Altered effective connectivity among face-processing systems in major depressive disorder.

BACKGROUND: Neuroimaging studies have revealed abnormal functional interaction during the processing of emotional faces in patients with major depressive disorder (MDD), thereby enhancing our comprehension of the pathophysiology of MDD. However, it is unclear whether there is abnormal directional interaction among face-processing systems in patients with MDD. METHODS: A group of patients with MDD and a healthy control group underwent a face-matching task during functional magnetic resonance imaging. Dynamic causal modelling (DCM) analysis was used to investigate effective connectivity between 7 regions in the face-processing systems. We used a Parametric Empirical Bayes model to compare effective connectivity between patients with MDD and controls. RESULTS: We included 48 patients and 44 healthy controls in our analyses. Both groups showed higher accuracy and faster reaction time in the shape-matching condition than in the face-matching condition. However, no significant behavioural or brain activation differences were found between the groups. Using DCM, we found that, compared with controls, patients with MDD showed decreased self-connection in the right dorsolateral prefrontal cortex (DLPFC), amygdala, and fusiform face area (FFA) across task conditions; increased intrinsic connectivity from the right amygdala to the bilateral DLPFC, right FFA, and left amygdala, suggesting an increased intrinsic connectivity centred in the amygdala in the right side of the face-processing systems; both increased and decreased positive intrinsic connectivity in the left side of the face-processing systems; and comparable task modulation effect on connectivity. LIMITATIONS: Our study did not include longitudinal neuroimaging data, and there was limited region of interest selection in the DCM analysis. CONCLUSION: Our findings provide evidence for a complex pattern of alterations in the face-processing systems in patients with MDD, potentially involving the right amygdala to a greater extent. The results confirm some previous findings and highlight the crucial role of the regions on both sides of face-processing systems in the pathophysiology of MDD.

Effects of repetitive transcranial magnetic stimulation on individual variability of resting-state functional connectivity in major depressive disorder.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), but substantial heterogeneity in outcomes remains. We examined a potential mechanism of action of rTMS to normalize individual variability in resting-state functional connectivity (rs-fc) before and after a course of treatment. METHODS: Variability in rs-fc was examined in healthy controls (baseline) and individuals with MDD (baseline and after 4-6 weeks of rTMS). Seed-based connectivity was calculated to 4 regions associated with MDD: left dorsolateral prefrontal cortex (DLPFC), right subgenual anterior cingulate cortex (sgACC), bilateral insula, and bilateral precuneus. Individual variability was quantified for each region by calculating the mean correlational distance of connectivity maps relative to the healthy controls; a higher variability score indicated a more atypical/idiosyncratic connectivity pattern. RESULTS: We included data from 66 healthy controls and 252 individuals with MDD in our analyses. Patients with MDD did not show significant differences in baseline variability of rs-fc compared with controls. Treatment with rTMS increased rs-fc variability from the right sgACC and precuneus, but the increased variability was not associated with clinical outcomes. Interestingly, higher baseline variability of the right sgACC was significantly associated with less clinical improvement (p = 0.037, uncorrected; did not survive false discovery rate correction).Limitations: The linear model was constructed separately for each region of interest. CONCLUSION: This was, to our knowledge, the first study to examine individual variability of rs-fc related to rTMS in individuals with MDD. In contrast to our hypotheses, we found that rTMS increased the individual variability of rs-fc. Our results suggest that individual variability of the right sgACC and bilateral precuneus connectivity may be a potential mechanism of rTMS.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Differences in fractional amplitude of low-frequency fluctuations (fALFF) and cognitive function between untreated major depressive disorder and schizophrenia with depressive mood patients.

