The boon and bane of nitrous oxide.

Nitrous oxide (N2O) has been known since the end of the eighteenth century. Today, N2O plays a huge role as a greenhouse gas and an ozone-depleting stratospheric molecule. The main sources of anthropogenic N2O emissions are agriculture, fuel combustion, wastewater treatment, and various industrial processes. By contrast, the contribution of medical N2O to the greenhouse effect appears to be small. The recreational and medical uses of N2O gradually diverged over time. N2O has analgesic and anesthetic effects, making it widely used in modern dentistry and surgery. New research has also begun studying N2O's antidepressant actions. N-methyl-D-aspartate (NMDA) antagonism and opioid effects are believed to be the main underlying biochemical mechanisms. At this point, numerous questions remain open and, in particular, the conduct of larger clinical trials will be essential to confirm N2O's use as a rapid-acting antidepressant. The N2O concentration delivered, the duration of a single inhalation, as well as the number of inhalations ultimately required, deserve to be better understood. Finally, the non-medical use of N2O has gained significant attention in recent years. Sudden deaths directly attributed to N2O are primarily due to asphyxia. Heavy, chronic N2O use may result in vitamin B12 deficiency, which, among other things, may cause megaloblastic anemia, venous thrombosis, myeloneuropathy, and skin pigmentation. Helpful biochemical tests include homocysteine and methylmalonic acid. The centerpiece of treatment is complete cessation of N2O use together with parenteral administration of vitamin B12.

EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder.

Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from baseline of >33% on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 h after infusion. EEG recordings were obtained pre-, start-, end- and 24 h post- infusion, and the resting EEG was automatically scored using the Vigilance Algorithm Leipzig (VIGALL). Relative to placebo (sodium chloride 0.9%), ketamine increased the amount of low-vigilance stage B1 at end-infusion. This increase in B1 was positively related to serum concentrations of ketamine, but not to norketamine, and was independent of clinical response. In contrast, treatment responders showed a distinct EEG pattern characterized by a decrease in high-vigilance stage A1 and an increase in low-vigilance B2/3, regardless of whether placebo or ketamine had been given. Furthermore, pretreatment EEG differed between responders and non-responders with responders showing a higher percentage of stage A1 (53% vs. 21%). The logistic regression fitted on the percent of A1 stages was able to predict treatment outcomes in the testing dataset with an area under the ROC curve of 0.7. Ketamine affects EEG vigilance in a distinct pattern observed only in responders. Consequently, the percentage of pretreatment stage A1 shows significant potential as a predictive biomarker of treatment response.Clinical Trials Registration: https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-000952-17/CZ Registration number: EudraCT Number: 2013-000952-17.

Outcomes of electroconvulsive therapy in patients with depressive symptoms with versus without comorbid personality disorders/traits: A systematic review and meta-analysis.

AIMS: To assess electroconvulsive therapy (ECT) outcomes in patients affected by depressive symptoms with versus without additional comorbid personality disorders/traits. METHODS: We identified observational studies investigating ECT clinical outcomes in patients affected by depressive symptoms with versus without comorbid personality disorders/traits in Embase/Medline in 11/2022. Our protocol was registered with PROSPERO (CRD42023390833). Study quality was evaluated using the Newcastle-Ottawa-Scale. Our primary outcomes were ECT response and remission rates. Meta-regression analyses included effects of in/outpatient percentages, age, number of ECT sessions, and electrode placement; subgroup analyses included the assessment methods for personality disorders/traits. We performed sensitivity analyses after excluding poor-quality studies. RESULTS: A total of 20 studies (n = 11,390) were included in our analysis. Patients with comorbid personality disorders/traits had lower remission rates (OR = 0.42, 95% CI = 0.31, 0.58, p < 0.001) with substantial heterogeneity (I2 = 93.0%) as well as lower response rates (OR = 0.35, 95% CI = 0.24, 0.51, n = 5129, p < 0.001) with substantial heterogeneity (I2 = 93.0%) compared with patients without comorbid personality disorders/traits. Relapse rates were higher in patients with versus without comorbid personality disorders/traits (OR = 3.23, 95% CI = 1.40, 7.45, k = 4, n = 239, p = 0.006) with moderate heterogeneity (I2 = 75.0%) and post-ECT memory impairment was more frequent in patients with versus without comorbid personality disorders/traits (OR = 1.41, 95% CI = 1.36, 1.46, k = 4, n = 471, p < 0.001) with minimal heterogeneity (I2 = 0.0%). Dropout rates were higher in patients with versus without comorbid personality disorders/traits (OR = 1.58, 95% CI = 1.13, 2.21, k = 3, n = 6145, p = 0.008). CONCLUSIONS: Patients with comorbid personality disorders/traits treated with ECT are reported to have lower response and remission rates and higher rates of side effects and relapse rates compared with patients without personality disorders/traits.

