Aberrant functional connectivity of dorsolateral prefrontal and cingulate networks in patients with major depression during working memory processing

The association between dietary quality, sleep duration, and depression symptoms in the general population: findings from cross-sectional NHANES study

BACKGROUND

Unhealthy lifestyles, which include unhealthy diet and unhealthy sleep duration, have been widely recognized as modifiable risk factors for depressive symptoms. This study aims to explore the associations between dietary quality, sleep duration, and depression symptoms, as well as their combined effects.

METHODS

The NHANES 2007 to 2014 cycles provided the data for this study, in which 19,134 adults aged 20 years and older were included. Unhealthy diet, measured using an average Healthy Eating Index (HEI)-2015 score below the 60th percentile, and unhealthy sleep duration, measured as sleep duration at night less than 7 h or greater than/equal to 9 h, were the primary exposures. Then participants were divided into four different lifestyles. A weighted-multivariable logistic regression was employed, controlling for relevant variables. Furthermore, stratified analyses were conducted to assess the robustness of the findings and identify potential high-risk groups.

RESULTS

The overall prevalence of depressive symptoms among all participants was 8.44%. Among the participants, 56.58% met the criteria for healthy sleep duration, and 24.83% scored at or above the 60th percentile on the HEI-2015. Unhealthy diet (OR: 1.40, 95% CI: 1.18-1.67, p < 0.001) and unhealthy sleep duration (OR: 1.94, 95% CI: 1.63-2.31, p < 0.001) exhibited positive associations with depression symptoms. Individuals who maintained an unhealthy diet but healthy sleep duration (OR: 1.60, 95% CI: 1.20-2.13, p = 0.002), healthy diet but unhealthy sleep duration (OR: 2.50, 95% CI: 1.64-3.80, p < 0.001), or an unhealthy diet and unhealthy sleep duration (OR: 2.91, 95% CI: 2.16-3.92, p < 0.001) were significantly associated with depressive symptoms compared to those with a healthy diet and healthy sleep duration, respectively. In stratified analyses, females, middle-aged individuals, those with a college degree or higher education, and individuals who do not meet the recommended level of physical activity exhibit heightened susceptibility to depressive symptoms when exposed to unhealthy diet and unhealthy sleep duration.

CONCLUSION

In summary, our study suggests that individuals affected by the individual and synergistic effect of an unhealthy diet and unhealthy sleep duration are more susceptible to experiencing depressive symptoms.

The role of serotonin in depression-A historical roundup and future directions

Depression is one of the most common psychiatric disorders worldwide, affecting approximately 280 million people, with probably much higher unrecorded cases. Depression is associated with symptoms such as anhedonia, feelings of hopelessness, sleep disturbances, and even suicidal thoughts. Tragically, more than 700 000 people commit suicide each year. Although depression has been studied for many decades, the exact mechanisms that lead to depression are still unknown, and available treatments only help a fraction of patients. In the late 1960s, the serotonin hypothesis was published, suggesting that serotonin is the key player in depressive disorders. However, this hypothesis is being increasingly doubted as there is evidence for the influence of other neurotransmitters, such as noradrenaline, glutamate, and dopamine, as well as larger systemic causes such as altered activity in the limbic network or inflammatory processes. In this narrative review, we aim to contribute to the ongoing debate on the involvement of serotonin in depression. We will review the evolution of antidepressant treatments, systemic research on depression over the years, and future research applications that will help to bridge the gap between systemic research and neurotransmitter dynamics using biosensors. These new tools in combination with systemic applications, will in the future provide a deeper understanding of the serotonergic dynamics in depression.

Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression

Understanding the neural basis of major depressive disorder (MDD) is vital to guiding neuromodulatory treatments. The available evidence supports the hypothesis that MDD is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neural systems lead to diminished emotion regulation and intrusive ruminations. Recent research also points towards network changes in the brain, especially within the prefrontal cortex (PFC), as primary sources of MDD etiology. However, due to limitations in spatiotemporal resolution and clinical opportunities for intracranial recordings, this hypothesis has not been directly tested. We recorded intracranial EEG from the dorsolateral (dlPFC), orbitofrontal (OFC), and anterior cingulate cortices (ACC) in neurosurgical patients with MDD. We measured daily fluctuations in self-reported depression severity alongside directed connectivity between these PFC subregions. We focused primarily on delta oscillations (1-3 Hz), which have been linked to GABAergic inhibitory control and intracortical communication. Depression symptoms worsened when connectivity within the left vs. right PFC became imbalanced. In the left hemisphere, all directed connectivity towards the ACC, from the dlPFC and OFC, was positively correlated with depression severity. In the right hemisphere, directed connectivity between the OFC and dlPFC increased with depression severity as well. This is the first evidence that delta oscillations flowing between prefrontal subregions transiently increase intensity when people are experiencing more negative mood. These findings support the overarching hypothesis that MDD worsens with prefrontal disinhibition.

Altered activation of the dorsal anterior cingulate cortex during oddball performance in individuals at risk for Alzheimer's disease

INTRODUCTION

The neural mechanisms underlying neurodegenerative disorders in the elderly remain elusive, despite extensive neuroimaging research in recent decades. Amnestic type mild cognitive impairment (aMCI) and late-life major depressive disorder (MDD) are two such conditions characterized by intersecting cognitive and affective symptomatology, and they are at a higher risk for Alzheimer's disease.

MATERIALS AND METHODS

This study analyzed the neural underpinnings of cognitive and depressive symptoms in a cohort comprising 12 aMCI subjects, 24 late-life MDD patients, and 26 healthy controls (HCs). Participants underwent a detailed neuropsychological assessment and completed a visual attentional oddball task during functional magnetic resonance imaging (fMRI), with evaluations at baseline and at 2-year follow-up.

RESULTS

Initial findings showed that aMCI subjects had reduced dACC activation during oddball (target) stimulus detection, a pattern that persisted in longitudinal analyses and correlated with cognitive functioning measures. For HCs, subsequent dACC activation was linked to depression scores. Furthermore, in the affective-cognitive altered groups, later dACC activation correlated with oddball and memory performance.

CONCLUSIONS

These findings enhance our comprehension of the neurobiological basis of cognitive and depressive disturbances in aging, indicating that dACC activation in response to a visual attentional oddball task could serve as a neural marker for assessing cognitive impairment and depression in conditions predisposing to Alzheimer's disease.