BACKGROUND: Distinguishing untreated major depressive disorder without medication (MDD) from schizophrenia with depressed mood (SZDM) poses a clinical challenge. This study aims to investigate differences in fractional amplitude of low-frequency fluctuations (fALFF) and cognition in untreated MDD and SZDM patients. METHODS: The study included 42 untreated MDD cases, 30 SZDM patients, and 46 healthy controls (HC). Cognitive assessment utilized the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Resting-state functional magnetic resonance imaging (rs-fMRI) scans were conducted, and data were processed using fALFF in slow-4 and slow-5 bands. RESULTS: Significant fALFF changes were observed in four brain regions across MDD, SZDM, and HC groups for both slow-4 and slow-5 fALFF. Compared to SZDM, the MDD group showed increased slow-5 fALFF in the right gyrus rectus (RGR). Relative to HC, SZDM exhibited decreased slow-5 fALFF in the left gyrus rectus (LGR) and increased slow-5 fALFF in the right putamen. Changes in slow-5 fALFF in both RGR and LGR were negatively correlated with RBANS scores. No significant correlations were found between remaining fALFF (slow-4 and slow-5 bands) and RBANS scores in MDD or SZDM groups. CONCLUSIONS: Alterations in slow-5 fALFF in RGR may serve as potential biomarkers for distinguishing MDD from SZDM, providing preliminary insights into the neural mechanisms of cognitive function in schizophrenia.

Relationship between cognitive function and functional outcomes in remitted major depression.

BACKGROUND: Few studies have focused on functional impairment in depressed patients during symptomatic remission. The exact relationship between cognitive performance and functional outcomes of patients with Major depressive disorder (MDD) remains unclear. METHODS: Participants diagnosed with MDD were included and interviewed at both baseline and follow-up. Cognitive function was assessed during acute episodes using the Cambridge Neuropsychological Test Automated Battery (CANTAB), which targeted attention (Rapid Visual Processing - RVP), visual memory (Pattern Recognition Memory - PRM), and executive function (Intra-Extra Dimensional Set Shift - IED). The 17-item Hamilton Depression Scale (HAMD) was used for symptom assessment. Participants were divided into two groups based on their SDSS (Social Disability Screening Schedule) scores, and the differences between their demographic information, HAMD scores, and baseline CANTAB test results were compared. Logistic regression analysis was used to identify cognitive predictors of social function during symptomatic remission. RESULTS: According to the SDSS score at follow-up, 103 patients were divided into the normal social function group (n = 81,78.6%) and the poor social function group (n = 22, 21.4%) during clinical remission. Participants with poorer social function performed worse in the visual memory (PRM) and executive function tests (IED) at the baseline. Logistic regression analysis suggested that performance on the PRM (95%CI = 0.31-0.93, p = 0.030) and IED (95%CI = 1.01-1.13, p = 0.014) tests, instead of less severe symptoms, significantly contributed to functional outcomes. CONCLUSION: Better performance in visual memory and executive function during acute episodes may predict better social functional outcomes in individuals with MDD. A potential early intervention to improve social function in individuals with MDD could include the treatments for executive function and visual memory.

Major depressive disorder recognition by quantifying EEG signal complexity using proposed APLZC and AWPLZC.

BACKGROUND: Seeking objective quantitative indicators is important for accurately recognizing major depressive disorder (MDD). Lempel-Ziv complexity (LZC), employed to characterize neurological disorders, faces limitations in tracking dynamic changes in EEG signals due to defects in the coarse-graining process, hindering its precision for MDD objective quantitative indicators. METHODS: This work proposed Adaptive Permutation Lempel-Ziv Complexity (APLZC) and Adaptive Weighted Permutation Lempel-Ziv Complexity (AWPLZC) algorithms by refining the coarse-graining process and introducing weight factors to effectively improve the precision of LZC in characterizing EEGs and further distinguish MDD patients better. APLZC incorporated the ordinal pattern, while False Nearest Neighbor and Mutual Information algorithms were introduced to determine and adjust key parameters adaptively. Furthermore, we proposed AWPLZC by assigning different weights to each pattern based on APLZC. Thirty MDD patients and 30 healthy controls (HCs) were recruited and their 64-channel resting EEG signals were collected. The complexities of gamma oscillations were then separately computed using LZC, APLZC, and AWPLZC algorithms. Subsequently, a multi-channel adaptive K-nearest neighbor model was constructed for identifying MDD patients and HCs. RESULTS: LZC, APLZC, and AWPLZC algorithms achieved accuracy rates of 78.29 %, 90.32 %, and 95.13 %, respectively. Sensitivities reached 67.96 %, 85.04 %, and 98.86 %, while specificities were 88.62 %, 95.35 %, and 89.92 %, respectively. Notably, AWPLZC achieved the best performance in accuracy and sensitivity, with a specificity limitation. LIMITATION: The sample size is relatively small. CONCLUSION: APLZC and AWPLZC algorithms, particularly AWPLZC, demonstrate superior effectiveness in differentiating MDD patients from HCs compared with LZC. These findings hold significant clinical implications for MDD diagnosis.

