable mild stress with pregnancy was used. ESK was injected into postpartum mice, and behavioral tests were conducted to predict the severity of symptoms at the end of lactation and in the offspring after adulthood. Both mice with PPD and their offspring showed significant anxiety- and depression-like behaviors that were ameliorated with the ESK intervention. ESK enhanced exploratory behavior in unfamiliar environments, increased the preference for sucrose, and ameliorated the impaired BDNF/AKT/mTOR signaling in the frontal and hippocampal regions in mice. Thus, ESK may have great potential in treating PPD and decreasing the incidence of depression in offspring.

Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial

OBJECTIVE

Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment.

METHODS

This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores.

RESULTS

A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models.

CONCLUSIONS

The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.

Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment

BACKGROUND

The high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume.

METHODS

We enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors.

RESULTS

In contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146-0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800-0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination.

CONCLUSIONS

Capturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.

Effects of electroconvulsive therapy on cortical thickness in depression: a systematic review

OBJECTIVE

Electroconvulsive therapy (ECT) is one of the most studied and validated available treatments for severe or treatment-resistant depression. However, little is known about the neural mechanisms underlying ECT. This systematic review aims to critically review all structural magnetic resonance imaging studies investigating longitudinal cortical thickness (CT) changes after ECT in patients with unipolar or bipolar depression.

METHODS

We performed a search on PubMed, Medline, and Embase to identify all available studies published before April 20, 2023. A total of 10 studies were included.

RESULTS

The investigations showed widespread increases in CT after ECT in depressed patients, involving mainly the temporal, insular, and frontal regions. In five studies, CT increases in a non-overlapping set of brain areas correlated with the clinical efficacy of ECT. The small sample size, heterogeneity in terms of populations, comorbidities, and ECT protocols, and the lack of a control group in some investigations limit the generalisability of the results.

CONCLUSIONS

Our findings support the idea that ECT can increase CT in patients with unipolar and bipolar depression. It remains unclear whether these changes are related to the clinical response. Future larger studies with longer follow-up are warranted to thoroughly address the potential role of CT as a biomarker of clinical response after ECT.

Brain ventricle and choroid plexus morphology as predictor of treatment response in major depression: Findings from the EMBARC study

Recent observations suggest a role of the volume of the cerebral ventricle volume, corpus callosum (CC) segment volume, in particular that of the central-anterior part, and choroid plexus (CP) volume for treatment resistance of major depressive disorder (MDD). An increased CP volume has been associated with increased inflammatory activity and changes in the structure of the ventricles and corpus callosum. We attempt to replicate and confirm that these imaging markers are associated with clinical outcome in subjects from the EMBARC study, as implied by a recent pilot study. The EMBARC study is a placebo controlled randomized study comparing sertraline vs. placebo in patients with MDD to identify biological markers of therapy resistance. Association of baseline volumes of the lateral ventricles (LVV), choroid plexus volume (CPV) and volume of segments of the CC with treatment response after 4 weeks treatment was evaluated. 171 subjects (61 male, 110 female) completed the 4 week assessments; gender and age were taken into account for this analyses. As previously reported, no treatment effect of sertraline vs. placebo was observed, therefore the study characterized prognostic markers of response in the pooled population. Change in depression severity was identified by the ratio of the Hamilton-Depression rating scale 17 (HAMD-17) at week 4 divided by the HAMD-17 at baseline (HAMD-17 ratio). Volumes of the lateral ventricles and choroid plexi were positively correlated with the HAMD-17 ratio, indication worse outcome with larger ventricles and choroid plexus volumes, whereas the volume of the central-anterior corpus callosum was negatively correlated with the HAMD-17 ratio. Responders (n = 54) had significantly smaller volumes of the lateral ventricles and CP compared to non-responders (n = 117), whereas the volume of mid-anterior CC was significantly larger compared to non-responders (n = 117), confirming our previous findings. In an exploratory way associations between enlarged LVV and CPV and signs of lipid dysregulation were observed. In conclusion, we confirmed that volumes of lateral ventricles, choroid plexi and the mid-anterior corpus callosum are associated with clinical improvement of depression and may be indicators of metabolic/inflammatory activity.

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.

Chronic psychosocial stress triggers microglial-/macrophage-induced inflammatory responses leading to neuronal dysfunction and depressive-related behavior

Chronic environmental stress and traumatic social experiences induce maladaptive behavioral changes and is a risk factor for major depressive disorder (MDD) and various anxiety-related psychiatric disorders. Clinical studies and animal models of chronic stress have reported that symptom severity is correlated with innate immune responses and upregulation of neuroinflammatory cytokine signaling in brain areas implicated in mood regulation (mPFC; medial Prefrontal Cortex). Despite increasing evidence implicating impairments of neuroplasticity and synaptic signaling deficits into the pathophysiology of stress-related mental disorders, how microglia may modulate neuronal homeostasis in response to chronic stress has not been defined. Here, using the repeated social defeat stress (RSDS) mouse model we demonstrate that microglial-induced inflammatory responses are regulating neuronal plasticity associated with psychosocial stress. Specifically, we show that chronic stress induces a rapid activation and proliferation of microglia as well as macrophage infiltration in the mPFC, and these processes are spatially related to neuronal activation. Moreover, we report a significant association of microglial inflammatory responses with susceptibility or resilience to chronic stress. In addition, we find that exposure to chronic stress exacerbates phagocytosis of synaptic elements and deficits in neuronal plasticity. Importantly, by utilizing two different CSF1R inhibitors (the brain penetrant PLX5622 and the non-penetrant PLX73086) we highlight a crucial role for microglia (and secondarily macrophages) in catalyzing the pathological manifestations linked to psychosocial stress in the mPFC and the resulting behavioral deficits usually associated with depression.

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.

Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review

The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.

