Toward a neuroimaging treatment selection biomarker for major depressive disorder

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

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

Psychotherapy or medication for depression? Using individual symptom meta-analyses to derive a Symptom-Oriented Therapy (SOrT) metric for a personalised psychiatry

BACKGROUND

Antidepressant medication (ADM) and psychotherapy are effective treatments for major depressive disorder (MDD). It is unclear, however, if treatments differ in their effectiveness at the symptom level and whether symptom information can be utilised to inform treatment allocation. The present study synthesises comparative effectiveness information from randomised controlled trials (RCTs) of ADM versus psychotherapy for MDD at the symptom level and develops and tests the Symptom-Oriented Therapy (SOrT) metric for precision treatment allocation.

METHODS

First, we conducted systematic review and meta-analyses of RCTs comparing ADM and psychotherapy at the individual symptom level. We searched PubMed Medline, PsycINFO, and the Cochrane Central Register of Controlled Trials databases, a database specific for psychotherapy RCTs, and looked for unpublished RCTs. Random-effects meta-analyses were applied on sum-scores and for individual symptoms for the Hamilton Rating Scale for Depression (HAM-D) and Beck Depression Inventory (BDI) measures. Second, we computed the SOrT metric, which combines meta-analytic effect sizes with patients' symptom profiles. The SOrT metric was evaluated using data from the Munich Antidepressant Response Signature (MARS) study (n = 407) and the Emory Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study (n = 234).

RESULTS

The systematic review identified 38 RCTs for qualitative inclusion, 27 and 19 for quantitative inclusion at the sum-score level, and 9 and 4 for quantitative inclusion on individual symptom level for the HAM-D and BDI, respectively. Neither meta-analytic strategy revealed significant differences in the effectiveness of ADM and psychotherapy across the two depression measures. The SOrT metric did not show meaningful associations with other clinical variables in the MARS sample, and there was no indication of utility of the metric for better treatment allocation from PReDICT data.

CONCLUSIONS

This registered report showed no differences of ADM and psychotherapy for the treatment of MDD at sum-score and symptom levels. Symptom-based metrics such as the proposed SOrT metric do not inform allocation to these treatments, but predictive value of symptom information requires further testing for other treatment comparisons.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

OBJECTIVES

The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement.

METHOD

We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019.

RESULT

We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%.

CONCLUSIONS

Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.

Deep brain stimulation for treatment-resistant depression: a safe and effective option

Introduction: Major depressive disorder (MDD) is the leading cause of years lost to disability worldwide. Pharmacotherapy and psychotherapy are effective treatments in most depressive episodes; but, about 30% of MDD patients remain symptomatic, and relapse is a common event. Recently, deep brain stimulation (DBS) has emerged as a valid therapeutic option in treatment-resistant depression (TRD) patients.Areas covered: In this paper, the authors summarize the findings of studies focused on these pathophysiologic phenomena and specifically on the role of DBS as a therapeutic option in TRD patients. The authors simply reviewed RCTs, open-label studies, neurophysiological mechanisms of DBS in MDD, and the possible role of different targets. Finally, we suggest possible future options.Expert opinion: Depression is a systems-level disorder, involving several brain structures. Neuroimaging studies demonstrate multiple interconnected regions that modulate different neural networks. DBS can modulate different targets, and others are under investigation. Among these subcallosal cingulate gyrus (SCG), ventral capsule and ventral striatum (VC/VS) seems to be the most relevant targets. We believe that, in the next future, DBS for TRD might become a first-line of treatment, especially using directional leads, that may help us to improve therapeutic effects.

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.

Individual Patterns of Abnormality in Resting-State Functional Connectivity Reveal Two Data-Driven PTSD Subgroups

OBJECTIVE

A major challenge in understanding and treating posttraumatic stress disorder (PTSD) is its clinical heterogeneity, which is likely determined by various neurobiological perturbations. This heterogeneity likely also reduces the effectiveness of standard group comparison approaches. The authors tested whether a statistical approach aimed at identifying individual-level neuroimaging abnormalities that are more prevalent in case subjects than in control subjects could reveal new clinically meaningful insights into the heterogeneity of PTSD.

METHODS

Resting-state functional MRI data were recorded from 87 unmedicated PTSD case subjects and 105 war zone-exposed healthy control subjects. Abnormalities were modeled using tolerance intervals, which referenced the distribution of healthy control subjects as the "normative population." Out-of-norm functional connectivity values were examined for enrichment in cases and then used in a clustering analysis to identify biologically defined PTSD subgroups based on their abnormality profiles.

RESULTS

The authors identified two subgroups among PTSD cases, each with a distinct pattern of functional connectivity abnormalities with respect to healthy control subjects. Subgroups differed clinically on levels of reexperiencing symptoms and improved case-control discriminability and were detectable using independently recorded resting-state EEG data.

CONCLUSIONS

The results provide proof of concept for the utility of abnormality-based approaches for studying heterogeneity within clinical populations. Such approaches, applied not only to neuroimaging data, may allow detection of subpopulations with distinct biological signatures so that further clinical and mechanistic investigations can be focused on more biologically homogeneous subgroups.

Escitalopram ameliorates differences in neural activity between healthy comparison and major depressive disorder groups on an fMRI Emotional conflict task: A CAN-BIND-1 study

BACKGROUND

Identifying objective biomarkers can assist in predicting remission/non-remission to treatment, improving remission rates, and reducing illness burden in major depressive disorder (MDD).

METHODS

Sixteen MDD 8-week remitters (MDD-8), twelve 16-week remitters (MDD-16), 14 non-remitters (MDD-NR) and 30 healthy comparison participants (HC) completed a functional magnetic resonance imaging emotional conflict task at baseline, prior to treatment with escitalopram, and 8 weeks after treatment initiation. Patients were followed 16 weeks to assess remitter status.

RESULTS

All groups demonstrated emotional Stroop in reaction time (RT) at baseline and Week 8. There were no baseline differences between HC and MDD-8, MDD-16, or MDD-NR in RT or accuracy. By Week 8, MDD-8 demonstrated poorer accuracy compared to HC. Compared to HC, the baseline blood-oxygen level dependent (BOLD) signal was decreased in MDD-8 in brain-stem and thalamus; in MDD-16 in lateral occipital cortex, middle temporal gyrus, and cuneal cortex; in MDD-NR in lingual and occipital fusiform gyri, thalamus, putamen, caudate, cingulate gyrus, insula, cuneal cortex, and middle temporal gyrus. By Week 8, there were no BOLD activity differences between MDD groups and HC.

