Frontocingulate dysfunction in depression: toward biomarkers of treatment response

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

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

The alteration of cognitive function networks in remitted patients with major depressive disorder: an independent component analysis

INTRODUCTION

Dysfunctional connectivity of resting-state functional networks has been observed in patients with major depressive disorder (MDD), particularly in cognitive function networks including the central executive network (CEN), default mode network (DMN) and salience network (SN). Findings from studies examining how aberrant functional connectivity (FC) changed after antidepressant treatment, however, have been inconsistent. Thus, the purpose of the present study was to explore potential mechanisms of altered cognitive function networks during resting-state between remitted major depressive disorder (rMDD) patients and healthy controls (HCs) and furthermore, the relationship between dysfunctional connectivity patterns in rMDD and clinical symptoms.

METHODOLOGY

In this study, 19 HCs and 19 rMDD patients were recruited for resting-state functional magnetic resonance imaging (fMRI) scanning. FC was evaluated with independent component analysis for CEN, DMN and SN. Two sample t tests were conducted to compare differences between rMDD and HCs. A Pearson correlation analysis was also performed to examine the relationship between connectivity of networks and cognitive function scores and clinical symptoms.

RESULTS

Compared to healthy controls, remitted patients showed lower connectivity in CEN, mostly in the superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior parietal lobule (IPL) and part of the supramarginal gyrus (SMG). Conversely, the bilateral insula, part of the SMG (a key node of the CEN) and dorsal anterior cingulate cortex (dACC) of the DMN showed higher connectivity in rMDD patients. Pearson correlation results demonstrated that connectivity of the right IPL in CEN was positively correlated with cognitive function scores, and connectivity of the left insula was negatively correlated with BDI scores.

CONCLUSIONS

Though rMDD patients reached the standard of clinal remission, unique impairments of FC in cognitive function networks remained. Aberrant FC between cognitive function networks responsible for executive control was observed in rMDD and may be associated with residual clinical symptoms.

Loneliness and depression dissociated on parietal-centered networks in cognitive and resting states

BACKGROUND

Perceived loneliness, an increasingly prevalent social issue, is closely associated with major depressive disorder (MDD). However, the neural mechanisms previously implicated in key cognitive and affective processes in loneliness and MDD still remain unclear. Such understanding is critical for delineating the psychobiological basis of the relationship between loneliness and MDD.

METHODS

We isolated the unique and interactive cognitive and neural substrates of loneliness and MDD among 27 MDD patients (mean age = 51.85 years, 20 females), and 25 matched healthy controls (HCs; mean age = 48.72 years, 19 females). We assessed participants' behavioral performance and neural regional and network functions on a Stroop color-word task, and their resting-state neural connectivity.

RESULTS

Behaviorally, we found greater incongruence-related accuracy cost in MDD patients, but reduced incongruence effect on reaction time in lonelier individuals. When performing the Stroop task, loneliness positively predicted prefrontal-anterior cingulate-parietal connectivity across all participants, whereas MDD patients showed a decrease in connectivity compared to controls. Furthermore, loneliness negatively predicted parietal and cerebellar activities in MDD patients, but positively predicted the same activities in HCs. During resting state, MDD patients showed reduced parietal-anterior cingulate connectivity, which again positively correlated with loneliness in this group.

CONCLUSIONS

We speculate the distinct neurocognitive profile of loneliness might indicate increase in both bottom-up attention and top-down executive control functions. However, the upregulated cognitive control processes in lonely individuals may eventually become exhausted, which may in turn predispose to MDD onset.

Transdiagnostic time-varying dysconnectivity across major psychiatric disorders

Dynamic functional connectivity (DFC) analysis can capture time‐varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting‐state functional magnetic resonance imaging and a sliding‐window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k‐means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4‐group differences (SZ, BD, MDD, and HC groups; q < .05, false‐discovery rate [FDR]‐corrected) in DFC were nearly only in State 1. Post hoc analyses (q < .05, FDR‐corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state‐dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.

Personality and psychopathology: In defense of a practical path toward integrating psychometric and biological approaches to advance a comprehensive model

Personality and psychopathology each reflect patterns of internal experience and outward behavior that differ between people and affect functioning. Drawing strict distinctions between the two concepts is not only difficult, but it may prove unnecessary for advancing an integrated model of psychological experiences associated with mental illness. We argue that developing such a model will be critical for improving treatment outcomes, and we discuss a practical path forward. Proponents of psychometric approaches to developing models of psychological experience focus on observable phenotypes and utilize statistical methods to describe patterns of covariation among a broad range of symptoms and dispositions. Advocates of biologically based approaches emphasize neuroscientific tools for identifying abnormalities in brain function that give rise to an individual's experience. There is substantial evidence that measures of personality and measures of symptoms capture nonoverlapping, clinically important information for understanding how and for whom treatments for mental illness work. In this article, we highlight the importance of combining psychometric and neurobiological approaches in order to understand which features of an individual those measures reflect, which aspects of neurobiology generate and maintain those features, how they relate to each other, and critically, how best to alter them to reduce distress and dysfunction.

Construction of gene-classifier and co-expression network analysis of genes in association with major depressive disorder

Because the pathogenesis of major depressive disorder (MDD) is still unclear and the accurate diagnosis remains unavailable, we aimed to analyze its molecular mechanisms and develop a gene classifier to improve diagnostic accuracy. We extracted differentially expressed genes from two datasets, GSE45642 (from brain tissue samples) and GSE98793 (from blood samples), and found three key modules to have a significant correlation with MDD traits by weighted gene coexpression network analysis. Hub genes were identified from the key modules according to the connectivity degree in the network and subjected to least absolute shrinkage and selection operator regression analysis. A total of eighty-five hub genes were selected to construct the gene classifier, which had considerable ability to recognize MDD patients in the training set and test set. In addition, the relationship between the key MDD modules and brain tissues indicated that the anterior cingulate should be a notable region in the study of MDD pathogenesis. The results of Gene Ontology (GO) and pathway enrichment analyses reiterate the relationship between depression and immunity. Therefore we identified MDD hub genes in the InnateDB database, and found 14 genes involved in both MDD and the inflammatory response.

EEG Frontal Asymmetry and Theta Power in Unipolar and Bipolar Depression

BACKGROUND

Distinguishing between unipolar and bipolar depression is of high clinical relevance. However, there is sparse research directly comparing these groups in terms of EEG activity.

METHOD

We investigated 87 participants' left and right EEG frontal alpha-1, alpha-2, and theta activity related to happy and sad face stimuli in unipolar (UD, n=33) and bipolar (BD, n=22) depressed participants, and controls without depression (HC, n=32).

RESULTS

Post-hoc analysis of an observed hemisphere x group interaction (p< .037) showed significant differences in alpha-1 asymmetry only for the comparison of UD and HC (p< .006). Further analysis of a significant emotion x group interaction (p= .001) revealed a differential impact of stimulus valence on theta power between the groups (p< .001). The valence dependent theta power of the BD differed from that of the UD (p< .0002) and the HC (p< .004). Alpha-1 asymmetry classified HC and both depressed groups with an accuracy of .69. Valence-related theta classified BD from UD with an accuracy of .83. Leave-one-out cross validation resulted in slightly reduced accuracy.

LIMITATIONS

Important limitations were the small sample size and that subjects were not medication-free.

CONCLUSIONS

Our results demonstrate the value of simple, task related EEG activity for differentiating not only healthy individuals from those with depression, but also individuals with unipolar depression from those with bipolar depression.

Neurocognitive impact of ketamine treatment in major depressive disorder: A review on human and animal studies

BACKGROUND

Most recent evidence support a rapid and sustained antidepressant effect of subanesthetic dose of intravenous ketamine in patients with major depressive disorder (MDD). However, clinical and animal studies investigating the effects of intravenous ketamine on specific functional domains disrupted by depression reported conflicting results. Therefore, the aim of this review is to provide an overview of the recent findings exploring the cognitive effects of ketamine in depression.

