Neuroimaging-based biomarkers for treatment selection in major depressive disorder

Different activation in dorsolateral prefrontal cortex between anxious depression and non-anxious depression during an autobiographical memory task: A fNIRS study

OBJECTIVE

Using functional near-infrared spectroscopy (fNIRS) previous studies have found that activation differences in the dorsolateral prefrontal cortex (DLPFC) during an autobiographical memory task (AMT) under the condition of different emotional valences may be neurophysiological markers of depression and different depression subtypes. Additionally, compared with non-anxious depression, anxious depression presents abnormal hemodynamic activation in the DLPFC. This study aimed to use fNIRS to investigate hemodynamic activation in the DLPFC of depression patients with and without anxiety during AMT triggered by different emotional valence stimuli.

METHODS

We recruited 194 patients with depression (91 with non-anxious depression, 103 with anxious depression) and 110 healthy controls from Chinese college students. A 53-channel fNIRS was used to detect cerebral hemodynamic differences in the three groups during AMT.

RESULTS

The results showed that: (1) the activation of oxy-Hb in the left DLPFC was significantly higher under positive emotional valence than under negative emotional valence for healthy controls and patients with non-anxious depression, while there was no significant difference between positive and negative emotional valence observed in response to anxious depression; and (2) Oxy-Hb activation under negative emotional valence was significantly higher in the anxious depression group than in the non-anxious depression group.

CONCLUSIONS

This study revealed that the hemodynamic hyperactivation of negative emotional valence in the left DLPFC may be due to the neurophysiological differences between anxious and non-anxious patients with depression.

Neural activity during inhibitory control predicts suicidal ideation with machine learning

Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.

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

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

Neural responses to facial emotions and subsequent clinical outcomes in difficult-to-treat depression

BACKGROUND

Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD.

METHODS

Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item).

RESULTS

We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels.

CONCLUSIONS

We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

A review of new insights into existing major depressive disorder biomarkers

As major depressive disorder (MDD) is such a diverse condition, there are currently no clear ways for determining its severity, endophenotype, or therapy response. The distinctive nature of depression, the variability of analysis in literature and the large number of conceptually complicated biomarkers are some of the many reasons for the lack of progress. Markers are involved in the process of neurotrophic, metabolic, and inflammation as well as neuroendocrine and neurotransmitter systems' components. Some clinical indicators are strong enough so that can be measured using assessments of proteomic, genetic, metabolomics, neuroimaging, epigenetic and transcriptomic. Markers of oxidative stress, endocrine, inflammatory, proteomic, and growth indicators are currently among the promising biologic systems/markers identified in this analysis. This narrative review examines succinct studies which investigated cytokines of inflammatory factors, peripheral factors of development, metabolic and endocrine markers as pathophysiological biomarkers of MDD, and treatment responses. Endocrine and metabolic alterations have also been linked to MDD in various studies. So, this study summarizes all of the numerous biomarkers that are significant in the detection or treatment of MDD patients. The paper also provides an overview of various biomarkers which are important for the regulation and its effects on MDD.

Precision Neuroimaging for Localization-Related Psychiatry

This Viewpoint describes how precision functional mapping may be helpful for associating neuroanatomical regions with specific psychiatric disorders.

Neuroscience-based psychotherapy: A position paper

In the recent years, discoveries in neuroscience have greatly impacted upon the need to modify therapeutic practice starting from the evidence showing some cerebral mechanisms capable of coping with mental health crisis and traumatic events of the individual's life history by redesigning the narrative plot and the person's sense of the Self. The emerging dialogue between neuroscience and psychotherapy is increasingly intense and modern psychotherapy cannot ignore the heritage deriving from studies about neuropsychological modification of memory traces, neurobiology of attachment theory, cognitive mechanisms involved in psychopathology, neurophysiology of human empathy, neuroimaging evidence about psychotherapeutic treatment, and somatoform disorders connecting the brain and the body. In the present article, we critically examined sectorial literature and claimed that psychotherapy has to referred to a neuroscience-based approach in order to adopt the most tailored interventions for specific groups of patients or therapy settings. We also provided recommendations for care implementation in clinical practice and illustrated challenges of future research.

Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM

INTRODUCTION

Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules.

METHODS

We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly.

RESULTS

Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions.

DISCUSSION

These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

Advancing brain network models to reconcile functional neuroimaging and clinical research

Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

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

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

Metabolomic signatures of intravenous racemic ketamine associated remission in treatment-resistant depression: A pilot hypothesis generating study

In this pilot study (N = 9), we highlight new insights gained on ketamine's mechanism of action where we have mapped biochemical processes that are affected within 40 min of intravenous ketamine exposure. Targeting acylcarnitines, we demonstrated rapid utilization of short-chain acylcarnitines within 40 min of ketamine treatment followed by restoration within 24 h; this change in short chain acylcarnitine with rapid-acting antidepressant treatment is consistent with previous work identifying similar change but at 8-weeks with slower-acting SSRI treatment. Using a non-targeted metabolomics platform, we defined broader effects of ketamine on lipid metabolism and identified changes in triglyceride that correlate with ketamine response. This study provides novel insights into ketamine's action and highlighting a possible role for mitochondrial function and energy metabolism in ketamine's mechanism of action.

A longitudinal multi-scanner multimodal human neuroimaging dataset

Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of "travelling human phantoms" had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches.