LIMITATIONS

The Emotional Conflict Task lacks a neutral (non-emotional) condition, restricting interpretation of how mood may influence perception of non-emotionally valenced stimuli.

CONCLUSIONS

The Emotional Conflict Task is not an objective biomarker for remission trajectory in patients with MDD receiving escitalopram treatment. Escitalopram may have influenced emotion recognition in MDD groups in terms of augmented accuracy and BOLD signal in response to an Emotional Conflict Task, following 8 weeks of escitalopram treatment.

Magnetoencephalographic Correlates of Suicidal Ideation in Major Depression

BACKGROUND

Defining the neurobiological underpinnings of suicidal ideation (SI) is crucial to improving our understanding of suicide. This study used magnetoencephalographic gamma power as a surrogate marker for population-level excitation-inhibition balance to explore the underlying neurobiology of SI and depression. In addition, effects of pharmacological intervention with ketamine, which has been shown to rapidly reduce SI and depression, were assessed.

METHODS

Data were obtained from 29 drug-free patients with major depressive disorder who participated in an experiment comparing subanesthetic ketamine (0.5 mg/kg) with a placebo saline infusion. Magnetoencephalographic recordings were collected at baseline and after ketamine and placebo infusions. During scanning, patients rested with their eyes closed. SI and depression were assessed, and a linear mixed-effects model was used to identify brain regions where gamma power and both SI and depression were associated. Two regions of the salience network (anterior insula, anterior cingulate) were then probed using dynamic causal modeling to test for ketamine effects.

RESULTS

Clinically, patients showed significantly reduced SI and depression after ketamine administration. In addition, distinct regions in the anterior insula were found to be associated with SI compared with depression. In modeling of insula-anterior cingulate connectivity, ketamine lowered the membrane capacitance for superficial pyramidal cells. Finally, connectivity between the insula and anterior cingulate was associated with improvements in depression symptoms.

CONCLUSIONS

These findings suggest that the anterior insula plays a key role in SI, perhaps via its role in salience detection. In addition, transient changes in superficial pyramidal cell membrane capacitance and subsequent increases in cortical excitability might be a mechanism through which ketamine improves SI.

Rostral Anterior Cingulate Thickness Predicts the Emotional Psilocybin Experience

Psilocybin is the psychoactive compound of mushrooms in the psilocybe species. Psilocybin directly affects a number of serotonin receptors, with highest affinity for the serotonin 2A receptor (5HT-2Ar). Generally, the effects of psilocybin, and its active metabolite psilocin, are well established and include a range of cognitive, emotional, and perceptual perturbations. Despite the generality of these effects, there is a high degree of inter-individual variability in subjective psilocybin experiences that are not well understood. Others have shown brain morphology metrics derived from magnetic resonance imaging (MRI) can predict individual drug response. Due to high expression of serotonin 2A receptors (5HT-2Ar) in the cingulate cortex, and its prior associations with psilocybin, we investigate if cortical thickness of this structure predicts the psilocybin experience in healthy adults. We hypothesized that greater cingulate thickness would predict higher subjective ratings in sub-scales of the Five-Dimensional Altered State of Consciousness (5D-ASC) with high emotionality in healthy participants (n = 55) who received oral psilocybin (either low dose: 0.160 mg/kg or high dose: 0.215 mg/kg). After controlling for sex, age, and using false discovery rate (FDR) correction, we found the rostral anterior cingulate predicted all four emotional sub-scales, whereas the caudal and posterior cingulate did not. How classic psychedelic compounds induce such large inter-individual variability in subjective states has been a long-standing question in serotonergic research. These results extend the traditional set and setting hypothesis of the psychedelic experience to include brain structure metrics.

Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future

Purpose of Review

This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression.

Recent Findings

Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures.

Summary

Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.

Emotional distress, brain functioning, and biobehavioral processes in cancer patients: a neuroimaging review and future directions

Despite emerging evidence that distress and adversity can contribute to negative health outcomes in cancer, little is known about the brain networks, regions, or circuits that can contribute to individual differences in affect/distress states and health outcomes in treated cancer patients. To understand the state-of-the-science in this regard, we reviewed neuroimaging studies with cancer patients that examined the associations between negative affect (distress) and changes in the metabolism or structure of brain regions. Cancer patients showed changes in function and/or structure of key brain regions such as the prefrontal cortex, thalamus, amygdala, hippocampus, cingulate cortex (mainly subgenual area), hypothalamus, basal ganglia (striatum and caudate), and insula, which are associated with greater anxiety, depression, posttraumatic stress disorder (PTSD) symptoms, and distress. These results provide insights for understanding the effects of these psychological and emotional factors on peripheral stress-related biobehavioral pathways known to contribute to cancer progression and long-term health outcomes. This line of work provides leads for understanding the brain-mediated mechanisms that may explain the health effects of psychosocial interventions in cancer patients and survivors. A multilevel and integrated model for distress management intervention effects on psychological adaptation, biobehavioral processes, cancer pathogenesis, and clinical outcomes is proposed for future research.

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.

State-Independent Microstructural White Matter Abnormalities in Major Depressive Disorder

BACKGROUND

Even with continuous antidepressant treatment, residual symptoms and the risk of relapse can persist in remitted major depressive disorder (MDD) patients. Hence, having a clear recognition of the persistent abnormalities of the underlying neural substrate in MDD through a longitudinal investigation is of great importance.

METHODS

A total of 127 adult medication-free MDD patients with an acute depressive episode and 118 matched healthy controls (HCs) underwent diffusion tensor imaging. Over a 6-month treatment course, 62 remitted patients underwent a second scan. Remission was defined as a 24-item Hamilton Depression Rating Scale (HAMD₂₄) score ≤7 for at least two weeks. Diffusion tensor imaging was performed with a 3.0 T scanner. Differences in whole-brain fractional anisotropy (FA) between MDD patients and HCs were assessed by an independent t-test using gender, age, and education as covariates.

RESULTS

Significant FA reductions in the left insula, left middle occipital gyrus, right thalamus, left pallidum and left precuneus were observed in current MDD (cMDD) patients compared with HCs. Moreover, significant FA reductions in the left insula were observed in remitted (rMDD) patients compared to HCs. However, no significant differences in FA values were found when comparing cMDD and rMDD patients.

CONCLUSIONS

The abnormalities in the insula showed state-independent characteristics, while the abnormalities in the middle occipital gyrus, thalamus, pallidum and precuneus seemed to be state-dependent impairments in MDD patients.

Connectivity between the anterior insula and dorsolateral prefrontal cortex links early symptom improvement to treatment response

BACKGROUND

Early improvement (EI) following treatment with antidepressants is a widely reported predictor to the treatment response. This study aimed to identify the resting-state functional connectivity (rs-FC) and its related clinical features that link the treatment response at the time of EI.