Aberrant brain dynamics in major depressive disorder during working memory task

Working memory (WM) is a distributed and dynamic process, and WM deficits are recognized as one of the top-ranked endophenotype candidates for major depressive disorders (MDD). However, there is a lack of knowledge of brain temporal-spatial profile of WM deficits in MDD. We used the dynamical degree centrality (dDC) to investigate the whole-brain temporal-spatial profile in 40 MDD and 40 controls during an n-back task with 2 conditions (i.e., '0back' and '2back'). We explored the dDC temporal variability and clustered meta-stable states in 2 groups during different WM conditions. Pearson's correlation analysis was used to evaluate the relationship between the altered dynamics with clinical symptoms and WM performance. Compared with controls, under '2back vs. 0back' contrast, patients showed an elevated dDC variability in wide range of brain regions, including the middle frontal gyrus, orbital part of inferior frontal gyrus (IFGorb), hippocampus, and middle temporal gyrus. Furthermore, the increased dDC variability in the hippocampus and IFGorb correlated with worse WM performance. However, there were no significant group-related differences in the meta-stable states were observed. This study demonstrated the increased WM-related instability (i.e., the elevated dDC variability) was represented in MDD, and enhancing stability may help patients achieve better WM performance.

Deep brain stimulation for refractory major depressive disorder: a comprehensive review

Deep brain stimulation (DBS) has emerged as a promising treatment for select patients with refractory major depressive disorder (MDD). The clinical effectiveness of DBS for MDD has been demonstrated in meta-analyses, open-label studies, and a few controlled studies. However, randomized controlled trials have yielded mixed outcomes, highlighting challenges that must be addressed prior to widespread adoption of DBS for MDD. These challenges include tracking MDD symptoms objectively to evaluate the clinical effectiveness of DBS with sensitivity and specificity, identifying the patient population that is most likely to benefit from DBS, selecting the optimal patient-specific surgical target and stimulation parameters, and understanding the mechanisms underpinning the therapeutic benefits of DBS in the context of MDD pathophysiology. In this review, we provide an overview of the latest clinical evidence of MDD DBS effectiveness and the recent technological advancements that could transform our understanding of MDD pathophysiology, improve the clinical outcomes for MDD DBS, and establish a path forward to develop more effective neuromodulation therapies to alleviate depressive symptoms.

Impact of on-call shifts on working memory and the role of burnout, sleep, and mental well-being in trainee physicians

BACKGROUND

Optimal cognitive functions, including working memory (WM), are crucial to enable trainee physicians to perform and excel in their clinical practice. Several risk factors, including on-call shifts, poor mental health, burnout, and sleep problems, can impair clinical practice in trainee physicians, potentially through cognitive impairment; however, these associations have not been fully explored.

OBJECTIVE

This study investigated the effect of on-call shifts on WM among trainee physicians and its association with burnout, depression, anxiety, affect, and sleep.

MATERIALS AND METHODS

This cross-sectional study involved 83 trainee physicians (45% male). We measured demographic and training-related factors including on-call shifts and working hours. We also assessed depressive symptoms (PHQ-9), both state and trait anxiety (STAI total score), burnout (OLBI total score), positive and negative affect scores (PANAS), and sleep disturbances (PSQI total score). WM was evaluated using spatial working memory (SWM) strategy scores that reflected performance and total error counts.

RESULTS

Trainee physicians with more on-calls per month had significantly worse depressive symptoms, burnout scores, and sleep, as well as more negative affect. While controlling for covariates, being on-call more times per month was significantly associated with worse WM. Worse depressive symptoms and burnout scores were also significantly associated with impaired WM.

CONCLUSION

Working more on-call shifts is associated with compromised WM. Trainee physicians who experienced more depressive symptoms and burnout had worse WM.

Theta oscillatory dynamics serving cognitive control index psychosocial distress in youth

Background

Psychosocial distress among youth is a major public health issue characterized by disruptions in cognitive control processing. Using the National Institute of Mental Health's Research Domain Criteria (RDoC) framework, we quantified multidimensional neural oscillatory markers of psychosocial distress serving cognitive control in youth.

Methods

The sample consisted of 39 peri-adolescent participants who completed the NIH Toolbox Emotion Battery (NIHTB-EB) and the Eriksen flanker task during magnetoencephalography (MEG). A psychosocial distress index was computed with exploratory factor analysis using assessments from the NIHTB-EB. MEG data were analyzed in the time-frequency domain and peak voxels from oscillatory maps depicting the neural cognitive interference effect were extracted for voxel time series analyses to identify spontaneous and oscillatory aberrations in dynamics serving cognitive control as a function of psychosocial distress. Further, we quantified the relationship between psychosocial distress and dynamic functional connectivity between regions supporting cognitive control.

Results

The continuous psychosocial distress index was strongly associated with validated measures of pediatric psychopathology. Theta-band neural cognitive interference was identified in the left dorsolateral prefrontal cortex (dlPFC) and middle cingulate cortex (MCC). Time series analyses of these regions indicated that greater psychosocial distress was associated with elevated spontaneous activity in both the dlPFC and MCC and blunted theta oscillations in the MCC. Finally, we found that stronger phase coherence between the dlPFC and MCC was associated with greater psychosocial distress.

Conclusions

Greater psychosocial distress was marked by alterations in spontaneous and oscillatory theta activity serving cognitive control, along with hyperconnectivity between the dlPFC and MCC.

Neural correlates of episodic memory decline following electroconvulsive therapy: An exploratory functional magnetic resonance imaging study

BACKGROUND

Electroconvulsive therapy (ECT) is an efficient and rapid-acting treatment indicated for severe depressive disorders. While ECT is commonly accompanied by transient memory decline, the brain mechanisms underlying these side effects remain unclear.

AIMS

In this exploratory functional magnetic resonance (fMRI) study, we aimed to compare effects of ECT versus pharmacological treatment on neural response during episodic memory encoding in patients with affective disorders.

METHODS

This study included 32 ECT-treated patients (major depressive disorder (MDD), n = 23; bipolar depression, n = 9) and 40 partially remitted patients in pharmacological treatment (MDD, n = 24; bipolar disorder, n = 16). Participants underwent neuropsychological assessment, a strategic picture encoding fMRI scan paradigm, and mood rating. The ECT group was assessed before ECT (pre-ECT) and 3 days after their eighth ECT session (post-ECT).

RESULTS

Groups were comparable on age, gender, and educational years (ps ⩾ 0.05). Within-group analyses revealed a selective reduction in verbal learning and episodic memory pre- to post-ECT (p = 0.012) but no decline in global cognitive performance (p = 0.3). Functional magnetic resonance imaging analyses adjusted for mood symptoms revealed greater activity in ECT-treated patients than pharmacologically treated No-ECT patients across left precentral gyrus (PCG), right dorsomedial prefrontal cortex (dmPFC), and left middle frontal gyrus (MFG). In ECT-treated patients, greater decline in verbal learning and memory performance from pre- to post-ECT correlated with higher PCG response (r = -0.46, p = 0.008), but not with dmPFC or MFG activity (ps ⩾ 0.1), post-ECT.