Beyond the neuron: Role of non-neuronal cells in stress disorders

Stress disorders are leading causes of disease burden in the U.S. and worldwide, yet available therapies are fully effective in less than half of all individuals with these disorders. Although to date, much of the focus has been on neuron-intrinsic mechanisms, emerging evidence suggests that chronic stress can affect a wide range of cell types in the brain and periphery, which are linked to maladaptive behavioral outcomes. Here, we synthesize emerging literature and discuss mechanisms of how non-neuronal cells in limbic regions of brain interface at synapses, the neurovascular unit, and other sites of intercellular communication to mediate the deleterious, or adaptive (i.e., pro-resilient), effects of chronic stress in rodent models and in human stress-related disorders. We believe that such an approach may one day allow us to adopt a holistic "whole body" approach to stress disorder research, which could lead to more precise diagnostic tests and personalized treatment strategies. Stress is a major risk factor for many psychiatric disorders. Cathomas et al. review new insight into how non-neuronal cells mediate the deleterious effects, as well as the adaptive, protective effects, of stress in rodent models and human stress-related disorders.

Anterior cingulate cortex in individuals with depressive symptoms: A structural MRI study

Several magnetic resonance imaging (MRI) studies have reported reduction in anterior cingulate cortex (ACC) volume in individuals with major depressive disorder (MDD). However, some MRI studies did not find significant ACC volumetric changes in MDD, and sample sizes were generally small. This cross-sectional structural MRI study examined the relationship between current depressive symptoms and ACC volume in a large community sample of 1803 adults. A series of multiple linear regression analyses were conducted to predict right and left ACC volumes using Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) scores, intracranial volume, age, sex, race/ethnicity, alcohol use, tobacco use, and psychotropic medications as predictor variables. Right ACC volume was significantly negatively associated with QIDS-SR scores, while no significant association was found between left ACC volume and QIDS-SR scores. In addition, there was a significant negative association between QIDS-SR scores and right but not left ACC volumes in males, and no significant association between QIDS-SR scores and right or left ACC volumes in females. These findings suggest that right ACC volume is reduced in people with greater self-reported depressive symptom severity, and that this association is only significant in men.

Greater baseline connectivity of the salience and negative affect circuits are associated with natural improvements in anxiety over time in untreated participants

BACKGROUND

There is limited research examining the natural trajectories of depression and anxiety, how these trajectories relate to baseline neural circuit function, and how symptom trajectory-circuit relationships are impacted by engagement in lifestyle activities including exercise, hobbies, and social interactions. To address these gaps, we assessed these relations over three months in untreated participants.

METHODS

262 adults (59.5% female, mean age 35) with symptoms of anxiety and depression, untreated with pharmacotherapy or behavioral therapy, completed the DASS-42, WHOQOL, and custom surveys at baseline and follow-up to assess symptoms, psychosocial function, and lifestyle activity engagement. At baseline, participants underwent fMRI under task-free and task-evoked conditions. We quantified six circuits implicated in these symptoms: default mode, salience, negative and positive affect, attention, and cognitive control.

RESULTS

From baseline to 3 months, some participants demonstrated a natural improvement in anxiety (24%) and depression (26%) symptoms. Greater baseline salience circuit connectivity (pFDR=0.045), specifically between the left and right insula (pFDR=0.045), and greater negative affect circuit connectivity elicited by sad faces (pFDR=0.030) were associated with anxiety symptom improvement. While engagement in lifestyle activities were not associated with anxiety improvements, engagement in hobbies moderated the association between negative affect circuit connectivity and anxiety symptom improvement (p = 0.048).

LIMITATIONS

The observational design limits causal inference.

CONCLUSIONS

Our findings highlight the role of the salience and negative affect circuits as potential circuit markers of natural anxiety symptom improvements over time. Future studies that identify biomarkers associated with symptom improvements are critical for the development of personalized treatment targets.

Altered effective connectivity of the extended face processing system in depression and its association with treatment response: findings from the YoDA-C randomized controlled trial

BACKGROUND

Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response.

METHODS

Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders.

RESULTS

Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing.

CONCLUSIONS

Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response.

Abnormal interhemispheric homotopic functional connectivity is correlated with gastrointestinal symptoms in patients with major depressive disorder

The severity of major depressive disorder (MDD) can be aggravated by gastrointestinal (GI) symptoms, but the neuroimaging mechanism underlying GI symptoms still remains unclear. In this study, we recruited 52 medication-free and first-episode MDD patients (35 with GI symptoms and 17 without GI symptoms) and 28 age-, sex-, and education-matched healthy controls to explore the inter-group differences in neuroimaging findings. All the participants underwent resting-state functional magnetic resonance imaging (fMRI) scan, and the functional connectivities that were reported to be abnormal in MDD were our focus of exploration. Voxel-mirrored homotopic connectivity (VMHC) method was used to explore the interhemispheric homotopic functional connectivity of all the subjects. Patients with MDD showed significantly different VMHC in brain regions in the default mode network (DMN), including the middle frontal gyrus, precuneus, inferior parietal lobule, and posterior cingulate cortex. Patients with GI symptoms exhibited significantly decreased interhemispheric homotopic functional connectivity in the middle frontal gyrus and superior frontal gyrus, compared with patients without GI symptoms. These results suggested that the DMN is involved in the neuropathology of MDD. Interhemispheric homotopic connectivity in specific regions could be applied as a biomarker to distinguish MDD patients with GI symptoms from those without GI symptoms.

Grey matter volume abnormalities in the first depressive episode of medication-naïve adult individuals: a systematic review of voxel based morphometric studies

BACKGROUND

To identify the reliable and consistent grey matter volume (GMV) abnormalities associated with major depressive disorder (MDD), we excluded the influence of confounding clinical characteristics, comorbidities and brain degeneration on brain morphological abnormalities by inclusion of non-comorbid and non-geriatric drug-naïve MDD individuals experiencing first episode depressive.

METHODS

The PubMed, Scopus, Web of Science, Science Direct and Google scholar databases were searched for papers published in English up to April 2020.

