Contribution of resting-state functional connectivity of the subgenual anterior cingulate to prediction of antidepressant efficacy in patients with major depressive disorder.

This study investigated how resting-state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC) predicts antidepressant response in patients with major depressive disorder (MDD). Eighty-seven medication-free MDD patients underwent baseline resting-state functional MRI scans. After 12 weeks of escitalopram treatment, patients were classified into remission depression (RD, n = 42) and nonremission depression (NRD, n = 45) groups. We conducted two analyses: a voxel-wise rsFC analysis using sgACC as a seed to identify group differences, and a prediction model based on the sgACC rsFC map to predict treatment efficacy. Haufe transformation was used to interpret the predictive rsFC features. The RD group showed significantly higher rsFC between the sgACC and regions in the fronto-parietal network (FPN), including the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral inferior parietal lobule (IPL), compared to the NRD group. These sgACC rsFC measures correlated positively with symptom improvement. Baseline sgACC rsFC also significantly predicted treatment response after 12 weeks, with a mean accuracy of 72.64% (p < 0.001), mean area under the curve of 0.74 (p < 0.001), mean specificity of 0.82, and mean sensitivity of 0.70 in 10-fold cross-validation. The predictive voxels were mainly within the FPN. The rsFC between the sgACC and FPN is a valuable predictor of antidepressant response in MDD patients. These findings enhance our understanding of the neurobiological mechanisms underlying treatment response and could help inform personalized treatment strategies for MDD.

Electroencephalography-based endogenous phenotype of diagnostic transition from major depressive disorder to bipolar disorder.

The neuropathology of mood disorders, including the diagnostic transition from major depressive disorder (MDD) to bipolar disorder (BD), is poorly understood. This study investigated resting-state electroencephalography (EEG) activity in patients with MDD and those whose diagnosis changed from MDD to BD. Among sixty-eight enrolled patients with MDD, the diagnosis of 17 patients converted to BD during the study period. We applied machine learning techniques to differentiate the two groups using sensor- and source-level EEG features. At the sensor level, patients with BD showed higher theta band power at the AF3 channel and low-alpha band power at the FC5 channel compared to patients with MDD. At the source level, patients with BD showed higher theta band activity in the right anterior cingulate and low-alpha band activity in the left parahippocampal gyrus. These four EEG features were selected for discriminating between BD and MDD with the best classification performance showing an accuracy of 80.88%, a sensitivity of 76.47%, and a specificity of 82.35%. Our findings revealed distinct theta and low-alpha band activities in patients with BD and MDD. These differences could potentially serve as candidate neuromarkers for the diagnosis and diagnostic transition between the two distinct mood disorders.

Psychopharmacological Approaches for Neural Plasticity and Neurogenesis in Major Depressive Disorders.

Major depressive disorder (MDD) is a mental health disorder associated with cognitive impairment, dysregulated appetite, fatigue, insomnia or hypersomnia, and severe mood changes that significantly impact the ability of the affected individual to perform day-to-day tasks, leading to suicide in the worst-case scenario. As MDD is becoming more prevalent, affecting roughly 300 million individuals worldwide, its treatment has become a major point of interest. Antidepressants acting as selective serotonin reuptake inhibitors (SSRIs) are currently used as the first line of treatment for MDD. Other antidepressants currently used for the treatment of MDD include the serotonin and norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), and monoamine oxidase inhibitors (MAOIs). However, although effective in alleviating symptoms of MDD, most antidepressants require weeks or even months of regular administration prior to eliciting a rational clinical effect. Owing to the strong evidence showing a relationship between neural plasticity, neurogenesis, and MDD, researchers have also looked at the possibility of using treatment modalities that target these processes in an attempt to improve clinical outcome. The overarching aim of this chapter is to highlight the role of neural plasticity and neurogenesis in the pathophysiology of MDD and discuss the most recently studied treatment strategies that target these processes by presenting supporting evidence from both animal and human studies.

Application of Transcranial Direct and Alternating Current Stimulation (tDCS and tACS) on Major Depressive Disorder.

