Antidepressant effect on connectivity of the mood-regulating circuit: an FMRI study

Prophylactic (R,S)-ketamine and (2S,6S)-HNK decrease fear expression by differentially modulating fear neural ensembles

BACKGROUND

We previously reported that a single injection of (R,S)-ketamine or its metabolite (2S,6S)-hydroxynorketamine (HNK) prior to stress attenuates learned fear. However, whether these drugs attenuate learned fear through divergent or convergent effects on neural activity remains to be determined.

METHODS

129S6/SvEv male mice were injected with saline, (R,S)-ketamine, or (2S,6S)-HNK one week before a 3-shock contextual fear conditioning (CFC) paradigm. Five days later, mice were re-exposed to the aversive context, and euthanized one hour later to quantify active cells. Brains were processed for c-fos immunoreactivity, and neural networks were built with a novel, wide-scale imaging pipeline.

RESULTS

We found that (R,S)-ketamine and (2S,6S)-HNK attenuate learned fear. Fear-related neural activity was altered in: dorsal CA3 following (2S,6S)-HNK; ventral CA3 and CA1, infralimbic (IL) and prelimbic (PL) regions, insular cortex (IC), retrosplenial cortex (RSP), piriform cortex (PIR), nucleus reuniens (RE), and periaqueductal grey (PAG) following both (R,S)-ketamine and (2S,6S)-HNK; and in the paraventricular nucleus of thalamus (PVT) following (R,S)-ketamine. Dorsal CA3 and ventral hippocampus activation correlated with freezing in the (R,S)-ketamine group, and RSP activation correlated with freezing in both (R,S)-ketamine and (2S,6S)-HNK groups. (R,S)-ketamine increased connectivity between cortical and subcortical regions while (2S,6S)-HNK increased connectivity within these regions.

CONCLUSIONS

This work identifies novel nodes in fear networks, involving the RE, PIR, IC, PAG and RSP, that can be targeted with neuromodulatory strategies or pharmaceutical compounds to treat fear-induced disorders. This approach could be used to optimize target engagement and dosing strategies of existing medications.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BACKGROUND

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

METHODS

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

RESULTS

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

CONCLUSIONS

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Longitudinal changes in resting-state functional connectivity as markers of vulnerability or resilience in first-degree relatives of patients with bipolar disorder

BACKGROUND

There is a significant contribution of genetic factors to the etiology of bipolar disorder (BD). Unaffected first-degree relatives of patients (UR) with BD are at increased risk of developing mental disorders and may manifest cognitive impairments and alterations in brain functional and connective dynamics, akin to their affected relatives.

METHODS

In this prospective longitudinal study, resting-state functional connectivity was used to explore stable and progressive markers of vulnerability i.e. abnormalities shared between UR and BD compared to healthy controls (HC) and resilience i.e. features unique to UR compared to HC and BD in full or partial remission (UR n = 72, mean age = 28.0 ± 7.2 years; HC n = 64, mean age = 30.0 ± 9.7 years; BD patients n = 91, mean age = 30.6 ± 7.7 years). Out of these, 34 UR, 48 BD, and 38 HC were investigated again following a mean time of 1.3 ± 0.4 years.

RESULTS

At baseline, the UR showed lower connectivity values within the default mode network (DMN), frontoparietal network, and the salience network (SN) compared to HC. This connectivity pattern in UR remained stable over the follow-up period and was not present in BD, suggesting a resilience trait. The UR further demonstrated less negative connectivity between the DMN and SN compared to HC, abnormality that remained stable over time and was also present in BD, suggesting a vulnerability marker.

CONCLUSION

Our findings indicate the coexistence of both vulnerability-related abnormalities in resting-state connectivity, as well as adaptive changes possibly promoting resilience to psychopathology in individual at familial risk.

Functional connectomics in depression: insights into therapies

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation

Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.

Review: The use of functional magnetic resonance imaging (fMRI) in clinical trials and experimental research studies for depression

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that can be used to examine neural responses with and without the use of a functional task. Indeed, fMRI has been used in clinical trials and pharmacological research studies. In mental health, it has been used to identify brain areas linked to specific symptoms but also has the potential to help identify possible treatment targets. Despite fMRI's many advantages, such findings are rarely the primary outcome measure in clinical trials or research studies. This article reviews fMRI studies in depression that sought to assess the efficacy and mechanism of action of compounds with antidepressant effects. Our search results focused on selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed treatments for depression and ketamine, a fast-acting antidepressant treatment. Normalization of amygdala hyperactivity in response to negative emotional stimuli was found to underlie successful treatment response to SSRIs as well as ketamine, indicating a potential common pathway for both conventional and fast-acting antidepressants. Ketamine's rapid antidepressant effects make it a particularly useful compound for studying depression with fMRI; its effects on brain activity and connectivity trended toward normalizing the increases and decreases in brain activity and connectivity associated with depression. These findings highlight the considerable promise of fMRI as a tool for identifying treatment targets in depression. However, additional studies with improved methodology and study design are needed before fMRI findings can be translated into meaningful clinical trial outcomes.

Effects of 12-week escitalopram treatment on resting-state functional connectivity of large-scale brain networks in major depressive disorder

In this study, the effects of antidepressants on large-scale brain networks and the neural basis of individual differences in response were explored. A total of 41 patients with major depressive disorder (MDD) and 42 matched healthy controls (HCs) were scanned by resting-state functional magnetic resonance imaging separately at baseline and after a 12-week follow-up. The patients with MDD received escitalopram for 12 weeks. After treatment, patients were classified into those with MDD in remission [MDDr, endpoint 17-item Hamilton Depression Rating Scale (HAMD) total score ≤7] and those in nonremission (MDDnr). The human Brainnetome Atlas was used to define large-scale networks and compute within- and between-network resting-state functional connectivity (rsFC). Results showed the decreased subcortical network (SCN)-ventral attention network (VAN) connectivity at baseline increased in patients with MDD after 12-week treatment, and it was comparable with that of HCs. This change was only observed in patients with MDDr. However, the decreased within-network rsFC in SCN and default mode network (DMN) persisted in all patients with MDD, including those with MDDr and MDDnr, after treatment. The strength of SCN-VAN connectivity at baseline was significantly negatively correlated with the reduction rate of HAMD score in all patients with MDD. Thus, SCN-VAN connectivity may be an antidepressant target associated with depressive state changes and a predictor of treatment response to serotonin reuptake inhibitors. The within-network rsFC in SCN and DMN may reflect a trait-like abnormality in MDD. These findings provide further insights into the mechanism of antidepressants and their individual differences in response. The trial name is "Appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement" (URL: http://www.chictr.org.cn/showproj.aspx?proj=21377; registration number: ChiCTR-OOC-17012566).

