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

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Specific Patterns of Endogenous Functional Connectivity Are Associated With Harm Avoidance in Obsessive-Compulsive Disorder

BACKGROUND

Individuals with obsessive-compulsive disorder (OCD) show persistent avoidance behaviors, often in the absence of actual threat. Quality-of-life costs and heterogeneity support the need for novel brain-behavior intervention targets. Informed by mechanistic and anatomical studies of persistent avoidance in rodents and nonhuman primates, our goal was to test whether connections within a hypothesized persistent avoidance-related network predicted OCD-related harm avoidance (HA), a trait measure of persistent avoidance. We hypothesized that 1) HA, not an OCD diagnosis, would be associated with altered endogenous connectivity in at least one connection in the network; 2) HA-specific findings would be robust to comorbid symptoms; and 3) reliable findings would replicate in a holdout testing subsample.

METHODS

Using resting-state functional connectivity magnetic resonance imaging, cross-validated elastic net for feature selection, and Poisson generalized linear models, we tested which connections significantly predicted HA in our training subsample (n = 73; 71.8% female; healthy control group n = 36, OCD group n = 37); robustness to comorbidities; and replicability in a testing subsample (n = 30; 56.7% female; healthy control group n = 15, OCD group n = 15).

RESULTS

Stronger inverse connectivity between the right dorsal anterior cingulate cortex and right basolateral amygdala and stronger positive connectivity between the right ventral anterior insula and left ventral striatum were associated with greater HA across groups. Network connections did not discriminate OCD diagnostic status or predict HA-correlated traits, suggesting sensitivity to trait HA. The dorsal anterior cingulate cortex-basolateral amygdala relationship was robust to controlling for comorbidities and medication in individuals with OCD and was also predictive of HA in our testing subsample.

CONCLUSIONS

Stronger inverse dorsal anterior cingulate cortex-basolateral amygdala connectivity was robustly and reliably associated with HA across groups and in OCD. Results support the relevance of a cross-species persistent avoidance-related network to OCD, with implications for precision-based approaches and treatment.

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Comparison of Brain Activation According to fMRI Data in Patient with Depression (After Acute Coronary Syndrome and Somatically Healthy) and Healthy Volunteers

Objective: The present study is devoted to the study of brain activation using fMRI in patients with depression (after acute coronary syndrome and somatically healthy) and in healthy volunteers. Method : The study enrolled a total of 51 patients: 11 with depression after acute coronary syndrome, 16 with primary depressive episode and recurrent depression without prior coronary event, and 24 with ACS without depression. The groups were matched by sex and age. The emotional information processing was evaluated with the Pennsylvania Test of Emotion Recognition. All patients underwent fMRI at the time of this test. The data processing was performed with SPM12 and xjView applications. Results: During the processing of emotional information in the depressed patients after ACS, specific activation zones in the frontal cortex (P < 0.001), right fusiform gyrus (P < 0.001), and right insular lobe were identified (P = 0.017). In the patients with primary depressive episode and recurrent depression without ACS, certain zones of activation were identified in frontal cortex (P < 0.001; 0.001), left fusiform gyrus (P < 0.001), occipital cortex (P < 0.001). In the patients who had ACS, without depression, some zones of activation were specified in the right middle occipital gyrus (P < 0.001), the right superior frontal gyrus (P = 0.088), and the putamen projection on the right (P < 0.001) and on the left (P = 0.009), as well as the left insular lobe (P = 0.015). Conclusion: The pathogenesis of depression is significantly associated with the peculiarities of processing emotionally significant information, regardless of the conditions under which it develops.