RESULTS

A total of 21 voxel based morphometric (VBM) studies comparing 845 individuals in the first depressive episode and medication-naïve with 940 healthy control subjects were included. The results showed a grey matter volumes reductions in the orbitofrontal cortex (OFC), prefrontal cortex (PFC), frontal and temporal gyri, temporal pole, insular lobe, thalamus, basal ganglia, cerebellum, hippocampus, cingulate cortex, and amygdala. In addition, increased grey matter volumes in the postcentral gyrus, superior frontal gyrus, insula, basal ganglia, thalamus, amygdala, cuneus, and precuneus differentiated the first depressive episode in medication-naïve individuals from healthy subjects.

CONCLUSION

The present systematic review provided additional support for the involvement of grey matter structural abnormalities in limbic-cortical circuits as possibly specific structural abnormalities in the early stage of MDD.Key pointsDistinct brain regions in MDD patients might be associated with the early stages of illness, and thus it is critical to study the causal relationship between brain structures and the onset of the disease to improve the evaluation in clinic.Grey matter alterations in the fronto-limbic networks in the first episode, medication-naïve MDD might suggest that these abnormalities may play an important role in the neuropathophysiology of MDD at its onset.First episode, medically naïve depressive patients show grey matter volume alterations in brain regions mainly associated with emotion regulation including parietal-temporal regions, PFC, insular lobe, thalamus, basal ganglia, cerebellum and limbic structures that may be specific changes in early stage of MDD.Genotype-diagnosis interaction effects on brain morphology in the cortico-limbic-striatal circuits, including the PFC, amygdala, hippocampus and striatum that might be implicated in the dysfunctional regulation of emotion in first-episode MDD patients.Future longitudinal and prospective studies should be conducted to identify the core structural brain changes in people at-risk for MDD and explore the association of their brain volumes with symptom onset.

Synthesizing the Evidence for Ketamine and Esketamine in Treatment-Resistant Depression: An International Expert Opinion on the Available Evidence and Implementation

Replicated international studies have underscored the human and societal costs associated with major depressive disorder. Despite the proven efficacy of monoamine-based antidepressants in major depression, the majority of treated individuals fail to achieve full syndromal and functional recovery with the index and subsequent pharmacological treatments. Ketamine and esketamine represent pharmacologically novel treatment avenues for adults with treatment-resistant depression. In addition to providing hope to affected persons, these agents represent the first non-monoaminergic agents with proven rapid-onset efficacy in major depressive disorder. Nevertheless, concerns remain about the safety and tolerability of ketamine and esketamine in mood disorders. Moreover, there is uncertainty about the appropriate position of these agents in treatment algorithms, their comparative effectiveness, and the appropriate setting, infrastructure, and personnel required for their competent and safe implementation. In this article, an international group of mood disorder experts provides a synthesis of the literature with respect to the efficacy, safety, and tolerability of ketamine and esketamine in adults with treatment-resistant depression. The authors also provide guidance for the implementation of these agents in clinical practice, with particular attention to practice parameters at point of care. Areas of consensus and future research vistas are discussed.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.

Biomarkers of Major Depressive Disorder: Knowing is Half the Battle

Major depressive disorder (MDD) is a heterogeneous disease which is why there are currently no specific methods to accurately test the severity, endophenotype or therapy response. This lack of progress is partly attributed to the com-plexity and variability of depression, in association with analytical variability of clinical literature and the wide number of theoretically complex biomarkers. The literature accessible, indicates that markers involved in inflammatory, neuro-trophic and metabolic processes and components of neurotransmitters and neuroendocrine systems are rather strong indicators to be considered clinically and can be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomics and neuroimaging assessments. Promising biologic systems/markers found were i.e., growth biomarkers, endocrine markers, oxidant stress markers, proteomic and chronic inflammatory markers, are discussed in this review. Several lines of evidence suggest that a portion of MDD is a dopamine agonist-responsive subtype. This review analyzes concise reports on the pathophysiological biomarkers of MDD and therapeutic reactions via peripheral developmental factors, inflammative cytokines, endocrine factors and metabolic markers. Various literatures also support that endocrine and metabolism changes are associated with MDD. Accumulating evidence suggests that at least a portion of MDD patients show characteristics pathological changes regarding different clinical pathological biomarkers. By this review we sum up all the different biomarkers playing an important role in the detection or treatment of the different patients suffering from MDD. The review also gives an overview of different biomarker's playing a potential role in modulating effect of MDD.

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

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

Device-Based Modulation of Neurocircuits as a Therapeutic for Psychiatric Disorders

Device-based neuromodulation of brain circuits is emerging as a promising new approach in the study and treatment of psychiatric disorders. This work presents recent advances in the development of tools for identifying neurocircuits as therapeutic targets and in tools for modulating neurocircuits. We review clinical evidence for the therapeutic efficacy of circuit modulation with a range of brain stimulation approaches, including subthreshold, subconvulsive, convulsive, and neurosurgical techniques. We further discuss strategies for enhancing the precision and efficacy of neuromodulatory techniques. Finally, we survey cutting-edge research in therapeutic circuit modulation using novel paradigms and next-generation devices.

Circuit-Based Biomarkers for Mood and Anxiety Disorders

Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using brain-wide activity patterns in humans have proven useful in distinguishing between disorder subtypes and identifying effective treatments. In order to improve biomarker identification, it is crucial to understand the basic circuitry underpinning brain-wide activity patterns. Leveraging a large repertoire of techniques, animal studies have examined roles of specific cell types and circuits in driving maladaptive behavior. Recent advances in multiregion recording techniques, data-driven analysis approaches, and machine-learning-based behavioral analysis tools can further push the boundary of animal studies and bridge the gap with human studies, to assess how brain-wide activity patterns encode and drive emotional behavior. Together, these efforts will allow identifying more precise biomarkers to enhance diagnosis and treatment.