CONCLUSIONS

Episodic memory decline was related to greater neural activity in the left PCG, but unrelated to increased dmPFC and MFG activity, immediately after ECT.

White and Gray Matter Abnormalities in Young Adult Females with Dependent Personality Disorder: A Diffusion-Tensor Imaging and Voxel-Based Morphometry Study

We applied diffusion-tensor imaging (DTI) including measurements of fractional anisotropy (FA), a parameter of neuronal fiber integrity, mean diffusivity (MD), a parameter of brain tissue integrity, as well as voxel-based morphometry (VBM), a measure of gray and white matter volume, to provide a basis to improve our understanding of the neurobiological basis of dependent personality disorder (DPD). DTI was performed on young girls with DPD (N = 17) and young female healthy controls (N = 17). Tract-based spatial statistics (TBSS) were used to examine microstructural characteristics. Gray matter volume differences between the two groups were investigated using voxel-based morphometry (VBM). The Pearson correlation analysis was utilized to examine the relationship between distinct brain areas of white matter and gray matter and the Dy score on the MMPI. The DPD had significantly higher fractional anisotropy (FA) values than the HC group in the right retrolenticular part of the internal capsule, right external capsule, the corpus callosum, right posterior thalamic radiation (include optic radiation), right cerebral peduncle (p < 0.05), which was strongly positively correlated with the Dy score of MMPI. The volume of gray matter in the right postcentral gyrus and left cuneus in DPD was significantly increased (p < 0.05), which was strongly positively correlated with the Dy score of MMPI (r1,2= 0.467,0.353; p1,2 = 0.005,0.04). Our results provide new insights into the changes in the brain structure in DPD, which suggests that alterations in the brain structure might implicate the pathophysiology of DPD. Possible visual and somatosensory association with motor nerve circuits in DPD.

Physical fitness is associated with neural activity during working memory performance in major depressive disorder

BACKGROUND

Deficits in cognition like working memory (WM) are highly prevalent symptoms related to major depressive disorder (MDD). Neuroimaging studies have described frontoparietal abnormalities in patients with MDD as a basis for these deficits. Based on research in healthy adults, it is hypothesized that increased physical fitness might be a protective factor for these deficits in MDD. However, the relationship between physical fitness and WM-related neural activity and performance has not been tested in MDD, to date. Understanding these associations could inform the development of physical exercise interventions in MDD.

METHODS

Within a larger project, 111 (53female) MDD outpatients and 56 (34female) healthy controls performed an n-back task (0-, 1-, 2-, 3-back) during functional Magnetic Resonance Imaging. Physical fitness from a graded exercise test on a cycle ergometer was performed by 106 MDD patients.

RESULTS

Patients showed reduced performance particularly at high loads of the n-back WM task and prolonged reaction times at all n-back loads. A whole-brain interaction analysis of group by WM load revealed reduced neural activity in six frontoparietal clusters at medium and high WM loads in MDD patients compared to healthy controls. Analysis of covariance within the MDD sample showed that physical fitness was associated with neural activity in right and left superior parietal lobules. Externally defined Regions of Interest confirmed this analysis.

CONCLUSIONS

Results indicate frontoparietal hypoactivity in MDD at high demands, arguing for decreased WM capacity. We demonstrate a parietal fitness correlate which could be used to guide future research on effects of exercise on cognitive functioning in MDD.

The role of dlPFC laterality in the expression and regulation of anxiety

Anxiety disorders are the most common mental health disorder. Therefore, elucidating brain mechanisms implicated in anxiety disorders is important avenue for developing novel treatments and improving care. The dorsolateral prefrontal cortex (dlPFC) is thought to be critically involved in working memory processes (i.e. maintenance, manipulation, suppression, etc.). In addition, there is evidence that this region is involved in anxiety regulation. However, it is unclear how working memory related dlPFC processes contribute to anxiety regulation. Furthermore, we know that laterality plays an important role in working memory related dlPFC processing, however there is no current model of dlPFC mediated anxiety regulation that accounts for potential laterality effects. To address this gap, we propose a potential framework where the dlPFC contributes to emotion regulation via working memory processing. According to this framework, working memory is a fundamental process executed by the dlPFC. However, the domain of content differs across the left and right dlPFC, with the left dlPFC sensitive to primarily verbal content, and the right dlPFC sensitive to primarily non-verbal (affective content). Critically, working memory processes allow for both the retention and suppression of affective information in working memory and the overall net effect of processing on mood will depend on the balance of retention and suppression, the valence of the information being processed (positive vs. negative), and the domain of the information (verbal vs. non-verbal). If accurate, the proposed framework predicts that effects of neuromodulation targeting the dlPFC may be dependent upon the context during which the stimulation is presented. This article is part of the Special Issue on 'Fear, Anxiety and PTSD'.

The centrality of working memory networks in differentiating bipolar type I depression from unipolar depression: A task-fMRI study

OBJECTIVES

Up to 70%-80% of patients with bipolar disorder are misdiagnosed as having major depressive disorder (MDD), leading to both delayed intervention and worsening disability. Differences in the cognitive neurophysiology may serve to distinguish between the depressive phase of type 1 bipolar disorder (BDD-I) from MDD, though this remains to be demonstrated. To this end, we investigate the discriminatory signal in the topological organization of the functional connectome during a working memory (WM) task in BDD-I and MDD, as a candidate identification approach.

METHODS

We calculated and compared the degree centrality (DC) at the whole-brain voxel-wise level in 31 patients with BDD-I, 35 patients with MDD, and 80 healthy controls (HCs) during an n-back task. We further extracted the distinct DC patterns in the two patient groups under different WM loads and used machine learning approaches to determine the distinguishing ability of the DC map.

RESULTS

Patients with BDD-I had lower accuracy and longer reaction time (RT) than HCs at high WM loads. BDD-I is characterized by decreased DC in the default mode network (DMN) and the sensorimotor network (SMN) when facing high WM load. In contrast, MDD is characterized by increased DC in the DMN during high WM load. Higher WM load resulted in better classification performance, with the distinct aberrant DC maps under 2-back load discriminating the two disorders with 90.91% accuracy.

CONCLUSIONS

The distributed brain connectivity during high WM load provides novel insights into the neurophysiological mechanisms underlying cognitive impairment of depression. This could potentially distinguish BDD-I from MDD if replicated in future large-scale evaluations of first-episode depression with longitudinal confirmation of diagnostic transition.