METHODS

After a bibliographic search on PubMed, Medline and PsycInfo, we retrieved 11 original studies meeting our research criteria, 7 in humans with MDD or Treatment Resistant Disorder and 4 using rats models for depression.

RESULTS

Overall the results showed that a) ketamine reduced activation and normalized connectivity measures of several brain regions related to depressive behaviors and reversed deficits in cognitive flexibility and coping response strategy in rats with depressive features, and b) ketamine leads to a no significant impairment on neurocognitive functions in most of the studies, with only three studies observing improvements in speed of processing, verbal learning, sustained attention and response control, verbal and working memory.

LIMITATIONS

The methodological heterogeneity, in terms of neuropsychological tests used and cognitive domain explored, of the studies included.

CONCLUSIONS

Most of the studies included showed no significant cognitive impairments in MDD patients after ketamine treatment. Furthermore, the results of the fMRI studies considered suggest that ketamine may have a normalizing effect on brain functions during attentional and emotional processing in MDD patients. However, further studies are needed to confirm these preliminary evidences.

Nonhuman primate meso-circuitry data: a translational tool to understand brain networks across species

The foundation for understanding brain connections and related psychiatric diseases lies in human and animal circuitry studies. In rodents and nonhuman primates (NHPs), axonal tracing methods provide the ground-truth connectivity information of brain circuits, coupled with the ability to experimentally manipulate them when combined with other methods. In humans, neuroimaging approaches have taken the lead for studying connectivity patterns in vivo and the changes in network profiles associated with disease. To integrate knowledge from animal models and humans, a critical question is how similar the animal brains and circuits are to the humans'. In this review, we demonstrate the use of meso-circuitry information from tracing studies in NHPs to understand common network connections across species. We show that the meso-circuitry information help establish homologies of cortical and striatal regions and fiber pathways between rodents and NHPs, facilitate the translation of connections that are detailed in animal models to humans, and can locate critical hubs in large-scale brain networks. This review combines anatomic studies across animal models and imaging studies across NHPs and humans to provide a more comprehensive understanding of the hard-wired connectivity that underlies neuroimaging-derived brain networks.

Pretreatment Reward Sensitivity and Frontostriatal Resting-State Functional Connectivity Are Associated With Response to Bupropion After Sertraline Nonresponse

BACKGROUND

Standard guidelines recommend selective serotonin reuptake inhibitors as first-line antidepressants for adults with major depressive disorder, but success is limited and patients who fail to benefit are often switched to non-selective serotonin reuptake inhibitor agents. This study investigated whether brain- and behavior-based markers of reward processing might be associated with response to bupropion after sertraline nonresponse.

METHODS

In a two-stage, double-blinded clinical trial, 296 participants were randomized to receive 8 weeks of sertraline or placebo in stage 1. Individuals who responded continued on another 8-week course of the same intervention in stage 2, while sertraline and placebo nonresponders crossed over to bupropion and sertraline, respectively. Data from 241 participants were analyzed. The stage 2 sample comprised 87 patients with major depressive disorder who switched medication and 38 healthy control subjects. A total of 116 participants with major depressive disorder treated with sertraline in stage 1 served as an independent replication sample. The probabilistic reward task and resting-state functional magnetic resonance imaging were administered at baseline.

RESULTS

Greater pretreatment reward sensitivity and higher resting-state functional connectivity between bilateral nucleus accumbens and rostral anterior cingulate cortex were associated with positive response to bupropion but not sertraline. Null findings for sertraline were replicated in the stage 1 sample.

CONCLUSIONS

Pretreatment reward sensitivity and frontostriatal connectivity may identify patients likely to benefit from bupropion following selective serotonin reuptake inhibitor failures. Results call for a prospective replication based on these biomarkers to advance clinical care.

Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy

Electroconvulsive therapy (ECT) is one of the most effective treatments in cases of severe and treatment resistant major depression. 60-80% of patients respond to ECT, but the procedure is demanding and robust prediction of ECT responses would be of great clinical value. Predictions based on neuroimaging data have recently come into focus, but still face methodological and practical limitations that are hampering the translation into clinical practice. In this retrospective study, we investigated the feasibility of ECT response prediction using structural magnetic resonance imaging (sMRI) data that was collected during ECT routine examinations. We applied machine learning techniques to predict individual treatment outcomes in a cohort of N = 71 ECT patients, N = 39 of which responded to the treatment. SMRI-based classification of ECT responders and non-responders reached an accuracy of 69% (sensitivity: 67%; specificity: 72%). Classification on additionally investigated clinical variables had no predictive power. Since dichotomisation of patients into ECT responders and non-responders is debatable due to many patients only showing a partial response, we additionally performed a post-hoc regression-based prediction analysis on continuous symptom improvements. This analysis yielded a significant relationship between true and predicted treatment outcomes and might be a promising alternative to dichotomization of patients. Based on our results, we argue that the prediction of individual ECT responses based on routine sMRI holds promise to overcome important limitations that are currently hampering the translation of such treatment biomarkers into everyday clinical practice. Finally, we discuss how the results of such predictive data analysis could best support the clinician's decision on whether a patient should be treated with ECT.

Individual resting-state frontocingular functional connectivity predicts the intermittent theta burst stimulation response to stress in healthy female volunteers

Intermittent theta burst stimulation (iTBS) delivered to the dorsolateral prefrontal cortex (DLPFC) has been investigated as a promising treatment for stress and stress‐related mental disorders such as major depression, yet large individual differences in responsiveness demand further exploration and optimization of its effectiveness. Clinical research suggests that resting‐state functional connectivity (rsFC) between the DLPFC and the anterior cingulate cortex (ACC) can predict iTBS treatment response in depression. The present study aimed to investigate whether rsFC between the left DLPFC and ACC subregions could predict the degree to which the stress system is affected by iTBS. After assessment of baseline resting‐state fMRI data, 34 healthy female participants performed the Trier Social Stress Test on two separate days, each followed by active or sham iTBS over the left DLPFC. To evaluate iTBS effects on the stress‐system, salivary cortisol was measured throughout the procedure. Our results showed that a stronger negative correlation between the left DLPFC and the caudal ACC was linked to a larger attenuation of stress‐system sensitivity during active, but not during sham iTBS. In conclusion, based on individual rsFC between left DLPFC and caudal ACC, iTBS could be optimized to more effectively attenuate deregulation of the stress system.

Shape and Volumetric Differences in the Corpus Callosum between Patients with Major Depressive Disorder and Healthy Controls

OBJECTIVE

This study aimed to investigate the morphometric differences in the corpus callosum between patients with major depressive disorder (MDD) and healthy controls and analyze their relationship to gray matter changes.

METHODS

Twenty female MDD patients and 21 healthy controls (HCs) were included in the study. To identify the difference in the regional gray matter concentration (GMC), VBM was performed with T1 magnetic resonance imaging. The shape analysis of the corpus callosum was processed. Diffusion tensor imaging (DTI) fiber-tracking was performed to identify the regional tract pathways in the damaged corpus callosal areas.

RESULTS

In the shape analysis, regional shape contractions in the rostrum and splenium were found in the MDD patients. VBM analysis showed a significantly lower white matter concentration in the genu and splenium, and a significantly lower GMC in the frontal, limbic, insular, and temporal regions of the MDD patients compared to the HCs. In DTI fiber-tracking, the fibers crossing the damaged areas of the genu, rostrum, and splenium were anatomically connected to the areas of lower GMC in MDD patients.

CONCLUSION

These findings support that major depressive disorder may be due to disturbances in multiple neuronal circuits, especially those associated with the corpus callosum.

Neurochemical correlates of behavioral treatment of pediatric trichotillomania

BACKGROUND

Trichotillomania (TTM) is a chronic and impairing psychiatric disorder with suspected dysfunctional cortico-striato-thalamo-cortical (CSTC) circuit activity reflecting excitatory/inhibitory signaling imbalance. TTM neurochemistry is understudied, with no prior research using magnetic resonance spectroscopy (MRS). This pilot investigation examined associations between TTM diagnosis, symptom severity, and response to behavioral treatment with MRS neurometabolites glutamate (Glu) and γ-aminobutyric acid (GABA) in CSTC structures.