Exploring the role of neuronal-enriched extracellular vesicle miR-93 and interoception in major depressive disorder.

Major depressive disorder (MDD) is associated with interoceptive processing dysfunctions, but the molecular mechanisms underlying this dysfunction are poorly understood. This study combined brain neuronal-enriched extracellular vesicle (NEEV) technology and serum markers of inflammation and metabolism with Functional Magnetic Resonance Imaging (fMRI) to identify the contribution of gene regulatory pathways, in particular micro-RNA (miR) 93, to interoceptive dysfunction in MDD. Individuals with MDD (n = 41) and healthy comparisons (HC; n = 35) provided blood samples and completed an interoceptive attention task during fMRI. EVs were separated from plasma using a precipitation method. NEEVs were enriched by magnetic streptavidin bead immunocapture utilizing a neural adhesion marker (L1CAM/CD171) biotinylated antibody. The origin of NEEVs was validated with two other neuronal markers - neuronal cell adhesion molecule (NCAM) and ATPase Na+/K+ transporting subunit alpha 3 (ATP1A3). NEEV specificities were confirmed by flow cytometry, western blot, particle size analyzer, and transmission electron microscopy. NEEV small RNAs were purified and sequenced. Results showed that: (1) MDD exhibited lower NEEV miR-93 expression than HC; (2) within MDD but not HC, those individuals with the lowest NEEV miR-93 expression had the highest serum concentrations of interleukin (IL)-1 receptor antagonist, IL-6, tumor necrosis factor, and leptin; and (3) within HC but not MDD, those participants with the highest miR-93 expression showed the strongest bilateral dorsal mid-insula activation during interoceptive versus exteroceptive attention. Since miR-93 is regulated by stress and affects epigenetic modulation by chromatin re-organization, these results suggest that healthy individuals but not MDD participants show an adaptive epigenetic regulation of insular function during interoceptive processing. Future investigations will need to delineate how specific internal and external environmental conditions contribute to miR-93 expression in MDD and what molecular mechanisms alter brain responsivity to body-relevant signals.

Functional connectivity of the amygdala subregions and the antidepressant effects of repeated ketamine infusions in major depressive disorder.

BACKGROUND: Amygdala subregion-based network dysfunction has been determined to be centrally implicated in major depressive disorder (MDD). Little is known about whether ketamine modulates amygdala subarea-related networks. We aimed to investigate the relationships between changes in the resting-state functional connectivity (RSFC) of amygdala subregions and ketamine treatment and to identify important neuroimaging predictors of treatment outcomes. METHODS: Thirty-nine MDD patients received six doses of ketamine (0.5 mg/kg). Depressive symptoms were assessed, and magnetic resonance imaging (MRI) scans were performed before and after treatment. Forty-five healthy controls underwent one MRI scan. Seed-to-voxel RSFC analyses were performed on the amygdala subregions, including the centromedial amygdala (CMA), laterobasal amygdala (LBA), and superficial amygdala subregions. RESULTS: Abnormal RSFC between the left LBA and the left precuneus in MDD patients is related to the therapeutic efficacy of ketamine. There were significant differences in changes in bilateral CMA RSFC with the left orbital part superior frontal gyrus and in changes in the left LBA with the right middle frontal gyrus between responders and nonresponders following ketamine treatment. Moreover, there was a difference in the RSFC of left LBA and the right superior temporal gyrus/middle temporal gyrus (STG/MTG) between responders and nonresponders at baseline, which could predict the antidepressant effect of ketamine on Day 13. CONCLUSIONS: The mechanism by which ketamine improves depressive symptoms may be related to its regulation of RSFC in the amygdala subregion. The RSFC between the left LBA and right STG/MTG may predict the response to the antidepressant effect of ketamine.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder.