The exploration of brain stimulation methods offers a promising avenue to overcome the shortcomings of traditional drug therapies and psychological treatments for major depressive disorder (MDD). Over the past years, there has been an increasing focus on transcranial electrical stimulation (tES), notably for its ease of use and potentially fewer side effects. This chapter delves into the use of transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), which are key components of tES, in managing depression. It begins by introducing tDCS and tACS, summarizing their action mechanisms. Following this introduction, the chapter provides an in-depth analysis of existing meta-analyses, systematic reviews, clinical studies, and case reports that have applied tES in MDD treatment. It also considers the role of tES in personalized medicine by looking at specific patient groups and evaluating research on possible biomarkers that could predict how patients with MDD respond to tES therapy.

Repetitive Transcranial Magnetic Stimulation (rTMS) in Major Depression.

Major depressive disorder (MDD) is a psychiatric disorder with several effective therapeutic approaches, being antidepressants and psychotherapies the first-line treatments. Nonetheless, due to side effects, limited efficacy, and contraindications for these treatments, alternative treatment options are required. Neurostimulation is a non-pharmacological and non-psychotherapeutic approach that has been under study for diverse neuropsychiatric conditions in the form of electrical or magnetic stimulation of the brain. Repetitive transcranial magnetic stimulation (rTMS) is a neurostimulation method designed to generate magnetic fields and deliver magnetic pulses to stimulate the brain cortex. The magnetic pulses produce electrical currents in the brain which are not intense enough to provoke seizures, differentiating this method from other forms of neurostimulation that produce seizures. Although the exact rTMS mechanisms of action are not completely understood, rTMS seems to cause its beneficial effects through changes in neuroplasticity. Devices and protocols for rTMS are still evolving, becoming more efficient over time. There are still some challenges to be addressed, including further refinement of parameters (coil/device type, location, intensity, frequency, number of sessions, and duration of treatment); treatment cost and burden for patients; and treatment resistance. However, the efficacy, tolerability, and safety of some rTMS protocols have been demonstrated in different double-blind sham-controlled randomized controlled trials and meta-analyses for treatment-resistant depression.

Electroconvulsive Therapy (ECT) in Major Depression: Oldies but Goodies.

Electroconvulsive therapy is one of the useful treatment methods for symptom improvement and remission in patients with treatment-resistant depression. Considering the various clinical characteristics of patients experiencing depression, key indicators are extracted from structural brain magnetic resonance imaging, functional brain magnetic resonance imaging, and electroencephalography (EEG) data taken before treatment, and applied as explanatory variables in machine learning and network analysis. Studies that attempt to make reliable predictions about the degree of response to electroconvulsive treatment and the possibility of remission in patients with treatment-resistant depression are continuously being published. In addition, studies are being conducted to identify the correlation with clinical improvement by taking structural-functional brain magnetic resonance imaging after electroconvulsive therapy in depressed patients. By reviewing and integrating the results of the latest studies on the above matters, we aim to present the usefulness of electroconvulsive therapy for improving the personalized prognosis of patients with treatment-resistant depression.

Temporal dynamic alterations of regional homogeneity in major depressive disorder: a study integrating machine learning.

Previous studies have found alterations in the local regional homogeneity of brain activity in individuals diagnosed with major depressive disorder. However, many studies have failed to consider that even during resting states, brain activity is dynamic and time-varying. The lack of investigation into the dynamic regional homogeneity has hindered the discovery of biomarkers for depression. This study aimed to assess the utility of the dynamic regional homogeneity by a machine learning model (support vector machine). Sixty-five individuals with dynamic regional homogeneity and 57 healthy controls participated in resting-state functional magnetic resonance rescanning and scale estimating. The dynamic regional homogeneity and receiver operating characteristic curve methods were used for analysis of the imaging data. Relative to healthy controls, major depressive disorder patients displayed increased dynamic regional homogeneity values in the left precuneus and right postcentral gyrus. Additionally, receiver operating characteristic curve results of the dynamic regional homogeneity values in the left precuneus and right postcentral gyrus could distinguish major depressive disorder patients from healthy controls; furthermore, changes in the dynamic regional homogeneity were correlated with depression severity.

Investigating changes of functional brain networks in major depressive disorder by graph theoretical analysis of resting-state fMRI.