METHODS

Proton echo-planar spectroscopic imaging (PEPSI) MRS was acquired from bilateral pregenual anterior cingulate cortex (pACC), caudate, putamen, globus pallidus, thalamus, and proximal white matter in 10 unmedicated girls with TTM, ages 9-17 years, before and after treatment, and from 13 age- and sex-matched healthy controls.

RESULTS

Nine of 10 TTM patients were treatment responders. Pretreatment mean Glu and GABA did not differ significantly between participants and controls. Pretreatment TTM symptoms were correlated with Glu in (left + right) pACC (r = 0.88, p = 0.02) and thalamus (r = 0.82, p = 0.012), and were negatively correlated with pACC GABA (r = -0.84, p = 0.034). Mean GABA in putamen increased 69% (baseline to post-treatment) (p = 0.027). Higher pretreatment Glu in caudate, putamen, globus pallidus, and thalamus predicted greater symptom decreases with treatment (all r < -0.6, p < 0.05); higher caudate GABA predicted less treatment-related symptom decline (r = 0.86, p = 0.014).

LIMITATIONS

Small sample, GABA quantified with spectral fitting rather than editing.

CONCLUSION

Consistent with other neuroimaging, MRS reveals discrete CSTC chemical changes with effective behavior therapy, and possibly with TTM etiology. TTM symptoms relate to excess excitatory versus inhibitory signaling in pACC and thalamus; symptom improvement may reflect reduced excitatory drive of the CSTC direct-pathway activity.

Connectome-based models can predict early symptom improvement in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a debilitating mental illness with more than 50% of patients not achieving an adequate response using first-line treatments. Reliable models that predict antidepressant treatment outcome are needed to guide clinical decision making. We aimed to build predictive models of treatment improvement for MDD patients using machine learning approaches based on fMRI resting-state functional connectivity patterns.

METHODS

Resting-state fMRI data were acquired from 192 untreated MDD patients at recruitment, and their severity of depression was assessed by Hamilton Rating Scale for Depression (HAMD) at baseline. Patients were given medication after the initial MR scan and their symptoms were monitored through HAMD for a period of six months. Connectome-based predictive modeling (CPM) algorithms were implemented to predict the improvement in HAMD score at one month from resting-state connectivity at baseline. Additionally, by selectively combining the features from all leave-one-out iterations in the model building stage, we created a consensus model that could be generalized to predict improvement in HAMD score in samples of non-overlapping subjects at different time points.

RESULTS

Using baseline functional connectivity, CPM successfully predicted symptom improvement of depression at one month. In addition, a consensus 'MDD improvement model' could predict symptom improvement for novel individuals at the two-week, one-month, two-month and three-month time points after antidepressant treatment.

CONCLUSIONS

Individual pre-treatment functional brain networks contain meaningful information that can be gleaned to build predictors of treatment outcome. The identified MDD improvement networks could be an appropriate biomarker for predicting individual therapeutic response of antidepressant treatment. Replication and validation using other large datasets will be a key next step before these models can be used in clinical practice.

Measurement of executive functioning with the National Institute of Health Toolbox and the association to anxiety/depressive symptomatology in childhood/adolescence

INTRODUCTION

Despite preliminary research, there remain inconsistent findings with regard to the role of executive functioning (EF) deficits in childhood anxiety and depression. This report examined the association of The National Institute of Health (NIH) Toolbox to clinical neuropsychological measures and to childhood, anxiety/depressive symptomatology. Methods: One-hundred eight children and adolescents completed the three EF measures from the NIH Toolbox (List Sorting Working Memory Test [LSWMT], Dimensional Change Card Sorting Test [DCCST], and Flanker Test of Attention and Inhibition [Flanker]) in an outpatient neuropsychology program. These tests were compared to established measures of EF in terms of linear correlations and detection of impairment. Heaton's Global Deficit Score (GDS) was utilized to calculate impairment. The Toolbox-EF measures were paired with parent-reported EF symptoms (Behavior Rating Inventory of Executive Function [BRIEF2]) to identify the role of EF in childhood anxiety/depressive symptomatology.

RESULTS

Toolbox-EF measures displayed medium sized correlations with their clinically comparable counterparts, and generally did not differ in their detection of impairment. Toolbox-GDS was associated with depression diagnosis and clinically significant child-reported anxiety and depressive symptoms. Together, Toolbox/BRIEF2 accounted for 26.8-30.9% of elevated depressive symptom variance, but only 13.2-14% of elevated anxiety symptom variance. Further, EF impairment was associated with depression across self report, parent report, and clinical diagnosis.

DISCUSSION

The NIH Toolbox-EF measures display comparable psychometric properties to clinically available EF measures in a pediatric (primarily psychiatric) neuropsychology setting. The Toolbox appears to display an appropriate ability to detect EF deficits secondary to self-reported depression in childhood.

The Functional Role of Large-scale Brain Network Coordination in Placebo-induced Anxiolysis

Anxiety reduction through mere expectation of anxiolytic treatment effects (placebo anxiolysis) has enormous clinical importance. Recent behavioral and electrophysiological data suggest that placebo anxiolysis involves reduced vigilance and enhanced internalization of attention; however, the underlying neurobiological mechanisms are not yet clear. Given the fundamental function of intrinsic connectivity networks (ICNs) in basic cognitive processes, we investigated ICN activity patterns associated with externally and internally directed mental states under the influence of an anxiolytic placebo medication. Based on recent findings, we specifically analyzed the functional role of the rostral anterior cingulate cortex (rACC) in coordinating placebo-dependent cue-related (phasic) and cue-unrelated (sustained) network activity. Under placebo, we observed a down-regulation of the entire salience network (SN), particularly in response to threatening cues. The rACC exhibited enhanced cue-unrelated functional connectivity (FC) with the SN, which correlated with reductions in tonic arousal and anxiety. Hence, apart from the frequently reported modulation of aversive cue responses, the rACC appears to be crucially involved in exerting a tonically dampening control over salience-responsive structures. In line with a more internally directed mental state, we also found enhanced FC within the default mode network (DMN), again predicting reductions in anxiety under placebo.

Reward related ventral striatal activity and differential response to sertraline versus placebo in depressed individuals

Medications to treat major depressive disorder (MDD) are not equally effective across patients. Given that neural response to rewards is altered in MDD and given that reward-related circuitry is modulated by dopamine and serotonin, we examined, for the first time, whether reward-related neural activity moderated response to sertraline, an antidepressant medication that targets these neurotransmitters. A total of 222 unmedicated adults with MDD randomized to receive sertraline (n = 110) or placebo (n = 112) in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study completed demographic and clinical assessments, and pretreatment functional magnetic resonance imaging while performing a reward task. We tested whether an index of reward system function in the ventral striatum (VS), a key reward circuitry region, moderated differential response to sertraline versus placebo, assessed with the Hamilton Rating Scale for Depression (HSRD) over 8 weeks. We observed a significant moderation effect of the reward index, reflecting the temporal dynamics of VS activity, on week-8 depression levels (Fs ≥ 9.67, ps ≤ 0.002). Specifically, VS responses that were abnormal with respect to predictions from reinforcement learning theory were associated with lower week-8 depression symptoms in the sertraline versus placebo arms. Thus, a more abnormal pattern of pretreatment VS dynamic response to reward expectancy (expected outcome value) and prediction error (difference between expected and actual outcome), likely reflecting serotonergic and dopaminergic deficits, was associated with better response to sertraline than placebo. Pretreatment measures of reward-related VS activity may serve as objective neural markers to advance efforts to personalize interventions by guiding individual-level choice of antidepressant treatment.

Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback

Simultaneous real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) is an emerging neuromodulation approach, that enables simultaneous volitional regulation of both hemodynamic (BOLD fMRI) and electrophysiological (EEG) brain activities. Here we report the first application of rtfMRI-EEG-nf for emotion self-regulation training in patients with major depressive disorder (MDD). In this proof-of-concept study, MDD patients in the experimental group (n = 16) used rtfMRI-EEG-nf during a happy emotion induction task to simultaneously upregulate two fMRI and two EEG activity measures relevant to MDD. The target measures included BOLD activities of the left amygdala (LA) and left rostral anterior cingulate cortex (rACC), and frontal EEG asymmetries in the alpha band (FAA, [7.5-12.5] Hz) and high-beta band (FBA, [21-30] Hz). MDD patients in the control group (n = 8) were provided with sham feedback signals. An advanced procedure for improved real-time EEG-fMRI artifact correction was implemented. The experimental group participants demonstrated significant upregulation of the LA BOLD activity, FAA, and FBA during the rtfMRI-EEG-nf task, as well as significant enhancement in fMRI connectivity between the LA and left rACC. Average individual FAA changes during the rtfMRI-EEG-nf task positively correlated with depression and anhedonia severities, and negatively correlated with after-vs-before changes in depressed mood ratings. Temporal correlations between the FAA and FBA time courses and the LA BOLD activity were significantly enhanced during the rtfMRI-EEG-nf task. The experimental group participants reported significant mood improvements after the training. Our results suggest that the rtfMRI-EEG-nf may have potential for treatment of MDD.

The effect of mGlu2/3 receptors on synaptic activities to different types of GABAergic interneurons in the anterior cingulate cortex

Antagonists of the group II metabotropic glutamate (mGlu) 2/3 receptors have been shown to have a rapid antidepressant effect. GABAergic interneurons play a crucial role in major depressive disorder (MDD) and possibly mediate the rapid antidepressant effect. However, how mGlu2/3 receptors regulate synaptic activities to GABAergic interneurons is not fully understood. In the present work, we studied the effect of mGlu2/3 receptors on excitatory and inhibitory synaptic activities to somatostatin (SST)- and parvalbumin (PV)-expressing interneurons, two major types of GABAergic interneurons, in the anterior cingulate cortex (ACC) that is strongly indicated in MDD. We found that activation of mGlu2/3 receptors by (2S,2'R,3'R)-2-(2',3'-dicarboxycyclopropyl) glycine (DCG-IV), an agonist of mGlu2/3 receptors, remarkably reduced the frequency, but not the amplitude, of spontaneous and miniature excitatory postsynaptic currents (sEPSCs and mEPSCs) and the amplitude of evoked EPSCs in both types. The reduction in the frequency of sEPSCs and the amplitude of evoked EPSCs was more pronounced in SST interneurons. DCG-IV, however, did not affect spontaneous and miniature inhibitory postsynaptic currents (sIPSCs and mIPSCs) and evoked IPSCs in both types. LY341495, an antagonist of mGlu2/3 receptors, enhanced the amplitude of evoked EPSCs without affecting sEPSCs and mEPSCs in both types. It also did not affect sIPSCs and evoked IPSCs except slightly increasing the frequency of mIPSCs in SST interneurons. Our results indicate that mGlu2/3 receptors primarily regulate excitatory synaptic activities to the two types of GABAergic interneurons in the ACC.

Altered spontaneous activity and effective connectivity of the anterior cingulate cortex in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a disabling neuropsychiatric disorder whose neurobiological basis remains unclear. Magnetic resonance imaging (MRI) studies have reported functional and structural alterations of the anterior cingulate cortex (ACC) in OCD. In this study, we explored the functional activity of subregions of the ACC and effective connectivity (EC) between ACC subregions and the whole brain in OCD. We used a Granger causality analysis (GCA) to identify the direction of information flow and whether the impact of that flow was excitatory or inhibitory. We performed resting-state functional MRI in 31 patients with OCD and 36 healthy controls and analyzed the amplitude of low-frequency fluctuation (ALFF) and coefficient-based GCA. The left pregenual ACC (pACC) in patients with OCD showed decreased ALFF relative to controls. There was significantly decreased excitatory output from the left pACC to both right dorsal superior frontal gyrus (dSFG) and left precuneus in patients compared with controls. Patients also had decreased inhibitory input to left pACC from left ventral SFG and left thalamus and caudate relative to controls. Results were similar in drug-naive patients and those with prior but not current psychopharmacological treatment. In patients, path coefficients of GCA from left pACC to right dSFG showed significant negative correlations with obsession and anxiety ratings. Decreased spontaneous neural activity and altered EC of pACC with widely distributed cortical circuitry, and associations with clinical ratings highlight the importance of pACC functional alteration in OCD.

Global connectivity and local excitability changes underlie antidepressant effects of repetitive transcranial magnetic stimulation

Repetitive transcranial magnetic stimulation (rTMS) is a commonly- used treatment for major depressive disorder (MDD). However, our understanding of the mechanism by which TMS exerts its antidepressant effect is minimal. Furthermore, we lack brain signals that can be used to predict and track clinical outcome. Such signals would allow for treatment stratification and optimization. Here, we performed a randomized, sham-controlled clinical trial and measured electrophysiological, neuroimaging, and clinical changes before and after rTMS. Patients (N = 36) were randomized to receive either active or sham rTMS to the left dorsolateral prefrontal cortex (dlPFC) for 20 consecutive weekdays. To capture the rTMS-driven changes in connectivity and causal excitability, resting fMRI and TMS/EEG were performed before and after the treatment. Baseline causal connectivity differences between depressed patients and healthy controls were also evaluated with concurrent TMS/fMRI. We found that active, but not sham rTMS elicited (1) an increase in dlPFC global connectivity, (2) induction of negative dlPFC-amygdala connectivity, and (3) local and distributed changes in TMS/EEG potentials. Global connectivity changes predicted clinical outcome, while both global connectivity and TMS/EEG changes tracked clinical outcome. In patients but not healthy participants, we observed a perturbed inhibitory effect of the dlPFC on the amygdala. Taken together, rTMS induced lasting connectivity and excitability changes from the site of stimulation, such that after active treatment, the dlPFC appeared better able to engage in top-down control of the amygdala. These measures of network functioning both predicted and tracked clinical outcome, potentially opening the door to treatment optimization.

Shape Deformation in the Brainstem of Medication-Naïve Female Patients with Major Depressive Disorder

OBJECTIVE

Although neuroimaging studies have shown volumetric reductions, such as the anterior cingulate, prefrontal cortices, and hippocampus in patients with major depressive disorder (MDD), few studies have investigated the volume of or shape alterations in the subcortical regions and the brainstem. We hypothesized that medication-naïve female adult patients with MDD might present with shape and volume alterations in the subcortical regions, including the brainstem, compared to healthy controls (HCs).

METHODS

A total of 20 medication-naïve female patients with MDD and 21 age-matched female HCs, underwent 3D T1-weighted structural magnetic resonance scanning. We analyzed the volumes of each subcortical region and each brainstem region, including the midbrain, pons, and medulla oblongata. We also performed surface-based vertex analyses on the subcortical areas and brainstem.

RESULTS

Female patients with MDD showed non-significant volumetric differences in the subcortical regions, whole brainstem, and each brainstem region compared to the HCs. However, in the surface-based vertex analyses, significant shape contractions were observed in both cerebellar peduncles located on the lateral wall of the posterior brainstem [threshold-free cluster enhancement, corrected for family-wise error (FWE) at p<0.05] in patients with MDD.

CONCLUSION

We revealed shape alterations in the posterior brainstem in female patients with MDD.

Depression-related anterior cingulate prefrontal resting state connectivity normalizes following cognitive behavioral therapy

Background.

Aberrant activity of the subcallosal cingulate (SCC) is a common theme across pharmacologic treatment efficacy prediction studies. The functioning of the SCC in psychotherapeutic interventions is relatively understudied, as are functional differences among SCC subdivisions. We conducted functional connectivity analyses (rsFC) on resting-state functional magnetic resonance imaging (fMRI) data, collected before and after a course of cognitive behavioral therapy (CBT) in patients with major depressive disorder (MDD), using seeds from three SCC subdivisions.