METHODS

This study included 23 first-episode treatment-naive patients with MDD. After 2 weeks of antidepressant treatment, these patients received 3.0 Tesla resting-state functional magnetic resonance imaging scanning and were subgrouped into an EI group (N = 13) and a non-EI group (N = 10). Using the anterior insula (rAI) as a seed region, this study identified the rs-FC that were associated with both EI and the treatment response at week 12, and further tested the associations of the identified rs-FC with either the clinical features or the early symptom improvement.

RESULTS

Rs-FC between rAI and the left dorsolateral prefrontal cortex (dlPFC) was associated with EI (t₂₁ = -6.091, p = 0.022 after FDR correction for multiple comparisons). This rs-FC was also associated with an interaction between EI and the treatment response at the week 12 (t₂₁ = -5.361, p = 6.37e-5). Moreover, among the clinical features, this rs-FC was associated with the early symptom improvement in the insomnia, somatic symptoms, and anxiety symptoms, and these early symptom improvements were associated with the treatment response.

CONCLUSION

Rs-FC between the rAI and the left dlPFC played a crucial role in the early antidepressant effect, which linked the treatment response. The early treatment effect relating to rAI may represent an early symptom improvement in self-perceptual anxiety, somatic symptoms and insomnia.

Neurocognitive working mechanisms of the prevention of relapse in remitted recurrent depression (NEWPRIDE): protocol of a randomized controlled neuroimaging trial of preventive cognitive therapy

BACKGROUND

Major Depressive Disorder (MDD) is a psychiatric disorder with a highly recurrent character, making prevention of relapse an important clinical goal. Preventive Cognitive Therapy (PCT) has been proven effective in preventing relapse, though not for every patient. A better understanding of relapse vulnerability and working mechanisms of preventive treatment may inform effective personalized intervention strategies. Neurocognitive models of MDD suggest that abnormalities in prefrontal control over limbic emotion-processing areas during emotional processing and regulation are important in understanding relapse vulnerability. Whether changes in these neurocognitive abnormalities are induced by PCT and thus play an important role in mediating the risk for recurrent depression, is currently unclear. In the Neurocognitive Working Mechanisms of the Prevention of Relapse In Depression (NEWPRIDE) study, we aim to 1) study neurocognitive factors underpinning the vulnerability for relapse, 2) understand the neurocognitive working mechanisms of PCT, 3) predict longitudinal treatment effects based on pre-treatment neurocognitive characteristics, and 4) validate the pupil dilation response as a marker for prefrontal activity, reflecting emotion regulation capacity and therapy success.

METHODS

In this randomized controlled trial, 75 remitted recurrent MDD (rrMDD) patients will be included. Detailed clinical and cognitive measurements, fMRI scanning and pupillometry will be performed at baseline and three-month follow-up. In the interval, 50 rrMDD patients will be randomized to eight sessions of PCT and 25 rrMDD patients to a waiting list. At baseline, 25 healthy control participants will be additionally included to objectify cross-sectional residual neurocognitive abnormalities in rrMDD. After 18 months, clinical assessments of relapse status are performed to investigate which therapy induced changes predict relapse in the 50 patients allocated to PCT.

DISCUSSION

The present trial is the first to study the neurocognitive vulnerability factors underlying relapse and mediating relapse prevention, their value for predicting PCT success and whether pupil dilation acts as a valuable marker in this regard. Ultimately, a deeper understanding of relapse prevention could contribute to the development of better targeted preventive interventions.

TRIAL REGISTRATION

Trial registration: Netherlands Trial Register, August 18, 2015, trial number NL5219.

Dimensional anhedonia and the adolescent brain: reward and aversion anticipation, effort and consummation

BACKGROUND

Given the heterogeneity of depression the Research Domain Criteria Framework suggests a dimensional approach to understanding the nature of mental illness. Neural reward function has been suggested as underpinning the symptom of anhedonia in depression but how anhedonia is related to aversion processing is unclear.

AIMS

To assess how the dimensional experience of anhedonia and depression severity relate to reward and aversion processing in the human brain.

METHOD

We examined adolescents and emerging adults (n = 84) in the age range 13-21 years. Using a dimensional approach we examined how anhedonia and depression related to physical effort to gain reward or avoid aversion and neural activity during the anticipation, motivation/effort and consummation of reward and aversion.

RESULTS

As anhedonia increased physical effort to gain reward decreased. As anhedonia increased neural activity decreased during effort to avoid in the precuneus and insula (trend) and increased in the caudate during aversive consummation. We found participants with depression symptoms invested less physical effort than controls and had blunted neural anticipation of reward and aversion in the precuneus, insula and prefrontal cortex and blunted neural activity during effort for reward in the putamen.

CONCLUSIONS

We show for the first time that both physical effort and neural activity during effort correlate with anhedonia in adolescents and that amotivation might be a specific deficit of anhedonia irrespective of valence. Future work will assess if these neural mechanisms can be used to predict blunted approach and avoidance in adolescents at risk of depression.

Decreased functional connectivity of the insula within the salience network as an indicator for prospective insufficient response to antidepressants

•

Connectivity analyses complemented with a metric exploring switching in brain activity.

•

Lower insula-salience connectivity predicts insufficient antidepressant response.

•

This same insula region is activated less when switching from task to a rest.

•

This could be a potential biomarkers for predicting future antidepressant response.

Insufficient response to treatment is the main cause of prolonged suffering from major depressive disorder (MDD). Early identification of insufficient response could result in faster and more targeted treatment strategies to reduce suffering. We therefore explored whether baseline alterations within and between resting state functional connectivity networks could serve as markers of insufficient response to antidepressant treatment in two years of follow-up. We selected MDD patients (N = 17) from the NEtherlands Study of Depression and Anxiety (NESDA), who received ≥ two antidepressants, indicative for insufficient response, during the two year follow-up, a group of MDD patients who received only one antidepressant (N = 32) and a healthy control group (N = 19) matched on clinical characteristics and demographics. An independent component analysis (ICA) of baseline resting-state scans was conducted after which functional connectivity within the components was compared between groups. We observed lower connectivity of the right insula within the salience network in the group with ≥ two antidepressants compared to the group with one antidepressant. No difference in connectivity was found between the patient groups and healthy control group. Given the suggested role of the right insula in switching between task-positive mode (activation during attention-demanding tasks) and task-negative mode (activation during the absence of any task), we explored whether right insula activation differed during switching between these two modes. We observed that in the ≥2 antidepressant group, the right insula was less active compared to the group with one antidepressant, when switching from task-positive to task-negative mode than the other way around. These findings imply that lower right insula connectivity within the salience network may serve as an indicator for prospective insufficient response to antidepressants. This result, supplemented by the diminished insula activation when switching between task and rest related networks, could indicate an underlying mechanism that, if not sufficiently targeted by current antidepressants, could lead to insufficient response. When replicated, these findings may contribute to the identification of biomarkers for early detection of insufficient response.