BACKGROUND: Major Depressive Disorder (MDD), as a chronic mental disorder, causes changes in mood, thoughts, and behavior. The pathophysiology of the disorder and its treatment are still unknown. One of the most notable changes observed in patients with MDD through fMRI is abnormal functional brain connectivity. METHODS: Preprocessed data from 60 MDD patients and 60 normal controls (NCs) were selected, which has been performed using the DPARSF toolbox. The whole-brain functional networks and topologies were extracted using graph theory-based methods. A two-sample, two-tailed t-test was used to compare the topological features of functional brain networks between the MDD and NCs groups using the DPABI-Net/Statistical Analysis toolbox. RESULTS: The obtained results showed a decrease in both global and local efficiency in MDD patients compared to NCs, and specifically, MDD patients showed significantly higher path length values. Acceptable p-values were obtained with a small sample size and less computational volume compared to the other studies on large datasets. At the node level, MDD patients showed decreased and relatively decreased node degrees in the sensorimotor network (SMN) and the dorsal attention network (DAN), respectively, as well as decreased node efficiency in the SMN, default mode network (DMN), and DAN. Also, MDD patients showed slightly decreased node efficiency in the visual networks (VN) and the ventral attention network (VAN), which were reported after FDR correction with Q < 0.05. LIMITATIONS: All participants were Chinese. CONCLUSIONS: Collectively, increased path length, decreased global and local efficiency, and also decreased nodal degree and efficiency in the SMN, DAN, DAN, VN, and VAN were found in patients compared to NCs.

Transdiagnostic depression severity and its relationship to global and prefrontal-amygdala structural properties in people with major depression and post-traumatic stress disorder.

While some studies have used a transdiagnostic approach to relate depression to metabolic or functional brain alterations, the structural substrate of depression across clinical diagnostic categories is underexplored. In a cross-sectional study of 52 patients with major depressive disorder and 51 with post-traumatic stress disorder, drug-naïve, and spanning mild to severe depression severity, we examined transdiagnostic depressive correlates with regional gray matter volume and the topological properties of gray matter-based networks. Locally, transdiagnostic depression severity correlated positively with gray matter volume in the right middle frontal gyrus and negatively with nodal topological properties of gray matter-based networks in the right amygdala. Globally, transdiagnostic depression severity correlated positively with normalized characteristic path length, a measure implying brain integration ability. Compared with 62 healthy control participants, both major depressive disorder and post-traumatic stress disorder patients showed altered nodal properties in regions of the fronto-limbic-striatal circuit, and global topological organization in major depressive disorder in particular was characterized by decreased integration and segregation. These findings provide evidence for a gray matter-based structural substrate underpinning depression, with the prefrontal-amygdala circuit a potential predictive marker for depressive symptoms across clinical diagnostic categories.

Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Interaction of the gut microbiota and brain functional connectivity in late-life depression.

BACKGROUND: Increasing evidence suggests an important role of the gut microbiome in the pathogenesis of mental disorders, including depression, along the microbiota-gut-brain axis. We sought to explore the interactions between gut microbe composition and neural circuits in late-life depression (LLD). METHODS: We performed fecal 16S ribosomal RNA (rRNA) sequencing and resting-state functional magnetic resonance imaging in a case-control cohort of older adults with LLD and healthy controls to characterize the association between gut microbiota and brain functional connectivity (FC). We used the Hamilton Depression Rating Scale (HAMD) to assess depressive symptoms. RESULTS: We included 32 adults with LLD and 16 healthy controls. At the genus level, the relative abundance of Enterobacter, Akkermansiaceae, Hemophilus, Burkholderia, and Rothia was significantly higher among patients with LDD than controls. Reduced FC within mood regulation circuits was mainly found in the frontal cortex (e.g., the right superior and inferior frontal gyrus, right lateral occipital cortex, left middle frontal gyrus, and left caudate) among patients with MDD. Group-characterized gut microbes among controls and patients showed opposite correlations with seed-based FC, which may account for the aberrant emotion regulation among patients with LDD. The abundance of Enterobacter (dominant genus among patients with LLD) was positively correlated with both HAMD scores (r = 0.49, p = 0.0004) and group-characterized FC (r = -0.37, p < 0.05), while Odoribacter (dominant genus among controls) was negatively correlated with both HAMD scores (r = -0.30, p = 0.04) and group-characterized FC. LIMITATIONS: The study's cross-sectional design and small sample size limit causal inferences; larger longitudinal studies are required for detailed subgroup analyses. CONCLUSION: We identified significant correlations between LDD-characterized gut microbes and brain FC, as well as depression severity, which may contribute to the pathophysiology of depression development among patients with LLD. Specific microbes were linked to altered brain connectivity, suggesting potential targets for treating LLD.

Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study.

BACKGROUND: Patients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD. METHODOLOGY: In a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls. RESULTS: FC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG. CONCLUSIONS: Differences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Is lack of goal-conflict-specific rhythmicity a biomarker for treatment resistance in generalised anxiety but not social anxiety or major depression?

BACKGROUND: Anxiety and depression cause major detriment to the patient, family, and society - particularly in treatment-resistant (TR) cases, which are highly prevalent. TR prevalence may be due to current diagnoses being based not on biological measures but on symptom lists that suffer from clinical subjectivity, variation in symptom presentation, and comorbidity. AIMS: Goal-conflict-specific rhythmicity (GCSR) measured using the Stop-Signal Task (SST) may provide the first neural biomarker for an anxiety process and disorder. This GCSR has been validated with selective drugs for anxiety. So, we proposed that GCSR could differ between TR and non-TR individuals and do so differently between those diagnoses normally sensitive to selective anxiolytics and those not. METHODS: We recorded electroencephalograms (EEG) from 20 TR participants (4 GAD, 5 SAD and 11 MDD) and 24 non-TR participants (4 GAD, 5 SAD and 15 Comorbid GAD/MDD (GMD)) while they performed the SST. RESULTS: There was significant positive GCSR in all groups except the GAD-TR group. GAD-TR lacked GCSR in the low-frequency range. However, TR had little effect in SAD or MDD/GMD populations with apparent increases not decreases. CONCLUSIONS: Overall, these results suggest that GAD may occur in two forms: one resulting from excessive GCSR and so being drug sensitive, and the other resulting from some other mechanism and so being TR. In SAD and MDD groups, heightened GCSR could be a consequence rather than the cause, driven by mechanisms that are normally more sensitive to non-selective panicolytic antidepressants.

Kynurenines are altered in the brain in depression, in a subgroup dependent manner: Implications for targeted treatment approaches.

Major depressive disorder (MDD) is a complex and multi-faceted disorder with a high level of heterogeneity at both the clinical and molecular level. Emerging evidence suggests a significant role of the kynurenine pathway in MDD neurobiology that may be associated with specific subgroups. In a recent study, we examined the kynurenine pathway in postmortem anterior cingulate cortex tissue obtained from subjects with and without MDD. We identified significant changes in MDD that were associated with sex and suicide but found minimal changes in the kynurenine pathway when grouping our cohort as a general classification of MDD. Furthermore, we identified significant correlations between age and quinolinic acid that were specific to MDD. In this commentary, we discuss the importance of considering a range of subgroups in the design and analysis of molecular studies in psychiatric disorders. Future studies should examine the extent of subgroup-specific changes to advance our understanding of MDD and explore targeted therapeutic approaches designed to address the specific changes in these subgroups.

Interactive effect of air pollution and genetic risk of depression on processing speed by resting-state functional connectivity of occipitoparietal network.