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.

Distinct brain activity alterations of treatment for bipolar disorders with psychotherapy and drug therapy: activation likelihood estimation meta-analysis

BACKGROUNDS

Many studies suggest that both psychotherapy and drug therapy are effective in the treatment of bipolar disorders (BDs). However, the pathophysiology of both types of intervention has not been established definitively.

METHODS

An activation likelihood estimation meta-analysis was performed to identify the distinct brain activity alterations between psychotherapy and drug therapy for the treatment of BDs. Articles were identified by searching databases including PubMed, Embase, Cochrane Library, and Web of Science databases. Eligible studies on BDs were published up until 10 June 2021.

RESULTS

21 studies were included and we conducted a meta-analysis for different therapies and imaging tasks. After receiving psychotherapy, BD patients showed increased activation in the inferior frontal gyrus (IFG) and superior temporal gyrus. While after taking drug therapy, BD patients displayed increased activation in the anterior cingulate cortex, medial frontal gyrus, IFG, and decreased activation in the posterior cingulate cortex. The regions of brain activity changes caused by psychotherapy were mostly focused on the frontal areas, while drug therapy mainly impacted on the limbic areas. Different type of tasks also affected brain regions which were activated.

CONCLUSIONS

Our comprehensive meta-analysis indicates that these two treatments might have effect on BD in their own therapeutic modes. Psychotherapy might have a top-down effect, while drug therapy might have a bottom-up effect. This study may contribute to differential diagnosis of BDs and would be helpful to finding more accurate neuroimaging biomarkers for BD treatment.

A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures

BACKGROUND

Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies.

AIMS

In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies.

DESIGN

The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies.

RESULTS

The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms.

LIMITATIONS

The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings.

CONCLUSIONS

In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease.

The thalamus is the causal hub of intervention in patients with major depressive disorder: Evidence from the Granger causality analysis

Major depressive disorder (MDD) is the leading mental disorder and afflicts more than 350 million people worldwide. The underlying neural mechanisms of MDD remain unclear, hindering the accurate treatment. Recent brain imaging studies have observed functional abnormalities in multiple brain regions in patients with MDD, identifying core brain regions is the key to locating potential therapeutic targets for MDD. The Granger causality analysis (GCA) measures directional effects between brain regions and, therefore, can track causal hubs as potential intervention targets for MDD. We reviewed literature employing GCA to investigate abnormal brain connections in patients with MDD. The total degree of effective connections in the thalamus (THA) is more than twice that in traditional targets such as the superior frontal gyrus and anterior cingulate cortex. Altered causal connections in patients with MDD mainly included enhanced bottom-up connections from the thalamus to various cortical and subcortical regions and reduced top-down connections from these regions to the THA, indicating excessive uplink sensory information and insufficient downlink suppression information for negative emotions. We suggest that the thalamus is the most crucial causal hub for MDD, which may serve as the downstream target for non-invasive brain stimulation and medication approaches in MDD treatment.

Altered resting-state functional connectivity within corticostriatal and subcortical-striatal circuits in chronic pain

Brain corticostriatal circuits are important for understanding chronic pain and highly relevant to motivation and cognitive processes. It has been demonstrated that in patients with chronic back pain, altered nucleus accumbens (NAcc)-medial prefrontal cortex (MPFC) circuit fMRI-based activity is predictive of patient outcome. We evaluated the NAcc-MPFC circuit in patients with another chronic pain condition, fibromyalgia, to extend these important findings. First, we compared fMRI-based NAcc-MPFC resting-state functional connectivity in patients with fibromyalgia (N = 32) vs. healthy controls (N = 37). Compared to controls, the NAcc-MPFC circuit's connectivity was significantly reduced in fibromyalgia. In addition, within the fibromyalgia group, NAcc-MPFC connectivity was significantly correlated with trait anxiety. Our expanded connectivity analysis of the NAcc to subcortical brain regions showed reduced connectivity of the right NAcc with mesolimbic circuit regions (putamen, thalamus, and ventral pallidum) in fibromyalgia. Lastly, in an exploratory analysis comparing our fibromyalgia and healthy control cohorts to a separate publicly available dataset from patients with chronic back pain, we identified reduced NAcc-MPFC connectivity across both the patient groups with unique alterations in NAcc-mesolimbic connectivity. Together, expanding upon prior observed alterations in brain corticostriatal circuits, our results provide novel evidence of altered corticostriatal and mesolimbic circuits in chronic pain.

Diagnostic delay in functional seizures is associated with abnormal processing of facial emotions

PURPOSE

In patients with functional seizures (FS), delay in diagnosis (DD) may negatively affect outcomes. Altered brain responses to emotional stimuli have been shown in adults with FS. We hypothesized that DD would be associated with differential fMRI activation in emotion processing circuits.

METHODS

Fifty-two adults (38 females) with video-EEG confirmed FS prospectively completed assessments related to symptoms of depression (BDI-II), anxiety (BAI), post-traumatic stress disorder (PCL-S), a measure of how their symptoms affect day-to-day life (GAF), and fMRI at 3T with emotional faces task (EFT). During fMRI, subjects indicated "male" or "female" via button press while implicitly processing happy, sad, fearful, and neutral faces. Functional magnetic resonance imaging (FMRI) response to each emotion was modeled and group analyses were performed in AFNI within pre-specified regions-of-interest involved in emotion processing. A median split (507 days) defined short- (s-DD) and long-delay diagnosis (l-DD) groups. Voxelwise regression analyses were also performed to examine linear relationship between DD and emotion processing. FMRI signal was extracted from clusters showing group differences and Spearman's correlations assessed relationships with symptom scores.

RESULTS

Groups did not differ in FS age of onset, sex distribution, years of education, TBI characteristics, EFT in-scanner or post-test performance, or scores on the GAF, BDI-II, BAI, and PCL-S measures. The s-DD group was younger than l-DD (mean age 32.6 vs. 40.1; p = 0.022) at the time of study participation. After correcting for age, compared to s-DD, the l-DD group showed greater fMRI activation to sad faces in the bilateral posterior cingulate cortex (PCC) and to neutral faces in the right anterior insula. Within-group linear regression revealed that with increasing DD, there was increased fMRI activation to sad faces in the PCC and to happy faces in the right anterior insula/inferior frontal gyrus (AI/IFG). There were positive correlations between PCC response to sad faces and BDI-II scores in the l-DD group (rho = 0.48, p = 0.012) and the combined sample (rho = 0.30, p = 0.029). Increased PCC activation to sad faces in those in the l-DD group was associated with worse symptoms of depression (i.e. higher BDI-II score).