Neuroimaging Correlates of Depression-Implications to Clinical Practice

The growth of the literature about neuroimaging of major depressive disorder (MDD) over the last several decades has contributed to the progress in recognizing precise brain areas, networks, and neurotransmitter processes related to depression. However, there are still doubts about the etiology and pathophysiology of depression that need answering. The authors did a nonsystematic review of the literature using PubMed database, with the following search terms: "major depressive disorder," "neuroimaging," "functional imaging," "magnetic resonance imaging," "functional magnetic resonance imaging," and "structural imaging," being selected the significant articles published on the topic. Anterior cingulate cortex, hippocampus, orbitomedial prefrontal cortex, amygdala basal ganglia, and the cerebellum were the main affected areas across the selected studies. These areas respond to particular neurotransmitter systems, neurochemicals, hormones, and other signal proteins; even more, the evidence supports a distorted frontolimbic mood regulatory pathway in MDD patients. Despite the positive findings, translation to treatment of MDD remains illusory. In conclusion, this article aims to be a critical review of the neuroimaging correlates of depression in clinical research with the purpose to improve clinical practice.

Do Relational and Self-Definitional Traits Influence Deep Brain Stimulation Device Preference?

Personality psychodynamics have been shown to influence individual responses to psychiatric treatments, including medication. Increasingly, neuromodulation therapies have become available for severe and treatment-resistant depression. This study aims to evaluate patient response to an implanted neurostimulator battery within the framework of relational versus self-definitional personality traits. Relational development is interpersonally oriented and disruptions along this pathway lead to dependency on others for a sense of security and self-worth. Self-definitional development is characterized by autonomy strivings and disruptions lead to self-critical feelings of failing to meet expectations. Patients drawn from a larger study of deep brain stimulation (DBS) for treatment-resistant depression were switched from a non-rechargeable to a rechargeable battery type to maintain stimulation therapy. This switch entailed taking greater personal responsibility for device maintenance and allowed for fewer battery replacement surgeries. Twenty-six patients completed the Depressive Experiences Questionnaire (DEQ) and a questionnaire surveying their preference for DBS battery type. Results show that the DEQ dependency subscale, and more specifically the neediness component of the subscale, is associated with patient preference for the non-rechargeable battery. This suggests that individuals with higher relational needs prefer treatment options that increase contact with and need for medical caregivers and may prioritize this aspect of an intervention over alternative considerations. In contrast, individuals with more self-critical personality traits did not have a battery type preference, indicating that self-definitional needs were not predictive of battery preference. The link between an individual's personality psychodynamics and response to biomedical interventions, including neuromodulation and treatments that incorporate medical devices, deserves further attention.

Promising Neuroimaging Biomarkers in Depression

The neuroimaging has been applied in the study of pathophysiology in major depressive disorder (MDD). In this review article, several kinds of methodologies of neuroimaging would be discussed to summarize the promising biomarkers in MDD. For the magnetic resonance imaging (MRI) and magnetoencephalography field, the literature review showed the potentially promising roles of frontal lobes, such as anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). In addition, the limbic regions, such as hippocampus and amygdala, might be the potentially promising biomarkers for MDD. The structures and functions of ACC, DLPFC, OFC, amygdala and hippocampus might be confirmed as the biomarkers for the prediction of antidepressant treatment responses and for the pathophysiology of MDD. The functions of cognitive control and emotion regulation of these regions might be crucial for the establishment of biomarkers. The near-infrared spectroscopy studies demonstrated that blood flow in the frontal lobe, such as the DLPFC and OFC, might be the biomarkers for the field of near-infrared spectroscopy. The electroencephalography also supported the promising role of frontal regions, such as the ACC, DLPFC and OFC in the biomarker exploration, especially for the sleep electroencephalogram to detect biomarkers in MDD. The positron emission tomography (PET) and single-photon emission computed tomography (SPECT) in MDD demonstrated the promising biomarkers for the frontal and limbic regions, such as ACC, DLPFC and amygdala. However, additional findings in brainstem and midbrain were also found in PET and SPECT. The promising neuroimaging biomarkers of MDD seemed focused in the fronto-limbic regions.

Pre-stimulus Brain Activity Is Associated With State-Anxiety Changes During Single-Session Transcranial Direct Current Stimulation

Transcranial direct current stimulation is a promising neuromodulation method for treating depression. However, compared with pharmacological treatment, previous studies have reported that a relatively limited proportion of patients respond to tDCS treatment. In addition, the neurophysiological mechanisms underlying tDCS treatment remain unclear, making it difficult to identify response predictors for tDCS treatment based on neurophysiological function. Because treatment effects are achieved by repetitive application of tDCS, studying the immediate effects of tDCS in depressive patients could extend understanding of its treatment mechanisms. However, immediate changes in a single session of tDCS are not well documented. Thus, in the current study, we focused on the immediate impact of tDCS and its association with pre-stimulus brain activity. To address this question, we applied anodal tDCS to the left dorsolateral prefrontal cortex (DLPFC) or dorsomedial prefrontal cortex (DMPFC) in 14 patients with major depressive disorder (MDD) and 19 healthy controls (HCs), at an intensity of 1.0 mA for 20 min in a single session. To evaluate anxiety, the state trait anxiety inventory was completed before and after tDCS. We recorded resting electroencephalography before tDCS, and calculated electrical neuronal activity in the theta and alpha frequency bands using standardized low-resolution electromagnetic tomography. We found that, during application of left DLPFC tDCS to patients with MDD, the anxiety reduction effect of tDCS was related to higher baseline theta-band activity in the rostral anterior cingulate cortex (rACC) and no medication with benzodiazepine used as hypnotic. For DMPFC stimulation in MDD, the anxiety reduction effect was associated with lower baseline alpha-band activity in the left inferior parietal lobule. In contrast, in HCs, the anxiety reduction effect was associated with higher baseline alpha activity in the precuneus during DMPFC stimulation. The current results suggest that the association between pre-tDCS brain activity and the anxiety reduction effect of tDCS depends on psychopathology (depressed or non-depressed) as well as the site of stimulation (DMPFC or left DLPFC) and insomnia. Furthermore, the results suggest that tDCS response might be associated with baseline resting state electrophysiological neural activity.