EEG wakefulness regulation in transdiagnostic patients after a recent suicide attempt.

OBJECTIVE: Decades of research have not yet produced statistically reliable predictors of preparatory behavior eventually leading to suicide attempts or deaths by suicide. As the nature of suicidal behavior is complex, it is best investigated in a transdiagnostic approach, while assessing objective markers, as proposed by the Research Domain Criteria (Cuthbert, 2013). METHODS: A 15-min resting-state EEG was recorded in 45 healthy controls, and 49 transdiagnostic in-patients with a recent (<6 months) suicide attempt. Brain arousal regulation in eyes-closed condition was assessed with the Vigilance Algorithm Leipzig (VIGALL) (Sander et al., 2015). RESULTS: A significant incline of median vigilance and vigilance slope was observed in patients within the first 3-min of the EEG recording. Additionally, a significant positive correlation of self-reported suicidal ideation with the vigilance slope over 15-min recording time, as well as a significant negative correlation with EEG vigilance stage A1 during the first 3-min was found. CONCLUSIONS: Transdiagnostic patients with a recent suicide attempt show a distinct vigilance regulation pattern. Further studies including a control group consisting of patients without life-time suicide attempts are needed to increase the clinical utility of the findings. SIGNIFICANCE: These findings might serve as potential objective markers of suicidal behavior.

EEG source functional connectivity in patients after a recent suicide attempt.

OBJECTIVE: Electroencephalogram (EEG) based frequency measures within the alpha frequency range (AFR), including functional connectivity, show potential in assessing the underlying pathophysiology of depression and suicide-related outcomes. We investigated the association between AFR connectivity, suicidal thoughts and behaviors, and depression in a transdiagnostic sample of patients after a recent suicide attempt (SA). METHODS: Lagged source-based measures of linear and nonlinear whole-brain connectivity within the standard AFR ([sAFR], 8-12 Hz) and the individually referenced AFR (iAFR) were applied to 70 15-minute resting-state EEGs from patients after a SA and 70 age- and gender-matched healthy controls (HC). Hypotheses were tested using network-based statistics and multiple regression models. RESULTS: Results showed no significant differences between patients after a SA and HC in any of the assessed connectivity modalities. However, a subgroup analysis revealed significantly increased nonlinear connectivity within the sAFR for patients after a SA with a depressive disorder or episode ([DD], n = 53) compared to matched HC. Furthermore, a multiple regression model, including significant main effects for group and global nonlinear connectivity within the sAFR outperformed all other models in explaining variance in depressive symptom severity. CONCLUSIONS: Our study further supports the importance of the AFR in pathomechanisms of suicidality and depression. The iAFR does not seem to improve validity of phase-based connectivity. SIGNIFICANCE: Our results implicate distinct neurophysiological patterns in suicidal subgroups. Exploring the potential of these patterns for treatment stratification might advance targeted interventions for suicidal thoughts and behaviors.

Deep Learning in the Identification of Electroencephalogram Sources Associated with Sexual Orientation.

INTRODUCTION: It is unclear if sexual orientation is a biological trait that has neurofunctional footprints. With deep learning, the power to classify biological datasets without an a priori selection of features has increased by magnitudes. The aim of this study was to correctly classify resting-state electroencephalogram (EEG) data from males with different sexual orientation using deep learning and to explore techniques to identify the learned distinguishing features. METHODS: Three cohorts (homosexual men, heterosexual men, and a mixed sex cohort), one pretrained network on sex classification, and one newly trained network for sexual orientation classification were used to classify sex. Further, Grad-CAM methodology and source localization were used to identify the spatiotemporal patterns that were used for differentiation by the networks. RESULTS: Using a pretrained network for classification of males and females, no differences existed between classification of homosexual and heterosexual males. The newly trained network was able, however, to correctly classify the cohorts with a total accuracy of 83%. The retrograde activation using Grad-CAM technology yielded distinctive functional EEG patterns in the Brodmann area 40 and 1 when combined with Fourier analysis and a source localization. DISCUSSION: This study shows that electrophysiological trait markers of male sexual orientation can be identified using deep learning. These patterns are different from the differentiating signatures of males and females in a resting-state EEG.