Methods.

Resting-state data were collected from unmedicated patients with current MDD (Hamilton Depression Rating Scale-17 > 16) before and after 14-sessions of CBT monotherapy. Treatment outcome was assessed using the Beck Depression Inventory (BDI). Rostral anterior cingulate (rACC), anterior subcallosal cingulate (aSCC), and Brodmann’s area 25 (BA25) masks were used as seeds in connectivity analyses that assessed baseline rsFC and symptom severity, changes in connectivity related to symptom improvement after CBT, and prediction of treatment outcomes using whole-brain baseline connectivity.

Results.

Pretreatment BDI negatively correlated with pretreatment rACC ~ dorsolateral prefrontal cortex and aSCC ~ lateral prefrontal cortex rsFC. In a region-of-interest longitudinal analysis, rsFC between these regions increased post-treatment (p < 0.05_FDR). In whole-brain analyses, BA25 ~ paracentral lobule and rACC ~ paracentral lobule connectivities decreased post-treatment. Whole-brain baseline rsFC with SCC did not predict clinical improvement.

Conclusions.

rsFC features of rACC and aSCC, but not BA25, correlated inversely with baseline depression severity, and increased following CBT. Subdivisions of SCC involved in top-down emotion regulation may be more involved in cognitive interventions, while BA25 may be more informative for interventions targeting bottom-up processing. Results emphasize the importance of subdividing the SCC in connectivity analyses.

Pregenual or subgenual anterior cingulate cortex as potential effective region for brain stimulation of depression

BACKGROUND

The dorsolateral prefrontal cortex (DLPFC) is the standard stimulation target for the repetitive transcranial magnetic stimulation (rTMS) treatment of major depression disorder (MDD). A retrospective study by Fox and colleagues found that a more negative resting-state functional magnetic resonance imaging (RS-fMRI) functional connectivity (FC) between left DLPFC and the subgenual anterior cingulate cortex (sgACC) in a large group of healthy participants is associated with a better curative effects of rTMS in MDD, suggesting that the sgACC may be an effective region. However, a recent meta-analysis on RS-fMRI studies found that the pregenual ACC (pgACC), rather than the sgACC, of MDD patients showed increased local activity.

METHODS

We used the stimulation coordinates in the left DLPFC analyzed by Fox et al. to perform RS-fMRI FC between the stimulation targets obtained from previous rTMS MDD studies and the potential effective regions (sgACC and pgACC, respectively) on the RS-fMRI data from 88 heathy participants.

RESULTS

(a) Both the pgACC and the sgACC were negatively connected to the left DLPFC; (b) both FCs of sgACC-DLPFC and pgACC-DLPFC were more negative in responders than in nonresponders; and (c) the associations between DLPFC-sgACC functional connectivity and clinical efficacy were clustered around the midline sgACC.

CONCLUSIONS

Both the pgACC and the sgACC may be potential effective regions for rTMS on the left DLPFC for treatment of MDD. However, individualized ACC-DLPFC FC-based rTMS on depression should be performed in the future to test the pgACC or the sgACC as effective regions.

Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice.

OBJECTIVE

To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment.

DESIGN, SETTING, AND PARTICIPANTS

In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019.

INTERVENTIONS

Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks.

MAIN OUTCOMES AND MEASURES

Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit.

RESULTS

Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points.

CONCLUSIONS AND RELEVANCE

These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01407094.

Rostral anterior cingulate glutamate predicts response to subcallosal deep brain stimulation for resistant depression

BACKGROUND

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) provided benefit for treatment-resistant depression (TRD) in open-label studies but failed in a recent randomized sham-controlled trial. Informed patient selection, based on reliable biomarkers, is needed to optimize outcome. We investigated if rostral anterior cingulate (rACC) glutamate/glutamine concentration could serve as a potential biomarker of response.

METHODS

Sixteen adults with TRD (Major Depression; MDD = 14; Bipolar Depression; BD =2) underwent proton magnetic resonance spectroscopy using a short-echo proton spectroscopy with a voxel placed in the rACC, prior to DBS. Improvement in depression was assessed using the 17-item Hamilton Rating Scale for Depression (HDRS). Glutamate and glutamine concentrations at baseline in the rACC were examined in relation to clinical outcomes at six months.

RESULTS

Lower baseline glutamate predicted significant reduction in HDRS scores in all TRD patients (p = 0.018), and predicted both HDRS reduction (p = 0.002) and 6-month response outcome in MDD-TRD patients (p = 0.013). Neither baseline glutamine nor glutamine/glutamate ratio significantly related to outcome or symptom improvement.

LIMITATIONS

Our study was limited by sample size, though it is large for a DBS study. We measured from a single voxel in the brain, so we cannot be certain our findings are specific to the rACC.

CONCLUSIONS

These preliminary results suggest that baseline rACC-glutamate concentration could serve as a response-predictive biomarker for SCC-DBS, particularly in patients with resistant major depression. If our findings are replicated and validated, rACC-glutamate may provide a basis to prospectively select TRD patients to improve likelihood of response to SCC-DBS.

Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation

BACKGROUND

Quality of life is severely impaired in patients with depressive disorders. Previous studies have focused on biomarkers predicting depressive symptomatology; however, studies investigating biomarkers predicting quality of life outcomes are limited. Improving quality of life is important because it is related not only to mental health but also to physical health. We need to develop a biomarker related to quality of life as a therapeutic target for patients with depressive disorders. Resting-state electroencephalography (EEG) is easy to record in clinical settings. The index of bandwidth spectral power predicts treatment response in depressive disorders and thus may be a candidate biomarker. However, no longitudinal studies have investigated whether EEG-recorded power could predict quality of life outcomes in patients with depressive disorders.

METHODS

The resting-state EEG-recorded bandwidth spectral power at baseline and the World Health Organization Quality of Life (QOL)-26 scores at 3-year follow-up were measured in 44 patients with depressive disorders.

RESULTS

The high beta band power (20-30 Hz) at baseline significantly predicted QOL at the 3-year follow-up after considering depressive symptoms and medication effects in a longitudinal investigation in patients with depressive disorders (β = 0.38, p = 0.01).

LIMITATIONS

We did not have healthy subjects as a comparison group in this study.

CONCLUSIONS

Our findings suggest that resting-state beta activity has the potential to be a useful biomarker for predicting future quality of life outcomes in patients with depressive disorders.

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{R}}}^{{\bf{2}}}{\boldsymbol{=}}{\bf{0.59}}\,{\bf{and}}\,{\bf{p}}{\boldsymbol{=}}{\bf{1.52}}{\bf{e}}{\boldsymbol{ \mbox{-} }}{\bf{05}}$$\end{document}R2=0.59andp=1.52e‐05). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

Comparison of shape alterations of the thalamus and caudate nucleus between drug-naïve major depressive disorder patients and healthy controls

BACKGROUND

Although structural alterations have been reported in patients with major depressive disorder (MDD), very few studies have compared the shape alterations of the subcortical regions between drug-naïve MDD patients and healthy controls (HCs). Therefore, we investigated and compared the subcortical shape alterations and volumetric changes between drug-naïve MDD patients and HCs in this study.

METHODS

This study included 45 drug-naïve MDD patients and 83 HCs, who underwent three-dimensional (3-D) T1-weighted structural magnetic resonance imaging. Surface-based vertex analysis (SVA) was performed with automated segmentation of the bilateral caudate nuclei, putamina, nuclei accumbens, thalami, pallidum, hippocampi, amygdalae, and brainstem. SVA revealed regional contractions of the thalamus (bilateral medial and lateral nuclei) and right caudate nucleus (medial wall and anterosuperior areas) in the drug-naïve MDD patients when compared to HCs RESULTS: In volume analysis, the drug-naïve MDD patients showed a significant decrease in the volume of bilateral thalami compared with HCs (after Bonferroni correction p < 0.003). We identified morphometric contractions in bilateral thalami and right caudate nucleus in the drug-naïve MDD patients (p < 0.05).