CONCLUSIONS

Delay in FS diagnosis is associated with fMRI changes in PCC and AI/IFG. As part of the default mode network, PCC is implicated in mood control, self-referencing, and other emotion-relevant processes. In our study, PCC changes are linked to depression. Future studies should assess the effects of interventions on these abnormalities.

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

•

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

•

This poor baseline performance contradicts results from smaller studies.

•

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

•

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

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

Gender differences in major depressive disorders: A resting state fMRI study

BACKGROUND

Major depressive disorder (MDD) has a high disability rate and brings a large disease burden to patients and the country. Significant sex differences exist in both the epidemiological and clinical features in MDD. The effect of sex on brain function in MDD is not clear now. Regional homogeneity (ReHo) and ALFF are widely used research method in the study of brain function. This research aimed to use ReHo and ALFF to explore gender differences in brain function images in MDD.

METHODS

Eighty first-episode drug-naive patients (47 women and 30 men) with MDD and 85 age, education matched healthy volunteers (47 women and 31 men) were recruited in our study and participated in resting-state functional magnetic resonance imaging scans. ReHo and ALFF were used to assess brain activity, two-way ANOVA and post hoc analysis was conducted to explore the sex difference in MDD. Correlation analysis was used to explore the relationship between abnormal brain functioning and clinical symptoms.

RESULTS

We observed sex-specific patterns and diagnostic differences in MDD Patients, further post hoc comparisons indicated that women with MDD showed decreased ALFF value in the right superior occipital gyrus and decreased ReHo value in the left calcarine and left dorsolateral superior frontal gyrus compared with HC females and men with MDD. Men with MDD showed decreased ReHo value in the right median cingulate gyrus compared with HC males and increased ReHo value in the left dorsolateral superior frontal gyrus compared with HC males, we also found that HC males showed higher ReHo value in the right median cingulate gyrus than HC females.

CONCLUSIONS

Men and women do have sex differences in brain function, the occipital lobe, calcarine, DLPFC, and DCG were the main different brain regions found between male and female in MDD, which may be the biomarker brain regions that can help diagnose and treat MDD in men and women.

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.

Behavioral, cortical and autonomic effects of single-dose escitalopram on the induction and regulation of fear and disgust: Comparison with single-session psychological emotion regulation with reappraisal

INTRODUCTION

Adaptive and successful emotion regulation, the ability to flexibly exert voluntary control over emotional experience and the ensuing behavior, is vital for optimal daily functioning and good mental health. In clinical settings, pharmacological and psychological interventions are widely employed to modify pathological emotion processing and ameliorate its deleterious consequences.

METHODS

In this study, we investigated the acute effects of single-dose escitalopram on the induction and regulation of fear and disgust in healthy subjects. Furthermore, we compared these pharmacological effects with psychological emotion regulation that utilized a cognitive strategy with reappraisal. Emotion induction and regulation tasks were performed before and 4 h after ingestion of placebo or 10 mg escitalopram in a randomized, double-blind design. The International Affective Picture System (IAPS) was used as a source of images, with threat-related pictures selected for fear and disease and contamination-related pictures for disgust. Behavioral data, electrodermal activity (EDA), and functional near-infrared spectroscopy (fNIRS) recordings were collected.

RESULTS

Escitalopram significantly reduced emotion intensity for both fear and disgust during emotion induction, albeit with differing electrodermal and hemodynamic activity patterns for the two negative emotions. At rest, i.e., in the absence of emotive stimuli, escitalopram increased sympathetic activity during the fear but not during the disgust experiments. For both fear and disgust, emotion regulation with reappraisal was more effective in reducing emotion intensity compared to pharmacological intervention with escitalopram or placebo.

DISCUSSION

We concluded that emotion regulation with reappraisal and acute administration of escitalopram, but not placebo, reduce emotion intensity for both fear and disgust, with cognitive regulation being significantly more efficient compared to pharmacological regulation under the conditions of this study. Results from the fNIRS and EDA recordings support the concept of differential mechanisms of emotion regulation that could be emotion-specific.

Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder

Antidepressants are often the first-line medications prescribed for patients with major depressive disorder (MDD). Given the critical role of the default mode network (DMN) in the physiopathology of MDD, the current study aimed to investigate the effects of antidepressants on the resting-state functional connectivity (rsFC) within and between the DMN subsystems. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 36 unmedicated MDD patients at baseline and after escitalopram treatment for 12 weeks. The rs-fMRI data were also collected from 61 matched healthy controls at the time point with the same interval. Then, we decomposed the DMN into three subsystems based on a template from previous studies and computed the rsFC within and between the three subsystems. Finally, repeated measures analysis of covariance was conducted to identify the main effect of group and time and their interaction effect. We found that the significantly reduced within-subsystem rsFC in the DMN core subsystem in patients with MDD at baseline was increased after escitalopram treatment and became comparable with that in the healthy controls, whereas the reduced within-subsystem rsFC persisted in the DMN dorsal medial prefrontal cortex (dMPFC) and medial temporal subsystems in patients with MDD following escitalopram treatment. In addition, the reduced between-subsystem rsFC between the core and dMPFC subsystem showed a similar trend of change after treatment in patients with MDD. Moreover, our main results were confirmed using the DMN regions from another brain atlas. In the current study, we found different effects of escitalopram on the rsFC of the DMN subsystems. These findings deepened our understanding of the neuronal basis of antidepressants' effect on brain function in patients with MDD. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.

Spontaneous thought-related network connectivity predicts sertraline effect on major depressive disorder

Sertraline is one of the most commonly prescribed antidepressants. Major depressive disorder (MDD) is characterized by spontaneous thoughts that are laden with negative affect-a "malignant sadness". Prior neuroimaging studies have identified abnormal resting-state functional connectivity (rsFC) in the spontaneous brain networks of MDD patients. But how antidepressant medication acts to relieve the experience of depression as well as adjust its associated spontaneous networks and mood-regulation circuits remains an open question. In this study, we recruited 22 drug-naïve MDD patients along with 35 normal controls and investigated whether the functional integrity of cortical networks associated with spontaneous thoughts is modulated by sertraline treatment. We attempted to predict post-treatment effects based upon what we observed in the pre-treatment rsFC of drug-naïve MDD patients. In the result, we demonstrated that (1) after the sertraline treatment, the medial temporal lobe of default network (DN_MTL) and mood regulation pathway-the fronto-parietal control network (FPCN), the thalamus, and the salience network (SN)-were restored to normal connectivity, relative to the pre-treatment condition; however, the altered connections of FPCN-core DN (DN_CORE), FPCN-SN, and intra-FPCN among MDD patients remained impaired; (2) thalamo-prefrontal connectivity provides moderate predictive power (r² = 0.63) for the effectiveness of sertraline treatment. In summary, our findings contribute to a body of evidence that suggests salubrious effects of sertraline treatment primarily involve the FPCN-thalamus-SN pathway. The pre-treatment rsFC in this pathway could serve as a predictor of sertraline treatment outcome.

Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity

Previous studies have suggested that resting-state functional connectivity plays a central role in the physiopathology of major depressive disorder (MDD). However, the individualized diagnosis of MDD based on resting-state functional connectivity is still unclear, especially in first episode drug-naive patients with MDD. Resting state functional magnetic resonance imaging was enrolled from 30 first episode drug-naive patients with MDD and age- and gender-matched 31 healthy controls. Whole brain functional connectivity was computed and viewed as classification features. Multivariate pattern analysis (MVPA) was performed to discriminate patients with MDD from controls. The experimental results exhibited a correct classification rate of 82.25% (p < 0.001) with sensitivity of 83.87% and specificity of 80.64%. Almost all of the consensus connections (125/128) were cross-network interaction among default mode network (DMN), salience network (SN), central executive network (CEN), visual cortex network (VN), Cerebellum and Other. Moreover, the supramarginal gyrus exhibited high discriminative power in classification. Our findings suggested cross-network interaction can be used as an effective biomarker for MDD clinical diagnosis, which may reveal the potential pathological mechanism for major depression. The current study further confirmed reliable application of MVPA in discriminating MDD patients from healthy controls.

Aerobic Exercise Decreases Negative Affect by Modulating Orbitofrontal-Amygdala Connectivity in Adolescents

Long-term negative affect in adolescence is associated with impairment in quality of life, interpersonal function, and social adaptation. Although physical exercise could decrease negative emotion, the underlying mechanism remains largely unknown. Acute exercise with controlled intensity might be a good experimental paradigm to unravel the potential neural mechanisms underlying the effects of physical exercise on negative affect. In this study, twenty-three males in late adolescence were randomly assigned to acute exercise group (AG) or control group. The experiment contained pre-test and post-test session interleaved with 30-min moderate-intensity exercise or seated rest. In each session, a resting-state fMRI scanning was conducted followed by completing Positive and Negative Affect Schedule and Profile of Mood State. Bilateral amygdala was used as seed region to calculate t voxel-wised functional connectivity (FC) of amygdala to whole brain. The results demonstrated, for the first time, that AG exhibited increased FC between right amygdala and right orbital frontal cortex. Significantly decreased negative affect was also observed in AG. Moreover, the increased rOFC-amygdala FC was also associated with the decreased depression score. Our findings suggest that exercise-induced decreased negative affect might be modulated by functional interactions of amygdala with both cognitive control and limbic networks, which offers a meaningful insight for clinical treatment and prevention of emotional disorders in late adolescence.

Functional impairment-based segmentation of anterior cingulate cortex in depression and its relationship with treatment effects

In major depressive disorder (MDD), the anterior cingulate cortex (ACC) is widely related to depression impairment and antidepressant treatment response. The multiplicity of ACC subdivisions calls for a fine‐grained investigation of their functional impairment and recovery profiles. We recorded resting state fMRI signals from 59 MDD patients twice before and after 12‐week antidepressant treatment, as well as 59 healthy controls (HCs). With functional connectivity (FC) between each ACC voxel and four regions of interests (bilateral dorsolateral prefrontal cortex [DLPFC] and amygdalae), subdivisions with variable impairment were identified based on groups' dissimilarity values between MDD patients before treatment and HC. The ACC was subdivided into three impairment subdivisions named as MedialACC, DistalACC, and LateralACC according to their dominant locations. Furthermore, the impairment pattern and the recovery pattern were measured based on group statistical analyses. DistalACC impaired more on its FC with left DLPFC, whereas LateralACC showed more serious impairment on its FC with bilateral amygdalae. After treatment, FCs between DistalACC and left DLPFC, and between LateralACC and right amygdala were normalized while impaired FC between LateralACC and left amygdala kept dysfunctional. Subsequently, FC between DistalACC and left DLPFC might contribute to clinical outcome prediction. Our approach could provide an insight into how the ACC was impaired in depression and partly restored after antidepressant treatment, from the perspective of the interaction between ACC subregions and critical frontal and subcortical regions.

Limbic cortico-striato-thalamo-cortical functional connectivity in drug-naïve patients of obsessive-compulsive disorder

BACKGROUND

The pathophysiology of obsessive-compulsive disorder (OCD) remains unclear despite extensive neuroimaging work on the disorder. Exposure to medication and comorbid mental disorders can confound the results of OCD studies. The goal of this study was to explore differences in brain functional connectivity (FC) within the cortico-striato-thalamo-cortical (CSTC) loop of drug-naïve and drug-free OCD patients and healthy controls (HCs).

METHODS

A total of 29 drug-naïve OCD patients, 22 drug-free OCD patients, and 25 HCs matched on age, gender and education level underwent functional magnetic resonance imaging scanning at resting state. Seed-based connectivity analyses were conducted among the three groups. The Yale Brown Obsessive Compulsive Scale and clinical inventories were used to assess the clinical symptoms.

RESULTS

Compared with HCs, the drug-naïve OCD patients had reduced FC within the limbic CSTC loop. In the drug-naïve OCD participants, we also found hyperconnectivity between the supplementary motor area and ventral and dorsal putamen (p < 0.05, corrected for multiple comparisons).

CONCLUSIONS

Exposure to antidepressants such as selective serotonin reuptake inhibitors may affect the function of some brain regions. Future longitudinal studies could help to reveal the pharmacotherapeutic mechanisms in these loops.