BACKGROUND: International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS: To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD: Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS: Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS: The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data.

BACKGROUND: Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown. METHODS: We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method. FINDINGS: The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms. INTERPRETATION: Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively. FUNDING: Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

Brain stimulation over the left DLPFC enhances motivation for effortful rewards in patients with major depressive disorder.

BACKGROUND: Amotivation is a typical feature in major depressive disorder (MDD), which produces reduced willingness to exert effort. The dorsolateral prefrontal cortex (DLPFC) is a crucial structure in goal-directed actions and therefore is a potential target in modulating effortful motivation. However, it remains unclear whether the intervention is effective for patients with MDD. METHODS: We employed transcranial magnetic stimulation (TMS), computational modelling and event-related potentials (ERPs) to reveal the causal relationship between the left DLPFC and motivation for effortful rewards in MDD. Fifty patients underwent both active and sham TMS sessions, each followed by performing an Effort-Expenditure for Rewards Task, during which participants chose and implemented between low-effort/low-reward and high-effort/high-reward options. RESULTS: The patients showed increased willingness to exert effort for rewards during the DLPFC facilitated session, compared with the sham session. They also had a trend in larger P3 amplitude for motivated attention toward chosen options, larger CNV during preparing for effort exertion, and larger SPN during anticipating a high reward. Besides, while behavior indexes for effortful choices were negatively related to depression severity in the sham session, this correlation was weakened in the active stimulation session. CONCLUSIONS: These findings provide behavioral, computational, and neural evidence for the left DLPFC on effortful motivation for rewards. Facilitated DLPFC improves motor preparation and value anticipation after making decisions especially for highly effortful rewards in MDD. Facilitated DLPFC also has a potential function in enhancing motivated attention during cost-benefit trade-off. This neuromodulation effect provides a potential treatment for improving motivation in clinics.

Effects of anhedonia on health-related quality of life and functional outcomes in major depressive disorder: A systematic review and meta-analysis.

BACKGROUND: Major depressive disorder (MDD) is a heterogeneous group of mood disorders. A prominent symptom domain is anhedonia narrowly defined as a loss of interest and ability to experience pleasure. Anhedonia is associated with depressive symptom severity, MDD prognosis, and suicidality. We perform a systematic review and meta-analysis of extant literature investigating the effects of anhedonia on health-related quality of life (HRQoL) and functional outcomes in persons with MDD. METHODS: A literature search was conducted on PubMed, OVID databases, and SCOPUS for published articles from inception to November 2023, reporting on anhedonia and patient-reported outcomes in persons with MDD. The reported correlation coefficients between anhedonia and self-reported measures of both HRQoL and functional outcomes were pooled using a random effects model. RESULTS: We identified 20 studies that investigated anhedonia with HRQoL and/or functional outcomes in MDD. Anhedonia as measured by the Snaith-Hamilton Pleasure Scale (SHAPS) scores had a statistically significant correlation with patient-reported HRQoL (r = -0.41 [95 % CI = -0.60, -0.18]) and functional impairment (r = 0.39 [95 % CI = 0.22, 0.54]). LIMITATIONS: These preliminary results primarily investigate correlations with consummatory anhedonia and do not distinguish differences in anticipatory anhedonia, reward valuation or reward learning; therefore, these results require replication. CONCLUSIONS: Persons with MDD experiencing symptoms of anhedonia are more likely to have worse prognosis including physical, psychological, and social functioning deficits. Anhedonia serves as an important predictor and target for future therapeutic and preventative tools in persons with MDD.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

BACKGROUND: Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS: Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD: A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS: Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS: Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Exosomal miR-1202 mediates Brodmann Area 44 functional connectivity changes in medication-free patients with major depressive disorder: An fMRI study.