ComBat Harmonization: Empirical Bayes versus fully Bayes approaches

Studying small effects or subtle neuroanatomical variation requires large-scale sample size data. As a result, combining neuroimaging data from multiple datasets is necessary. Variation in acquisition protocols, magnetic field strength, scanner build, and many other non-biologically related factors can introduce undesirable bias into studies. Hence, harmonization is required to remove the bias-inducing factors from the data. ComBat is one of the most common methods applied to features from structural images. ComBat models the data using a hierarchical Bayesian model and uses the empirical Bayes approach to infer the distribution of the unknown factors. The empirical Bayes harmonization method is computationally efficient and provides valid point estimates. However, it tends to underestimate uncertainty. This paper investigates a new approach, fully Bayesian ComBat, where Monte Carlo sampling is used for statistical inference. When comparing fully Bayesian and empirical Bayesian ComBat, we found Empirical Bayesian ComBat more effectively removed scanner strength information and was much more computationally efficient. Conversely, fully Bayesian ComBat better preserved biological disease and age-related information while performing more accurate harmonization on traveling subjects. The fully Bayesian approach generates a rich posterior distribution, which is useful for generating simulated imaging features for improving classifier performance in a limited data setting. We show the generative capacity of our model for augmenting and improving the detection of patients with Alzheimer's disease. Posterior distributions for harmonized imaging measures can also be used for brain-wide uncertainty comparison and more principled downstream statistical analysis.Code for our new fully Bayesian ComBat extension is available at https://github.com/batmanlab/BayesComBat.

Altered topology of individual brain structural covariance networks in major depressive disorder

BACKGROUND

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks.

METHODS

We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics.

RESULTS

Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex.

CONCLUSIONS

The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI

BACKGROUND

Due to individual differences and lack of objective biomarkers, only 30-40% patients with major depressive disorder (MDD) achieve remission after initial antidepressant medication (ADM). We aimed to employ radiomics analysis after ComBat harmonization to predict early improvement to ADM in adolescents with MDD by using brain multiscale structural MRI (sMRI) and identify the radiomics features with high prediction power for selection of selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs).

METHODS

121 MDD patients were recruited for brain sMRI, including three-dimensional T1 weighted imaging (3D-T₁WI)and diffusion tensor imaging (DTI). After receiving SSRIs or SNRIs for 2 weeks, the subjects were divided into ADM improvers (SSRIs improvers and SNRIs improvers) and non-improvers according to reduction rate of the Hamilton Depression Rating Scale, 17 item (HAM-D₁₇) score. Then, sMRI data were preprocessed, and conventional imaging indicators and radiomics features of gray matter (GM) based on surface-based morphology (SBM) and voxel-based morphology (VBM) and diffusion properties of white matter (WM) were extracted and harmonized with ComBat harmonization. Two-level reduction strategy with analysis of variance (ANOVA) and recursive feature elimination (RFE) was utilized sequentially to decrease high-dimensional features. Support vector machine with radial basis function kernel (RBF-SVM) was used to integrate multiscale sMRI features to construct models for early improvement prediction. Area under the curve (AUC), accuracy, sensitivity, and specificity based on the leave-one-out cross-validation (LOO-CV) and receiver operating characteristic (ROC) curve analysis were calculated to evaluate the model performance. Permutation tests were used for assessing the generalization rate.

RESULTS

After 2-week ADM, 121 patients were divided into 67 ADM improvers (31 SSRIs improvers and 36 SNRIs improvers) and 54 ADM non-improvers. After two-level dimensionality reduction, 8 conventional indicators (2 VBM-based features and 6 diffusion features) and 49 radiomics features (16 VBM-based features and 33 diffusion features) were selected. The overall accuracy of RBF-SVM models based on conventional indicators and radiomics features was 74.80% and 88.19%. The radiomics model achieved the AUC, sensitivity, specificity, and accuracy of 0.889, 91.2%, 80.1% and 85.1%, 0.954, 89.2%, 87.4% and 88.5%, 0.942, 91.9%, 82.5% and 86.8% for predicting ADM improvers, SSRIs improvers and SNRIs improvers, respectively. P value of permutation tests were less than 0.001. The radiomics features predicting ADM improver were mainly located in the hippocampus, medial orbitofrontal gyrus, anterior cingulate gyrus, cerebellum (lobule vii-b), body of corpus callosum, etc. The radiomics features predicting SSRIs improver were primarily distributed in hippocampus, amygdala, inferior temporal gyrus, thalamus, cerebellum (lobule vi), fornix, cerebellar peduncle, etc. The radiomics features predicting SNRIs improver were primarily located in the medial orbitofrontal cortex, anterior cingulate gyrus, ventral striatum, corpus callosum, etc. CONCLUSIONS: These findings suggest the radiomics analysis based on brain multiscale sMRI after ComBat harmonization could effectively predict the early improvement of ADM in adolescent MDD patients with a high accuracy, which was superior to the model based on the conventional indicators. The radiomics features with high prediction power may help for the individual selection of SSRIs and SNRIs.

Abnormal brain spontaneous activity in major depressive disorder adolescents with non-suicidal self injury and its changes after sertraline therapy

Background

Non-suicidal self-injury (NSSI) commonly occurs among adolescents with major depressive disorder (MDD), causing adverse effects on the physical and mental health of the patients. However, the underlying neurobiological mechanism of NSSI in adolescents with MDD (nsMDDs) remains unclear, and there are still challenges in the treatment. Studies have suggested that sertraline administration could be an effective way for treatment.

Methods

To verify the effectiveness and to explore the neurobiological processes, we treated a group of adolescents with nsMDDs with sertraline in this study. The brain spontaneous activity alteration was then investigated in fifteen unmedicated first-episode adolescent nsMDDs versus twenty-two healthy controls through the resting-state functional magnetic resonance imaging. Besides the baseline scanning for all participants, the nsMDDs group was scanned again after eight weeks of sertraline therapy to examine the changes after treatment.

Results

At pre-treatment, whole brain analysis of mean amplitude of low-frequency fluctuation (mALFF) was performed to examine the neuronal spontaneous activity alteration, and increased mALFF was found in the superior occipital extending to lingual gyrus in adolescent nsMDDs compared with controls. Meanwhile, decreased mALFF was found in the medial superior frontal in adolescent nsMDDs compared with controls. Compared with the pre-treatment, the nsMDDs group was found to have a trend of, respectively, decreased and increased functional neuronal activity at the two brain areas after treatment through the region of interest analysis. Further, whole brain comparison of mALFF at pre-treatment and post-treatment showed significantly decreased spontaneous activity in the orbital middle frontal and lingual gyrus in adolescent nsMDDs after treatment. Also, depression severity was significantly decreased after treatment.