Whole brain network effects of subcallosal cingulate deep brain stimulation for treatment-resistant depression

Ongoing experimental studies of subcallosal cingulate deep brain stimulation (SCC DBS) for treatment-resistant depression (TRD) show a differential timeline of behavioral effects with rapid changes after initial stimulation, and both early and delayed changes over the course of ongoing chronic stimulation. This study examined the longitudinal resting-state regional cerebral blood flow (rCBF) changes in intrinsic connectivity networks (ICNs) with SCC DBS for TRD over 6 months and repeated the same analysis by glucose metabolite changes in a new cohort. A total of twenty-two patients with TRD, 17 [15 O]-water and 5 [18 F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) patients, received SCC DBS and were followed weekly for 7 months. PET scans were collected at 4-time points: baseline, 1-month after surgery, and 1 and 6 months of chronic stimulation. A linear mixed model was conducted to examine the differential trajectory of rCBF changes over time. Post-hoc tests were also examined to assess postoperative, early, and late ICN changes and response-specific effects. SCC DBS had significant time-specific effects in the salience network (SN) and the default mode network (DMN). The rCBF in SN and DMN was decreased after surgery, but responder and non-responders diverged thereafter, with a net increase in DMN activity in responders with chronic stimulation. Additionally, the rCBF in the DMN uniquely correlated with depression severity. The glucose metabolic changes in a second cohort show the same DMN changes. The trajectory of PET changes with SCC DBS is not linear, consistent with the chronology of therapeutic effects. These data provide novel evidence of both an acute reset and ongoing plastic effects in the DMN that may provide future biomarkers to track clinical improvement with ongoing treatment.

Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder

BACKGROUND

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD.

METHODS

Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD.

RESULTS

Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up.

CONCLUSION

Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Network effects of Stanford Neuromodulation Therapy (SNT) in treatment-resistant major depressive disorder: a randomized, controlled trial

Here, we investigated the brain functional connectivity (FC) changes following a novel accelerated theta burst stimulation protocol known as Stanford Neuromodulation Therapy (SNT) which demonstrated significant antidepressant efficacy in treatment-resistant depression (TRD). In a sample of 24 patients (12 active and 12 sham), active stimulation was associated with significant pre- and post-treatment modulation of three FC pairs, involving the default mode network (DMN), amygdala, salience network (SN) and striatum. The most robust finding was the SNT effect on amygdala-DMN FC (group*time interaction F(1,22) = 14.89, p < 0.001). This FC change correlated with improvement in depressive symptoms (rho (Spearman) = -0.45, df = 22, p = 0.026). The post-treatment FC pattern showed a change in the direction of the healthy control group and was sustained at the one-month follow-up. These results are consistent with amygdala-DMN connectivity dysfunction as an underlying mechanism of TRD and bring us closer to the goal of developing imaging biomarkers for TMS treatment optimization.Trial registration: ClinicalTrials.gov NCT03068715.

Whole Brain Network effects of subcallosal cingulate deep brain stimulation for treatment-resistant depression

Ongoing experimental studies of subcallosal cingulate deep brain stimulation (SCC DBS) for treatment-resistant depression (TRD) show a differential timeline of behavioral effects with rapid changes after initial stimulation, and both early and delayed changes over the course of ongoing chronic stimulation. This study examined the longitudinal resting-state regional cerebral blood ow (rCBF) changes in intrinsic connectivity networks (ICNs) with SCC DBS for TRD over 6 months and repeated the same analysis by glucose metabolite changes in a new cohort. A total of twenty-two patients with TRD, 17 [15O]-water and 5 [18]-Fluorodeoxyglucose (FDG) positron emission tomography (PET) patients, received SCC DBS and were followed weekly for 7 months. PET scans were collected at 4-time points: baseline, 1-month after surgery, and 1 and 6 months of chronic stimulation. A linear mixed model was conducted to examine the differential trajectory of rCBF changes over time. Post-hoc tests were also examined to assess postoperative, early, and late ICN changes and response-specific effects. SCC DBS had significant time-specific effects in the salience network (SN) and the default mode network (DMN). The rCBF in SN and DMN was decreased after surgery, but responder and non-responders diverged thereafter, with a net increase in DMN activity in responders with chronic stimulation. Additionally, the rCBF in the DMN uniquely correlated with depression severity. The glucose metabolic changes in a second cohort show the same DMN changes. The trajectory of PET changes with SCC DBS is not linear, consistent with the chronology of therapeutic effects. These data provide novel evidence of both an acute reset and ongoing plastic effects in the DMN that may provide future biomarkers to track clinical improvement with ongoing treatment.

Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients.

METHODS

This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively.

RESULTS

The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity.

LIMITATIONS

Environmental factors and differences in locations of functional areas across individuals were not taken into account.

CONCLUSIONS

This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.

Antidepressant treatment-related brain activity changes in remitted major depressive disorder

Recent evidence has shown that some brain regions are core hubs and play a key role in the treatment of depression. Twenty-five unmedicated patients with major depressive disorder (MDD) were included, and telephone follow-up was performed at 8, 24, and 48 weeks after enrollment. After reaching clinical remission, they were scheduled for a second magnetic resonance imaging scan and clinical evaluation. Thirty-one healthy controls were also investigated. The intrinsic functional connectivity (degree centrality) of each participant was mapped using a computationally efficient approach. Then, functional connectivity of patients was calculated between the identified regions of interest by degree centrality analysis and every voxel. Later, linear regression analysis was used to identify potential variables predictive of an improvement in disease severity. The prominent hubs identified by degree centrality analysis included the cerebellum, inferior temporal gyrus, lingual gyrus, dorsal medial prefrontal cortex (DMPFC), and dorsal lateral prefrontal cortex. We also found that the increased degree centrality of DMPFC was associated with improvement in depressive symptoms. The brain activity associated with antidepressant effects, especially brain connectivity changes in the left DMPFC, can potentially be used to monitor treatment response and predict treatment outcomes.

Predicting Antidepressant Treatment Response Using Functional Brain Controllability Analysis

Introduction: For decades, predicting response to the antidepressant medication has been a critical unmet need in depression treatment in clinic, and a technical challenge in depression research. Methods: In this study, a recently developed functional brain network controllability (fBNC) analysis approach was employed to identify the antidepressant treatment responders and nonresponders from depression patients at the pretreatment period. The fBNC, which captures the ability of brain regions to guide the brain's behavior from an initial state to a desired state with suitable choice of inputs, may provide valuable features for antidepressant response prediction. The performance of prediction was evaluated using resting-state functional magnetic resonance imaging data collected from a 6-week longitudinal clinical trial with escitalopram in treating unmedicated depression patients (n = 20). Treatment outcomes were assessed using the Hamilton Depression Rating Scale (HAMD) scores. Patients were considered as the treatment responders if their post-treatment HAMD scores were decreased by 50% or more at 6 weeks post-treatment. Results: Results showed significantly larger global average controllability and lower global modal controllability, greater regional average controllability, and smaller regional modal controllability of default mode network in treatment responders compared with the treatment nonresponders at the pretreatment period. By performing optimal control analysis, our results showed no significant difference of the neuromodulation effects between the treatment responders and nonresponders. Discussion: Our results suggest that the fBNC measures may be utilized as novel biomarkers to predict antidepressant response on depression and provide theoretical support to employ neuromodulation for treating antidepressant nonresponders. Impact statement In this study, by employing the novel functional brain controllability analysis on top of the brain connectivity network, we identified a set of biomarkers to identify the groups of depressive patients who responded to the antidepressant treatments from those who did not. We further provided the theoretical support to utilize neuromodulation for treating antidepressant nonresponders. These findings have clinical implications as accurate identification of antidepressant treatment response before starting the treatment may reduce patients' suffering and costs and increase the treatment outcomes by adjusting and personalizing the treatment protocol.

Common and differential neural mechanisms underlying mood disorders

BACKGROUND

Despite homogenous clinical presentations between bipolar and unipolar disorders, there are distinct neurobiological differences. Chronicity of illness may be a factor impacting and sustaining certain neural features. The goal of this study was to investigate common and shared neural mechanisms underlying mood disorders, and possible sustained neural changes relating to illness chronicity by investigating a cohort of euthymic patients with bipolar disorder (BD), unipolar depression who had responded to treatment (treatment-sensitive depression, TSD), and a chronically treatment-resistant depressed (TRD) group.