Toward biophysical markers of depression vulnerability

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Neural correlates of N-back task performance and proposal for corresponding neuromodulation targets in psychiatric and neurodevelopmental disorders

AIM

Working memory (WM) deficit represents the most common cognitive impairment in psychiatric and neurodevelopmental disorders, making the identification of its neural substrates a crucial step towards the conceptualization of restorative interventions. We present a meta-analysis focusing on neural activations associated with the most commonly used task to measure WM, the N-back task, in patients with schizophrenia, depressive disorder, bipolar disorder, and attention-deficit/hyperactivity disorder. Showing qualitative similarities and differences in WM processing between patients and healthy controls, we propose possible targets for cognitive enhancement approaches.

METHODS

Selected studies, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, were analyzed through the activation likelihood estimate statistical framework, with subsequent generation of disorder-specific N-back activation maps.

RESULTS

Despite similar WM deficits shared across all disorders, results highlighted different brain activation patterns for each disorder compared with healthy controls. In general, results showed brain activity in frontal, parietal, subcortical, and cerebellar regions; however, reduced engagement of specific nodes of the fronto-parietal network emerged in patients compared with healthy controls. In particular, neither bipolar nor depressive disorders showed detectable activations in the dorsolateral prefrontal cortices, while their parietal activation patterns were lateralized to the left and right hemispheres, respectively. On the other hand, patients with attention-deficit/hyperactivity disorder showed a lack of activation in the left parietal lobe, whereas patients with schizophrenia showed lower activity over the left prefrontal cortex.

CONCLUSION

These results, together with biophysical modeling, were then used to discuss the design of future disorder-specific cognitive enhancement interventions based on noninvasive brain stimulation.

Using multisession tDCS stimulation as an early intervention on memory bias processing in subthreshold depression

Transcranial direct current stimulation (tDCS) as an intervention tool has gained promising results in major depression disorder. However, studies related to subthreshold depression's (SD) cognitive deficits and neuromodulation approaches for the treatment of SD are still rare. We adopted Beck's cognitive model of depression and tested the tDCS stimulation effects on attentional and memory deficits on SD. First, this was a single-blinded, randomized, sham-controlled clinical trial to determine a 13-day tDCS modulation effect on 49 SD (27: Stimulation; 22: Sham) and 17 healthy controls. Second, the intervention effects of the consecutive and single-session tDCS were compared. Furthermore, the attentional and memory biases were explored in SD. Anodal tDCS was administrated over left dorsolateral prefrontal cortex for 13 consecutive days. Attentional and memory bias were assessed through a modified Sternberg task and a dot-probe task on the 1st, 2nd, and 15th day while their EEG was being recorded. After the 13-day tDCS stimulation (not after single-session stimulation), we found reduced memory bias (Stimulation vs. Sham, p = .02, r²  = .09) and decreased mid-frontal alpha power (p < .01, r²  = .13). In contrast, tDCS did not affect any attentional related behavioral or neural indexes (all ps > .15). Finally, reduced depressive symptoms (e.g., BDI score) were found for both groups. The criteria of SD varied across studies; the efficacy of this protocol should be tested in elderly patients. Our study suggests memory bias of SD can be modulated by the multisession tDCS and alpha power could serve as a neural index for intervention.

Disrupted fronto-parietal network and default-mode network gamma interactions distinguishing suicidal ideation and suicide attempt in depression

BACKGROUND

Precise suicide risk evaluation struggled in Major depressive disorder (MDD), especially for patients with only suicidal-ideation (SI) but without suicide attempt (SA). MDD patients have deficits in negative emotion processing, which is associated with the generation of SI and SA. Given the critical role of gamma oscillations in negative emotion processing, we hypothesize that the transition from SI to SA in MDD could be characterized by abnormal gamma interactions.

METHODS

We recruited 162 participants containing 106 MDD patients and 56 healthy controls (HCs). Participants performed facial recognition tasks while magnetoencephalography data were recorded. Time-frequency-representation (TFR) analysis was conducted to identify the dominant spectra differences between MDD and HCs, and then source analysis was applied to localize the region of interests. Furthermore, frequency-specific functional connectivity network were constructed and a semi-supervised clustering algorithm was utilized to predict potential suicide risk.

RESULTS

Gamma (50-70 Hz) power was found significantly increased in MDD, mainly residing in regions from fronto-parietal-control-network (FPN), visual-network (VN), default-mode-network (DMN) and salience-network (SN). Based on impaired gamma functional connectivity network between well-established SA group and non-SI group, semi-supervised algorithm clustered patients with only SI into two groups with different suicide risks. Moreover, Inter-network gamma connectivity between FPN and DMN significantly negatively correlated with suicide risk and not confounded by depression severity.

CONCLUSION

Inter-network gamma connectivity with FPN and DMN might be the key neuropathological interactions underling the progression from SI to SA. By applying semi-supervised clustering to electrophysiological data, it is possible to predict individual suicide risk.

The Relationship Between Default Mode and Dorsal Attention Networks Is Associated With Depressive Disorder Diagnosis and the Strength of Memory Representations Acquired Prior to the Resting State Scan

Previous research indicates that individuals with depressive disorders (DD) have aberrant resting state functional connectivity and may experience memory dysfunction. While resting state functional connectivity may be affected by experiences preceding the resting state scan, little is known about this relationship in individuals with DD. Our study examined this question in the context of object memory. 52 individuals with DD and 45 healthy controls (HC) completed clinical interviews, and a memory encoding task followed by a forced-choice recognition test. A 5-min resting state fMRI scan was administered immediately after the forced-choice task. Resting state networks were identified using group Independent Component Analysis across all participants. A network modeling analysis conducted on 22 networks using FSLNets examined the interaction effect of diagnostic status and memory accuracy on the between-network connectivity. We found that this interaction significantly affected the relationship between the network comprised of the medial prefrontal cortex, posterior cingulate cortex, and hippocampal formation and the network comprised of the inferior temporal, parietal, and prefrontal cortices. A stronger positive correlation between these two networks was observed in individuals with DD who showed higher memory accuracy, while a stronger negative correlation (i.e., anticorrelation) was observed in individuals with DD who showed lower memory accuracy prior to resting state. No such effect was observed for HC. The former network cross-correlated with the default mode network (DMN), and the latter cross-correlated with the dorsal attention network (DAN). Considering that the DMN and DAN typically anticorrelate, we hypothesize that our findings indicate aberrant reactivation and consolidation processes that occur after the task is completed. Such aberrant processes may lead to continuous "replay" of previously learned, but currently irrelevant, information and underlie rumination in depression.

Global Functional Connectivity Analysis Indicating Dysconnectivity of the Hate Circuit in Major Depressive Disorder

Background

Abnormalities of functional connectivity (FC) in certain brain regions are closely related to the pathophysiology of major depressive disorder (MDD). Findings are inconsistent with different presuppositions in regions of interest. Our research focused on voxel-wise brain-wide FC changes in patients with MDD in an unbiased manner.