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

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

Enhanced functional connectivity between insular subregions correlates with the efficacy of music and instruction-guided relaxation in depression

Music and instruction-guided relaxation (MIGR) is a complementary therapeutic tool used in the treatment of the major depressive disorder (MDD). However, the neural mechanism that underlies the effect of MIGR on MDD patients is not known. Twenty-three right-handed MDD patients and 23 age-, sex-, handedness-, and educational level-matched healthy controls were enrolled. Resting-state functional MRI data were acquired from patients before and after MIGR and from healthy controls. The relationships between insular subregion-based functional connectivity and Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale (HAM-A), Automatic Thoughts Questionnaire, and Ruminative Responses Scale scores were examined. One-way analysis of variance exhibited significant differences among the three groups in functional connectivity between the left dorsal anterior insula (dAI) and left superior medial frontal gyrus (SMFG), left dAI and left precuneus, left posterior insula and left gyrus rectus, right ventral anterior insula (vAI) and left posterior cingulate cortex (PCC), right vAI and right inferior frontal gyrus (R-IFG). Further comparisons in regions of interest showed that MDD patients before MIGR showed decreased functional connectivity between the left dAI and left SMFG, left dAI and left precuneus, left posterior insula, and left gyrus rectus, right vAI and left PCC, right vAI and R-IFG relative to those in healthy controls. The strength of functional connectivity between the right dAI and left putamen also exhibited a negative correlation with the HAM-A score in MDD cases before MIGR. MIGR may result in enhanced functional connectivity in insular subregions, thereby potentially increasing the regulatory influence of cognitive reappraisal.

Functional connectivity of the prefrontal cortex and amygdala is related to depression status in major depressive disorder

BACKGROUND

We used resting-state functional magnetic resonance imaging to examine possible amygdala-prefrontal cortex functional connectivity abnormalities and to clarify the correlation of the abnormal connectivity with response to antidepressant medications.

METHODS

We recruited 40 drug-naïve patients with first-episode depression, had a 17-item Hamilton Rating Scale for Depression (HRSD17) score>17 for participation in a magnetic resonance imaging scan. Remission was defined as an HRSD17 score <7 following 8 weeks of fluoxetine antidepressant treatment. Gender- and age-matched healthy subjects (n = 26) also underwent MRI scanning. Finally, the association between the change in HRSD17 scores and a change in connectivity between the amygdala and prefrontal cortex from pre to post-treatment was evaluated in major depressive disorder (MDD).

RESULTS

After controlling for age, gender and years of education, a statistically significant increase in functional connectivity to the right prefrontal cortex from the amygdala was observed in the MDD group compared with the healthy control group (p<0.05, corrected). After 8 weeks of antidepressant treatment and remission in the MDD group, a significant decrease in functional connectivity to the right prefrontal cortex and the left prefrontal cortex from the amygdala was observed, compared with the level of connectivity in the drug-naïve MDD group(p<0.05,corrected). There were no significant associations between the difference in HRSD17 scores rMDD and fMDD with the change in connectivity.

LIMITATIONS

The design of this study lack resistance to treatment for the depressed group.

CONCLUSIONS

Increased functional connectivity of PFC-AMY is a promise to be a biomarker of MDD, however weather it could be a biomarker of fluoxetine treatment needs future studying.

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.

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.

Neuropsychiatric aspects of Parkinson disease psychopharmacology: Insights from circuit dynamics

Parkinson disease (PD) is a neurodegenerative disorder with a complex pathophysiology characterized by the progressive loss of dopaminergic neurons within the substantia nigra. Persons with PD experience several motoric and neuropsychiatric symptoms. Neuropsychiatric features of PD include depression, anxiety, psychosis, impulse control disorders, and apathy. In this chapter, we will utilize the National Institutes of Mental Health Research Domain Criteria (RDoC) to frame and integrate observations from two prevailing disease constructions: neurotransmitter anomalies and circuit physiology. When there is available evidence, we posit how unified translational observations may have clinical relevance and postulate importance outside of PD. Finally, we review the limited evidence available for pharmacologic management of these symptoms.

Response inhibition during processing of sexual stimuli in males with problematic hypersexual behavior

BACKGROUND AND AIMS

Individuals with problematic hypersexual behavior (PHB) are unable to control their sexual cravings, regardless of other situational factors. This inability to control cravings is a common trait in patients with neurological pathologies related to response inhibition. Until recently, however, it was unclear whether individuals with PHB have decreased inhibition and altered neural responses in the brain regions associated with inhibition compared to healthy control individuals, especially in the presence of distracting sexual stimuli. In this study, we examined the neural and psychological underpinnings of inhibition in individuals with PHB.

METHODS

Thirty individuals with PHB and 30 healthy subjects underwent functional magnetic resonance imaging while performing a modified go/no-go task with neutral or sexual backgrounds used as distractors.

RESULTS

Individuals with PHB showed poorer response inhibition than healthy subjects, especially when sexual distractors were present. Further, compared to healthy control subjects, individuals with PHB showed decreased activation in the right inferior frontal gyrus (IFG) and reduced functional connectivity between the IFG and the pre-supplementary motor area (preSMA) when response inhibition was required. Finally, the reduced activation and connectivity were more pronounced in the presence of sexual distractors than in the presence of neutral distractors.

DISCUSSION

These findings suggest that individuals with PHB show reduced ability to inhibit responses that might be related to lower IFG activation and IFG-preSMA connectivity during response inhibition. Our results provide insights into the neurobiological underpinnings of poor response inhibition in individuals with PHB.

The relationship between sleep efficiency and clinical symptoms is mediated by brain function in major depressive disorder

BACKGROUND

Sleep disturbance is a common and key symptom that affects most of patients with major depressive disorder (MDD). However, neural substrates underlying sleep disturbance and their clinical relevance in depression remain unclear.

METHODS

Ninety-six MDD patients underwent resting-state functional MRI. Fractional amplitude of low-frequency fluctuation (fALFF) and resting-state functional connectivity (rsFC) were used to measure brain function. Overnight polysomnography was performed to objectively measure sleep efficiency (SE), which was used to classify patients into normal sleep efficiency (NSE) and low sleep efficiency (LSE) groups. Between-group differences in fALFF and rsFC were examined using two-sample t-tests. Moreover, correlation and mediation analyses were conducted to test for potential associations between SE, brain functional changes, and clinical variables.

RESULTS

LSE group showed decreased fALFF in right cuneus, thalamus, and middle temporal gyrus compared to NSE group. MDD patients with low SE also exhibited lower rsFC of right cuneus to right lateral temporal cortex, which was associated with more severe depression and anxiety symptoms. More importantly, mediation analyses revealed that the relationships between SE and severity of depression and anxiety symptoms were significantly mediated by the altered rsFC. In addition, these low SE-related brain functional alterations were not affected by antidepressant medication and were independent of structural changes.

LIMITATIONS

The lack of healthy controls because of "first-night effect".

CONCLUSION

These findings not only may expand existing knowledge about neuropathology of sleep disturbance in depression, but also may inform real-world clinical practice by improving depression and anxiety symptoms through sleep regulation.

Heterogeneity of Preictal Dynamics in Human Epileptic Seizures

It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.