Conclusion

The abnormal functional neuronal activity found at frontal and occipital cortex implied cognitive and affective disturbances in adolescent nsMDDs. The trend of upregulation of frontal neuronal activity and downregulation of occipital neuronal activity after sertraline treatment indicated that the therapy could be effective in regulating the abnormality. Notably, the significantly decreased neuronal activity in the decision related orbital middle frontal and anxiety-depression related lingual gyrus could be suggestive of reduced NSSI in adolescent MDD after therapy.

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.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Distinct and shared patterns of brain plasticity during electroconvulsive therapy and treatment as usual in depression: an observational multimodal MRI-study

Electroconvulsive therapy (ECT) is a highly effective treatment for depression. Previous studies point to ECT-induced volume increase in the hippocampi and amygdalae, and to increase in cortical thickness. However, it is unclear if these neuroplastic changes are associated with treatment response. This observational study aimed to address this research question by comparing neuroplasticity between patients with depression receiving ECT and patients with depression that respond to treatment as usual (TAU-responders). Twenty ECT-patients (16 major depressive disorder (MDD), 4 depressed bipolar disorder), 20 TAU-responders (20 MDD) and 20 healthy controls (HC) were scanned twice with multimodal magnetic resonance imaging (structure: MP2RAGE; perfusion: arterial spin labeling). ECT-patients were scanned before and after an ECT-index series (ECT-group). TAU-responders were scanned during a depressive episode and following remission or treatment response. Volumes and cerebral blood flow (CBF) of the hippocampi and amygdalae, and global mean cortical thickness were compared between groups. There was a significant group × time interaction for hippocampal and amygdalar volumes, CBF in the hippocampi and global mean cortical thickness. Hippocampal and amygdalar enlargements and CBF increase in the hippocampi were observed in the ECT-group but neither in TAU-responders nor in HC. Increase in global mean cortical thickness was observed in the ECT-group and in TAU-responders but not in HC. The co-occurrence of increase in global mean cortical thickness in both TAU-responders and in ECT-patients may point to a shared mechanism of antidepressant response. This was not the case for subcortical volume and CBF increase.

Long-term depressive symptoms and midlife brain age

BACKGROUND

Evidence suggests that depression may be a risk factor for dementia in older adults, but the link between depressive symptoms and brain health earlier in life is less understood. Our aim was to investigate the association between long-term depressive symptoms in young to mid-adulthood and a measure of brain age derived from structural MRI.

METHODS

From the Coronary Artery Risk Development in Young Adults study, we identified 649 participants (age 23-36 at baseline) with brain MRI and cognitive testing. Long-term depressive symptoms were measured with the Center for Epidemiological Studies Depression scale (CESD) six times across 25 years and analyzed as time-weighted averages (TWA). Brain age was derived using previously validated high dimensional neuroimaging pattern analysis, quantifying individual differences in age-related atrophy. Elevated depressive symptoms were defined as CES-D ≥16. Linear regression was used to test the association between TWA depressive symptoms, brain aging, and cognition.

RESULTS

Each standard deviation (5-points) increment in TWA depression symptoms over 25 years was associated with one-year greater brain age (β: 1.14, 95 % confidence interval [CI]: 0.57 to 1.71). Participants with elevated TWA depressive symptoms had on average a 3-year greater brain age (β: 2.75, 95 % CI: 0.43 to 5.08). Moreover, elevated depressive symptoms were associated with higher odds of poor cognitive function in midlife (OR: 3.30, 95 % CI: 1.37 to 7.97).

LIMITATIONS

Brain age was assessed at one time, limiting our ability to evaluate the temporality of depressive symptoms and brain aging.

CONCLUSIONS

Elevated depressive symptoms in early adulthood may have implications for brain health as early as in midlife.

Generalizability of treatment outcome prediction in major depressive disorder using structural MRI: A NeuroPharm study

Brain morphology has been suggested to be predictive of drug treatment outcome in major depressive disorders (MDD). The current study aims at evaluating the performance of pretreatment structural brain magnetic resonance imaging (MRI) measures in predicting the outcome of a drug treatment of MDD in a large single-site cohort, and, importantly, to assess the generalizability of these findings in an independent cohort. The random forest, boosted trees, support vector machines and elastic net classifiers were evaluated in predicting treatment response and remission following an eight week drug treatment of MDD using structural brain measures derived with FastSurfer (FreeSurfer). Models were trained and tested within a nested cross-validation framework using the NeuroPharm dataset (n = 79, treatment: escitalopram); their generalizability was assessed using an independent clinical dataset, EMBARC (n = 64, treatment: sertraline). Prediction of antidepressant treatment response in the Neuropharm cohort was statistically significant for the random forest (p = 0.048), whereas none of the models could significantly predict remission. Furthermore, none of the models trained using the entire NeuroPharm dataset could significantly predict treatment outcome in the EMBARC dataset. Although our primary findings in the NeuroPharm cohort support some, but limited value in using pretreatment structural brain MRI to predict drug treatment outcome in MDD, the models did not generalize to an independent cohort suggesting limited clinical applicability. This study emphasizes the importance of assessing model generalizability for establishing clinical utility.