A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression

The neurobiology of major depressive disorder (MDD) remains incompletely understood, and many individuals fail to respond to standard treatments. Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) has emerged as a promising antidepressant therapy. However, the heterogeneity of response underscores a pressing need for biomarkers of treatment outcome. We acquired resting state functional magnetic resonance imaging (rsfMRI) data in 47 MDD individuals prior to 5-8 weeks of rTMS treatment targeted using the F3 beam approach and in 29 healthy comparison subjects. The caudate, prefrontal cortex, and thalamus showed significantly lower blood oxygenation level-dependent (BOLD) signal power in MDD individuals at baseline. Critically, individuals who responded best to treatment were associated with lower pre-treatment BOLD power in these regions. Additionally, functional connectivity (FC) in the default mode and affective networks was associated with treatment response. We leveraged these findings to train support vector machines (SVMs) to predict individual treatment responses, based on learned patterns of baseline FC, BOLD signal power and clinical features. Treatment response (responder vs. nonresponder) was predicted with 85-95% accuracy. Reduction in symptoms was predicted to within a mean error of ±16% (r = .68, p < .001). These preliminary findings suggest that therapeutic outcome to DLPFC-rTMS could be predicted at a clinically meaningful level using only a small number of core neurobiological features of MDD, warranting prospective testing to ascertain generalizability. This provides a novel, transparent and physiologically plausible multivariate approach for classification of individual response to what has become the most commonly employed rTMS treatment worldwide. This study utilizes data from a larger clinical study (Australian New Zealand Clinical Trials Registry: Investigating Predictors of Response to Transcranial Magnetic Stimulation for the Treatment of Depression; ACTRN12610001071011; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336262).

Insula serotonin 2A receptor binding and gene expression contribute to serotonin transporter polymorphism anxious phenotype in primates

Genetic variation in the serotonin transporter gene (SLC6A4) is associated with vulnerability to affective disorders and pharmacotherapy efficacy. We recently identified sequence polymorphisms in the common marmoset SLC6A4 repeat region (AC/C/G and CT/T/C) associated with individual differences in anxiety-like trait, gene expression, and response to antidepressants. The mechanisms underlying the effects of these polymorphisms are unknown, but a key mediator of serotonin action is the serotonin 2A receptor (5HT_2A). Thus, we correlated 5HT_2A binding potential (BP) and RNA gene expression in 16 SLC6A4 genotyped marmosets with responsivity to 5HT_2A antagonism during the human intruder test of anxiety. Voxel-based analysis and RNA measurements showed a reduction in 5HT_2A BP and gene expression specifically in the right posterior insula of individuals homozygous for the anxiety-related variant AC/C/G. These same marmosets displayed an anxiogenic, dose-dependent response to the human intruder after 5HT_2A pharmacological antagonism, while CT/T/C individuals showed no effect. A voxel-based correlation analysis, independent of SLC6A4 genotype, revealed that 5HT_2A BP in the adjacent right anterior insula and insula proisocortex was negatively correlated with trait anxiety scores. Moreover, 5HT_2A BP in both regions was a good predictor of the size and direction of the acute emotional response to the human intruder threat after 5HT_2A antagonism. Our findings suggest that genetic variation in the SLC6A4 repeat region may contribute to the trait anxious phenotype via neurochemical changes in brain areas implicated in interoceptive and emotional processing, with a critical role for the right insula 5HT_2A in the regulation of affective responses to threat.

Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression

While cognitive behavioral therapy (CBT) is an effective treatment for major depressive disorder, only up to 45% of depressed patients will respond to it. At present, there is no clinically viable neuroimaging predictor of CBT response. Notably, the lack of a mechanistic understanding of treatment response has hindered identification of predictive biomarkers. To obtain mechanistically meaningful fMRI predictors of CBT response, we capitalize on pretreatment neural activity encoding a weighted reward prediction error (RPE), which is implicated in the acquisition and processing of feedback information during probabilistic learning. Using a conventional mass-univariate fMRI analysis, we demonstrate that, at the group level, responders exhibit greater pretreatment neural activity encoding a weighted RPE in the right striatum and right amygdala. Crucially, using multivariate methods, we show that this activity offers significant out-of-sample classification of treatment response. Our findings support the feasibility and validity of neurocomputational approaches to treatment prediction in psychiatry.

Prediction of Response to Drug Therapy in Psychiatric Disorders

Reprinted with permission from Open Biol. 8: 180031. The Royal Society.

Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity

BACKGROUND

Recent studies showed that major depressive disorder (MDD) has been involved in abnormal functional and structural connections in specific brain regions. However, comprehensive researches on MDD-related alterations in the topological organization of brain functional and structural networks are still limited.

METHODS

Functional network (FN) was constructed from resting-state functional MRI temporal series correlations and structural network (SN) was established by Diffusion tensor imaging (DTI) data in 58 MDD patients and 71 healthy controls (HC). The measurements of the network properties were calculated for two networks respectively. Correlations were conducted between altered network parameters and Hamilton depression scale (HAMD) score. Additionally, network resilient analysis were conducted on FN and SN.

RESULTS

The losses of small-worldness charateristics and the decline of nodal efficiency across FN and SN were found in MDD patients. Based on network-based statistic (NBS) approach, the decreased connections in MDD patients were mainly found in the superior occipital gyrus, superior temporal gyrus for FN and SN, while the increased connections were distributed in putamen, superior frontal gyrus only for SN. Compared with the FN, the SN showed less resilient to targeted or random node failure. Besides, altered edges in NBS and regions with decreased nodal efficiency were negatively associated with HAMD score in MDD patients.

LIMITATIONS

The samples size is small and most of the MDD patients take different antidepressant medications.

CONCLUSIONS

Alterations of SN in the brain of MDD patients preceded that of FN to some extent, and reorganization of the brain network was a mechanism which compensated for functional and structural alterations during disease progression.