Abnormal functional connectivity strength in first-episode, drug-naïve adult patients with major depressive disorder

BACKGROUND

The pathogenesis of major depressive disorder (MDD) is complicated and equivocal. Previous studies have found an incidence of abnormal changes of neural networks, with plentiful evidence pointing the finger of suspicion firmly at the default mode network (DMN) and cortico-limbic networks. The aim of the present study was to use the approach of functional connectivity strength (FCS) to directly investigate the features of spontaneous brain activity in the case of first-episode, drug-naïve adult patients with MDD at rest.

METHODS

Resting-state functional magnetic resonance imaging (MRI) scans were performed on 23 first-episode drug-naïve major depressive disorder (MDD) patients and 20 healthy controls (HCs). In this study, using graph-theory approaches(FCS), we computed the characteristics of brain connectivity. Simultaneously, we used a series of validated test procedures to evaluate the patients' cognitive function. Subsequently, the results were compared with the peak of FCS value and a correlation analysis was conducted.

RESULTS

Compared with the HCs group, MDD patients showed significantly decreased FCS in bilateral posterior cingulate cortex (PCC)/precuneus and bilateral prefrontal cortex(PFC) and increased FCS in right posterior central gyrus, left thalamus and left temporal lobe. These brain regions belongs to the default-mode network and cortico-limbic networks. Finally, the correlation analyses showed the negative correlation of the FCS values in the left posterior cingulate cortex (PCC)/precuneus and Hamilton Anxiety Rating Scale (HAMA, r = -0.472, p = .023), Stroop Color Word Test-A(SCWT-A, r = -0.451, p = .031), Stroop Color Word Test-B(SCWT-B, r = -0.588, p = .003).Meanwhile, there was negative correlation between the FCS values in the left thalamus and SCWT-A(r = -0.473, p = .023), SCWT-B(r = -0.465, p = .025), SCWTC(r = -0.524, p = .010).In addition, the FCS values in the right PCC has negative correlation with Montgomery Asberg Depression Rating Scale (MADRS) (r = -0.433, p = .039).

CONCLUSIONS

DMN is an important node of MDD. FCS within the default mode network and cortico-limbic networks in patients with major depressive disorder has been changed in the early stage of MDD. FCS can provide favourable and additional evidence in the investigation of brain pathophysiology and therapy in depression.

Functional Connectivity of the Striatum as a Neural Correlate of Symptom Severity in Patient with Obsessive-Compulsive Disorder

OBJECTIVE

It is well established that the cortico-striato-thalamo-cortical (CSTC) circuit is implicated in the pathophysiology of obsessive- compulsive disorder (OCD). However, reports on corticostriatal functional connectivity (FC) in OCD have been inconsistent due to the structural and functional heterogeneity of the striatum. Therefore, in the present study, we investigated corticostriatal FC using a fine 12-seed striatal parcellation to overcome this heterogeneity and discover the neural correlates of symptoms in OCD patients.

METHODS

We recruited 23 OCD patients and 23 healthy controls (HCs). Whole-brain FC based on striatal seeds was examined using resting-state functional magnetic resonance imaging data and compared across OCD patients and HCs. We conducted correlation analysis between FCs of striatal subregions with significant group differences and symptom severity scores on the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Hamilton Rating Scale for Depression, and Hamilton Rating Scale for Anxiety (HAM-A).

RESULTS

Compared to HCs, patients demonstrated increased FC of the dorsal caudal putamen and ventral rostral putamen (VRP) with several cortical regions, such as the intracalcarine cortex, inferior frontal gyrus, supramarginal/angular gyrus (SMG/AG), and postcentral gyrus (PCG). Furthermore, FC between the VRP and SMG/AG and between the VRP and PCG was negatively correlated with scores on the Y-BOCS compulsive subscale and the HAM-A, respectively.

CONCLUSION

These findings suggest that striatal subregions have strengthened FC with extensive cortical regions, which may reflect neural correlates of compulsive and anxious symptoms in OCD patients. These results contribute to an improved understanding of OCD pathophysiology by complementing the current evidence regarding striatal FC.

Treatment-naïve first episode depression classification based on high-order brain functional network

BACKGROUND

Recent functional connectivity (FC) studies have proved the potential value of resting-state functional magnetic resonance imaging (rs-fMRI) in the study of major depressive disorder (MDD); yet, the rs-fMRI-based individualized diagnosis of MDD is still challenging.

METHODS

We enrolled 82 treatment-naïve first episode depression (FED) adults and 72 matched normal control (NC). A computer-aided diagnosis framework was utilized to classify the FEDs from the NCs based on the features extracted from not only traditional "low-order" FC networks (LON) based on temporal synchronization of original rs-fMRI signals, but also "high-order" FC networks (HON) that characterize more complex functional interactions via correlation of the dynamic (time-varying) FCs. We contrasted a classifier using HON feature (C_HON) and compared its performance with using LON only (C_LON). Finally, an integrated classification model with both features was proposed to further enhance FED classification.

RESULTS

The C_HON had significantly improved diagnostic accuracy compared to the C_LON (82.47% vs. 67.53%). Joint classification further improved the performance (83.77%). The brain regions with potential diagnostic values mainly encompass the high-order cognitive function-related networks. Importantly, we found previously less-reported potential imaging biomarkers that involve the vermis and the crus II in the cerebellum.

LIMITATIONS

We only used one imaging modality and did not examine data from different subtypes of depression.

CONCLUSIONS

Depression classification could be significantly improved by using HON features that better capture the higher-level brain functional interactions. The findings suggest the importance of higher-level cerebro-cerebellar interactions in the pathophysiology of MDD.

The association between antidepressant treatment and brain connectivity in two double-blind, placebo-controlled clinical trials: a treatment mechanism study

BACKGROUND

Antidepressant medications offer an effective treatment for depression, yet nearly 50% of patients either do not respond or have side-effects rendering them unable to continue the course of treatment. Mechanistic studies might help advance the pharmacology of depression by identifying pathways through which treatments exert their effects. Toward this goal, we aimed to identify the effects of antidepressant treatment on neural connectivity, the relationship with symptom improvement, and to test whether these effects were reproducible across two studies.

METHODS

We completed two double-blind, placebo-controlled trials of SNRI antidepressant medications with MRI scans obtained before and after treatment. One was a 10-week trial of duloxetine (30-120 mg daily; mean 92·1 mg/day [SD 30·00]) and the other was a 12-week trial of desvenlafaxine (50-100 mg daily; 93·6 mg/day [16·47]). Participants consisted of adults with persistent depressive disorder. Adjusting for sex and age, we examined the effect of treatment on whole-brain functional connectivity. We also examined correlations between change in functional connectivity and improvement in symptoms of depression (24-item Hamilton Depression Rating Scale) and pain symptom severity (Symptom Checklist-90-Revised).