CONCLUSIONS

The present study implied that with cortical shape changes, the subcortical brain alterations could contribute to emotional dysregulation in the drug-naïve MDD patients.

Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder

Alterations in cortical thickness have been identified in major depressive disorder (MDD), but findings have been variable and inconsistent. To date, no reliable tools have been available for the meta-analysis of surface-based morphometric (SBM) studies to effectively characterize what has been learned in previous studies, and drug treatments may have differentially impacted findings. We conducted a comprehensive meta-analysis of magnetic resonance imaging (MRI) studies that explored cortical thickness in medication-free patients with MDD, using a newly developed meta-analytic mask compatible with seed-based d mapping (SDM) meta-analytic software. We performed the meta-regression to explore the effects of demographics and clinical characteristics on variation in cortical thickness in MDD. Fifteen studies describing 529 patients and 586 healthy controls (HCs) were included. Medication-free patients with MDD, relative to HCs, showed a complex pattern of increased cortical thickness in some areas (posterior cingulate cortex, ventromedial prefrontal cortex, and anterior cingulate cortex) and decreased cortical thickness in others (gyrus rectus, orbital segment of the superior frontal gyrus, and middle temporal gyrus). Most findings in the whole sample analysis were confirmed in a meta-analysis of studies recruiting medication-naive patients. Using the new mask specifically developed for SBM studies, this SDM meta-analysis provides evidence for regional cortical thickness alterations in MDD, mainly involving increased cortical thickness in the default mode network and decreased cortical thickness in the orbitofrontal and temporal cortex.

Structural changes induced by electroconvulsive therapy are associated with clinical outcome

BACKGROUND

Electroconvulsive therapy (ECT) is the most effective treatment option for major depressive disorder, so understanding whether its clinical effect relates to structural brain changes is vital for current and future antidepressant research.

OBJECTIVE

To determine whether clinical response to ECT is related to structural volumetric changes in the brain as measured by structural magnetic resonance imaging (MRI) and, if so, which regions are related to this clinical effect. We also determine whether a similar model can be used to identify regions associated with electrode placement (unilateral versus bilateral ECT).

METHODS

Longitudinal MRI and clinical data (Hamilton Depression Rating Scale) was collected from 10 sites as part of the Global ECT-MRI research collaboration (GEMRIC). From 192 subjects, relative changes in 80 (sub)cortical areas were used as potential features for classifying treatment response. We used recursive feature elimination to extract relevant features, which were subsequently used to train a linear classifier. As a validation, the same was done for electrode placement. We report accuracy as well as the structural coefficients of regions included in the discriminative spatial patterns obtained.

RESULTS

A pattern of structural changes in cortical midline, striatal and lateral prefrontal areas discriminates responders from non-responders (75% accuracy, p < 0.001) while left-sided mediotemporal changes discriminate unilateral from bilateral electrode placement (81% accuracy, p < 0.001).

CONCLUSIONS

The identification of a multivariate discriminative pattern shows that structural change is relevant for clinical response to ECT, but this pattern does not include mediotemporal regions that have been the focus of electroconvulsive therapy research so far.

Circuits, Networks, and Neuropsychiatric Disease: Transitioning From Anatomy to Imaging

Since the development of cellular and myelin stains, anatomy has formed the foundation for understanding circuitry in the human brain. However, recent functional and structural studies using magnetic resonance imaging have taken the lead in this endeavor. These innovative and noninvasive approaches have the advantage of studying connectivity patterns under different conditions directly in the human brain. They demonstrate dynamic and structural changes within and across networks linked to normal function and to a wide range of psychiatric illnesses. However, these indirect methods are unable to link networks to the hardwiring that underlies them. In contrast, anatomic invasive experimental studies can. Following a brief review of prefrontal cortical, anterior cingulate, and striatal connections and the different methodologies used, this article discusses how data from anatomic studies can help inform how hardwired connections are linked to the functional and structural networks identified in imaging studies.

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.

Behavioral and neural correlates of memory suppression in subthreshold depression

Many studies have demonstrated that healthy individuals can intentionally control memory. However, little is known about the behavioral and neural mechanisms of memory control in those with subthreshold depression (SD), a highly prevalent condition associated with severe impairments and a significant social burden. In this study, we used functional magnetic resonance imaging (fMRI) and a generalized form of task-dependent psychophysiological interaction (gPPI) analysis during the think/no-think task to examine the brain mechanism of memory suppression in SD participants. The behavioral results revealed that SD participants were unable to suppress negative memories. Neuroimaging data revealed that the SD group showed greater activation than the healthy control (HC) group in the prefrontal gyrus during memory processing. Moreover, gPPI analysis showed that the SD group had significantly lower right hippocampal functional coupling with the dorsolateral prefrontal cortex during negative memory suppression than the HC group. These results indicated that SD participants recruited more frontal control resources for memory suppression because of executive and prefrontal inhibitory dysfunction. However, the abnormal prefrontal-hippocampal inhibitory pathway resulted in a failure of the memory control process when the stimuli were negative. These findings provide some evidence for understanding why SD individuals have inefficient memory control of negative memories.

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

IMPORTANCE

Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression.

OBJECTIVE

To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment.

INTERVENTIONS

All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment.

MAIN OUTCOMES AND MEASURES

The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%.

RESULTS

Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%).

CONCLUSIONS AND RELEVANCE

These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Frontocingulate Dysfunction Is Associated with Depression and Decreased Serum PON1 in Methamphetamine-Dependent Patients

INTRODUCTION

Studies have been reported that frequent use of methamphetamine (MA) is associated with brain function impairment, mood disorders and excessive free radical production accompanied by the decreased level of the antioxidant response elements, but no study investigated their correlations simultaneously. In the current study, the correlation of brain function, depression and anxiety levels, and the serum levels of PON1 (an antioxidant) in MA-dependent patients were investigated.

METHODS

Nineteen active MA abusers and 18 control subjects performed color-word Stroop task during fMRI and the state of their depression, anxiety, and stress were measured by the Depression, Anxiety and Stress Scale-21 Items (DASS-21) questionnaire. Their blood samples were collected to measure the level of PON1 by the human enzyme-linked immunosorbent assay (ELISA) kit and its correlation with the measured variables was studied.

RESULTS

Analysis of fMRI findings showed frontocingulate dysfunction in Stroop effect condition, including left anterior cingulate cortex, paracingulate gyrus, superior frontal gyrus, and frontal pole in MA-dependent patients, which was associated with a higher level of depression and decreased level of serum PON1 in these patients.

DISCUSSION

The results of the current study showed that MA-dependency is associated with frontocingulate dysfunction, decreased serum PON1 concentration, and increased depression/anxiety, which is worth to be more studied to elucidate their roles in the pathophysiology of MA addiction.

Resting State Quantitative Electroencephalogram Power Spectra in Patients with Depressive Disorder as Compared to Normal Controls: An Observational Study

INTRODUCTION

A significant number of quantitative electroencephalogram (qEEG) studies indicate that increased spectral activities distinguish patients with depressive disorder from control subjects. But they did not yield consistent findings in the delta, theta, alpha, or beta bands.

METHODS

A total of 30 drug-naïve or drug-free subjects with a depressive episode or recurrent depressive disorder were compared with 30 age, sex, education, and handedness-matched healthy controls using qEEG power spectra in six frequency bands (delta, theta, alpha, beta, slow beta, and fast beta) and total activities separately. Spectral analysis was performed on a section of 180 s of qEEG digitized at the rate of 512 samples/s/channel, and absolute powers were log-transformed before statistical analysis.