Method

We examined resting-state functional MRI in 23 patients with MDD and 26 healthy controls. Imaging data were analyzed by using global-brain FC (GFC) and used to explore the correlation of abnormal GFC values with clinical variables.

Results

Increased GFC values in the left medial superior frontal gyrus (SFGmed) and decreased GFC values in the right supplementary motor area (SMA) were observed in the patients with MDD compared with the controls. The decreased GFC values in the right SMA had a positive correlation with vitamin D and Hamilton Anxiety Scale (HAM-A) scores.

Conclusion

Abnormal GFC in the hate circuit, particularly increased GFC in the left SFGmed and decreased GFC in the right SMA, appears to be a new sight for comprehending the pathological alterations in MDD.

The self on its axis: a framework for understanding depression

The self is experienced differently in depression. It is infused with pervasive low mood, and structured by negative self-related thoughts. The concept of the self has been difficult to define-one of the reasons it is now infrequently an object of enquiry for psychiatry-but findings from functional brain imaging and other neuroscience studies have provided new insights. They have elucidated how the self is supported by complex, hierarchical brain processes. Bodily sensations rise through the spinal cord, brainstem, and subcortical regions through to cortical networks, with the default mode network sitting at the apex, integrating interoceptive signals with information about the extended social environment. We discuss how this forms a "self axis", and demonstrate how this axis is set awry by depression. Our self-axis model of depression establishes a new perspective on the disorder. It emphasises the multi-level nature of depression, and how impacts made at different explanatory levels influence others along the axis. It suggests that diverse treatments might be effective for depression, from lifestyle interventions to psychotherapies to medications: they target different aspects of the self, but changes at one level of the self axis can affect others along it. Our framework for depression establishes a central role for the self, which might again become a useful focus of investigation.

Prefrontal cortex and depression

The prefrontal cortex (PFC) has emerged as one of the regions most consistently impaired in major depressive disorder (MDD). Although functional and structural PFC abnormalities have been reported in both individuals with current MDD as well as those at increased vulnerability to MDD, this information has not translated into better treatment and prevention strategies. Here, we argue that dissecting depressive phenotypes into biologically more tractable dimensions - negative processing biases, anhedonia, despair-like behavior (learned helplessness) - affords unique opportunities for integrating clinical findings with mechanistic evidence emerging from preclinical models relevant to depression, and thereby promises to improve our understanding of MDD. To this end, we review and integrate clinical and preclinical literature pertinent to these core phenotypes, while emphasizing a systems-level approach, treatment effects, and whether specific PFC abnormalities are causes or consequences of MDD. In addition, we discuss several key issues linked to cross-species translation, including functional brain homology across species, the importance of dissecting neural pathways underlying specific functional domains that can be fruitfully probed across species, and the experimental approaches that best ensure translatability. Future directions and clinical implications of this burgeoning literature are discussed.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

The observational design limits causal inference.

CONCLUSIONS

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

Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression

Research into neurobiology of depression primarily focuses on its complex psychological aspects. Here we propose an alternative approach and target sensorimotor alterations—a prominent but often neglected feature of depression. We demonstrated using resting-state functional MRI data and computational modeling that top-down and bottom-up information flow in sensory and motor cortices is altered with increasing depression severity in a way that is consistent with depression symptoms. Depression-associated changes were found to be consistent across sessions, amenable to treatment and of effect size sufficiently large to predict whether somebody has mild or severe depression. These results pave the way for an avenue of research into the neural underpinnings of mental health conditions.

Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder

Working memory (WM) impairments are common features of psychiatric disorders. A systematic meta-analysis was performed to determine common and disorder-specific brain fMRI response during performance of WM tasks in patients with SZ and patients with MDD relative to healthy controls (HC). Thirty-four published fMRI studies of WM in patients with SZ and 18 published fMRI studies of WM in patients with MDD, including relevant HC, were included in the meta-analysis. In both SZ and MDD there was common stronger fMRI response in right medial prefrontal cortex (MPFC) and bilateral anterior cingulate cortex (ACC), which are part of the default mode network (DMN). The effects were of greater magnitude in SZ than MDD, especially in prefrontal-temporal-cingulate-striatal-cerebellar regions. In addition, a disorder-specific weaker fMRI response was observed in right middle frontal gyrus (MFG) in MDD, relative to HC. For both SZ and MDD a significant correlation was observed between the severity of clinical symptoms and lateralized fMRI response relative to HC. These findings indicate that there may be common and distinct anomalies in brain function underlying deficits in WM in SZ and MDD, which may serve as a potential functional neuroimaging-based diagnostic biomarker with value in supporting clinical diagnosis, measuring illness severity and assessing the efficacy of treatments for SZ and MDD at the brain level.

Abnormal Cerebellar Volume in Patients with Remitted Major Depression with Persistent Cognitive Deficits

Cerebellar involvement in major depressive disorder (MDD) has been demonstrated by a growing number of studies, but it is unknown whether cognitive functioning in depressed individuals is related to cerebellar gray matter volume (GMV) abnormalities. Impaired attention and executive dysfunction are characteristic cognitive deficits in MDD, and critically, they often persist despite remission of mood symptoms. In this study, we investigated cerebellar GMV in patients with remitted MDD (rMDD) that showed persistent cognitive impairment. We applied cerebellum-optimized voxel-based morphometry in 37 patients with rMDD and with cognitive deficits, in 12 patients with rMDD and without cognitive deficits, and in 36 healthy controls (HC). Compared with HC, rMDD patients with cognitive deficits had lower GMV in left area VIIA, crus II, and in vermal area VIIB. In patients with rMDD, regression analyses demonstrated significant associations between GMV reductions in both regions and impaired attention and executive dysfunction. Compared with HC, patients without cognitive deficits showed increased GMV in bilateral area VIIIB. This study supports cerebellar contributions to the cognitive dimension of MDD. The data also point towards cerebellar area VII as a potential target for non-invasive brain stimulation to treat cognitive deficits related to MDD.

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states.

Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants.

Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03).

Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.

Decreased integration of the frontoparietal network during a working memory task in major depressive disorder

BACKGROUND

Working memory deficits are a common feature in major depressive disorder and are associated with poor functional outcomes. Intact working memory performance requires the recruitment of large-scale brain networks. However, it is unknown how the disrupted recruitment of distributed regions belonging to these large-scale networks at the whole-brain level brings about working memory impairment seen in major depressive disorder.

METHODS

We used graph theory to examine the functional connectomic metrics (local and global efficiency) at the whole-brain and large-scale network levels in 38 patients with major depressive disorder and 41 healthy controls during a working memory task. Altered connectomic metrics were studied in a moderation model relating to clinical symptoms and working memory accuracy in patients, and a machine learning method was employed to assess whether these metrics carry enough illness-specific information to discriminate patients from controls.