METHODS

One hundred and seventy-two participants (40 BD, 39 TSD, 40 TRD, and 53 age-gender-matched healthy controls) underwent resting-state fMRI scans. Seed-based and independent component analyses were performed to investigate group differences in resting-state connectivity between the four groups.

RESULTS

All three clinical groups had significantly lower connectivity within the frontoparietal network (FPN) relative to controls. TRD and BD were significantly different from TSD (TRD, BD > TSD) but were not significantly different from each other. TRDs were also significantly different from both BD and TSD for salience network connectivity with the posterior cingulate (DMN) and the FPN with frontal pole (DMN). Additionally, the BD group exhibited greater DMN-FPN (sgACC-RDLPFC) connectivity relative to TRD, TSD, and controls, which was correlated with a previous number of depressive episodes, in the BD group only.

CONCLUSIONS

BD demonstrated shared and differential connectivity features relative to symptomatic TRD and euthymic TSD groups. The increased sgACC-RDLPFC connectivity in BD and its correlation with a number of depressive episodes could be a neural feature associated with illness chronicity.

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

Introduction

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

Materials and methods

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

Results

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

Conclusion

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

Clinical trial registration

The ClinicalTrials.gov identifier was NCT03941301.

C-reactive protein is related to a distinct set of alterations in resting-state functional connectivity contributing to a differential pathophysiology of major depressive disorder

BACKGROUND

Several studies in major depressive disorder (MDD) have found inflammation, especially C-reactive protein (CRP), to be consistently associated with MDD and network dysfunction. The aim was to investigate whether CRP is linked to a distinct set of resting-state functional connectivity (RSFC) alterations.

METHODS

For this reason, we investigated the effects of diagnosis and elevated blood plasma CRP levels on the RSFC in 63 participants (40 females, mean age 31.4 years) of which were 27 patients with a primary diagnosis of MDD and 36 healthy control-subjects (HC), utilizing a seed-based approach within five well-established RSFC networks obtained using fMRI.

RESULTS

Of the ten network pairs examined, five showed increased between-network RSFC-values unambiguously connected either to a diagnosis of MDD or elevated CRP levels. For elevated CRP levels, increased RSFC between DMN and AN was found. Patients showed increased RSFC within DMN areas and between the DMN and ECN and VAN, ECN and AN and AN and DAN.

CONCLUSIONS

The results of this study show dysregulated neural circuits specifically connected to elevated plasma CRP levels and independent of other alterations of RSFC in MDD. This dysfunction in neural circuits might in turn result in a certain immune-inflammatory subtype of MDD.

Insula network connectivity mediates the association between childhood maltreatment and depressive symptoms in major depressive disorder patients

Childhood maltreatment (CM) is a major risk factor for developing the major depressive disorder (MDD), however, the neurobiological mechanism linking CM and MDD remains unclear. We recruited 34 healthy controls (HCs) and 44 MDD patients to complete the childhood maltreatment experience assessment with Childhood Trauma Questionnaire (CTQ) and resting-state fMRI scan. Multivariate linear regression analysis was employed to identify the main effects of CM and depressive symptoms total and subfactors scores on bilateral anterior and posterior insula functional connectivity (IFC) networks, respectively. Mediation analysis was performed to investigate whether IFC strength mediates the association between CM and depressive symptoms. MDD patients showed significantly decreased connectivity in the dorsal medial prefrontal cortex and increased connectivity in the medial frontal gyrus in the bipartite IFC networks, compared to HCs. The main effects of CM and depressive symptoms showed a large discrepancy on the anterior and posterior IFC networks, which primarily located in the frontal-limbic system. Further, conjunction analysis identified the overlapping regions linking CM and depressive symptoms were mainly implicated in self-regulation and cognitive processing circuits. More important, these IFC strengths could mediate the association between different types of CM, especially for childhood abuse and childhood neglect, and depressive symptoms in those overlapping regions. We demonstrated that early exposure to CM may increase the vulnerability to depression by influencing brain's self-regulating and cognitive processing circuitry. These findings provide new insight into the understanding of pathological mechanism underlying CM-induced depressive symptoms.