Evaluation of brain structure and metabolism in currently depressed adults with a history of childhood trauma

Structural differences in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), hippocampus, and amygdala were reported in adults who experienced childhood trauma; however, it is unknown whether metabolic differences accompany these structural differences. This multimodal imaging study examined structural and metabolic correlates of childhood trauma in adults with major depressive disorder (MDD). Participants with MDD completed the Childhood Trauma Questionnaire (CTQ, n = 83, n = 54 female (65.1%), age: 30.4 ± 14.1) and simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI). Structure (volume, n = 80, and cortical thickness, n = 81) was quantified from MRI using Freesurfer. Metabolism (metabolic rate of glucose uptake) was quantified from dynamic ¹⁸F-fluorodeoxyglucose (FDG)-PET images (n = 70) using Patlak graphical analysis. A linear mixed model was utilized to examine the association between structural/metabolic variables and continuous childhood trauma measures while controlling for confounding factors. Bonferroni correction was applied. Amygdala volumes were significantly inversely correlated with continuous CTQ scores. Specifically, volumes were lower by 7.44 mm³ (95% confidence interval [CI]: -12.19, -2.68) per point increase in CTQ. No significant relationship was found between thickness/metabolism and CTQ score. While longitudinal studies are required to establish causation, this study provides insight into potential consequences of, and therefore potential therapeutic targets for, childhood trauma in the prevention of MDD. This work aims to reduce heterogeneity in MDD studies by quantifying neurobiological correlates of trauma within MDD. It further provides biological targets for future interventions aimed at preventing MDD following trauma. To our knowledge, this is the first simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) study to assess both structure and metabolism associated with childhood trauma in adults with MDD.

Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis

BACKGROUND

Radiomics is an emerging image analysis framework that provides more details than conventional methods. In present study, we aimed to identify structural radiomics features of gray matter (GM) and white matter (WM), and to develop and validate the classification model for major depressive disorder (MDD) and subthreshold depression (StD) diagnosis using radiomics analysis.

METHODS

A consecutive cohort of 142 adolescents and young adults, including 43 cases with MDD, 49 cases with StD and 50 healthy controls (HC), were recruited and underwent the three-dimensional T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI). We extracted radiomics features representing the shape and diffusion properties of GM and WM from all participants. Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features.

RESULTS

The results showed that a total of 3030 features were extracted after preprocessing, including 2262 shape-related features from each T1-weighted image representing GM morphometry and 768 features from each DTI representing the diffusion properties of WM. 25 features were selected ultimately, including ten features for MDD versus HC, eight features for StD versus HC, and seven features for MDD versus StD. The accuracies and area under curve (AUC) the RFC achieved were 86.75%, 0.93 for distinguishing MDD from HC with significant radiomics features located in the left medial orbitofrontal cortex, right superior and middle temporal regions, right anterior cingulate, left cuneus and hippocampus, 70.51%, 0.69 for discriminating StD from HC within left cuneus, medial orbitofrontal cortex, cerebellar vermis, hippocampus, anterior cingulate and amygdala, right superior and middle temporal regions, and 59.15%, 0.66 for differentiating MDD from StD within left medial orbitofrontal cortex, middle temporal and cuneus, right superior frontal, superior temporal regions and hippocampus, anterior cingulate, respectively.

CONCLUSION

These findings provide preliminary evidence that radiomics features of brain structure are valid for discriminating MDD and StD subjects from healthy controls. The MRI-based radiomics approach, with further improvement and validation, might be a potential facilitating method to clinical diagnosis of MDD or StD.

Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders

Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.

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.

Static and treatment-responsive brain biomarkers of depression relapse vulnerability following prophylactic psychotherapy: Evidence from a randomized control trial

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A prospective study of neural biomarkers of relapse in remitted depressed patients.

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Assessed neural response to dysphoric mood-induction before and after psychotherapy.

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Relapse over a 2-year follow-up linked to dysphoria-evoked sensory inhibition.

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Relapse risk was lower when dorsolateral prefrontal reactivity decreased over time.

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Depression prophylaxis may involve reducing dysphoria-evoked sensory inhibition.

Background

Neural reactivity to dysphoric mood induction indexes the tendency for distress to promote cognitive reactivity and sensory avoidance. Linking these responses to illness prognosis following recovery from Major Depressive Disorder informs our understanding of depression vulnerability and provides engagement targets for prophylactic interventions.

Methods

A prospective fMRI neuroimaging design investigated the relationship between dysphoric reactivity and relapse following prophylactic intervention. Remitted depressed outpatients (N = 85) were randomized to 8 weeks of Cognitive Therapy with a Well-Being focus or Mindfulness Based Cognitive Therapy. Participants were assessed before and after therapy and followed for 2 years to assess relapse status. Neural reactivity common to both assessment points identified static biomarkers of relapse, whereas reactivity change identified dynamic biomarkers.

Results

Dysphoric mood induction evoked prefrontal activation and sensory deactivation. Controlling for past episodes, concurrent symptoms and medication status, somatosensory deactivation was associated with depression recurrence in a static pattern that was unaffected by prophylactic treatment, HR 0.04, 95% CI [0.01, 0.14], p < .001. Treatment-related prophylaxis was linked to reduced activation of the left lateral prefrontal cortex (LPFC), HR 3.73, 95% CI [1.33, 10.46], p = .013. Contralaterally, the right LPFC showed dysphoria-evoked inhibitory connectivity with the right somatosensory biomarker

Conclusions

These findings support a two-factor model of depression relapse vulnerability, in which: enduring patterns of dysphoria-evoked sensory deactivation contribute to episode return, but vulnerability may be mitigated by targeting prefrontal regions responsive to clinical intervention. Emotion regulation during illness remission may be enhanced by reducing prefrontal cognitive processes in favor of sensory representation and integration.

Acute neurofunctional effects of escitalopram during emotional processing in pediatric anxiety: a double-blind, placebo-controlled trial

Anxiety disorders are the most common mental disorders in adolescents. However, only 50% of pediatric patients with anxiety disorders respond to the first-line pharmacologic treatments-selective serotonin reuptake inhibitors (SSRIs). Thus, identifying the neurofunctional targets of SSRIs and finding pretreatment or early-treatment neurofunctional markers of SSRI treatment response in this population is clinically important. We acquired pretreatment and early-treatment (2 weeks into treatment) functional magnetic resonance imaging during a continuous processing task with emotional and neutral distractors in adolescents with generalized anxiety disorder (GAD, N = 36) randomized to 8 weeks of double-blind escitalopram or placebo. Generalized psychophysiological interaction analysis was conducted to examine the functional connectivity of the amygdala while patients viewed emotional pictures. Full-factorial analysis was used to investigate the treatment effect of escitalopram on amygdala connectivity. Correlation analyses were performed to explore whether pretreatment and early (week 2) treatment-related connectivity were associated with treatment response (improvement in anxiety) at week 8. Compared to placebo, escitalopram enhanced emotional processing speed and enhanced negative right amygdala-bilateral ventromedial prefrontal cortex (vmPFC) and positive left amygdala-right angular gyrus connectivity during emotion processing. Baseline amygdala-vmPFC connectivity and escitalopram-induced increased amygdala-angular gyrus connectivity at week 2 predicted the magnitude of subsequent improvement in anxiety symptoms. These findings suggest that amygdala connectivity to hubs of the default mode network represents a target of acute SSRI treatment. Furthermore, pretreatment and early-treatment amygdala connectivity could serve as biomarkers of SSRI treatment response in adolescents with GAD. The trial registration for the study is ClinicalTrials.gov Identifier: NCT02818751.