FINDINGS

Participants were enrolled between Jan 26, 2006, and Nov 22, 2011, for the duloxetine RCT and Aug 5, 2012, and Jan 28, 2016, for the desvenlafaxine RCT. Before and after treatment MRI scans were collected in 32 participants for the duloxetine RCT and 34 participants for the desvenlafaxine RCT. In both studies, antidepressants decreased functional connectivity compared with placebo (duloxetine study: β=-0·06; 95% CI -0·08 to -0·03; p<0·0001, ηp2=0·44; desvenlafaxine study: -0·06, -0·09 to -0·03; p<0·0001, ηp2=0·35) within a thalamo-cortico-periaqueductal network that has previously been associated with the experience of pain. Within the active drug groups, reductions in functional connectivity within this network correlated with improvements in depressive symptom severity in both studies (duloxetine study: r=0·38, 95% CI 0·01-0·65; p=0·0426; desvenlafaxine study: 0·44, 0·10-0·69; p=0·0138) and pain symptoms in the desvenlafaxine study (0·39, 0·04 to 0·65; p=0·0299).

INTERPRETATION

The findings suggest the thalamo-cortico-periaqueductal network associated with the experience of pain is a new and potentially important target for novel antidepressant therapeutics.

FUNDING

National Mental Health Institute, Eli Lilly and Company, Pfizer Pharmaceuticals, and the Edwin S Webster Foundation.

Characterizing Brain Connectivity From Human Electrocorticography Recordings With Unobserved Inputs During Epileptic Seizures

Epilepsy is a neurological disorder characterized by the sudden occurrence of unprovoked seizures. There is extensive evidence of significantly altered brain connectivity during seizure periods in the human brain. Research on analyzing human brain functional connectivity during epileptic seizures has been limited predominantly to the use of the correlation method. However, spurious connectivity can be measured between two brain regions without having direct connection or interaction between them. Correlations can be due to the apparent interactions of the two brain regions resulting from common input from a third region, which may or may not be observed. Hence, researchers have recently proposed a sparse-plus-latent-regularized precision matrix (SLRPM) when there are unobserved or latent regions interacting with the observed regions. The SLRPM method yields partial correlations of the conditional statistics of the observed regions given the latent regions, thus identifying observed regions that are conditionally independent of both the observed and latent regions. We evaluate the performance of the methods using a spring-mass artificial network and assuming that some nodes cannot be observed, thus constituting the latent variables in the example. Several cases have been considered, including both sparse and dense connections, short-range and long-range connections, and a varying number of latent variables. The SLRPM method is then applied to estimate brain connectivity during epileptic seizures from human ECoG recordings. Seventy-four clinical seizures from five patients, all having complex partial epilepsy, were analyzed using SLRPM, and brain connectivity was quantified using modularity index, clustering coefficient, and eigenvector centrality. Furthermore, using a measure of latent inputs estimated by the SLRPM method, it was possible to automatically detect 72 of the 74 seizures with four false positives and find six seizures that were not marked manually.

The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols

Studies of clinical populations that combine MRI data generated at multiple sites are increasingly common. The Canadian Biomarker Integration Network in Depression (CAN-BIND; www.canbind.ca) is a national depression research program that includes multimodal neuroimaging collected at several sites across Canada. The purpose of the current paper is to provide detailed information on the imaging protocols used in a number of CAN-BIND studies. The CAN-BIND program implemented a series of platform-specific MRI protocols, including a suite of prescribed structural and functional MRI sequences supported by real-time monitoring for adherence and quality control. The imaging data are retained in an established informatics and databasing platform. Approximately 1300 participants are being recruited, including almost 1000 with depression. These include participants treated with antidepressant medications, transcranial magnetic stimulation, cognitive behavioural therapy and cognitive remediation therapy. Our ability to analyze the large number of imaging variables available may be limited by the sample size of the substudies. The CAN-BIND program includes a multimodal imaging database supported by extensive clinical, demographic, neuropsychological and biological data from people with major depression. It is a resource for Canadian investigators who are interested in understanding whether aspects of neuroimaging — alone or in combination with other variables — can predict the outcomes of various treatment modalities.

Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression

BACKGROUND

Previously, we identified four depression subtypes defined by distinct functional connectivity alterations in depression-related brain networks, which in turn predicted clinical symptoms and treatment response. Optogenetic functional magnetic resonance imaging offers a promising approach for testing how dysfunction in specific circuits gives rise to subtype-specific, depression-related behaviors. However, this approach assumes that there are robust, reproducible correlations between functional connectivity and depressive symptoms-an assumption that was not extensively tested in previous work.

METHODS

First, we comprehensively reevaluated the stability of canonical correlations between functional connectivity and symptoms (N = 220 subjects) using optimized approaches for large-scale statistical hypothesis testing, and we validated methods for improving estimation of latent variables driving brain-behavior correlations. Having confirmed this necessary condition, we reviewed recent advances in optogenetic functional magnetic resonance imaging and illustrated one approach to formulating hypotheses regarding latent subtype-specific circuit mechanisms and testing them in animal models.

RESULTS

Correlations between connectivity features and clinical symptoms were robustly significant, and canonical correlation analysis solutions tested repeatedly on held-out data generalized. However, they were sensitive to data quality, preprocessing, and clinical heterogeneity, which can reduce effect sizes. Generalization could be markedly improved by adding L2 regularization, which decreased estimator variance, increased canonical correlations in left-out data, and stabilized feature selection. These improvements were useful for identifying candidate circuits for optogenetic interrogation in animal models.

CONCLUSIONS

Multiview, latent-variable approaches such as canonical correlation analysis offer a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic functional magnetic resonance imaging holds promise for testing hypotheses regarding latent, subtype-specific mechanisms driving depressive symptoms and behaviors.

Elevated resting-state connectivity in the medial temporal lobe and the prefrontal cortex among patients with Cushing's syndrome in remission

Objective Cushing's syndrome is associated with long-term cognitive deficits and affective symptoms such as depression and anxiety. The alterations in brain function underlying these deficits after Cushing's syndrome are unclear and therefore we aimed to explore alterations in resting-state functional connectivity in patients with Cushing's syndrome in remission. Design Cross-sectional case-control study. Methods Nineteen women with Cushing's syndrome in remission for a median time of 7 years (IQR: 6-10) and a mean age of 45 years were included at three university clinics. These patients and 38 age-matched female controls underwent brain imaging at a single center. The main outcome measure was functional connectivity at rest, measured with functional magnetic resonance imaging. Results The medial temporal lobe (MTL) and prefrontal cortex networks, exhibited elevated functional connectivity among patients compared to controls. The degree of elevated functional connectivity in the MTL was negatively associated with time in remission. Conclusions Resting-state functional connectivity within glucocorticoid receptor-rich regions, particularly the MTL and medial prefrontal cortex, was increased in patients. These differences in connectivity may provide a neural basis for the cognitive deficits and affective symptoms commonly experienced by patients with Cushing's syndrome in remission.