Convergent neurobiological predictors of mood and anxiety symptoms and treatment response

Introduction: Mood and anxiety disorders are leading contributors to the global burden of diseases. Comorbid mood and anxiety disorders have a lifetime prevalence of ~20% globally and increases the risk for suicide, a leading cause of death. Areas covered: In this review, authors highlight recent advances in the understanding of multilevel-neurobiological mechanisms for normal/pathological human affective-functioning. The authors then address the complex interplay between environmental-adversity and molecular-genetic mediators of brain correlates of affective-symptoms. The molecular focus is strategically limited to GTF2i, BDNF, and FKBP5 genes that are, respectively, involved in transcriptional-, neurodevelopmental- and neuroendocrine-pathway mediation of affective-functions. The importance of these genes is illustrated with studies of copy-number-variants, genome-wide association (GWAS), and candidate gene-sequence variant associations with disease etiology. Authors concluded by highlighting the predictive values of integrative neurobiological processing of gene-environment interactions for affective disorder symptom management. Expert opinion: Given the transcriptional, neurodevelopmental and neuroimmune relevance of GTF2i, BDNF, and FKBP5 genes, respectively, authors reviewed the putative roles of these genes in neurobiological mediation of adaptive affective-responses. Authors discussed the importance of studying gene-dosage effects in understanding affective disorder risk biology, and how such targeted neurogenetic studies could guide precision identification of novel pharmacotherapeutic targets and aid in prediction of treatment response.

Severity of anxiety moderates the association between neural circuits and maternal behaviors in the postpartum period

Neuroimaging research has suggested that activity in the amygdala, center of the socioemotional network, and functional connectivity between the amygdala and cortical regions are associated with caregiving behaviors in postpartum mothers. Anxiety is common in the early postpartum period, with severity ranging from healthy maternal preoccupation to clinical disorder. However, little is known about the influence of anxiety on the neural correlates of early caregiving. We examined these relationships in a community cohort of 75 postpartum women (ages 18-22; predominantly low-SES, minority race) who listened to infant cry sounds while undergoing an fMRI assessment. Maternal self-reported symptoms of anxiety were mostly within the subclinical range. Positive and negative caregiving behaviors during filmed face-to-face mother-infant interactions were coded by independent observers. The results from whole-brain analyses showed that anxiety severity moderated the brain-maternal behavior relationships. Specifically, our results showed that the higher a mother's anxiety, the stronger the association between positive caregiving (i.e., maternal warmth and involvement) and amygdala-right posterior superior temporal sulcus (amygdala-RpSTS) functional connectivity. These results remained significant when we controlled for symptoms of depression and contextual variables. These findings suggest that functional connectivity between the amygdala and a social perception region (RpSTS) plays a particularly important role for anxious mothers in facilitating their positive parenting. These findings extend our understanding of the specific neural circuits that support positive maternal caregiving in the context of maternal anxiety, and they may help inform the future design of personalized and effective interventions.

Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.

METHODS

Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.

RESULTS

Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).

CONCLUSIONS

A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

Baseline effective connectivity predicts response to repetitive transcranial magnetic stimulation in patients with treatment-resistant depression

Repetitive transcranial magnetic stimulation (rTMS) has become a popular treatment option for treatment-resistant depression (TRD). However, suboptimal response rates highlight the need for improved efficacy through optimisation of treatment protocol and patient selection. We investigate whether the limbic salience network and its connectivity with prefrontal stimulation sites predict immediate and longer-term responsiveness to rTMS. Twenty-seven patients with TRD were randomly allocated to receive 16 sessions of either conventional rTMS or intermittent theta-burst (iTBS) over 4 weeks; delivered using connectivity profiling and neuronavigation to target person-specific dorsolateral prefrontal cortex (DLPFC). At baseline and 3-month follow-up, patients underwent clinical assessment and scanning session, and 1-month clinical follow-up. Resting-state fMRI data were entered into seed-based functional and effective connectivity analyses between right anterior insula (rAI) and DLPFC target, and independent components analysis to extract resting-state networks. Cerebral blood flow (CBF) was also assessed in the rAI. All brain measures were compared between baseline and follow-up, and related to treatment response at 1- and 3-months. Baseline fronto-insular effective connectivity and salience network connectivity were significantly positively correlated, while baseline rAI CBF was negatively correlated, with early (1-month) response to rTMS treatment but not sustained response (3-months), suggesting persistence of therapeutic response is not associated with baseline features. Connectivity or CBF measures did not change between the two time points. We demonstrate that fronto-insular and salience-network interactions can predict early response to rTMS in TRD, suggesting that these network nodes may be key regions toward developing rTMS response biomarkers.

The pulvinar nucleus and antidepressant treatment: dynamic modeling of antidepressant response and remission with ultra-high field functional MRI

Functional magnetic resonance imaging (fMRI) successfully disentangled neuronal pathophysiology of major depression (MD), but only a few fMRI studies have investigated correlates and predictors of remission. Moreover, most studies have used clinical outcome parameters from two time points, which do not optimally depict differential response times. Therefore, we aimed to detect neuronal correlates of response and remission in an antidepressant treatment study with 7 T fMRI, potentially harnessing advances in detection power and spatial specificity. Moreover, we modeled outcome parameters from multiple study visits during a 12-week antidepressant fMRI study in 26 acute (aMD) patients compared to 36 stable remitted (rMD) patients and 33 healthy control subjects (HC). During an electrical painful stimulation task, significantly higher baseline activity in aMD compared to HC and rMD in the medial thalamic nuclei of the pulvinar was detected (p = 0.004, FWE-corrected), which was reduced by treatment. Moreover, clinical response followed a sigmoid function with a plateau phase in the beginning, a rapid decline and a further plateau at treatment end. By modeling the dynamic speed of response with fMRI-data, perigenual anterior cingulate activity after treatment was significantly associated with antidepressant response (p < 0.001, FWE-corrected). Temporoparietal junction (TPJ) baseline activity significantly predicted non-remission after 2 antidepressant trials (p = 0.005, FWE-corrected). The results underline the importance of the medial thalamus, attention networks in MD and antidepressant treatment. Moreover, by using a sigmoid model, this study provides a novel method to analyze the dynamic nature of response and remission for future trials.

Increased ventromedial prefrontal cortex activity and connectivity predict poor sertraline treatment outcome in late-life depression

OBJECTIVE

Previous studies of imaging predictors on acute treatment response in late-life depression (LLD) demonstrated that poor response to selective serotonin reuptake inhibitors (SSRIs) is associated with pre-treatment low functional connectivity (FC) within executive control network and high FC within default-mode network including the ventromedial prefrontal cortex (vmPFC). However, there is less research in regional resting-state functional activity that explains FC changes related to SSRI response.

METHODS

Thirty-six older major depressive disorder (MDD) patients not currently on antidepressant treatment had a baseline, pre-treatment resting-state functional magnetic resonance imaging scan, followed by sertraline treatment for 12 weeks. Depression severity was assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS). Subjects whose MADRS score decreased less than 50% from baseline or who discontinued sertraline for any reason were classified as nonresponders (n = 21). Subjects whose 12-week MADRS score dropped greater than or equal to 50% from baseline were defined as responders (n = 15). We conducted the amplitude of low-frequency fluctuation (ALFF) and region of interest (ROI)-to-ROI FC analyses independently. Significance threshold was set at P < 0.05 with false discovery rate (FDR) correction for multiple comparisons.