Region-specific associations between gamma-aminobutyric acid A receptor binding and cortical thickness in high-functioning autistic adults

The neurobiology of autism has been shown to involve alterations in cortical morphology and gamma-aminobutyric acid A (GABA_A ) receptor density. We hypothesized that GABA_A receptor binding potential (GABA_A R BP_ND ) would correlate with cortical thickness, but their correlations would differ between autistic adults and typically developing (TD) controls. We studied 50 adults (23 autism, 27 TD, mean age of 27 years) using magnetic resonance imaging to measure cortical thickness, and [¹⁸ F]flumazenil positron emission tomography imaging to measure GABA_A R BP_ND . We determined the correlations between cortical thickness and GABA_A R BP_ND by cortical lobe, region-of-interest, and diagnosis of autism spectrum disorder (ASD). We also explored potential sex differences in the relationship between cortical thickness and autism characteristics, as measured by autism spectrum quotient (AQ) scores. Comparing autism and TD groups, no significant differences were found in cortical thickness or GABA_A R BP_ND . In both autism and TD groups, a negative relationship between cortical thickness and GABA_A R BP_ND was observed in the frontal and occipital cortices, but no relationship was found in the temporal or limbic cortices. A positive correlation was seen in the parietal cortex that was only significant for the autism group. Interestingly, in an exploratory analysis, we found sex differences in the relationships between cortical thickness and GABA_A R BP_ND , and cortical thickness and AQ scores in the left postcentral gyrus. LAY SUMMARY: The thickness of the brain cortex and the density of the receptors associated with inhibitory neurotransmitter GABA have been hypothesized to underlie the neurobiology of autism. In this study, we found that these biomarkers correlate positively in the parietal cortex, but negatively in the frontal and occipital cortical regions of the brain. Furthermore, we collected preliminary evidence that the correlations between cortical thickness and GABA receptor density are sexdependent in a brain region where sensory inputs are registered.

Privacy-preserving harmonization via distributed ComBat

Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

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Baseline measures alone not able to predict escitalopram response above default.

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This poor baseline performance contradicts results from smaller studies.

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Accuracy improved using change in functional connectivity from baseline to week 2.

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Measures of early change following treatment may be crucial for accurate prediction.

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation.

Mitigating site effects in covariance for machine learning in neuroimaging data

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi‐site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site‐related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi‐center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within‐site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group.

Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis

BACKGROUND

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

METHODS

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

RESULTS

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

CONCLUSIONS

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report

OBJECTIVES

Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response.

METHODS

Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data.

RESULTS

Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD.

CONCLUSION

We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.

Psychedelics and Other Psychoplastogens for Treating Mental Illness

Psychedelics have inspired new hope for treating brain disorders, as they seem to be unlike any treatments currently available. Not only do they produce sustained therapeutic effects following a single administration, they also appear to have broad therapeutic potential, demonstrating efficacy for treating depression, post-traumatic stress disorder (PTSD), anxiety disorders, substance abuse disorder, and alcohol use disorder, among others. Psychedelics belong to a more general class of compounds known as psychoplastogens, which robustly promote structural and functional neural plasticity in key circuits relevant to brain health. Here we discuss the importance of structural plasticity in the treatment of neuropsychiatric diseases, as well as the evidence demonstrating that psychedelics are among the most effective chemical modulators of neural plasticity studied to date. Furthermore, we provide a theoretical framework with the potential to explain why psychedelic compounds produce long-lasting therapeutic effects across a wide range of brain disorders. Despite their promise as broadly efficacious neurotherapeutics, there are several issues associated with psychedelic-based medicines that drastically limit their clinical scalability. We discuss these challenges and how they might be overcome through the development of non-hallucinogenic psychoplastogens. The clinical use of psychedelics and other psychoplastogenic compounds marks a paradigm shift in neuropsychiatry toward therapeutic approaches relying on the selective modulation of neural circuits with small molecule drugs. Psychoplastogen research brings us one step closer to actually curing mental illness by rectifying the underlying pathophysiology of disorders like depression, moving beyond simply treating disease symptoms. However, determining how to most effectively deploy psychoplastogenic medicines at scale will be an important consideration as the field moves forward.

Increased medial prefrontal cortical thickness and resilience to traumatic experiences in North Korean refugees

Little is known regarding structural brain changes in traumatized refugees and the association with psychopathology. In the present study, the cortical thickness in North Korean refugees and the association with psychological symptoms were explored. North Korean refugees with lifetime post-traumatic stress disorder (PTSD group, n = 27), trauma-exposed North Korean refugees without lifetime PTSD (trauma-exposed control (TEC) group, n = 23), and healthy South Korean controls without traumatic experiences (HC group, n = 51) completed questionnaires assessing depression, anxiety, somatization, and PTSD symptoms. The cortical thickness was measured by magnetic resonance imaging (MRI) using FreeSurfer. Age- and sex-adjusted cortical thickness of the right medial prefrontal cortex (mPFC) was greater in the TEC group than in the HC group. However, significant differences were not observed between the PTSD and HC groups. Increased right mPFC thickness was significantly correlated with less anxiety and somatization after controlling for age and sex in the TEC group, but not in the PTSD or HC groups. North Korean refugees who did not develop PTSD after trauma showed increased right mPFC thickness, which was associated with less severe psychiatric symptoms. These findings indicate that increased mPFC thickness might have helped to reduce PTSD and psychiatric symptoms after trauma, and likely reflects resilience achieved by potentially enhancing emotional regulation in the mPFC.