Intrinsic Connectivity Networks of Glutamate-Mediated Antidepressant Response: A Neuroimaging Review

Conventional monoamine-based pharmacotherapy, considered the first-line treatment for major depressive disorder (MDD), has several challenges, including high rates of non-response. To address these challenges, preclinical and clinical studies have sought to characterize antidepressant response through monoamine-independent mechanisms. One striking example is glutamate, the brain's foremost excitatory neurotransmitter: since the 1990s, studies have consistently reported altered levels of glutamate in MDD, as well as antidepressant effects following molecular targeting of glutamatergic receptors. Therapeutically, this has led to advances in the discovery, testing, and clinical application of a wide array of glutamatergic agents, particularly ketamine. Notably, ketamine has been demonstrated to rapidly improve mood symptoms, unlike monoamine-based interventions, and the neurobiological basis behind this rapid antidepressant response is under active investigation. Advances in brain imaging techniques, including functional magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography, enable the identification of the brain network-based characteristics distinguishing rapid glutamatergic modulation from the effect of slow-acting conventional monoamine-based pharmacology. Here, we review brain imaging studies that examine brain connectivity features associated with rapid antidepressant response in MDD patients treated with glutamatergic pharmacotherapies in contrast with patients treated with slow-acting monoamine-based treatments. Trends in recent brain imaging literature suggest that the activity of brain regions is organized into coherent functionally distinct networks, termed intrinsic connectivity networks (ICNs). We provide an overview of major ICNs implicated in depression and explore how treatment response following glutamatergic modulation alters functional connectivity of limbic, cognitive, and executive nodes within ICNs, with well-characterized anti-anhedonic effects and the enhancement of "top-down" executive control. Alterations within and between the core ICNs could potentially exert downstream effects on the nodes within other brain networks of relevance to MDD that are structurally and functionally interconnected through glutamatergic synapses. Understanding similarities and differences in brain ICNs features underlying treatment response will positively impact the trajectory and outcomes for adults suffering from MDD and will facilitate the development of biomarkers to enable glutamate-based precision therapeutics.

Predicting treatment selections for individuals with major depressive disorder according to functional connectivity subgroups

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. Here, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations.

METHODS

All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery dataset (n=228), we firstly identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic, symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions.

RESULTS

Three subgroups with specific treatment recommendations were emerged including: (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities. (2) a stimulation-oriented subgroup with more alleviation in suicide. (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing respectively conducted on three testing datasets, results showed an overall accuracy of 72.83%.

CONCLUSIONS

Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

Neural changes in youth at high risk for bipolar disorder undergoing family-focused therapy or psychoeducation

BACKGROUND

Patients with mood disorders may benefit from psychosocial interventions through changes in brain networks underlying emotion processing. In this study, we used functional magnetic resonance imaging (fMRI) to investigate treatment-related changes in emotion processing networks in youth at familial high risk for bipolar disorder (BD).

METHODS

Youth, ages 9-17, were randomly assigned to family-focused therapy for high-risk youth (FFT-HR) or an active comparison treatment, Enhanced Care (EC). Before and after these 4-month treatments, participants underwent fMRI while viewing happy, fearful, and calm facial expressions. Twenty youth in FFT-HR and 20 in EC were included in analyses of pre- to post-treatment changes in activation across the whole brain. Significant clusters were assessed for correlation with mood symptom improvement.

RESULTS

In the dorsolateral prefrontal cortex (DLPFC), activation increased from pre- to post-treatment in the FFT-HR group and decreased in the EC group. Insula activation decreased in the FFT-HR group and did not change in the EC group. Across both treatments, decreasing activation in the hippocampus and amygdala was correlated with pre- to post-treatment improvement in hypomania, while increasing activation in the DLPFC was correlated with pre- to post-treatment improvement in depression.