RESULTS

Relative to the responder group, the nonresponder group showed significantly less ALFF in the dorsomedial prefrontal cortex (dmPFC) and greater ALFF in the vmPFC/subgenual cingulate area. For ROI-to-ROI connectivity, there was significantly greater connectivity between the vmPFC and the cerebellar vermis in the nonresponder group.

CONCLUSION

Our study highlighted the association of vmPFC resting-state activity and connectivity with SSRI response. Future studies are warranted for understanding the role of vmPFC-vermis connectivity in LLD.

Estimating patient-specific treatment advantages in the 'Treatment for Adolescents with Depression Study'

The 'Treatment for Adolescents with Depression Study' (TADS, ClinicalTrials.gov, identifier: NCT00006286) was a cornerstone, randomized controlled trial evaluating the effectiveness of standard treatment options for major depression in adolescents. Whereas previous TADS analyses examined primarily effect modifications of treatment-placebo differences by various patient characteristics, less is known about the modification of inter-treatment differences, and hence, patient characteristics that might guide treatment selection. We sought to fill this gap by estimating patient-specific inter-treatment differences as a function of patients' baseline characteristics. We did so by applying the 'model-based random forest', a recently-introduced machine learning-based method for evaluating effect heterogeneity that allows for the estimation of patient-specific treatment effects as a function of arbitrary baseline characteristics. Treatment conditions were cognitive-behavioural therapy (CBT) alone, fluoxetine (FLX) alone, and the combination of CBT and fluoxetine (COMB). All inter-treatment differences (CBT vs. FLX; CBT vs. COMB; FLX vs. COMB) were evaluated across 23 potential effect modifiers extracted from previous studies. Overall, FLX was superior to CBT, while COMB was superior to both CBT and FLX. Evidence for effect heterogeneity was found for the CBT-FLX difference and the FLX-COMB difference, but not for the CBT-COMB difference. Baseline depression severity modified the CBT-FLX difference; whereas baseline depression severity, patients' treatment expectations, and childhood trauma modified the FLX-COMB difference. All modifications were quantitative rather than qualitative, however, meaning that the differences varied only in magnitude, but not direction. These findings imply that combining CBT with fluoxetine may be superior to either therapy used alone across a broad range of patients.

Benefits of Sequentially Adding Cognitive-Behavioral Therapy or Antidepressant Medication for Adults With Nonremitting Depression

OBJECTIVE

Adults with major depressive disorder frequently do not achieve remission with an initial treatment. Addition of psychotherapy for patients who do not achieve remission with antidepressant medication alone can target residual symptoms and protect against recurrence, but the utility of adding antidepressant medication after nonremission with cognitive-behavioral therapy (CBT) has received little study. The authors aimed to evaluate the acute and long-term outcomes resulting from both sequences of combination treatments.

METHODS

Previously untreated adults with major depression who were randomly assigned to receive escitalopram, duloxetine, or CBT monotherapy and completed 12 weeks of treatment without achieving remission entered an additional 12 weeks of combination treatment. For patients who did not achieve remission with CBT, escitalopram was added (CBT plus medication group) to their treatment, and for those who did not achieve remission with an antidepressant, CBT was added (medication plus CBT group) to their treatment. Patients who responded to the combination treatment entered an 18-month follow-up phase to assess risk of recurrence.

RESULTS

A total of 112 patients who did not achieve remission with a monotherapy entered combination treatment (41 who responded to monotherapy but did not achieve remission and 71 who did not respond to monotherapy). Overall, remission rates after subsequent combination therapy were significantly higher among patients who responded to monotherapy but did not achieve remission (61%) than among patients who did not respond to monotherapy (41%). Among patients who responded to monotherapy but did not achieve remission, the remission rate in the CBT plus medication group (89%) was higher than in the medication plus CBT group (53%). However, among patients whose depression did not respond to monotherapy, rates of response and remission were similar between the treatment arms. Higher levels of anxiety, both prior to monotherapy and prior to beginning combination treatment, predicted poorer outcomes for both treatment groups.

CONCLUSIONS

The order in which CBT and antidepressant medication were sequentially combined did not appear to affect outcomes. Addition of an antidepressant is an effective approach to treating residual symptoms for patients who do not achieve remission with CBT, as is adding CBT after antidepressant monotherapy. Patients who do not respond to one treatment modality warrant consideration for addition of the alternative modality.

Perspektiven einer evidenzbasierten und personalisierten Psychotherapie

Zusammenfassung. Theoretischer Hintergrund: Der Diskurs um eine evidenz-basierte und personalisierte (bzw. „Precision“) Medizin sowie zur Umsetzung von Evaluation und Qualitätssicherung hat in den letzten Jahren auch Einfluss auf die Psychotherapieforschung genommen. Dies gilt in Bezug auf die patientenspezifische Auswahl von Behandlungen (u. a. personalisierte Vorhersagen) als auch für die dynamische Anpassung von Interventionen im Therapieverlauf (adaptive Indikation, Feedback, Problemlösetools). Fragestellung und Methode: Im Bereich der differentiellen Indikation sind mittlerweile unterschiedliche Algorithmen („machine learning“) und Netzwerkmodelle zur Vorhersage erprobt worden. Für eine empirisch gestützte adaptive Indikation bilden insbesondere die Studien zum psychometrischen Feedback sowie die Entwicklung von Problemlösetools für Risikopatient_innen die Grundlage. Ergebnisse: Diese Grundlagenforschung war die Basis für die Entwicklung eines Entscheidungssystems (Trierer Therapie Navigator, TTN) zur Vorhersage der optimalen Behandlungsstrategie und des Abbruchrisikos. Darüber hinaus enthält der TTN ein adaptives Modellierungselement des Behandlungsverlaufs. Es können damit Risikopatienten für einen Behandlungsmisserfolg identifiziert und Behandlungsoptimierungen über Problemlösetools unterstützt werden. Schlussfolgerungen: In vorliegender Arbeit werden zentrale neue Ansätze einer evidenz-basierten und personalisierten Psychotherapie zusammenfassend dargestellt sowie die Anwendung in der klinischen Praxis diskutiert.

Hippocampal subfield-specific connectivity findings in major depressive disorder: A 7 Tesla diffusion MRI study

OBJECTIVE

Diffusion magnetic resonance imaging (dMRI) enables non-invasive characterization of white matter (WM) structures in vivo. Prior studies suggest that certain WM tracts may be affected in major depressive disorder (MDD), however, hippocampal subfield-specific dMRI measures have not yet been explored in MDD. We use 7 Tesla dMRI to investigate differences in hippocampal subfield connectivity of MDD patients.