Long-term effects of early treatment with SSRIs on cognition and brain development in individuals with 22q11.2 deletion syndrome

Cognitive deficits in individuals at risk of psychosis represent a significant challenge for research, as current strategies for symptomatic treatment are often ineffective. Recent studies showed that atypical cognitive development predicts the occurrence of psychotic symptoms. Additionally, abnormal brain development is known to predate clinical manifestations of psychosis. Therefore, critical developmental stages may be the best period for early interventions expected to prevent cognitive decline and protect brain maturation. However, it is challenging to identify and treat individuals at risk of psychosis in the general population before the onset of the first psychotic symptoms. 22q11.2 deletion syndrome (22q11DS), the neurogenetic disorder with the highest genetic risk for schizophrenia, provides the opportunity to prospectively study the development of subjects at risk for psychosis. In this retrospective cohort study, we aimed to establish if early treatment with SSRIs in children and adolescents with 22q11DS was associated with long-term effects on cognition and brain development. We included 98 participants with a confirmed diagnosis of 22q11DS followed up 2-4 times (age range: 10-32). Thirty subjects without psychiatric disorders never received psychotropic medications, thirty had psychotic symptoms but were not treated with SSRIs, and 38 received SSRIs treatment. An increase in IQ scores characterized the developmental trajectories of participants receiving treatment with SSRIs, even those with psychotic symptoms. The thickness of frontal regions and hippocampal volume were also relatively increased. The magnitude of the outcomes was inversely correlated to the age at the onset of the treatment. We provide preliminary evidence that early long-term treatment with SSRIs may attenuate the cognitive decline associated with psychosis in 22q11DS and developmental brain abnormalities.

Examination of structural brain changes in recent suicidal behavior

We aimed to identify brain structural changes in cortical and subcortical regions linked to recent suicidal behavior. We performed secondary analyses of structural MRI data of two independent studies, namely the Establishing Moderators/Biosignatures of Antidepressant Response - Clinical Care (EMBARC) study and a Little Rock study on acute suicidal behavior. Study 1 (EMBARC, N = 187), compared individuals with remote suicide attempts (Remote-SA), individuals with lifetime suicide ideation but no attempts (Lifetime-SI only), and depressed individuals without lifetime suicide ideation or attempts (non-suicidal depressed). Study 2 (Little Rock data, N = 34) included patients recently hospitalized for suicide ideation or attempt constituted by: patients who recently attempted suicide (Recent-SA), individuals with remote suicide attempts (Remote-SA), and Lifetime-SI only. Study 3 combined the EMBARC and Little Rock datasets including Recent-SA, Remote-SA, Lifetime-SI only and non-suicidal depressed individuals. In Study 1 and Study 2, no significant differences were observed between groups. In Study 3, significantly lower middle temporal gyrus thickness, insular surface area, and thalamic volume and higher volume in the nucleus accumbens were observed in Recent-SA. This pattern of structural abnormalities may underlie pain and emotion dysregulation, which have been linked to the transition from suicidal thoughts to action.

Cortical Thickness Predicts Response Following 2 Weeks of SSRI Regimen in First-Episode, Drug-Naive Major Depressive Disorder: An MRI Study

OBJECTIVE

Major depression disorder (MDD) is a harmful disorder, and the pathological mechanism remains unclear. The primary pharmacotherapy regimen for MDD is selective serotonin reuptake inhibitors (SSRIs), but fewer than 40% of patients with MDD are in remission following initial treatment. Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in MDD. This study aims to determine if cortical thickness can be used as a predictor for SSRIs.

METHODS

A total of 126 first-episode, drug-naive MDD patients (MDDs) and 71 healthy controls (HCs) were enrolled in our study. Demographic data were collected according to the self-made case report form (CRF) at the baseline of all subjects. Magnetic resonance imaging (MRI) scanning was performed for all the participants at baseline, and all imaging was processed using the DPABISurf software. All MDDs were treated with SSRIs, and symptoms were assessed at both the baseline and 2 weeks using the 17-item Hamilton Rating Scale (HAMD-17). According to HAMD-17 total score improvement from baseline to the end of 2 weeks, the MDDs were divided into the non-responder group (defined as ≤ 20% HAMD-17 reduction) and responder group (defined as ≥50% HAMD-17 reduction). The receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of MDDs' and HCs' cortical thickness for MDD. Correlation analysis was performed for the responder group and the non-responder group separately to identify the relationship between cortical thickness and SSRI treatment efficacy. To analyze whether cortical thickness was sufficient to differentiate responders and non-responders at baseline, we used ROC curve analysis.

RESULTS

Significant decreases were found in the cortical thickness of the right supplementary motor area (SMA) in MDDs at the baseline (corrected by the Monte Carlo permutation correction, cluster-wise significant threshold at p < 0.025 and vertex-wise threshold at p = 0.001), area under the curve (AUC) = 0.732 [95% confidence interval (CI) = 0.233-0.399]. In the responder group, the cortical thickness of the right SMA was significantly thinner than in the non-responder group at baseline. There was a negative correlation (r = -0.373, p = 0.044) between the cortical thickness of SMA (0 weeks) and HAMD-17 reductive rate (2 weeks) in the responder group. The results of ROC curve analyses of the responder and non-responder groups were AUC = 0.885 (95% CI = 0.803-0.968), sensitivity = 73.5%, and specificity = 96.6%, and the cutoff value was 0.701.

CONCLUSION

Lower cortical thickness of the right SMA in MDD patients at the baseline may be a neuroimaging biomarker for MDD diagnosis, and a greater extent of thinner cortical thickness in the right SMA at baseline may predict improved SSRI treatment response. Our study shows the potential of cortical thickness as a possible biomarker that predicts a patient's clinical treatment response to SSRIs in MDD.