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

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

Resilience to Stress and Resilience to Pain: Lessons from Molecular Neurobiology and Genetics

What biological factors account for resilience to pain or to behavioral stress? Here, we discuss examples of cellular and molecular mechanisms within disparate parts of the nervous system as contributors to such resilience. In some especially well-studied humans, it is possible to identify particular neuronal cell types in the peripheral nervous system (PNS) and pinpoint specific genes that are major contributors to pain resilience. We also discuss more complex factors that operate within the central nervous system (CNS) to confer resilience to behavioral stress. We propose that genetic and neurobiological substrates for resilience are discoverable and suggest more generally that neurology and psychiatry hold lessons for each other as investigators search for actionable, biological underpinnings of disease.

Metabolic activity in subcallosal cingulate predicts response to deep brain stimulation for depression

Subcallosal cingulate (SCC) deep brain stimulation (DBS) is a promising therapy for treatment-resistant depression (TRD), but response rates in open-label studies were not replicated in a large multicenter trial. Identifying biomarkers of response could improve patient selection and outcomes. We examined SCC metabolic activity as both a predictor and marker of SCC DBS treatment response. Brain glucose metabolism (CMRGlu) was measured with [18F] FDG-PET at baseline and 6 months post DBS in 20 TRD patients in a double-blind randomized controlled trial where two stimulation types (long pulse width (LPW) n = 9 and short pulse width (SPW) n = 11) were used. Responders (n = 10) were defined by a ≥48% reduction in Hamilton Depression Rating Scale scores after 6 months. The response rates were similar with five responders in each stimulation group: LPW (55.6%) and SPW (44.5%). First, differences in SCC CMRGlu in responders and non-responders were compared at baseline. Then machine learning analysis was performed with a leave-one-out cross-validation using a Gaussian naive Bayes classifier to test whether baseline CMRGlu in SCC could categorize responders. Finally, we compared 6-month change in metabolic activity with change in depression severity. All analyses were controlled for age. Baseline SCC CMRGlu was significantly higher in responders than non-responders. The machine learning analysis predicted response with 80% accuracy. Furthermore, reduction in SCC CMRGlu 6 months post DBS correlated with symptom improvement (r(17) = 0.509; p = 0.031). This is the first evidence of an image-based treatment selection biomarker that predicts SCC DBS response. Future studies could utilize SCC metabolic activity for prospective patient selection.

Magnetic resonance-guided focused ultrasound capsulotomy for refractory obsessive compulsive disorder and major depressive disorder: clinical and imaging results from two phase I trials

Obsessive compulsive disorder (OCD) and major depressive disorder (MDD) are common, often refractory, neuropsychiatric conditions for which new treatment approaches are urgently needed. Magnetic resonance-guided focused ultrasound (MRgFUS) is a novel surgical technique permitting incisionless ablative neurosurgery. We examined the safety profile, clinical response, and imaging correlates of MRgFUS bilateral anterior capsulotomy in patients with refractory obsessive compulsive disorder (OCD, N = 6) and major depressive disorder (MDD, n = 6). There were no serious adverse events. Nonserious adverse events included headaches and pin-site swelling in 7/12 patients. The response rate was 4/6 and 2/6 in the OCD and MDD cohorts respectively. To delineate the white-matter tracts impacted by capsulotomy, a normative diffusion MRI-based structural connectome was used, revealing tracts terminating primarily in the frontal pole, medial thalamus, striatum, and medial-temporal lobe. Positron emission tomography (PET) analysis (nine subjects) revealed widespread decreases in metabolism bilaterally in the cerebral hemispheres at 6 months post treatment, as well as in the right hippocampus, amygdala, and putamen. A pretreatment seed-to-voxel resting-state functional magnetic resonance imaging (rs-fMRI) analysis (12 subjects) revealed three voxel clusters significantly associated with eventual clinical response. MRgFUS capsulotomy appears to be safe, well tolerated, and according to these initial results, may be an important treatment option for patients with refractory OCD and MDD. MRgFUS capsulotomy results in both targeted and widespread changes in neural activity, and neuroimaging may hold potential for the prediction of outcome.

Proposed Neuroimmune Roles of Dimethyl Fumarate, Bupropion, S-Adenosylmethionine, and Vitamin D3 in Affording a Chronically Ill Patient Sustained Relief from Inflammation and Major Depression

We had discussed earlier that, after most of the primary author's multiple sclerosis (MS) symptoms were lessened by prior neuroimmune therapies, use of dimethyl fumarate (DMF) gradually subdued his asthma and urticaria symptoms, as well as his MS-related intercostal cramping; and bupropion supplemented with S-adenosylmethionine (SAMe) and vitamin D3 (vit-D3) helped remit major depression (MD). Furthermore, the same cocktail (bupropion plus supplements), along with previously discussed routines (yoga, meditation, physical exercises, and timely use of medications for other illnesses), continued to subdue MD during new difficulties with craniopharyngioma, which caused bitemporal vision loss; sphenoid sinus infections, which caused cranial nerve-VI (CN6) palsy and diplopia; and through their treatments. Impressed by the benefit the four compounds provided, in this manuscript, we focus on explaining current neuroimmune literature proposals on how: (1) DMF impedes inflammation, oxidative stress, and cell death in CNS and peripheral tissues; (2) Bupropion curbs anxiety, MD, and enhances alertness, libido, and moods; (3) SAMe silences oxidative stress and depression by multiple mechanisms; and (4) Vit-D3 helps brain development and functioning and subdues inflammation. we realize that herein we have reviewed proposed mechanisms of remedies we discovered by literature searches and physician assisted auto-experimentation; and our methods might not work with other patients. We present our experiences so readers are heartened to reflect upon their own observations in peer-reviewed forums and make available a wide body of information for the chronically ill and their physicians to benefit from.