Previous large-sample postmortem study revealed that the expression of miR-1202 in brain tissues from Brodmann area 44 (BA44) was dysregulated in patients with major depressive disorder (MDDs). However, the specific in vivo neuropathological mechanism of miR-1202 as well as its interplay with BA44 circuits in the depressed brain are still unclear. Here, we performed a case-control study with imaging-genetic approach based on resting-state functional magnetic resonance imaging (MRI) data and miR-1202 quantification from 110 medication-free MDDs and 102 healthy controls. Serum-derived circulating exosomes that readily cross the blood-brain barrier were isolated to quantify miR-1202. For validation, repeated MR scans were performed after a six-week follow-up of antidepressant treatment on a cohort of MDDs. Voxelwise factorial analysis revealed two brain areas (including the striatal-thalamic region) in which the effect of depression on the functional connectivity with BA44 was significantly dependent on the expression level of exosomal miR-1202. Moreover, longitudinal change of the BA44 connectivity with the striatal-thalamic region in MDDs after antidepressant treatment was found to be significantly related to the level of miR-1202 expression. These findings revealed that the in vivo neuropathological effect of miR-1202 dysregulation in depression is possibly exerted by mediating neural functional abnormalities in BA44-striatal-thalamic circuits.

P300 parameters in major depressive disorder: A systematic review and meta-analysis.

OBJECTIVES: Event-related potential measures have been extensively studied in mental disorders. Among them, P300 amplitude and latency reflect impaired cognitive abilities in major depressive disorder (MDD). The present systematic review and meta-analysis was conducted to investigate whether patients with MDD differ from healthy controls (HCs) with respect to P300 amplitude and latency. METHODS: PubMed and Web of Science databases were searched from inception to 15 January 2023 for case-control studies comparing P300 amplitude and latency in patients with MDD and HCs. The primary outcome was the standard mean difference. A total of 13 articles on P300 amplitude and latency were included in the meta-analysis. RESULTS: Random effect models indicated that MDD patients had decreased P300 amplitude, but similar latency compared to healthy controls. According to regression analysis, the effect size increased with the severity of depression and decreased with the proportion of women in the MDD samples. Funnel plot asymmetry was not significant for publication bias. CONCLUSIONS: Decreased P300 amplitude may be a candidate diagnostic biomarker for MDD. However, prospective studies testing P300 amplitude as a monitoring biomarker for MDD are needed.

Multivariate association between psychosocial environment, behaviors, and brain functional networks in adolescent depression.

BACKGROUND: Adolescent depression shows high clinical heterogeneity. Brain functional networks serve as a powerful tool for investigating neural mechanisms underlying depression profiles. A key challenge is to characterize how variation in brain functional organization links to behavioral features and psychosocial environmental influences. METHODS: We recruited 80 adolescents with major depressive disorder (MDD) and 42 healthy controls (HCs). First, we estimated the differences in functional connectivity of resting-state networks (RSN) between the two groups. Then, we used sparse canonical correlation analysis to characterize patterns of associations between RSN connectivity and symptoms, cognition, and psychosocial environmental factors in MDD adolescents. Clustering analysis was applied to stratify patients into homogenous subtypes according to these brain-behavior-environment associations. RESULTS: MDD adolescents showed significantly hyperconnectivity between the ventral attention and cingulo-opercular networks compared with HCs. We identified one reliable pattern of covariation between RSN connectivity and clinical/environmental features in MDD adolescents. In this pattern, psychosocial factors, especially the interpersonal and family relationships, were major contributors to variation in connectivity of salience, cingulo-opercular, ventral attention, subcortical and somatosensory-motor networks. Based on this association, we categorized patients into two subgroups which showed different environment and symptoms characteristics, and distinct connectivity alterations. These differences were covered up when the patients were taken as a whole group. CONCLUSION: This study identified the environmental exposures associated with specific functional networks in MDD youths. Our findings emphasize the importance of the psychosocial context in assessing brain function alterations in adolescent depression and have the potential to promote targeted treatment and precise prevention.