RESULTS

Statistically significant differences between the patients and normal controls were found in the delta and the total bands, while Structured Interview Guide for the Hamilton Depression Rating Scale ( SIGH-D) score predicted the fast beta spectral power at the left temporal region. In the entire region of the brain, in the theta band, lesser absolute spectral power was found in patients than normal controls, whereas in the fast beta band, it was greater. In other bands, greater powers of spectral activities were found in patients than normal controls consistently in the parietal and occipital regions.

CONCLUSION

Various findings of qEEG absolute power spectra could demonstrate a difference between the patients with depressive disorder and the normal controls independently and efficiently. However, all the differences collectively showed stronger evidence. The findings may steer future studies to differentiate the patients with depressive disorder from controls.

Brainwaves Oscillations as a Potential Biomarker for Major Depression Disorder Risk

Major depressive disorder (MDD) is a multidimensional disorder that is characterized by the presence of alterations in mood, cognitive capacity, sensorimotor, and homeostatic functions. Given that about half of the patients diagnosed with MDD do not respond to the various current treatments, new techniques are being sought to predict not only the course of the disease but also the characteristics that differentiate responders from non-responders. Using the electroencephalogram, a noninvasive and inexpensive tool, most studies have proposed that patients with MDD have some lateralization in brain electrical activity, with alterations in alpha and theta rhythms being observed, which would be related to dysfunctions in emotional capacity such as the absence or presence of responses to the different existing treatments. These alterations help in the identification of subjects at high risk of suffering from depression, in the differentiation into responders and nonresponders to various therapies (pharmacological, electroconvulsive therapy, and so on), as well as to establish in which period of the disease the treatment will be more effective. Although the data are still inconclusive and more research is needed, these alpha and theta neurophysiological markers could support future clinical practice when it comes to establishing an early diagnosis and treating state disorders more successfully and accurately of mood disorders.

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

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

Predicting Treatment Outcome in Major Depressive Disorder Using Serotonin 4 Receptor PET Brain Imaging, Functional MRI, Cognitive-, EEG-Based, and Peripheral Biomarkers: A NeuroPharm Open Label Clinical Trial Protocol

BACKGROUND

Between 30 and 50% of patients with major depressive disorder (MDD) do not respond sufficiently to antidepressant regimens. The conventional pharmacological treatments predominantly target serotonergic brain signaling but better tools to predict treatment response and identify relevant subgroups of MDD are needed to support individualized and mechanistically targeted treatment strategies. The aim of this study is to investigate antidepressant-free patients with MDD using neuroimaging, electrophysiological, molecular, cognitive, and clinical examinations and evaluate their ability to predict clinical response to SSRI treatment as individual or combined predictors.

METHODS

We will include 100 untreated patients with moderate to severe depression (>17 on the Hamilton Depression Rating Scale 17) in a non-randomized open clinical trial. We will collect data from serotonin 4 receptor positron emission tomography (PET) brain scans, functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), cognitive tests, psychometry, and peripheral biomarkers, before (at baseline), during, and after 12 weeks of standard antidepressant treatment. Patients will be treated with escitalopram, and in case of non-response at week 4 or intolerable side effects, offered to switch to a second line treatment with duloxetine. Our primary outcome (treatment response) is assessed using the Hamilton depression rating subscale 6-item scores at week 8, compared to baseline. In a subset of the patients (n = ~40), we will re-assess the neurobiological response (using PET, fMRI, and EEG) 8 weeks after initiated pharmacological antidepressant treatment, to map neurobiological signatures of treatment responses. Data from matched controls will either be collected or is already available from other cohorts.

DISCUSSION

The extensive investigational program with follow-up in this large cohort of participants provides a unique possibility to (a) uncover potential biomarkers for antidepressant treatment response, (b) apply the findings for future stratification of MDD, (c) advance the understanding of pathophysiological underpinnings of MDD, and (d) uncover how putative biomarkers change in response to 8 weeks of pharmacological antidepressant treatment. Our data can pave the way for a precision medicine approach for optimized treatment of MDD and also provides a resource for future research and data sharing.

CLINICAL TRIAL REGISTRATION

The study was registered at clinicaltrials.gov prior to initiation (NCT02869035; 08.16.2016, URL: https://clinicaltrials.gov/ct2/results?cond=&term=NCT02869035&cntry=&state=&city=&dist=).

Atrophic Corpus Callosum Associated with Altered Functional Asymmetry in Major Depressive Disorder

PURPOSE

Impairments in intra- and inter-hemispheric information transfer circuits have been reported in patients with major depressive disorder (MDD). However, the specific anomalous connection (intra- and/or inter-hemispheric) and hemisphere (left and/or right) in which this connection plays a more dominant role in the pathogenic mechanism underlying MDD are still poorly understood.

PATIENTS AND METHODS

Structural magnetic resonance imaging and resting-state functional magnetic resonance imaging were performed in 33 patients with MDD and 33 healthy controls. The intra- and inter-hemispheric functional connectivity (FC) strength in the default mode network areas and volume of the callosal subregions were computed using independent samples t-tests. The partial correlations between the volumes and FCs were also computed.

RESULTS

The patients with MDD had smaller volumes in the genu of the corpus callosum than the controls. The intrahemispheric FCs of the bilateral posterior cingulate gyrus, left precuneus, left medial superior frontal gyrus, left medial orbitofrontal gyrus, left angular gyrus and left middle temporal gyrus, and interhemispheric FCs of the bilateral posterior cingulate gyrus in the patients with MDD were lower than those in the controls. Moreover, the intrahemispheric FCs of the precuneus and interhemispheric FCs of middle frontal gyrus, orbital middle frontal gyrus, and anterior cingulate gyrus in the patients with MDD showed right-lateralized asymmetry, which were opposite from the asymmetry patterns observed in the controls. The functional asymmetry of the anterior cingulate gyrus was correlated with the volume of the genu of the corpus callosum and disease duration.

CONCLUSION

These findings provide robust evidence that intra- and inter-hemispheric disconnections are involved in MDD, and that functional disruptions in the left hemisphere may be more relevant to the pathophysiology of MDD. Furthermore, imbalanced interhemispheric exchanges may contribute to the anatomical deficits in the corpus callosum in patients with MDD.

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

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

Pilot Study of An Intracranial Electroencephalography Biomarker of Depressive Symptoms in Epilepsy

OBJECTIVES

Adult patients with epilepsy have an increased prevalence of major depressive disorder (MDD). Intracranial EEG (iEEG) captured during extended inpatient monitoring of patients with treatment-resistant epilepsy offers a particularly promising method to study MDD networks in epilepsy.

METHODS

The authors used 24 hours of resting-state iEEG to examine the neural activity patterns within corticolimbic structures that reflected the presence of depressive symptoms in 13 adults with medication-refractory epilepsy. Principal component analysis was performed on the z-scored mean relative power in five standard frequency bands averaged across electrodes within a region.

RESULTS

Principal component 3 was a statistically significant predictor of the presence of depressive symptoms (R²=0.35, p=0.014). A balanced logistic classifier model using principal component 3 alone correctly classified 78% of patients as belonging to the group with a high burden of depressive symptoms or a control group with minimal depressive symptoms (sensitivity, 75%; specificity, 80%; area under the curve=0.8, leave-one-out cross validation). Classification was dependent on beta power throughout the corticolimbic network and low-frequency cingulate power.

CONCLUSIONS

These finding suggest, for the first time, that neural features across circuits involved in epilepsy may distinguish patients who have depressive symptoms from those who do not. Larger studies are required to validate these findings and to assess their diagnostic utility in MDD.

Connectivity patterns of cognitive control network in first episode medication-naive depression and remitted depression

BACKGROUND

Cognitive dysfunctions, such as impaired cognitive control, are frequently observed in patients with major depressive disorder (MDD). Although the cognitive control network (CCN) is widely considered a core feature of major depressive disorder (MDD), the relationship between cognitive dysfunction and symptom dimensions remains unclear. This study investigated differences in resting-state functional connectivity of the cognitive control network (CCN) between first-episode medication-naive MDD patients and remitted MDD.