Resting State Functional Connectivity and Outcomes of Psychotherapies for Late-Life Depression

BACKGROUND

Problem solving therapy (PST) and "Engage," a reward-exposure" based therapy, are important treatment options for late-life depression, given modest efficacy of antidepressants in this disorder. Abnormal function of the reward and default mode networks has been observed during depressive episodes. This study examined whether resting state functional connectivity (rsFC) of reward and DMN circuitries is associated with treatment outcomes.

METHODS

Thirty-two older adults with major depression (mean age = 72.7) were randomized to 9-weeks of either PST or "Engage." We assessed rsFC at baseline and week 6. We placed seeds in three a priori regions of interest: subgenual anterior cingulate cortex (sgACC), dorsal anterior cingulate cortex (dACC), and nucleus accumbens (NAcc). Outcome measures included the Hamilton Depression Rating Scale (HAMD) and the Behavioral Activation for Depression Scale (BADS).

RESULTS

In both PST and "Engage," higher rsFC between the sgACC and middle temporal gyrus at baseline was associated with greater improvement in depression severity (HAMD). Preliminary findings suggested that in "Engage" treated participants, lower rsFC between the dACC and dorsomedial prefrontal cortex at baseline was associated with HAMD improvement. Finally, in Engage only, increased rsFC from baseline to week 6 between NAcc and Superior Parietal Cortex was associated with increased BADS scores.

CONCLUSION

The results suggest that patients who present with higher rsFC between the sgACC and a structure within the DMN may benefit from behavioral psychotherapies for late life depression. "Engage" may lead to increased rsFC within the reward system reflecting a reconditioning of the reward systems by reward exposure.

Prefrontal-amygdala connectivity in trait anxiety and generalized anxiety disorder: Testing the boundaries between healthy and pathological worries

BACKGROUND

Current brain-based theoretical models of generalized anxiety disorder (GAD) suggest a dysfunction of amygdala-ventromedial prefrontal cortex emotional regulatory mechanisms. These alterations might be reflected by an altered resting state functional connectivity between both areas and could extend to vulnerable non-clinical samples such as high worriers without a GAD diagnosis. However, there is a lack of information in this regard.

METHODS

We investigated differences in resting state functional connectivity between the basolateral amygdala and the ventromedial prefrontal cortex (amygdala-vmPFC) in 28 unmedicated participants with GAD, 28 high-worriers and 28 low-worriers. We additionally explored selected clinical variables as predictors of amygdala-vmPFC connectivity, including anxiety sensitivity.

RESULTS

GAD participants presented higher left amygdala-vmPFC connectivity compared to both groups of non-GAD participants, and there were no differences between the latter two groups. In our exploratory analyses, concerns about the cognitive consequences of anxiety (the cognitive dimension of anxiety sensitivity) were found to be a significant predictor of the left amygdala-vmPFC connectivity.

LIMITATIONS

The cross-sectional nature of our study preclude us from assessing if functional connectivity measures and anxiety sensitivity scores entail an increased risk of GAD.

CONCLUSIONS

These results suggest a neurobiological qualitative distinction at the level of the amygdala-vmPFC emotional-regulatory system in GAD compared to non-GAD participants, either high- or low-worriers. At this neural level, they question previous hypotheses of continuity between high worries and GAD development. Instead, other anxiety traits such as anxiety sensitivity might confer a greater proneness to the amygdala-vmPFC connectivity alterations observed in GAD.

Psychedelic drugs-a new era in psychiatry?

This article covers the renaissance of classical psychedelic drugs such as psilocybin and LSD plus 3,4-methylene dioxymethamphetamine (MDMA-ecstasy) in psychiatric research. These drugs were used quite extensively before they became prohibited. This ban had little impact on recreational use, but effectively stopped research and clinical treatments, which up to that point had looked very promising in several areas of psychiatry. In the past decade a number of groups have been working to re-evaluate the utility of these substances in medicine. So far highly promising preliminary data have been produced with psilocybin in anxiety, depression, smoking, alcoholism, and with MDMA for post-traumatic stress disorder (PTSD) and alcoholism. These findings have led to the European Medicines Agency approving psilocybin for a phase 3 study in treatment-resistant depression and the Food and Drug Administration for PTSD with MDMA. Both trials should read out in 2020, and if the results are positive we are likely to see these medicines approved for clinical practice soon afterwards.
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Single and repeated ketamine treatment induces perfusion changes in sensory and limbic networks in major depressive disorder

Ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depressive disorder (MDD). Yet, how single and repeated ketamine treatment induces brain systems-level neuroplasticity underlying symptom improvement is unknown. Advanced multiband imaging (MB) pseudo-continuous arterial spin labeling (pCASL) perfusion MRI data was acquired from patients with treatment resistant depression (TRD) (N = 22, mean age=35.2 ± 9.95 SD, 27% female) at baseline, and 24 h after receiving single, and four subanesthetic (0.5 mg/kg) intravenous ketamine infusions. Changes in global and regional CBF were compared across time points, and relationships with overall mood, anhedonia and apathy were examined. Comparisons between patients at baseline and controls (N = 18, mean age=36.11 ± 14.5 SD, 57% female) established normalization of treatment effects. Results showed increased regional CBF in the cingulate and primary and higher-order visual association regions after first ketamine treatment. Baseline CBF in the fusiform, and acute changes in CBF in visual areas were related to symptom improvement after single and repeated ketamine treatment, respectively. In contrast, after serial infusion therapy, decreases in regional CBF were observed in the bilateral hippocampus and right insula with ketamine treatment. Findings demonstrate that neurophysiological changes occurring with single and repeated ketamine treatment follow both a regional and temporal pattern including sensory and limbic regions. Initial changes are observed in the posterior cingulate and precuneus and primary and higher-order visual areas, which relate to clinical responses. However, repeated exposure to ketamine, though not relating to clinical outcome, appears to engage deeper limbic structures and insula. ClinicalTrials.gov: Biomarkers of Fast Acting Therapies in Major Depression, https://clinicaltrials.gov/ct2/show/NCT02165449, NCT02165449.