METHODS

Eighteen MDD patients and eighteen matched healthy volunteers underwent 7 Tesla MRI. The hippocampal formations were segmented by subfields and tractography was performed to determine streamline count (SC), fractional anisotropy (FA), and mean diffusivity (MD) in patients and controls. Significant subfield connectivity measures were also correlated with age at depression onset.

RESULTS

Compared with controls, MDD patients exhibited reduced SC in the molecular layer of the left dentate gyrus (p < 0.001). SC count in the left dentate gyrus was shown to positively correlate with age at disease onset (p < 0.05). Increased MD was observed in streamlines emanating from both the left (p = 0.0001) and right (p < 0.001) fimbriae in MDD patients.

CONCLUSIONS

Increased MD of tracts in the fimbriae suggests compromised neuronal membranes in the major hippocampal output gate. Reduced SC of the dentate gyri indexes a disruption of normal cellular processes such as neurogenesis. These findings may have significant implications for identifying biomarkers of MDD and elucidating the neurobiological underpinnings of depression.

Prediction of response to drug therapy in psychiatric disorders

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study

Due to lacking predictors of depression recovery, successful treatment of major depressive disorder (MDD) is frequently only achieved after therapeutic optimization leading to a prolonged suffering of patients. This study aimed to determine neural prognostic predictors identifying non-remitters prior or early after treatment initiation. Moreover, it intended to detect time-sensitive neural mediators indicating depression recovery. This longitudinal, interventional, single-arm, open-label, phase IV, pharmacological functional magnetic resonance imaging (fMRI) study comprised four scans at important stages prior (day 0) and after escitalopram treatment initiation (day 1, 28, and 56). Totally, 22 treatment-free MDD patients (age mean ± SD: 31.5 ± 7.7; females: 50%) suffering from a concurrent major depressive episode without any comorbid DSM-IV axis I diagnosis completed the study protocol. Primary outcome were neural prognostic predictors of depression recovery. Enhanced de-activation of anterior medial prefrontal cortex (amPFC, single neural mediator) indicated depression recovery correlating with MADRS score and working memory improvements. Strong dorsolateral PFC (dlPFC) activation and weak dlPFC-amPFC, dlPFC-posterior cingulate cortex (PCC), dlPFC-parietal lobe (PL) coupling (three prognostic predictors) hinted at depression recovery at day 0 and 1. Preresponse prediction of continuous (dlPFC-PL: R²_day1 = 55.9%, 95% CI: 22.6-79%, P < 0.005) and dichotomous (specificity/sensitivity: SP/SN_day1 = 0.91/0.82) recovery definitions remained significant after leave-one-out cross-validation. Identified prefrontal neural predictors might propel the future development of fMRI markers for clinical decision making, which could lead to increased response rates and adherence during acute phase treatment periods. Moreover, this study underscores the importance of the amPFC in depression recovery.

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

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

Intrinsic Brain Network Biomarkers of Antidepressant Response: a Review

PURPOSE OF REVIEW

Poor treatment response is a hallmark of major depressive disorder. To tackle this problem, recent neuroimaging studies have sought to characterize antidepressant response in terms of pretreatment differences in intrinsic functional brain networks. Our aim is to review recent studies that predict antidepressant response using intrinsic network connectivity. We discuss current methodological limitations and directions for future antidepressant biomarker studies.

RECENT FINDINGS

Functional connectivity stemming from the subgenual and rostral anterior cingulate has shown particular consistency in predicting antidepressant response. Differences in this connectivity may prove fruitful in differentiating treatment responders to many antidepressant interventions. Future biomarker studies should integrate biological MDD subtypes to address the disorder's inherent clinical heterogeneity. These clinical and scientific advancements have the potential to address this population marked by limited treatment response. Methodological considerations, including patient selection, response criteria, and model overfitting, will require future investigation to ensure that biomarkers generalize for prospective prediction of treatment response.

Cognitive neuropsychological theory: Reconciliation of psychological and biological approaches for depression

New antidepressants and individualized approaches to treatment, matching specific therapies to individual patients, are urgently needed. For this, a better understanding of processes underpinning the development of depressive symptoms and response to medications are required. The cognitive neuropsychological model offers a novel approach uniquely combining biological and psychological approaches to explain how antidepressants exert their effect, why there is a delay in the onset of their clinical effect, and how changes in emotional processing are an essential step for a clinical antidepressant effect to take place. The paper presents the model and its underpinnings in the form of research in both healthy and depressed individuals, as well as the potential for its practical use.

Predicting Treatment Response in Depression: The Role of Anterior Cingulate Cortex

Background

Identification of biomarkers predicting therapeutic outcome of antidepressant treatment is one of the most important tasks in current research because it may transform the lengthy process of finding the right treatment for a given individual with depression. In the current study, we explored the potential of pretreatment pregenual anterior cingulate cortex activity as a putative biomarker of treatment response.

Methods

Thirty-two medication-free patients with depression were treated for 6 weeks with a selective serotonin reuptake inhibitor, escitalopram. Before treatment began, patients underwent an fMRI scan testing response to brief, masked, presentations of facial expression depicting sadness and happiness.

Results

After 6 weeks of treatment, there were 20 selective serotonin reuptake inhibitor responders and 12 nonresponders. Increased pretreatment pregenual anterior cingulate cortex activity to sad vs happy faces was observed in responders relative to nonresponders. A leave-one-out analysis suggested that activity in the anterior cingulate cortex was able to predict response status at the level of the individual participant.

Conclusions

The study supports the notion of pregenual anterior cingulate cortex as a promising predictor of antidepressant response.

Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown

Treatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To better understand this heterogeneity and to help in determining the best patient-specific treatments for a given disease, many clinical trials are collecting large amounts of patient-level data prior to administering treatment in the hope that some of these data can be used to identify moderators of treatment effect. These data can range from simple scalar values to complex functional data such as curves or images. Combining these various types of baseline data to discover "biosignatures" of treatment response is crucial for advancing precision medicine. Motivated by the problem of selecting optimal treatment for subjects with depression based on clinical and neuroimaging data, we present an approach that both (1) identifies covariates associated with differential treatment effect and (2) estimates a treatment decision rule based on these covariates. We focus on settings where there is a potentially large collection of candidate biomarkers consisting of both scalar and functional data. The validity of the proposed approach is justified via extensive simulation experiments and illustrated using data from a placebo-controlled clinical trial investigating antidepressant treatment response in subjects with depression.