MicroRNA-124 influenced depressive symptoms via large-scale brain connectivity in major depressive disorder patients.

This study aimed to investigate the neurobiological mechanisms by which microRNA 124 (miR-124) is involved in major depressive disorder (MDD). We enrolled 53 untreated MDD patients and 38 healthy control (HC) subjects who completed behavior assessments and resting-state functional MRI (rs-fMRI) scans. MiR-124 expression levels were detected in the peripheral blood of all participants. We determined that miR-124 levels could influence depressive symptoms via disrupted large-scale intrinsic intra- and internetwork connectivity, including the default mode network (DMN)-DMN, dorsal attention network (DAN)-salience network (SN), and DAN-cingulo-opercular network (CON). This study deepens our understanding of how miR-124 dysregulation contributes to depression.

Differences in resting-state brain activity in first-episode drug-naïve major depressive disorder patients with and without suicidal ideation.

Despite altered brain activities being associated with suicidal ideation (SI), the neural correlates of SI in major depressive disorder (MDD) have remained elusive. We enrolled 82 first-episode drug-naïve MDD patients including 41 with SI and 41 without SI, as well as 41 healthy controls (HCs). Resting-state functional and structural MRI data were collected. The measures of fractional amplitude of low-frequency fluctuation (fALFF) and grey matter volume (GMV) were calculated and compared. Compared with HCs, patients with SI exhibited increased fALFF values in the right rectus gyrus and left medial superior frontal gyrus, middle frontal gyrus and precuneus. Decreased GMV in the right parahippocampal gyrus, insula and middle occipital gyrus and increased GMV in the left superior frontal gyrus were detected in patients with SI. In addition, patients without SI demonstrated increased fALFF values in the right superior frontal gyrus and decreased fALFF values in the right postcentral gyrus. Decreased GMV in the left superior frontal gyrus, right medial superior frontal gyrus, opercular part of inferior frontal gyrus, postcentral gyrus, fusiform gyrus and increased left supplementary motor area, superior occipital gyrus, right anterior cingulate gyrus and superior temporal gyrus were revealed in patients with SI. Moreover, in comparison with patients without SI, increased fALFF values were identified in the left precuneus of patients with SI. However, no significant differences were found in GMV between patients with and without SI. These findings might be helpful for finding neuroimaging markers predicting individual suicide risk and detecting targeted brain regions for effective early interventions.

Frontal alpha asymmetry during emotion regulation in adults with lifetime major depression.

Emotion regulation (ER) often is impaired in current or remitted major depression (MD), although the extent of the deficits is not fully understood. Recent studies suggest that frontal alpha asymmetry (FAA) could be a promising electrophysiological measure to investigate ER. The purpose of this study was to investigate ER differences between participants with lifetime major depression (lifetime MD) and healthy controls (HC) for the first time in an experimental task by using FAA. We compared lifetime MD (n = 34) and HC (n = 25) participants aged 18-24 years in (a) an active ER condition, in which participants were instructed to reappraise negative images and (b) a condition in which they attended to the images while an EEG was recorded. We also report FAA results from an independent sample of adolescents with current MD (n = 36) and HC adolescents (n = 38). In the main sample, both groups were able to decrease self-reported negative affect in response to negative images through ER, without significant group differences. We found no differences between groups or conditions in FAA, which was replicated within the independent adolescent sample. The lifetime MD group also reported less adaptive ER in daily life and higher difficulty of ER during the task. The lack of differences between in self-reported affect and FAA between lifetime MD and HC groups in the active ER task indicates that lifetime MD participants show no impairments when instructed to apply an adaptive ER strategy. Implications for interventional aspects are discussed.

Cingulate dynamics track depression recovery with deep brain stimulation.

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.