Autism spectrum disorders in adults and the autonomic nervous system: Heart rate variability markers in the diagnostic procedure.

The diagnostic assessment of autism spectrum disorders (ASD) in adults is a challenging and time-consuming procedure. In order to address the lack of specialised health-care professionals and improve the waiting time, we aimed to identify specific electrocardiogram (ECG) derived Heart Rate Variability (HRV) parameters that could be used for diagnostic purposes. 152 patients were diagnosed based on a standardised clinical procedure and assigned to one of three groups: ASD (n = 56), any other psychiatric disorder (OD) (n = 72), and patients with no diagnosis (ND) (n = 24). Groups were compared using ANOVA. Discriminative power of biological parameters and the clinical assessment were compared using receiver operating characteristic curves (ROCs). Patients with ASD showed reduced parasympathetic and increased sympathetic activity compared to ND. The accuracy determined by the area under the curve (AUC) of the biological parameters for discrimination between ASD vs. pooled OD/ND was 0.736 (95% CI = 0.652-0.820), compared to .856 (95% CI = 0.795-0.917) for the extensive clinical assessment. Our results confirmed the dysregulation of the autonomic nervous system in ASD with reduced parasympathetic and increased sympathetic activity as compared to ND. The discriminative power of biological markers including HRV was considerable and could supplement less sophisticated clinical assessments.

Postpartum hemorrhage and postpartum depression: A systematic review and meta-analysis of observational studies.

OBJECTIVE: To assess the postpartum depression (PPD) risk in women with postpartum hemorrhage (PPH) and moderators. METHODS: We identified observational studies of PPD rates in women with versus without PPH in Embase/Medline/PsychInfo/Cinhail in 09/2022. Study quality was evaluated using the Newcastle-Ottawa-Scale. Our primary outcome was the odds ratio (OR, 95% confidence intervals [95%CI]) of PPD in women with versus without PPH. Meta-regression analyses included the effects of age, body mass index, marital status, education, history of depression/anxiety, preeclampsia, antenatal anemia and C-section; subgroup analyses were based on PPH and PPD assessment methods, samples with versus without history of depression/anxiety, from low-/middle- versus high-income countries. We performed sensitivity analyses after excluding poor-quality studies, cross-sectional studies and sequentially each study. RESULTS: One, five and three studies were rated as good-, fair- and poor-quality respectively. In nine studies (k = 10 cohorts, n = 934,432), women with PPH were at increased PPD risk compared to women without PPH (OR = 1.28, 95% CI = 1.13 to 1.44, p < 0.001), with substantial heterogeneity (I2 = 98.9%). Higher PPH-related PPD ORs were estimated in samples with versus without history of depression/anxiety or antidepressant exposure (OR = 1.37, 95%CI = 1.18 to 1.60, k = 6, n = 55,212, versus 1.06, 95%CI = 1.04 to 1.09, k = 3, n = 879,220, p < 0.001) and in cohorts from low-/middle- versus high-income countries (OR = 1.49, 95%CI = 1.37 to 1.61, k = 4, n = 9197, versus 1.13, 95%CI = 1.04 to 1.23, k = 6, n = 925,235, p < 0.001). After excluding low-quality studies the PPD OR dropped (1.14, 95%CI = 1.02 to 1.29, k = 6, n = 929,671, p = 0.02). CONCLUSIONS: Women with PPH had increased PPD risk amplified by history of depression/anxiety, whereas more data from low-/middle-income countries are required.

Association between the loudness dependence of auditory evoked potential, serotonergic neurotransmission and treatment outcome in patients with depression.