METHODS

We collected resting-state functional MRI (rs-fMRI) data from 22 first-episode medication-naive major depressive disorder (fMDD) patients, 20 patients previously diagnosed with MDD in the remitted phase of depression (rMDD), and 20 healthy controls (HC). The CCN was derived from fMRI images using independent component analysis (ICA), a data-driven image analysis method.

RESULTS

Changes in functional connectivity (FC) within the CCN was mainly attenuated in the right dorsolateral prefrontal cortex and the left inferior parietal lobule, while strengthened in the right dorsal anterior cingulate cortex and the right insula in both fMDD and rMDD groups. Compared with the fMDD group, the rMDD group had decreased FC in the bilateral insula and the right dorsolateral prefrontal cortex. Further analysis explored that the FC in the bilateral insula, the right dorsal anterior cingulate cortex and the right inferior parietal lobule were correlated positively cognitive disturbance factor scores in both patients groups.

CONCLUSIONS

These findings are in agreement with the previous findings that the cognitive control network are impaired in MDD. Furthermore, our results suggest that the alteration of CCN might underpin the cognitive disturbance and the distinct patterns of the CCN between fMDD and rMDD patients may be an important target for effective cognitive remediation in MDD.

Whole-cortex mapping of common genetic influences on depression and a social deficits dimension

Social processes are associated with depression, particularly understanding and responding to others, deficits in which can manifest as callousness/unemotionality (CU). Thus, CU may reflect some of the genetic risk to depression. Further, this vulnerability likely reflects the neurological substrates of depression, presenting biomarkers to capture genetic vulnerability of depression severity. However, heritability varies within brain regions, so a high-resolution genetic perspective is needed. We developed a toolbox that maps genetic and environmental associations between brain and behavior at high resolution. We used this toolbox to estimate brain areas that are genetically associated with both depressive symptoms and CU in a sample of 258 same-sex twin pairs from the Colorado Longitudinal Twin Study (LTS). We then overlapped the two maps to generate coordinates that allow for tests of downstream effects of genes influencing our clusters. Genetic variance influencing cortical thickness in the right dorsal lateral prefrontal cortex (DLFPC) sulci and gyri, ventral posterior cingulate cortex (PCC), pre-somatic motor cortex (PreSMA), medial precuneus, left occipital-temporal junction (OTJ), parietal-temporal junction (PTJ), ventral somatosensory cortex (vSMA), and medial and lateral precuneus were genetically associated with both depression and CU. Split-half replication found support for both DLPFC clusters. Meta-analytic term search identified "theory of mind", "inhibit", and "pain" as likely functions. Gene and transcript mapping/enrichment analyses implicated calcium channels. CU reflects genetic vulnerability to depression that likely involves executive and social functioning in a distributed process across the cortex. This approach works to unify neuroimaging, neuroinformatics, and genetics to discover pathways to psychiatric vulnerability.

Orexinergic modulation of serotonin neurons in the dorsal raphe of a diurnal rodent, Arvicanthis niloticus

The hypothalamic neuropeptide, orexin (or hypocretin), is implicated in numerous physiology and behavioral functions, including affective states such as depression and anxiety. The underlying mechanisms and neural circuits through which orexin modulates affective responses remain unclear. The objective of the present study was to test the hypothesis that the serotonin (5-HT) system of the dorsal raphe nucleus (DRN) is a downstream target through which orexin potentially manifests its role in affective states. Using a diurnal rodent, the Nile grass rat (Arvicanthis niloticus), we first characterized the expression of the orexin receptors OX1R and OX2R in the DRN using in situ hybridization. The results revealed distinct distributions of OX1R and OX2R mRNAs, with OX1R predominantly expressed in the dorsal and lateral wings of the DRN that are involved in affective processes, while OX2R was mostly found in the ventral DRN that is more involved in sensory-motor function. We next examined how the orexin-OX1R pathway regulates 5-HT in the DRN and some of its projection sites using a selective OX1R antagonist SB-334867 (10 mg/kg, i.p.). A single injection of SB-334867 decreased 5-HT-ir fibers within the anterior cingulate cortex (aCgC); five once-daily administrations of SB-334867 decreased 5-HT-ir not only in the aCgC but also in the DRN, oval bed nucleus of the stria terminalis (ovBNST), nucleus accumbens shell (NAcSh), and periaqueductal gray (PAG). HPLC analysis revealed that five once-daily administrations of SB-334867 did not affect 5-HT turnover to any of the five sites, although it increased the levels of both 5-HT and 5-HIAA in the NAcSh. These results together suggest that orexinergic modulation of DRN 5-HT neurons via OX1Rs may be one pathway through which orexin regulates mood and anxiety, as well as perhaps other neurobiological processes.

Baseline functional connectivity may predict placebo responses to accelerated rTMS treatment in major depression

Although in theory sham repetitive transcranial magnetic stimulation (rTMS) has no inherent therapeutic value, nonetheless, such placebo stimulations may have relevant therapeutic effects in clinically depressed patients. On the other hand, antidepressant responses to sham rTMS are quite heterogeneous across individuals and its neural underpinnings have not been explored yet. The current brain imaging study aims to detect baseline neural fingerprints resulting in clinically beneficial placebo rTMS treatment responses. We collected resting‐state functional magnetic resonance imaging data prior to a registered randomized clinical trial of accelerated placebo stimulation protocol in patients documented with treatment‐resistant depression (http://clinicaltrials.gov/show/NCT01832805). In addition to global brain connectivity and rostral anterior cingulate cortex (rACC) seed‐based functional connectivity (FC), elastic‐net regression and cross‐validation procedures were used to identify baseline intrinsic brain connectivity biomarkers for sham‐rTMS responses. Placebo responses to accelerated sham rTMS were correlated with baseline global brain connectivity in the rACC/ventral medial prefrontal cortex (vmPFC). Concerning the rACC seed‐based FC analysis, the placebo response was associated positively with the precuneus/posterior cingulate (PCun/PCC) cortex and negatively with the middle frontal gyrus. Our findings provide first brain imaging evidence for placebo responses to sham stimulation being predictable from rACC rsFC profiles, especially in brain areas implicated in (re)appraisal and self‐focus processes.

Functional connectivity of the anterior cingulate cortex predicts treatment outcome for rTMS in treatment-resistant depression at 3-month follow-up

BACKGROUND AND OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) is a first-line treatment for treatment-resistant depression (TRD). The mechanisms of action of rTMS are not fully understood, and no biomarkers are available to assist in clinical practice to predict response to rTMS. This study aimed to demonstrate that after-rTMS clinical improvement is associated with functional connectivity (FC) changes of the subgenual cingulate cortex (sgACC) and rostral anterior cingulate (rACC), and FC of sgACC and rACC might serve as potential predictors for treatment response.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected within 1 week before rTMS initiation in 50 TRD patients to predict subsequent response to rTMS on the left dorsolateral prefrontal cortex (DLPFC). Follow-up rs-fMRI was obtained 12 weeks after completion of rTMS and neural correlates of rTMS in sgACC- and rACC-related FC patterns were compared to before rTMS data and with rs-fMRI from healthy participants.

RESULTS

Treatment response was associated with lower FC of sgACC to right DLPFC and higher FC of rACC to left lateral parietal cortex (IPL) measured at baseline. Using sgACC-DLPFC and rACC-IPL connectivity as features, responder-nonresponder classification accuracies of 84% and 76% (end-of-treatment), 88% and 81% (3-month follow-up), respectively were achieved. Longitudinal rs-fMRI data analyses revealed that the hyperconnectivity between sgACC and visual cortex was normalized to a level which was comparable to that of healthy participants.

CONCLUSIONS

Brain activity patterns in depression are predictive of treatment response to rTMS, and longitudinal change of brain activity in relevant brain circuits after rTMS is associated with treatment response in depression. Target engagement paradigms may offer opportunities to increase the efficacy of rTMS in TRD by optimal selection of patients for treatment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifiers: NCT01887782 and NCT02800226.