A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies

In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.

Pilot Study of Metabolomic Clusters as State Markers of Major Depression and Outcomes to CBT Treatment

Major depressive disorder (MDD) is a common and disabling syndrome with multiple etiologies that is defined by clinically elicited signs and symptoms. In hopes of developing a list of candidate biological measures that reflect and relate closely to the severity of depressive symptoms, so-called “state-dependent” biomarkers of depression, this pilot study explored the biochemical underpinnings of treatment response to cognitive behavior therapy (CBT) in medication-free MDD outpatients. Plasma samples were collected at baseline and week 12 from a subset of MDD patients (N = 26) who completed a course of CBT treatment as part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Targeted metabolomic profiling using the AbsoluteIDQ^® p180 Kit and LC-MS identified eight “co-expressed” metabolomic modules. Of these eight, three were significantly associated with change in depressive symptoms over the course of the 12-weeks. Metabolites found to be most strongly correlated with change in depressive symptoms were branched chain amino acids, acylcarnitines, methionine sulfoxide, and α-aminoadipic acid (negative correlations with symptom change) as well as several lipids, particularly the phosphatidlylcholines (positive correlation). These results implicate disturbed bioenergetics as an important state marker in the pathobiology of MDD. Exploratory analyses contrasting remitters to CBT versus those who failed the treatment further suggest these metabolites may serve as mediators of recovery during CBT treatment. Larger studies examining metabolomic change patterns in patients treated with pharmacotherapy or psychotherapy will be necessary to elucidate the biological underpinnings of MDD and the -specific biologies of treatment response.

Mood episodes are associated with increased cortical thinning: A longitudinal study of bipolar disorder type II

OBJECTIVES

Previous studies found evidence for thinner frontotemporal cortices in bipolar disorder (BD), yet whether this represents a stable disease trait or an effect of mood episodes remains unknown. Here, we assessed the reproducibility of thinner frontotemporal cortices in BD type II, compared longitudinal changes in cortical thickness between individuals with BD type II and healthy controls (HCs), and examined the effect of mood episodes on cortical thickness change.

METHODS

Thirty-three HCs and 29 individuals with BD type II underwent 3T magnetic resonance imaging at baseline, as published previously, and 2.4 years later, at follow-up. Cross-sectional and longitudinal analyses of cortical thickness were performed using Freesurfer, and relationships with mood episodes from baseline to follow-up were assessed.

RESULTS

Individuals with BD type II had thinner left and right prefrontal and left temporal cortex clusters at follow-up (all corrected P < 0.001), consistent with baseline results. Both groups showed widespread longitudinal cortical thinning, and patients had increased thinning in a left temporal cortex cluster compared to HCs (corrected P < 0.001). Patients with more (>2) depressive episodes between baseline and follow-up had greater left temporal cortical thinning than patients with fewer depressive episodes (corrected P < 0.05). In addition, patients with more depressive episodes had greater thinning in bilateral ventromedial prefrontal clusters relative to HCs (uncorrected P < 0.05), yet these results did not survive correction for multiple comparisons.

CONCLUSIONS

Together, these findings support reduced frontotemporal cortical thickness in BD type II and provide the first preliminary evidence for an association between depressive episodes and increased cortical thinning.

Brain structural abnormalities in emotional regulation and sensory processing regions associated with anxious depression

BACKGROUND

Considerable patients with major depressive disorder (MDD) comorbid with anxious symptoms, referred as anxious depression. The neural structural basis of this MDD specifier remains largely unknown.

METHODS

104 patients with anxious depression, 57 MDD patients without significant anxious symptoms, and 160 healthy controls from single research center participated in the study with age and sex well-matched. We investigated gray matter alterations in anxious and non-anxious depression, explored different brain alterations between these two patient groups, and possible relationships between brain structural parameter and clinical information in patients.

RESULTS

Gray matter volumes differed in the right inferior frontal gyrus, right orbital frontal gyrus, left postcentral gyrus, bilateral culmen and left cuneus among the three groups. Anxious depression had smaller gray matter volumes in the right inferior frontal gyrus and orbital frontal gyrus relative to both non-anxious depression and healthy controls. Patients with anxious depression presented larger gray matter volumes in the left postcentral gyrus than non-anxious depression, and larger gray matter volumes in the left cuneus than healthy controls. In addition, both patient groups showed larger gray matter volumes in bilateral culmen relative to healthy controls. Gray matter volumes in the left postcentral gyrus were positively associated with overall depression severity and anxiety factor scores in anxious depression.

CONCLUSION

Our study revealed brain structural abnormalities in emotional regulation and sensory processing regions of anxious depression, which may suggested distinct neurobiological mechanisms of this MDD specifier and could help explain different clinical manifestations in anxious depression from pure depression.

Suicidal ideation and other persisting symptoms after CBT or antidepressant medication treatment for major depressive disorder

BACKGROUND

Persisting symptoms after treatment for major depressive disorder (MDD) contribute to ongoing impairment and relapse risk. Whether cognitive behavior therapy (CBT) or antidepressant medications result in different profiles of residual symptoms after treatment is largely unknown.

METHODS

Three hundred fifteen adults with MDD randomized to treatment with either CBT or antidepressant medication in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study were analyzed for the frequency of residual symptoms using the Montgomery Asberg Depression Rating Scale (MADRS) item scores at the end of the 12-week treatment period. Separate comparisons were made for treatment responders and non-responders.