BACKGROUND: Air pollution, a reversible environmental factor, was significantly associated with the cognitive domains that are impaired in major depressive disorder (MDD), notably processing speed. Limited evidence explores the interactive effect of air pollution and the genetic risk of depression on cognition. This cross-sectional study aims to extend the research by specifically examining how this interaction influences depression-related cognitive impairment and resting-state brain function. METHODS: Eligible participants were 497 healthy adult volunteers (48.7% males, mean age 24.5) living in Beijing for at least 1 year and exposed to relatively high air pollution from the local community controlling for socioeconomic and genomic. Six months' ambient air pollution exposures were assessed based on residential addresses using monthly averages of fine particulate matter with a diameter of less than or equal to 2.5 µm (PM2.5). A cross-sectional analysis was conducted using functional magnetic resonance imaging (fMRI) and cognitive performance assessments. The polygenic risk score (PRS) of MDD was used to estimate genetic susceptibility. RESULTS: Using a general linear model and partial least square regression, we observed a negative association between resting-state local connectivity in precuneus and PRS-by-PM2.5 interactive effect (PFWE = 0.028), indicating that PM2.5 exposure reduced the spontaneous activity in precuneus in individuals at high genetic risk for MDD. DNA methylation and gene expression of the SLC30A3 gene, responsible for maintaining zinc-glutamate homeostasis, was suggestively associated with this local connectivity. For the global functional connectivity, the polygenic risk for MDD augmented the neural impact of PM2.5 exposure, especially in the frontal-parietal and frontal-limbic regions of the default mode network (PFDR < 0.05). In those genetically predisposed to MDD, increased PM2.5 exposure positively correlated with resting-state functional connectivity between the left angular gyrus and left cuneus gyrus. This connectivity was negatively associated with processing speed. CONCLUSIONS: Our cross-sectional study suggests that air pollution may be associated with an increased likelihood of cognitive impairment in individuals genetically predisposed to depression, potentially through alterations in the resting-state function of the occipitoparietal and default mode network.

Whole-brain mechanism of neurofeedback therapy: predictive modeling of neurofeedback outcomes on repetitive negative thinking in depression.

Real-time fMRI neurofeedback (rtfMRI-NF) has emerged as a promising intervention for psychiatric disorders, yet its clinical efficacy remains underexplored due to an incomplete mechanistic understanding. This study aimed to delineate the whole-brain mechanisms underpinning the effects of rtfMRI-NF on repetitive negative thinking in depression. In a double-blind randomized controlled trial, forty-three depressed individuals underwent NF training targeting the functional connectivity (FC) between the posterior cingulate cortex and the right temporoparietal junction, linked to rumination severity. Participants were randomly assigned to active or sham groups, with the sham group receiving synthesized feedback mimicking real NF signal patterns. The active group demonstrated a significant reduction in brooding rumination scores (d = -1.52, p < 0.001), whereas the sham group did not (d = -0.23, p = 0.503). While the target FC did not show discernible training effects or group differences, connectome-based predictive modeling (CPM) analysis revealed that the interaction between brain activity during regulation and brain response to the feedback signal was the critical factor in explaining treatment outcomes. The model incorporating this interaction successfully predicted rumination changes across both groups. The FCs significantly contributing to the prediction were distributed across brain regions, notably the frontal control, salience network, and subcortical reward processing areas. These results underscore the importance of considering the interplay between brain regulation activities and brain response to the feedback signal in understanding the therapeutic mechanisms of rtfMRI-NF. The study affirms rtfMRI-NF's potential as a therapeutic intervention for repetitive negative thinking and highlights the need for a nuanced understanding of the whole-brain mechanisms contributing to its efficacy.

Aberrant high-beta band functional connectivity during reward processing in melancholic major depressive disorder: An MEG study.

OBJECTIVE: To identify the spatial-temporal pattern variation of whole-brain functional connectivity (FC) during reward processing in melancholic major depressive disorder (MDD) patients, and to determine the clinical correlates of connectomic differences. METHODS: 61 MDD patients and 32 healthy controls were enrolled into the study. During magnetoencephalography (MEG) scanning, all participants completed the facial emotion recognition task. The MDD patients were further divided into two groups: melancholic (n = 31) and non-melancholic (n = 30), based on the Mini International Neuropsychiatric Interview (M.I.N.I.) assessment. Melancholic symptoms were examined by using the 6-item melancholia subscale from the Hamilton Depression Rating Scale (HAM-D6). The whole-brain orthogonalized power envelope connections in the high-beta band (20-35 Hz) were constructed in each period after the happy emotional stimuli (0-200 ms, 100-300 ms, 200-400 ms, 300-500 ms, and 400-600 ms). Then, the network-based statistic (NBS) was used to determine the specific abnormal connection patterns in melancholic MDD patients. RESULTS: The NBS identified a sub-network difference at the mid-late period (300-500 ms) in response to happy faces among the three groups (corrected P = 0.035). Then, the post hoc and correlation analyses found five FCs were decreased in melancholic MDD patients and were related to HAM-D6 score, including FCs of left fusiform gyrus-right orbital inferior frontal gyrus (r = -0.52, P < 0.001), left fusiform gyrus-left amygdala (r = -0.26, P = 0.049), left posterior cingulate gyrus-right precuneus (r = -0.32, P = 0.025), left precuneus-right precuneus (r = -0.27, P = 0.049), and left precuneus-left inferior occipital gyrus (r = -0.32, P = 0.025). CONCLUSION: In response to happy faces, melancholic MDD patients demonstrated a disrupted functional connective pattern (20-35 Hz, 300-500 ms), which involved brain regions in visual information processing and the limbic system. The aberrant functional connective pattern in reward processing might be a biomarker of melancholic MDD.