Topologically state-independent and dependent functional connectivity patterns in current and remitted depression

OBJECTIVE

Identification of state-independent and -dependent neural biomarkers may provide insight into the pathophysiology and effective treatment of major depressive disorder (MDD), therefore we aimed to investigate the state-independent and -dependent topological alterations of MDD.

METHOD

Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 59 patients with unmedicated first episode current MDD (cMDD), 48 patients with remitted MDD (rMDD) and 60 demographically matched healthy controls (HCs). Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the global and nodal level.

RESULTS

At a global level, both patient groups showed decreased normalized clustering coefficient in relative to HCs. On a nodal level, both patient groups showed decreased nodal centrality, predominantly in cortex-mood-regulation brain regions including the dorsolateral prefrontal cortex, posterior parietal cortex and posterior cingulate cortex. By comparison to cMDD patients, rMDD group had a higher nodal centrality in right parahippocampal gyrus.

LIMITATIONS

The present study, an exploratory analysis, may require further confirmation with task-based and experimental studies.

CONCLUSIONS

Deficits in the topological organization of the whole brain and cortex-mood-regulation brain regions in both rMDD and cMDD represent state-independent biomarkers.

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

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

Functional MRI findings, pharmacological treatment in major depression and clinical response

Major depressive disorders are common conditions with relatively limited response to treatment. In order to improve response to treatment, a better understanding of functional neuroanatomy is necessary to improve treatment targets at brain level. This work summarises the literature of longitudinal functional magnetic resonance imaging studies in major depression to identify brain regions where aberrant neural activity normalises after clinical response following treatment with pharmacological compounds with known antidepressant properties. Hyperactivity in regions such as the amygdala and the ventral components of the anterior cingulate cortex were some of the most replicated findings of functional MRI studies in major depression and normalisation of aberrant activity one of the best predictive biomarkers of treatment response.

Regional default mode network connectivity in major depressive disorder: modulation by acute intravenous citalopram

The relationship between altered default mode network (DMN) connectivity and abnormal serotonin function in major depressive disorder (MDD) has not been investigated. Using intravenous citalopram and resting-state fMRI, we investigated DMN intra-network connectivity and serotonin function in 77 healthy controls and patients with MDD. There were no significant main effects of MDD or citalopram on DMN intra-network connectivity; however, significant interactions indicated that group differences under saline were modified by citalopram. In MDD patients during saline infusion, in contrast with controls, the DMN (i) did not include the precuneus that was instead part of an anti-correlated network but (ii) did include amygdala that was part of the anti-correlated network in controls. Citalopram infusion in MDD patients restored the pattern seen in controls under saline. In healthy controls, citalopram infusion disengaged the precuneus from the DMN and engaged the amygdala, partially reproducing the abnormalities seen under saline in MDD. In exploratory analyses within the MDD group, greater rumination self-ratings were associated with greater intra-network connectivity of the anterior cingulate cortex with the DMN. We hypothesise that, in MDD, disengagement of the precuneus from the DMN relates to overgeneral memory bias in rumination. The opposite effect, with greater engagement of the amygdala in the DMN, reflects the negative valence of rumination. Reversal of these abnormalities by citalopram suggests that they may be related to impaired serotonin function. That citalopram engaged the amygdala in the DMN in controls may relate to the paradoxical effects on aversive processing seen with acute SSRIs in healthy subjects.

The Impact of Whole Brain Global Functional Connectivity Density Following MECT in Major Depression: A Follow-Up Study

To explore the alteration of global functional connectivity density (gFCD) in depressive patients after modified electroconvulsive therapy (MECT) and analyze the relationship between gFCD and clinical outcome. Thirty-seven subjects were evaluated based on the diagnostic criteria of the International Classification of Diseases-10 (ICD-10), consisting of a depressive group (24 patients after follow-ups) and a healthy control group with 13 normal individuals. All participants received Hamilton Depression Scale (HAMD) scores and resting-state functional magnetic resonance imaging scans. The gFCD significantly increased in the posterior-middle insula, the supra-marginal gyrus and the dorsal medial prefrontal cortex (dmPFC) before MECT treatment compared to healthy controlled patients. The gFCD statistically expanded in the perigenual anterior cingulate cortex (pgACC), the orbitofrontal cortex bilaterally and the left-supra-marginal gyrus after MECT, and it decreased notably in the posterior insula. The gFCD in the pgACC and the right orbital frontal cortex of depressive group before MECT showed a positive correlation with HAMD scores with treatment. Conforming to the impact of gFCD in depressive patients after MECT, the aforementioned brain region may become an indicator of MECT effect.

Development of Neuroimaging-Based Biomarkers in Psychiatry

This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.

Magnetic resonance study on the brain structure and resting-state brain functional connectivity in primary insomnia patients

The aim of the study was to study the changes in brain structure and functional connectivity in primary insomnia (PI) patients, as well as to explore the biological characteristics of PI abnormality and the pathophysiological mechanism underlying the brain structure and the abnormal functional connectivity under depression.Voxel-based morphometry (VBM) technique and resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) techniques were used to investigate the brain structure and rs-fc in PI and light-moderate primary insomnia with depression (PID) patients; healthy individuals were used as the normal control (NC) group. The differences between the 3 groups, the correlation between the brain network connection of the anterior cingulate cortex (ACC), and clinical information were compared.Compared with the NC group, patients in PI and PID groups showed changes in brain structure and brain functional connectivity, which might be related to the pathophysiological mechanism of primary insomnia. PI patients had enhanced connections in the left anterior cingulate cortex/insula, left posterior cingulate, and the right limbic lobe/cingulate gyrus/paracingulate gyrus with ACC. Compared with PI patients, PID patients had weaker brain functional connectivity in the left corpus callosum/posterior cingulate with ACC and enhanced functional connectivity in the frontal and limbic lobes with ACC, suggesting that PI patients with depression had abnormal brain network connection.Primary insomnia has abnormalities in intracephalic multisystem structure and neural network connection. The interaction and influence between depression and insomnia aggravate the cognitive function damage. This study provided the theoretical basis for exploring the neuropathology underlying the PID disorder and cognitive function.