RESULTS

Among treatment completers (n = 250) who responded to CBT or antidepressant medication, there were no significant differences in the persistence of residual MADRS symptoms. However, non-responders treated with medication were significantly less likely to endorse suicidal ideation (SI) at week 12 compared with those treated with CBT (non-responders to medication: 0/54, 0%, non-responders to CBT: 8/30, 26.7%; p = .001). Among patients who terminated the trial early (n = 65), residual MADRS item scores did not significantly differ between the CBT- and medication-treated groups.

CONCLUSIONS

Depressed adults who respond to CBT or antidepressant medication have similar residual symptom profiles. Antidepressant medications reduce SI, even among patients for whom the medication provides little overall benefit.

Neural predictors of treatment response to brain stimulation and psychological therapy in depression: a double-blind randomized controlled trial

Standard depression treatments, including antidepressant medication and cognitive behavioural therapy (CBT), are ineffective for many patients. Prefrontal transcranial direct current stimulation (tDCS) has been proposed as an alternative treatment, but has shown inconsistent efficacy for depression, and its mechanisms are poorly understood. We recruited unmedicated patients with major depressive disorder (N = 71 approached; N = 39 randomised) for a mechanistic, double-blind, randomized controlled trial consisting of eight weekly sessions of prefrontal tDCS administered to the left prefrontal cortex prior to CBT. We probed (1) whether tDCS improved the efficacy of CBT relative to sham stimulation; and (2) whether neural measures predicted clinical response. We found a modest and non-significant effect of tDCS on clinical outcome over and above CBT (active: 50%; sham: 31.6%; odds ratio: 2.16, 95% CI = 0.59-7.99), but a strong relationship, predicted a priori, between baseline activation during a working memory task in the stimulated prefrontal region and symptom improvement. Repeating our analyses of symptom outcome splitting the sample according to this biomarker revealed that tDCS was significantly superior to sham in individuals with high left prefrontal cortex activation at baseline; we also show 86% accuracy in predicting clinical response using this measure. Exploratory analyses revealed several other regions where activation at baseline was associated with subsequent response to CBT, irrespective of tDCS. This mechanistic trial revealed variable, but predictable, clinical effects of prefrontal tDCS combined with CBT for depression. We have discovered a potential explanation for this variability: individual differences in baseline activation of the region stimulated. Such a biomarker could potentially be used to pre-select patients for trials and, eventually, in the clinic.

Prevention of Initial Depressive Disorders Among at-Risk Portuguese Adolescents

This study evaluated whether Arnarson and Craighead's (2009, 2011) developmentally based behavioral and cognitive program that prevented the initial episode of depressive disorders among Icelandic adolescents could be adapted to prevent depressive disorders among "at-risk" Portuguese adolescents. One hundred sixty-eight Portuguese mid-adolescents (primarily 14 to 15 years old), who had subsyndromal symptoms of depression but who had never met criteria for a depressive disorder, were identified by classroom screening with the CDI and subsequent K-SADS-PL interview. All 168 adolescents were offered participation in the 14-week prevention program; 70 agreed to participate in the program, and 98 agreed to participate only in an assessment control group. Psychological disorders were evaluated at baseline, 6-, 12-, 18-, and 24-month assessments. During the 2-year follow-up period, 12 students in the assessment-only group experienced an initial depressive disorder versus 2 in the prevention group. Survival analyses indicated a significantly lower rate of initial episodes of depressive disorders, χ²(1) = 4.261, p = .039, among the prevention group participants compared to the assessment only comparison group. The hazard ratio was .207, and the NNT was 11. Survival analyses indicated no significant differences between the prevention condition and the assessment only condition in the occurrence of other psychiatric disorders, χ2(1) = 1.080, p =.299. The findings indicate the program can be successfully adapted for use in Portuguese schools, and they provide a preliminary indication that those "at-risk" adolescents who chose to participate in the program, compared to those who chose to participate only in the assessments, developed fewer initial episodes of depressive disorders over the course of 24 months. The program effects were similar to the outcomes of the prior study of this program in Iceland. As in the Icelandic version of the program, its effects appeared to be specific to the depressive disorders for which the program was designed.

Rehabilitative compensatory mechanism of hierarchical subnetworks in major depressive disorder: A longitudinal study across multi-sites

BACKGROUND

Brain structural connectome comprise of a minority of efficiently interconnected rich club nodes that are regarded as 'high-order regions'. The remission of major depressive disorder (MDD) in response to selective serotonin reuptake inhibitor (SSRI) treatment could be investigated by the hierarchical structural connectomes' alterations of subnetworks.

METHODS

Fifty-five MDD patients who achieved remission underwent diffusion tensors imaging (DTI) scanning from 3 cohorts before and after 8-weeks antidepressant treatment. Five hierarchical subnetworks namely, rich, local, feeder, rich-feeder and feeder-local, were constructed according to the different combinations of connections and nodes as defined by rich club architecture. The critical treatment-related subnetwork pattern was explored by multivariate pattern analysis with support vector machine to differ the pre-/post-treatment patients. Then, relationships between graph metrics of discriminative subnetworks/ nodes and clinical variables were further explored.

RESULTS

The feeder-local subnetwork presented the most discriminative power in differing pre-/post- treatment patients, while the rich-feeder subnetwork had the highest discriminative power when comparing pre-treatment patients and controls. Furthermore, based on the feeder connection, which indicates the information transmission between the core and non-core architectures of brain networks, its topological measures were found to be significantly correlated with the reduction rate of 17-item Hamilton Rating Scale for Depression.

CONCLUSION

Although pathological lesion on MDD relied on abnormal core organization, disease remission was association with the compensation from non-core organization. These results suggested that the dysfunctions arising from hierarchical subnetworks are compensated by increased information interactions between core brain regions and functionally diverse regions.