RESULTS

Global efficiency of the frontoparietal network was reduced in major depressive disorder (false discovery rate corrected, p = 0.014); this reduction predicted worse working memory performance in patients with less severe illness burden indexed by Brief Psychiatric Rating Scale (β =-0.43, p = 0.035, t =-2.2, 95% confidence interval = [-0.043,-0.002]). We achieved a classification accuracy and area under the curve of 73.42% and 0.734, respectively, to discriminate patients from controls based on connectomic metrics, and the global efficiency of the frontoparietal network contributed most to the diagnostic classification.

CONCLUSIONS

We report a putative mechanistic link between the global efficiency of the frontoparietal network and impaired n-back performance in major depressive disorder. This relationship is more pronounced at lower levels of symptom burden, indicating the possibility of multiple pathways to cognitive deficits in severe major depressive disorder.

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.

Altered dynamics of the prefrontal networks are associated with the risk for postpartum psychosis: a functional magnetic resonance imaging study

Postpartum psychosis (PP) is a severe mental disorder that affects women in the first few weeks after delivery. To date there are no biomarkers that distinguish which women at risk (AR) develop a significant psychiatric relapse postpartum. While altered brain connectivity may contribute to the risk for psychoses unrelated to the puerperium, this remains unexplored in PP. We followed up 32 AR and 27 healthy (HC) women from pregnancy to 8-week postpartum. At this point, we classified women as AR-unwell (n = 15) if they had developed a psychiatric relapse meeting DSM-IV diagnostic criteria, or impacting on daily functioning and requiring treatment, or AR-well (n = 17) if they remained asymptomatic. Women also underwent an fMRI scan at rest and during an emotional-processing task, to study within- and between-networks functional connectivity. Women AR, and specifically those in the AR-well group, showed increased resting connectivity within an executive network compared to HC. During the execution of the emotional task, women AR also showed decreased connectivity in the executive network, and altered emotional load-dependent connectivity between executive, salience, and default-mode networks. AR-unwell women particularly showed increased salience network-dependent modulation of the default-mode and executive network relative to AR-well, who showed greater executive network-dependent modulation of the salience network. Our finding that the executive network and its interplay with other brain networks implicated in goal-directed behavior are intrinsically altered suggest that they could be considered neural phenotypes for postpartum psychosis and help advance our understanding of the pathophysiology of this disorder.

Disrupted hemispheric connectivity specialization in patients with major depressive disorder: Evidence from the REST-meta-MDD Project

BACKGROUND

Functional specialization is a feature of human brain for understanding the pathophysiology of major depressive disorder (MDD). The degree of human specialization refers to within and cross hemispheric interactions. However, most previous studies only focused on interhemispheric connectivity in MDD, and the results varied across studies. Hence, brain functional connectivity asymmetry in MDD should be further studied.

METHODS

Resting-state fMRI data of 753 patients with MDD and 451 healthy controls were provided by REST-meta-MDD Project. Twenty-five project contributors preprocessed their data locally with the Data Processing Assistant State fMRI software and shared final indices. The parameter of asymmetry (PAS), a novel voxel-based whole-brain quantitative measure that reflects inter- and intrahemispheric asymmetry, was reported. We also examined the effects of age, sex and clinical variables (including symptom severity, illness duration and three depressive phenotypes).

RESULTS

Compared with healthy controls, patients with MDD showed increased PAS scores (decreased hemispheric specialization) in most of the areas of default mode network, control network, attention network and some regions in the cerebellum and visual cortex. Demographic characteristics and clinical variables have significant effects on these abnormalities.

LIMITATIONS

Although a large sample size could improve statistical power, future independent efforts are needed to confirm our results.

CONCLUSIONS

Our results highlight the idea that many brain networks contribute to broad clinical pathophysiology of MDD, and indicate that a lateralized, efficient and economical brain information processing system is disrupted in MDD. These findings may help comprehensively clarify the pathophysiology of MDD in a new hemispheric specialization perspective.

Task MRI-Based Functional Brain Network of Major Depression

This chapter will focus on task magnetic resonance imaging (MRI) to understand the biological mechanisms and pathophysiology of brain in major depressive disorder (MDD), which would have minor alterations in the brain function. Therefore, the functional study, such as task MRI functional connectivity, would play a crucial role to explore the brain function in MDD. Different kinds of tasks would determine the alterations in functional connectivity in task MRI studies of MDD. The emotion-related tasks are linked with alterations in anterior cingulate cortex, insula, and default mode network. The emotional memory task is linked with amygdala-hippocampus alterations. The reward-related task would be related to the reward circuit alterations, such as fronto-straital. The cognitive-related tasks would be associated with frontal-related functional connectivity alterations, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, and other frontal regions. The visuo-sensory characteristics of tasks might be associated with the parieto-occipital alterations. The frontolimbic regions might be major components of task MRI-based functional connectivity in MDD. However, different scenarios and tasks would influence the representations of results.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Cerebellar-cerebral dynamic functional connectivity alterations in major depressive disorder

BACKGROUND

The cerebellum plays an important role in the neural mechanism of depression and its static functional connectivity (FC) with the cerebrum is disrupted in patients with major depressive disorder (MDD). However, cerebellar-cerebral dynamic FC alterations in MDD remain largely unknown.

METHODS

50 patients with MDD and 36 well-matched healthy controls underwent resting-state functional magnetic resonance imaging. Cerebellar-cerebral dynamic FC analyses were performed using the cerebellar seeds previously identified as being involved in the executive, default-mode, affective-limbic, and motor networks. Inter-group differences in the cerebellar dynamic FC and their associations with clinical and cognitive variables were examined.

RESULTS

Compared to healthy controls, patients with MDD had decreased cerebellar-cerebral dynamic FC of the cerebellar subregions connecting with the executive, default-mode and affective-limbic networks. The dynamic FC of the cerebellar subregion connecting with the affective-limbic network was related to severity of depression and anxiety symptoms in MDD patients. The dynamic FC of the cerebellar subregions connecting with the default-mode and affective-limbic networks were related to sustained attention and prospective memory in controls, while the correlations were inverse or non-significant in patients.

LIMITATIONS

The fairly modest sample size and potential medication effect may increase the instability of the results.

CONCLUSIONS

Our findings provide further evidence for the pivotal role of the cerebellum in the neuropathology of depression, pointing to potential targets of cerebellar-cerebral pathways for alternative intervention or monitoring therapeutic responses.