Previous studies have suggested that the loudness dependence of auditory evoked potential (LDAEP) is associated with the effectiveness of antidepressant treatment in patients with major depressive disorders (MDD). Furthermore, both LDAEP and the cerebral serotonin 4 receptor (5-HT4R) density is inversely related to brain serotonin levels. We included 84 patients with MDD and 22 healthy controls to examined the association between LDAEP and treatment response and its association with cerebral 5-HT4R density. Participants underwent both EEG and 5-HT4R neuroimaging with [11C]SB207145 PET. Thirty-nine patients with MDD were re-examined after 8 weeks of treatment with selective serotonin reuptake inhibitors/serotonin noradrenaline reuptake inhibitor (SSRI/SNRI). We found that the cortical source of LDAEP was higher in untreated patients with MDD compared to healthy controls (p=0.03). Prior to SSRI/SNRI treatment, subsequent treatment responders had a negative association between LDAEP and depressive symptoms and a positive association between scalp LDAEP and symptom improvement at week 8. This was not found in source LDAEP. In healthy controls, we found a positive correlation between both scalp and source LDAEP and cerebral 5-HT4R binding but that was not observed in patients with MDD. We did not see any changes in scalp and source LDAEP in response to SSRI/SNRI treatment. These results support a theoretical framework where both LDAEP and cerebral 5-HT4R are indices of cerebral 5-HT levels in healthy individuals while this association seems to be disrupted in MDD. The combination of the two biomarkers may be useful for stratifying patients with MDD. Clinical Trials Registration:https://clinicaltrials.gov/ct2/show/NCT02869035?draw=1Registration number: NCT0286903.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

A recent suicide attempt and the heartbeat: Electrophysiological findings from a trans-diagnostic cohort of patients and healthy controls.

Suicidal behavior is influenced by a multitude of factors, making prediction and prevention of suicide attempts (SA) a challenge. A useful tool to uncover underlying pathophysiology or propose new therapy approaches are biomarkers, especially within the context of point-of-care tests. Heart rate variability (HRV) is a well-established biomarker of mental health, and measures the activity of the sympathetic and parasympathetic nervous system (PNS). Previous studies reported a correlation between lower PNS activity and suicidality. However, most studies involved participants from a healthy population, patients without history of suicide attempts, or patients with a single diagnosis. 52 in-patients with a recent suicide attempt (<6 months), and 43 controls without history of SA or psychiatric diagnoses confirmed study participation. The included patients age ranged between 18 and 65 years, 65% had psychiatric comorbidities. Patients with dementia, cognitive impairments, acute psychosis, chronic non suicidal self-harming behavior, or current electroconvulsive therapy were excluded. A 15-min resting state electrocardiography was recorded with two bipolar electrodes attached to the right and left insides of the wrists. The multiple regression analyses showed lower parasympathetic, and higher sympathetic activity in patients compared to controls. Partial correlation found a positive trend result between self-reported suicidality and the very low frequency band. ROC curve analysis revealed an acceptable to excellent clinical accuracy of HRV parameters. Therefore, HRV parameters could be reliable discriminative biomarkers between in-patients with a recent SA and healthy controls. One limitation is the lack of a control group consisting of in-patients without life-time suicidal ideation or attempts.

The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database.

In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5-89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main indications included are Major Depressive Disorder (MDD; N = 426), attention deficit hyperactivity disorder (ADHD; N = 271), Subjective Memory Complaints (SMC: N = 119) and obsessive-compulsive disorder (OCD; N = 75). Demographic-, personality- and day of measurement data are included in the database. Thirty percent of clinical and treatment outcome data will remain blinded for prospective validation and replication purposes. The TDBRAIN database and code are available on the Brainclinics Foundation website at www.brainclinics.com/resources and on Synapse at www.synapse.org/TDBRAIN .

Fit for Work and Life-an eight-week program for improvement of functionality and quality of life : A two-stage study.