Fluoxetine-induced recovery of serotonin and norepinephrine projections in a mouse model of post-stroke depression

Chronic treatment with fluoxetine (FLX) is required for its antidepressant effects, but the role of serotonin (5-HT) axonal plasticity in FLX action is unknown. To address this, we examined mice with a stroke in the left medial prefrontal cortex (mPFC) resulting in persistent anxiety-like and depression-like behaviors and memory deficits as a model of post-stroke depression. Chronic treatment with FLX (but not exercise) completely reversed the behavioral phenotype and partially reversed changes in FosB-labeled cells in the mPFC, nucleus accumbens, septum, hippocampus, basolateral amygdala (BLA), and dorsal raphe. In these regions, 5-HT or norepinephrine (NE) innervation was quantified by staining for 5-HT or NE transporters, respectively. 5-HT synapses and synaptic triads were identified as synaptophysin-stained sites on 5-HT axons located proximal to gephyrin-stained or PSD95-stained spines. A week after stroke, 5-HT innervation was greatly reduced at the stroke site (left cingulate gyrus (CG) of the mPFC) and the left BLA. Chronically, 5-HT and NE innervation was reduced at the left CG, nucleus accumbens, and BLA, with no changes in other regions. In these areas, pre-synaptic and post-synaptic 5-HT synapses and triads to inhibitory (gephyrin+) sites were reduced, while 5-HT contacts at excitatory (PSD95+) sites were reduced in the CG and prelimbic mPFC. Chronic FLX, but not exercise, reversed these reductions in 5-HT innervation but incompletely restored NE projections. Changes in 5-HT innervation were verified using YFP staining in mice expressing YFP-tagged channelrhodopsin in 5-HT neurons. Thus, FLX-induced 5-HT axonal neuroplasticity of forebrain projections may help mediate recovery from brain injury.

Neuroimmune Mechanisms and Sex/Gender-Dependent Effects in the Pathophysiology of Mental Disorders

Innate and adaptive immune mechanisms have emerged as critical regulators of CNS homeostasis and mental health. A plethora of immunologic factors have been reported to interact with emotion- and behavior-related neuronal circuits, modulating susceptibility and resilience to mental disorders. However, it remains unclear whether immune dysregulation is a cardinal causal factor or an outcome of the pathologies associated with mental disorders. Emerging variations in immune regulatory pathways based on sex differences provide an additional framework for discussion in these psychiatric disorders. In this review, we present the current literature pertaining to the effects that disrupted immune pathways have in mental disorder pathophysiology, including immune dysregulation in CNS and periphery, microglial activation, and disturbances of the blood-brain barrier. In addition, we present the suggested origins of such immune dysregulation and discuss the gender and sex influence of the neuroimmune substrates that contribute to mental disorders. The findings challenge the conventional view of these disorders and open the window to a diverse spectrum of innovative therapeutic targets that focus on the immune-specific pathophenotypes in neuronal circuits and behavior. SIGNIFICANCE STATEMENT: The involvement of gender-dependent inflammatory mechanisms on the development of mental pathologies is gaining momentum. This review addresses these novel factors and presents the accumulating evidence introducing microglia and proinflammatory elements as critical components and potential targets for the treatment of mental disorders.

Prognosis and Improved Outcomes in Major Depression: A Review

(Reprinted from Transl Psychiatry. 2019 Apr 3; 9(1):127. Open access; is licensed under a Creative Commons Attribution 4.0 International License).

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.

Combat exposure, posttraumatic stress disorder, and head injuries differentially relate to alterations in cortical thickness in military Veterans

Combat-exposed Veterans are at increased risk for developing psychological distress, mood disorders, and trauma and stressor-related disorders. Trauma and mood disorders have been linked to alterations in brain volume, function, and connectivity. However, far less is known about the effects of combat exposure on brain health. The present study examined the relationship between severity of combat exposure and cortical thickness. Post-9/11 Veterans (N = 337; 80% male) were assessed with structural neuroimaging and clinically for combat exposure, depressive symptoms, prior head injury, and posttraumatic stress disorder (PTSD). Vertex-wide cortical thickness was estimated using FreeSurfer autosegmentation. FreeSurfer's Qdec was used to examine relationship between combat exposure, PTSD, and prior head injuries on cortical thickness (Monte Carlo corrected for multiple comparisons, vertex-wise cluster threshold of 1.3, p < 0.01). Covariates included age, sex, education, depressive symptoms, nonmilitary trauma, alcohol use, and prior head injury. Higher combat exposure uniquely related to lower cortical thickness in the left prefrontal lobe and increased cortical thickness in the left middle and inferior temporal lobe; whereas PTSD negatively related to cortical thickness in the right fusiform. Head injuries related to increased cortical thickness in the bilateral medial prefrontal cortex. Combat exposure uniquely contributes to lower cortical thickness in regions implicated in executive functioning, attention, and memory after accounting for the effects of PTSD and prior head injury. Our results highlight the importance of examining effects of stress and trauma exposure on neural health in addition to the circumscribed effects of specific syndromal pathology.

A study combining whole-exome sequencing and structural neuroimaging analysis for major depressive disorder

BACKGROUND

Genetic variations associated with major depressive disorder (MDD) may affect the structural aspects of neural networks mediated by the molecular pathways involved in neuronal survival and synaptic plasticity. However, few studies have applied a novel approach such as whole-exome sequencing (WES) analysis to investigate the genetic contribution to the neurostructural changes in MDD.

METHODS

In the first part of the study, we investigated rare variants of selected genes from previous WES studies using a WES analysis in 184 patients with MDD and 82 healthy controls. In the second part of the study, we explored the association between the common genetic variants from the WES analysis and cortical thickness in 91 patients with MDD and 75 healthy controls. The gray-matter thickness of each cortical region was measured using FreeSurfer.

RESULTS

We identified recurrent non-silent variants in 24 MDD-related genes including FASN, MYH13, UNC13D, LILRA1, CACNA1B, TRIO, HOMER3, and BCAR3, and observed eleven recurrently altered copy number alternations where a gain on 15q11.2 and losses on 7q34 and 15q11.1-q11.2 in MDD genomes. We also found that rs11592462 in CDH23, a calcium-dependent cell-adhesion molecule encoding gene, was significantly associated with thinning in the right anterior cingulate cortex.