Feeling left out: depressed adolescents may atypically recruit emotional salience and regulation networks during social exclusion

Depression is associated with negative attention and attribution biases and maladaptive emotion responsivity and regulation, which adversely impact self-evaluations and interpersonal relationships. Using functional magnetic resonance imaging, we investigated the neural substrates of these impairments. We compared neural activity recruited by 126 clinically depressed and healthy adolescents (ages 11-17 years) during social exclusion (Exclusion > Inclusion) using Cyberball. Results revealed significant interaction effects within left anterior insula (AI)/inferior frontal gyrus and left middle temporal gyrus. Insula hyperresponsivity was associated with peer exclusion for depressed adolescents but peer inclusion for healthy adolescents. In additional, healthy adolescents recruited greater lateral temporal activity during peer exclusion. Complementary effect size analyses within independent parcellations offered converging evidence, as well as highlighted medium-to-large effects within subgenual/ventral anterior cingulate cortex and lateral prefrontal, lateral temporal and lateral parietal regions implicated in emotion regulation. Depressogenic neural patterns were associated with negative self-perceptions and negative information processing biases. These findings suggest a neural mechanism underlying cognitive biases in depression, as reflected by emotional hyperresponsivity and maladaptive regulation/reappraisal of negative social evaluative information. This study lends further support for salience and central executive network dysfunction underlying social threat processing, and in particular, highlights the anterior insula as a key region of disturbance in adolescent depression.

Clinical Pharmacokinetics and Pharmacodynamics of Drugs in the Central Nervous System

Despite contributing significantly to the burden of global disease, the translation of new treatment strategies for diseases of the central nervous system (CNS) from animals to humans remains challenging, with a high attrition rate in the development of CNS drugs. The failure of clinical trials for CNS therapies can be partially explained by factors related to pharmacokinetics/pharmacodynamics (PK/PD), such as lack of efficacy or improper selection of the initial dosage. A focused assessment is needed for CNS-acting drugs in first-in-human studies to identify the differences in PK/PD from animal models, as well as to choose the appropriate dose. In this review, we summarize the available literature from human studies on the PK and PD in brain tissue, cerebrospinal fluid, and interstitial fluid for drugs used in the treatment of psychosis, Alzheimer's disease and neuro-HIV, and address critical questions in the field. We also explore newer methods to characterize PK/PD relationships that may lead to more efficient dose selection in CNS drug development.

Different spatial patterns of brain atrophy and global functional connectivity impairments in major depressive disorder

Alterations in grey matter structure and functional connectivity (FC) are pathological features of major depressive disorder (MDD). However, the relationship between brain structural and functional impairments in MDD remains uncertain. Thus, the aim of this study was to investigate the relationship between alterations in grey matter volume (GMV) and resting-state global functional connectivity density (rs-gFCD) in MDD. Forty-five patients with MDD and 48 healthy controls underwent structural and resting-state functional magnetic resonance imaging (fMRI). GMV and rs-gFCD maps of each subject were created, and the intergroup differences in these neuroimaging metrics were evaluated. Compared to healthy controls, patients with MDD displayed reduced GMV in the left insula, putamen, amygdala and hippocampus (mainly in the limbic system). In contrast, decreased rs-gFCD was observed in the left postcentral and precentral gyri, right fusiform gyrus and lingual gyrus (mainly in the sensory system). Furthermore, we found no significant correlations between GMV and rs-gFCD in these brain regions. Finally, overlap ratio analysis demonstrated that the spatial distribution patterns of intergroup differences on the GMV and rs-gFCD maps were distinct. Our findings suggest that the presence of distinct spatial distribution patterns of brain atrophy and global FC impairments might be pathological features of MDD.

Indicators to facilitate the early identification of patients with major depressive disorder in need of highly specialized care: A concept mapping study

BACKGROUND

Early identification of the subgroup of patients with major depressive disorder (MDD) in need of highly specialized care could enhance personalized intervention. This, in turn, may reduce the number of treatment steps needed to achieve and sustain an adequate treatment response. The aim of this study was to identify patient-related indicators that could facilitate the early identification of the subgroup of patients with MDD in need of highly specialized care.

METHODS

Initial patient indicators were derived from a systematic review. Subsequently, a structured conceptualization methodology known as concept mapping was employed to complement the initial list of indicators by clinical expertise and develop a consensus-based conceptual framework. Subject-matter experts were invited to participate in the subsequent steps (brainstorming, sorting, and rating) of the concept mapping process. A final concept map solution was generated using nonmetric multidimensional scaling and agglomerative hierarchical cluster analyses.

RESULTS

In total, 67 subject-matter experts participated in the concept mapping process. The final concept map revealed the following 10 major clusters of indicators: 1-depression severity, 2-onset and (treatment) course, 3-comorbid personality disorder, 4-comorbid substance use disorder, 5-other psychiatric comorbidity, 6-somatic comorbidity, 7-maladaptive coping, 8-childhood trauma, 9-social factors, and 10-psychosocial dysfunction.

CONCLUSIONS

The study findings highlight the need for a comprehensive assessment of patient indicators in determining the need for highly specialized care, and suggest that the treatment allocation of patients with MDD to highly specialized mental healthcare settings should be guided by the assessment of clinical and nonclinical patient factors.

Lactate in the brain: from metabolic end-product to signalling molecule

Lactate in the brain has long been associated with ischaemia; however, more recent evidence shows that it can be found there under physiological conditions. In the brain, lactate is formed predominantly in astrocytes from glucose or glycogen in response to neuronal activity signals. Thus, neurons and astrocytes show tight metabolic coupling. Lactate is transferred from astrocytes to neurons to match the neuronal energetic needs, and to provide signals that modulate neuronal functions, including excitability, plasticity and memory consolidation. In addition, lactate affects several homeostatic functions. Overall, lactate ensures adequate energy supply, modulates neuronal excitability levels and regulates adaptive functions in order to set the 'homeostatic tone' of the nervous system.