DISCUSSION

Psychosocial treatment addresses abnormalities in emotion regulation networks in youth at high risk for BD. Increased prefrontal cortex activation suggests enhanced emotion regulation from pre- to post-treatment with FFT-HR. Improvements in family interactions may facilitate the development of prefrontal resources that provide protection against future mood episodes.

A lateralized model of the pain-depression dyad

Chronic pain and depression are two frequently co-occurring and debilitating conditions. Even though the former is treated as a physical affliction, and the latter as a mental illness, both disorders closely share neural substrates. Here, we review the association of pain with depression, especially when symptoms are lateralized on either side of the body. We also explore the overlapping regions in the forebrain implicated in these conditions. Finally, we synthesize these findings into a model, which addresses gaps in our understanding of comorbid pain and depression. Our lateralized pain-depression dyad model suggests that individuals diagnosed with depression should be closely monitored for pain symptoms in the left hemibody. Conversely, for patients in pain, with the exception of acute pain with a known source, referrals in today's pain centers for psychological evaluation should be part of standard practice, within the framework of an interdisciplinary approach to pain treatment.

Fifteen years of NESDA Neuroimaging: An overview of results related to clinical profile and bio-social risk factors of major depressive disorder and common anxiety disorders

The longitudinal Netherlands Study of Depression and Anxiety (NESDA) Neuroimaging study was set up in 2003 to investigate whether neuroanatomical and functional abnormalities during tasks of primary emotional processing, executive planning and memory formation, and intrinsic brain connectivity are i) shared by individuals with major depressive disorder (MDD) and common anxiety disorders; and ii) characterized by symptomatology-specific abnormalities. Furthermore, questions related to individual variations in vulnerability for onset, comorbidity, and longitudinal course could be investigated. Between 2005 and 2007, 233 individuals fulfilling a diagnosis of MDD, panic disorder, social anxiety disorder and/or generalized anxiety disorder and 68 healthy controls aging between 18 and 57 were invited from the NESDA main sample (n = 2981). An emotional faces processing task, an emotional word-encoding task, and an executive planning task were administered during 3T BOLD-fMRI acquisitions. In addition, resting state BOLD-fMRI was acquired and T1-weighted structural imaging was performed. All participants were invited to participate in the two-year and nine-year follow-up MRI measurement. Fifteen years of NESDA Neuroimaging demonstrated common morphological and neurocognitive abnormalities across individuals with depression and anxiety disorders. It however provided limited support for the idea of more extensive abnormalities in patients suffering from both depression and anxiety, despite their worse prognosis. Risk factors including childhood maltreatment and specific risk genes had an emotion processing modulating effect, apparently stronger than effects of diagnostic labels. Furthermore, brain imaging data, especially during emotion processing seemed valuable for predicting the long-term course of affective disorders, outperforming prediction based on clinical information alone.

Differential Alterations in Resting State Functional Connectivity Associated with Depressive Symptoms and Early Life Adversity

Depression and early life adversity (ELA) are associated with aberrant resting state functional connectivity (FC) of the default mode (DMN), salience (SN), and central executive networks (CEN). However, the specific and differential associations of depression and ELA with FC of these networks remain unclear. Applying a dimensional approach, here we analyzed associations of FC between major nodes of the DMN, SN, and CEN with severity of depressive symptoms and ELA defined as childhood abuse and neglect in a sample of 83 healthy and depressed subjects. Depressive symptoms were linked to increased FC within the SN and decreased FC of the SN with the DMN and CEN. Childhood abuse was associated with increased FC within the SN, whereas childhood neglect was associated with decreased FC within the SN and increased FC between the SN and the DMN. Our study thus provides evidence for differential associations of depressive symptoms and ELA with resting state FC and contributes to a clarification of previously contradictory findings. Specific FC abnormalities may underlie specific cognitive and emotional impairments. Future research should link specific clinical symptoms resulting from ELA to FC patterns thereby characterizing depression subtypes with specific neurobiological signatures.