Neural responses to decision-making in suicide attempters with youth major depressive disorder.

An improved understanding of the factors associated with suicidal attempts in youth suffering from depression is crucial for the identification and prevention of future suicide risk. However, there is limited understanding of how neural activity is modified during the process of decision-making. Our study aimed to investigate the neural responses in suicide attempters with major depressive disorder (MDD) during decision-making. Electroencephalography (EEG) was recorded from 79 individuals aged 16-25 with MDD, including 39 with past suicide attempts (SA group) and 40 without (NSA group), as well as from 40 age- and sex- matched healthy controls (HCs) during the Iowa Gambling Task (IGT). All participants completed diagnostic interviews, self-report questionnaires. Our study examined feedback processing by measuring the feedback-related negativity (FRN), ΔFN (FRN-loss minus FRN-gain), and the P300 as electrophysiological indicators of feedback evaluation. The SA group showed poorest IGT performance. SA group and NSA group, compared with HC group, exhibited specific deficits in decision-making (i.e., exhibited smaller (i.e., blunted) ΔFN). Post hoc analysis found that the SA group was the least sensitive to gains and the most sensitive to losses. In addition, we also found that the larger the value of ΔFN, the better the decision-making ability and the lower the impulsivity. Our study highlights the link between suicide attempts and impaired decision-making in individuals with major depressive disorder. These findings constitute an important step in gaining a better understanding of the specific reward-related abnormalities that could contribute to the young MDD patients with suicide attempts.

The unique face of comorbid anxiety and depression: Increased frontal, insula and cingulate cortex response during Pavlovian fear-conditioning.

BACKGROUND: Dysregulation of fear processing through altered sensitivity to threat is thought to contribute to the development of anxiety disorders and major depressive disorder (MDD). However, fewer studies have examined fear processing in MDD than in anxiety disorders. The current study used propensity matching to examine the hypothesis that comorbid MDD and anxiety (AnxMDD) shows greater neural correlates of fear processing than MDD, suggesting that the co-occurrence of AnxMDD is exemplified by exaggerated defense related processes. METHODS: 195 individuals with MDD (N = 65) or AnxMDD (N = 130) were recruited from the community and completed multi-level assessments, including a Pavlovian fear learning task during functional imaging. Visual images paired with threat (conditioned stimuli: CS+) were compared to stimuli not paired with threat (CS-). RESULTS: MDD and AnxMDD showed significantly different patterns of activation for CS+ vs CS- in the dorsal anterior insula/inferior frontal gyrus (partial eta squared; ηp2 = 0.02), dorsolateral prefrontal cortex (ηp2 = 0.01) and dorsal anterior/mid cingulate cortex (ηp2 = 0.01). These differences were driven by greater activation to the CS+ in AnxMDD versus MDD. LIMITATIONS: Limitations include the cross-sectional design, a scream US rather than shock and half the number of MDD as AnxMDD participants. CONCLUSIONS: AnxMDD showed a pattern of increased activation in regions identified with fear processing. Effects were consistently driven by threat, further suggesting fear signaling as the emergent target process. Differences emerged in regions associated with salience processing, attentional orienting/conflict, self-relevant processing and executive functioning in comorbid anxiety and depression, thereby highlighting potential treatment targets for this prevalent and treatment resistant group.