Testing the Mechanism of Action of Computerized Cognitive Training in Young Adults with Depression: Protocol for a Blinded, Randomized, Controlled Treatment Trial

Background:

Depression is associated with a broad range of cognitive deficits, including processing speed (PS) and executive functioning (EF). Cognitive symptoms commonly persist with the resolution of affective symptoms and increase risk of relapse and recurrence. The cognitive control network is comprised of brain areas implicated in EF and mood regulatory functions. Prior research has demonstrated the effectiveness of computerized cognitive training (CCT) focused on PS and EF in mitigating both cognitive and affective symptoms of depression.

Methods:

Ninety participants aged 18–29 with a current diagnosis of major depressive disorder or persistent depressive disorder, or a Hamilton Depression Rating Scale score ≥12, will be randomized to either PS/EF CCT, verbal CCT, or waitlist control. Participants in the active groups will complete 15 min of training 5 days/week for 8 weeks. Clinical and neuropsychological assessments will be completed at baseline, week 4, week 8, and 3-month follow-up. Structural and functional magnetic resonance imaging (fMRI) will be completed at baseline and week 8. We will compare changes in mood, cognition, daily functioning, and fMRI data. We will explore cognitive control network functioning using resting-state and task-based fMRI.

Results:

Recruitment began in October 2019; we expect to finish recruitment by April 2022 and subsequently begin data analysis.

Conclusions:

This study is innovative in that it will include both active and waitlist control conditions and will explore changes in neural activation. Identifying the neural networks associated with improvements following CCT will allow for the development of more precise and effective interventions.

Trial Registration:

ClinicalTrials.govNCT03869463; https://clinicaltrials.gov/ct2/show/NCT03869463.

The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression

Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest ("Emotion", "Gambling" and "Continuous Performance" tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.

Rapid anti-depressant-like effects of ketamine and other candidates: Molecular and cellular mechanisms

Major depressive disorder takes at least 3 weeks for clinical anti‐depressants, such as serotonin selective reuptake inhibitors, to take effect, and only one‐third of patients remit. Ketamine, a kind of anaesthetic, can alleviate symptoms of major depressive disorder patients in a short time and is reported to be effective to treatment‐resistant depression patients. The rapid and strong anti‐depressant‐like effects of ketamine cause wide concern. In addition to ketamine, caloric restriction and sleep deprivation also elicit similar rapid anti‐depressant‐like effects. However, mechanisms about the rapid anti‐depressant‐like effects remain unclear. Elucidating the mechanisms of rapid anti‐depressant effects is the key to finding new therapeutic targets and developing therapeutic patterns. Therefore, in this review we summarize potential molecular and cellular mechanisms of rapid anti‐depressant‐like effects based on the pre‐clinical and clinical evidence, trying to provide new insight into future therapy.

Cerebellar neural markers of susceptibility to social isolation and positive affective processing

Chronic loneliness predicts mood disturbances and onset of major depressive disorder. However, little research has examined the neural correlates of individual difference in susceptibility to perceiving loneliness. In addition, the role of cerebellum, which is heavily implicated in social, cognitive and affective processes, in loneliness is unclear. We studied 99 healthy individuals divided into susceptible, concordant and robust groups depending on whether the participant’s loneliness level was greater, comparable or less than her/his objective social isolation level. The cerebellar gray matter structure, functional activity and connectivity patterns during performing an emotion stroop task were examined. We found greater posterior and medial cerebellar volume in the susceptible group than the other groups. In addition, the posterior and medial cerebellar activities when processing positive versus neutral words exhibited significant interactive effects of both loneliness and social network, and susceptibility to isolation. Loneliness and social network also had positive effects on the right posterior cerebellar functional connectivity with the visual and premotor cortices. Our findings provide novel evidence on the intricate role of the cerebellum in loneliness and susceptibility to isolation, suggesting that socio-cognitive processes of the cerebellum in the hedonic domain may be a key mechanism underlying loneliness proneness.

Lipocalin-2 is dispensable in inflammation-induced sickness and depression-like behavior

RATIONALE

While the relationship between inflammation and depression is well-established, the molecular mechanisms mediating this relationship remain unclear. RNA sequencing analysis comparing brains of vehicle- and lipopolysaccharide-treated mice revealed LCN2 among the most dysregulated genes. As LCN2 is known to be an important regulator of the immune response to bacterial infection, we investigated its role in the behavioral response to lipopolysaccharide.

OBJECTIVE

To explore the role of LCN2 in modulating behavior following lipopolysaccharide administration using wild type (WT) and lcn2-/- mice.

METHODS

Using a within-subjects design, mice were treated with 0.33 mg/kg liposaccharide (LPS) and vehicle. Primary outcome measures included body weight, food consumption, voluntary wheel running, sucrose preference, and the tail suspension test. To evaluate the inflammatory response, 1 week later, mice were re-administered either vehicle or LPS and terminated at 6 h.

RESULTS

While lcn2-/- mice had increased baseline food consumption and body weight, they showed a pattern of reduced food consumption and weight loss similar to WT mice in response to LPS. WT and lcn2-/- mice both recovered voluntary activity on the fourth day following LPS. LPS induced equivalent reductions in sucrose preference and TST immobility in the WT and lcn2-/- mice. Finally, there were no significant effects of genotype on inflammatory markers.

CONCLUSIONS

Our data demonstrate that lcn2 is dispensable for sterile inflammation-induced sickness and depression-like behavior. Specifically, lcn2-/- mice displayed sickness and immobility in the tail suspension test comparable to that of WT mice both in terms of intensity and duration.

Genetic contribution of catechol-O-methyltransferase in dorsolateral prefrontal cortex functional changes in the first episode schizophrenia

Catechol-O-methyltransferase (COMT) gene variants have been reported to be implicated in the pathogenesis of psychotic symptoms in schizophrenia, especially in negative symptoms. These symptoms including apathy, blunted affect, social withdrawal and motor retardation. Neuroimaging studies suggested that negative symptoms appear to be associated with impaired activities of the prefrontal cortex in particular the dorsolateral prefrontal cortex (DLPFC). Given that the COMT gene is highly expressed in the DLPFC, it is poorly understood whether the disease state and COMT val¹⁵⁸met polymorphisms have main and interactive effect on the resting state functional connectivity (RSFC) of DLPFC-related pathways. To this end, fifty-five first episode schizophrenia (FES) and fifty-three healthy controls were genotyped using blood samples and underwent magnetic resonance imaging scanning. Seed-based voxel wise functional connectivity analysis was performed by placing bilateral pairs of seeds with DLPFC in area 46 defined by Brodmann's atlas. A two-ways ANCOVA model was performed with val¹⁵⁸met genotypes and disease state as the between subjects factors. Significant disease × COMT interactive effect was found mainly in the left DLPFC with the left anterior cingulate cortex, right precuneus, right superior parietal gyrus, which were overlapped with disease main effect. And these RSFC had positive correlations with affective blunting scores in FES patients with val homozygotes, but not with met carriers. Our results showed that the disease and the genotypes in COMT gene have significant interactive effect on RSFC of DLPFC and provided evidence for a disease-dependent pattern of gene action.