LIMITATION

The small sample size may lead our findings to be underpowered regarding rare variants.

CONCLUSION

The present study identified that non-synonymous rare variants were significantly associated with risk of MDD and found that genetic contributions to the development of MDD may be mediated by alterations in cortical thickness of emotion-processing neural circuits.

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.

Increased Cortical Thickness in Patients With Major Depressive Disorder Following Antidepressant Treatment

Background

Considering the slow-acting properties of traditional antidepressants, an important challenge in the field is the identification of early treatment response biomarkers. Reduced cortical thickness has been reported in neuroimaging studies of depression. However, little is known whether antidepressants reverse this abnormality. In this brief report, we investigated early cortical thickness changes following treatment with sertraline compared to placebo.

Methods

Participants (n = 215) with major depressive disorder were randomized to a selective serotonin reuptake inhibitor, sertraline, or to placebo. Structural magnetic resonance imaging scans were acquired at baseline and one week following treatment. Response was defined as at least 50% improvement in Hamilton rating scale for depression score at week 8. In a vertex-wise approach, we examined the effects of treatment, response, and treatment × response.

Results

Following correction for multiple comparisons, we found a significant effect of treatment, with widespread increase in cortical thickness following sertraline compared to placebo. Clusters with increased thickness were found in the left medial prefrontal cortex, right medial and lateral prefrontal cortex, and within the right parieto-temporal lobes. There were no sertraline-induced cortical thinning, and no significant response effects or treatment × response interactions.

Conclusion

Our findings suggest that cortical thickness abnormalities may be responsive to antidepressant treatment. However, a relationship between these early cortical changes and later treatment response was not demonstrated. Future studies would be needed to investigate whether those early effects are maintained at eight weeks and are associated with enhanced response.

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.

Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: a CAN-BIND report

Finding a clinically useful neuroimaging biomarker that can predict treatment response in patients with major depressive disorder (MDD) is challenging, in part because of poor reproducibility and generalizability of findings across studies. Previous work has suggested that posterior hippocampal volumes in depressed patients may be associated with antidepressant treatment outcomes. The primary purpose of this investigation was to examine further whether posterior hippocampal volumes predict remission following antidepressant treatment. Magnetic resonance imaging (MRI) scans from 196 patients with MDD and 110 healthy participants were obtained as part of the first study in the Canadian Biomarker Integration Network in Depression program (CAN-BIND 1) in which patients were treated for 16 weeks with open-label medication. Hippocampal volumes were measured using both a manual segmentation protocol and FreeSurfer 6.0. Baseline hippocampal tail (Ht) volumes were significantly smaller in patients with depression compared to healthy participants. Larger baseline Ht volumes were positively associated with remission status at weeks 8 and 16. Participants who achieved early sustained remission had significantly greater Ht volumes compared to those who did not achieve remission by week 16. Ht volume is a prognostic biomarker for antidepressant treatment outcomes in patients with MDD.

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.

The PPM1F gene moderates the association between PTSD and cortical thickness

BACKGROUND

Evidence suggests that single nucleotide polymorphisms (SNPs) in genes involved in serotonergic signaling and stress response pathways moderate associations between PTSD and cortical thickness. This study examined a genetic regulator of these pathways, the PPM1F gene, which has also been implicated in mechanisms of stress responding and is differentially expressed in individuals with comorbid PTSD and depression compared to controls.

METHODS

Drawing from a sample of 240 white non-Hispanic trauma-exposed veterans, we tested 18 SNPs spanning the PPM1F gene for association with PTSD and cortical thickness.

RESULTS

Analyses revealed six PPM1F SNPs that moderated associations between PTSD symptom severity and cortical thickness of bilateral superior frontal and orbitofrontal regions as well as the right pars triangularis (all corrected p's < 0.05) such that greater PTSD severity was related to reduced cortical thickness as a function of genotype. A whole-cortex vertex-wise analysis using the most associated SNP (rs9610608) revealed this effect to be localized to a cluster in the right superior frontal gyrus (cluster-corrected p < 0.02).

LIMITATIONS

Limitations of this study include the small sample size and that the sample was all-white, non-Hispanic predominately male veterans.

CONCLUSIONS

These results extend prior work linking PPM1F to PTSD and suggest that variants in this gene may have bearing on the neural integrity of the prefrontal cortex (PFC).

Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial

BACKGROUND

Baseline rostral anterior cingulate cortex (rACC) activity is a well-replicated nonspecific predictor of depression improvement. The rACC is a key hub of the default mode network, which prior studies indicate is hyperactive in major depressive disorder. Because default mode network downregulation is reliant on input from the salience network and frontoparietal network, an important question is whether rACC connectivity with these systems contributes to depression improvement.

METHODS

Our study evaluated this hypothesis in outpatients (N = 238; 151 female) enrolled in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) 8-week randomized clinical trial of sertraline versus placebo for major depressive disorder. Depression severity was measured using the Hamilton Rating Scale for Depression, and electroencephalography was recorded at baseline and week 1. Exact low-resolution electromagnetic tomography was used to compute activity from the rACC, and key regions within the default mode network (posterior cingulate cortex), frontoparietal network (left dorsolateral prefrontal cortex), and salience network (right anterior insula [rAI]). Connectivity in the theta band (4.5-7 Hz) and beta band (12.5-21 Hz) was computed using lagged phase synchronization.

RESULTS

Stronger baseline theta-band rACC-rAI (salience network hub) connectivity predicted greater depression improvement across 8 weeks of treatment for both treatment arms (B = -0.57, 95% confidence interval = -1.07, -0.08, p = .03). Early increases in theta-band rACC-rAI connectivity predicted greater likelihood of achieving remission at week 8 (odds ratio = 2.90, p = .03).

CONCLUSIONS

Among patients undergoing treatment, theta-band rACC-rAI connectivity is a prognostic, albeit treatment-nonspecific, indicator of depression improvement, and early connectivity changes may predict clinically meaningful outcomes.