BACKGROUND: The current two-stage study focused on work integration and quality of life of patients in an acute psychiatric day care unit. There is evidence that a longer absence from work due to illness negatively affects job retention, life satisfaction and clinical prognosis. Furthermore, there are individual supportive methods that proved to be effective in work integration. We therefore developed a specific group program Fit for Work and Life (FWL) for patients in an acute psychiatric day care unit focusing on work integration in the first labor market (in contrast to work in institutions for people with disabilities/second labor market). METHODS: Between 2018 and 2020, 62 patients (intervention group; IG) were enrolled in an 8­week prospective job integration program and compared to 74 patients (control group; CG) who received treatment as usual (partly retrospective survey). Patients of both groups held a job when entering treatment. Main outcome was defined as their working status 4 weeks after the end of treatment as well as self-reported life satisfaction. RESULTS: At the end of treatment (i.e. the week prior to discharge), the IG participants reported higher overall life satisfaction as well as higher health-, self- and living condition-related satisfaction than controls. Functional and clinical improvement during treatment was linked to subsequently returning to work. Functional improvement was further linked to higher life satisfaction. Mediational analysis revealed an indirect path from functional improvement on life satisfaction via working status, i.e. the higher functional improvement during treatment, the higher the chance of successfully returning to work, which in turn increased life satisfaction. CONCLUSION: Our findings suggest that programs such as FWL are useful interventions for employed patients to improve reintegration into work and life and to help to increase life satisfaction.

Suicidal ideations and suicide attempts prior to admission to a psychiatric hospital in the first six months of the COVID-19 pandemic: interrupted time-series analysis to estimate the impact of the lockdown and comparison of 2020 with 2019.

BACKGROUND: There is a substantial burden on global mental health as a result of the Coronavirus disease 2019 (COVID-19) pandemic that has become putting pressure on healthcare systems. There is increasing concern about rising suicidality consequential to the COVID-19 pandemic and the measures taken. Existing research about the impact of earlier epidemics and economic crises as well as current studies about the effects of the pandemic on public mental health and populations at risk indicate rising suicidality, especially in the middle and longer term. AIMS: This study investigated the early impact of the COVID-19 pandemic on suicidality by comparing weekly in-patient admissions for individuals who were suicidal or who attempted suicide just before admission, for the first 6 months after the pandemic's onset in Switzerland with corresponding 2019 control data. METHOD: Data was collected at the Psychiatric University Hospital of Zurich. An interrupted time-series design was used to analyse the number of patients who were suicidal. RESULTS: Instead of a suggested higher rate of suicidality, fewer admissions of patients with suicidal thoughts were found during the first 6-months after the COVID-19 outbreak. However, the proportion of involuntary admissions was found to be higher and more patients have been admitted after a first suicide attempt than in the corresponding control period from 2019. CONCLUSIONS: Although admissions relating to suicidality decreased during the pandemic, the rising number of patients admitted with a first suicide attempt may be an early indicator for an upcoming extra burden on public mental health (and care). Being a multifactorial process, suicidality is influenced in several ways; low in-patient admissions of patients who are suicidal could also reflect fear of contagion and related uncertainty about seeking mental healthcare.

Stratified psychiatry: Tomorrow's precision psychiatry?

Here we review the paradigm-change from one-size-fits-all psychiatry to more personalized-psychiatry, where we distinguish between 'precision psychiatry' and 'stratified psychiatry'. Using examples in Depression and ADHD we argue that stratified psychiatry, using biomarkers to facilitate patients to best 'on-label' treatments, is a more realistic future for implementing biomarkers in clinical practice.

Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD).

Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry. Here, we tested the replicability of these meta-analytic rsfMRI findings by measuring functional connectivity during resting-state fMRI in a new sample of OCD patients (n = 24) and matched controls (n = 33). We performed seed-to-voxel analyses using 30 seed regions from the prior meta-analysis. OCD patients showed reduced functional connectivity between the SN and the DMN compared to controls, replicating previous findings. We did not observe significant group differences of functional connectivity within the DMN, SN, nor FPN. Additionally, we observed reduced connectivity between the visual network to both the DMN and SN in OCD patients, in particular reduced functional connectivity between lateral parietal seeds and the left inferior lateral occipital pole. Furthermore, the right lateral parietal seed (associated with the DMN) was more strongly correlated with a cluster in the right lateral occipital cortex and precuneus (a region partly overlapping with the Dorsal Attentional Network (DAN)) in patients. Importantly, this latter finding was positively correlated to OCD symptom severity. Overall, our study partly replicated prior meta-analytic findings, highlighting hypoconnectivity between SN and DMN as a potential biomarker for OCD. Furthermore, we identified changes between the SN and the DMN with the visual network. This suggests that abnormal connectivity between cortex regions associated with abstract functions (transmodal regions such as the DMN), and cortex regions associated with constrained neural processing (unimodal regions such as the visual cortex), may be important in OCD.

Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium.

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers ( www.psymri.com ). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 ± 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 ± 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.