Neuronal underpinnings of cognitive impairment and - improvement in mood disorders

Neuropsychiatric illnesses including mood disorders are accompanied by cognitive impairment, which impairs work capacity and quality of life. However, there is a lack of treatment options that would lead to solid and lasting improvement of cognition. This is partially due to the absence of valid and reliable neurocircuitry-based biomarkers for pro-cognitive effects. This systematic review therefore examined the most consistent neural underpinnings of cognitive impairment and cognitive improvement in unipolar and bipolar disorders. We identified 100 studies of the neuronal underpinnings of working memory and executive skills, learning and memory, attention, and implicit learning and 9 studies of the neuronal basis for cognitive improvements. Impairments across several cognitive domains were consistently accompanied by abnormal activity in dorsal prefrontal (PFC) cognitive control regions-with the direction of this activity depending on patients' performance levels-and failure to suppress default mode network (DMN) activity. Candidate cognition treatments seemed to enhance task-related dorsal PFC and temporo-parietal activity when performance increases were observed, and to reduce their activity when performance levels were unchanged. These treatments also attenuated DMN hyper-activity. In contrast, nonspecific cognitive improvement following symptom reduction was typically accompanied by decreased limbic reactivity and reversal of pre-treatment fronto-parietal hyper-activity. Together, the findings highlight some common neural correlates of cognitive impairments and cognitive improvements. Based on this evidence, studies are warranted to examine the reliability and predictive validity of target engagement in the identified neurocircuitries as a biomarker model of pro-cognitive effects.

A brain network model for depression: From symptom understanding to disease intervention

Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal-striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top-down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks-and corresponding symptom improvement-following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression.

Neural correlates of working memory in first episode and recurrent depression: An fMRI study

BACKGROUND

Patients suffering from major depressive disorder (MDD) show deficits in working memory (WM) performance accompanied by bilateral fronto-parietal BOLD signal changes. It is unclear whether patients with a first depressive episode (FDE) exhibit the same signal changes as patients with recurrent depressive episodes (RDE).

METHODS

We investigated seventy-four MDD inpatients (48 RDE, 26 FDE) and 74 healthy control (HC) subjects performing an n-back WM task (0-back, 2-back, 3-back condition) in a 3T-fMRI.

RESULTS

FMRI analyses revealed deviating BOLD signal in MDD in the thalamus (0-back vs. 2-back), the angular gyrus (0-back vs. 3-back), and the superior frontal gyrus (2-back vs. 3-back). Further effects were observed between RDE vs. FDE. Thus, RDE displayed differing neural activation in the middle frontal gyrus (2-back vs. 3-back), the inferior frontal gyrus, and the precentral gyrus (0-back vs. 2-back). In addition, both HC and FDE indicated a linear activation trend depending on task complexity.

CONCLUSIONS

Although we failed to find behavioral differences between the groups, results suggest differing BOLD signal in fronto-parietal brain regions in MDD vs. HC, and in RDE vs. FDE. Moreover, both HC and FDE show similar trends in activation shapes. This indicates a link between levels of complexity-dependent activation in fronto-parietal brain regions and the stage of MDD. We therefore assume that load-dependent BOLD signal during WM is impaired in MDD, and that it is particularly affected in RDE. We also suspect neurobiological compensatory mechanisms of the reported brain regions in (working) memory functioning.

Neuronal connectivity in major depressive disorder: a systematic review

BACKGROUND

The causes of major depressive disorder (MDD), as one of the most common psychiatric disorders, still remain unclear. Neuroimaging has substantially contributed to understanding the putative neuronal mechanisms underlying depressed mood and motivational as well as cognitive impairments in depressed individuals. In particular, analyses addressing changes in interregional connectivity seem to be a promising approach to capture the effects of MDD at a systems level. However, a plethora of different, sometimes contradicting results have been published so far, making general conclusions difficult. Here we provide a systematic overview about connectivity studies published in the field over the last decade considering different methodological as well as clinical issues.

METHODS

A systematic review was conducted extracting neuronal connectivity results from studies published between 2002 and 2015. The findings were summarized in tables and were graphically visualized.

RESULTS

The review supports and summarizes the notion of an altered frontolimbic mood regulation circuitry in MDD patients, but also stresses the heterogeneity of the findings. The brain regions that are most consistently affected across studies are the orbitomedial prefrontal cortex, anterior cingulate cortex, amygdala, hippocampus, cerebellum and the basal ganglia.

CONCLUSION

The results on connectivity in MDD are very heterogeneous, partly due to different methods and study designs, but also due to the temporal dynamics of connectivity. While connectivity research is an important step toward a complex systems approach to brain functioning, future research should focus on the dynamics of functional and effective connectivity.

Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics

Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.

Polygenic risk for depression and the neural correlates of working memory in healthy subjects

INTRODUCTION

Major depressive disorder (MDD) patients show impairments of cognitive functioning such as working memory (WM), and furthermore alterations during WM-fMRI tasks especially in frontal and parietal brain regions. The calculation of a polygenic risk score (PRS) can be used to describe the genetic influence on MDD, hence imaging genetic studies aspire to combine both genetics and neuroimaging data to identify the influence of genetic factors on brain functioning. We aimed to detect the effect of MDD-PRS on brain activation during a WM task measured with fMRI and expect healthy individuals with a higher PRS to be more resembling to MDD patients.

METHOD

In total, n=137 (80 men, 57 women, aged 34.5, SD=10.4years) healthy subjects performed a WM n-back task [0-back (baseline), 2-back and 3-back condition] in a 3T-MRI-tomograph. The sample was genotyped using the Infinium PsychArray BeadChip and a polygenic risk score was calculated for MDD using PGC MDD GWAS results.

RESULTS

A lower MDD risk score was associated with increased activation in the bilateral middle occipital gyri (MOG), the bilateral middle frontal gyri (MFG) and the right precentral gyrus (PCG) during the 2-back vs. baseline condition. Moreover, a lower PRS was associated with increased brain activation during the 3-back vs. baseline condition in the bilateral cerebellum, the right MFG and the left inferior parietal lobule. A higher polygenic risk score was associated with hyperactivation in brain regions comprising the right MFG and the right supplementary motor area during the 3-back vs. 2-back condition.

DISCUSSION

The results suggest that part of the WM-related brain activation patterns might be explained by genetic variants captured by the MDD-PRS. Furthermore we were able to detect MDD-associated activation patterns in healthy individuals depending on the MDD-PRS and the task complexity. Additional gene loci could contribute to these task-dependent brain activation patterns.