The Association Between Chronic Tobacco Smoking and Brain Alterations in Schizophrenia: A Systematic Review of Magnetic Resonance Imaging Studies.

BACKGROUND AND HYPOTHESIS: The high co-occurrence of tobacco smoking in patients with schizophrenia spectrum disorders (SSD) poses a serious health concern, linked to increased mortality and worse clinical outcomes. The mechanisms underlying this co-occurrence are not fully understood. STUDY DESIGN: Addressing the need for a comprehensive overview of the impact of tobacco use on SSD neurobiology, we conducted a systematic review of neuroimaging studies (including structural, functional, and neurochemical magnetic resonance imaging studies) that investigate the association between chronic tobacco smoking and brain alterations in patients with SSD. STUDY RESULTS: Eight structural and fourteen functional studies were included. Structural studies show widespread independent and additive reductions in gray matter in relation to smoking and SSD. The majority of functional studies suggest that smoking might be associated with improvements in connectivity deficits linked to SSD. However, the limited number of and high amount of cross-sectional studies, and high between-studies sample overlap prevent a conclusive determination of the nature and extent of the impact of smoking on brain functioning in patients with SSD. Overall, functional results imply a distinct neurobiological mechanism for tobacco addiction in patients with SSD, possibly attributed to differences at the nicotinic acetylcholine receptor level. CONCLUSIONS: Our findings highlight the need for more longitudinal and exposure-dependent studies to differentiate between inherent neurobiological differences and the (long-term) effects of smoking in SSD, and to unravel the complex interaction between smoking and schizophrenia at various disease stages. This could inform more effective strategies addressing smoking susceptibility in SSD, potentially improving clinical outcomes.

Quantitative MRI at 7-Tesla reveals novel frontocortical myeloarchitecture anomalies in major depressive disorder.

Whereas meta-analytical data highlight abnormal frontocortical macrostructure (thickness/surface area/volume) in Major Depressive Disorder (MDD), the underlying microstructural processes remain uncharted, due to the use of conventional MRI scanners and acquisition techniques. We uniquely combined Ultra-High Field MRI at 7.0 Tesla with Quantitative Imaging to map intracortical myelin (proxied by longitudinal relaxation time T1) and iron concentration (proxied by transverse relaxation time T2*), microstructural processes deemed particularly germane to cortical macrostructure. Informed by meta-analytical evidence, we focused specifically on orbitofrontal and rostral anterior cingulate cortices among adult MDD patients (N = 48) and matched healthy controls (HC; N = 10). Analyses probed the association of MDD diagnosis and clinical profile (severity, medication use, comorbid anxiety disorders, childhood trauma) with aforementioned microstructural properties. MDD diagnosis (p's < 0.05, Cohen's D = 0.55-0.66) and symptom severity (p's < 0.01, r = 0.271-0.267) both related to decreased intracortical myelination (higher T1 values) within the lateral orbitofrontal cortex, a region tightly coupled to processing negative affect and feelings of sadness in MDD. No relations were found with local iron concentrations. These findings allow uniquely fine-grained insights on frontocortical microstructure in MDD, and cautiously point to intracortical demyelination as a possible driver of macroscale cortical disintegrity in MDD.

Brain abnormalities in survivors of COVID-19 after 2-year recovery: a functional MRI study.

Background: A variety of symptoms, particularly cognitive, psychiatric and neurological symptoms, may persist for a long time among individuals recovering from COVID-19. However, the underlying mechanism of these brain abnormalities remains unclear. This study aimed to investigate the long-term neuroimaging effects of COVID-19 infection on brain functional activities using resting-state functional magnetic resonance imaging (rs-fMRI). Methods: Fifty-two survivors 27 months after infection (mild-moderate group: 25 participants, severe-critical: 27 participants), from our previous community participants, along with 35 healthy controls, were recruited to undergo fMRI scans and comprehensive cognitive function measurements. Participants were evaluated by subjective assessment of Cognitive Failures Questionnaire-14 (CFQ-14) and Fatigue Scale-14 (FS-14), and objective assessment of Montreal Cognitive Assessment (MoCA), N-back, and Simple Reaction Time (SRT). Each had rs-fMRI at 3T. Measures such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) were calculated. Findings: Compared with healthy controls, survivors of mild-moderate acute symptoms group and severe-critical group had a significantly higher score of cognitive complains involving cognitive failure and mental fatigue. However, there was no difference of cognitive complaints between two groups of COVID-19 survivors. The performance of three groups was similar on the score of MoCA, N-back and SRT. The rs-fMRI results showed that COVID-19 survivors exhibited significantly increased ALFF values in the left putamen (PUT.L), right inferior temporal gyrus (ITG.R) and right pallidum (PAL.R), while decreased ALFF values were observed in the right superior parietal gyrus (SPG.R) and left superior temporal gyrus (STG.L). Additionally, decreased ReHo values in the right precentral gyrus (PreCG.R), left postcentral gyrus (PoCG.L), left calcarine fissure and surrounding cortex (CAL.L) and left superior temporal gyrus (STG.L). Furthermore, significant negative correlations between the ReHo values in the STG.L, and CFQ-14 and mental fatigue were found. Interpretation: This long-term study suggests that individuals recovering from COVID-19 continue to experience cognitive complaints, psychiatric and neurological symptoms, and brain functional alteration. The rs-fMRI results indicated that the changes in brain function in regions such as the putamen, temporal lobe, and superior parietal gyrus may contribute to cognitive complaints in individuals with long COVID even after 2-year infection. Funding: The National Programs for Brain Science and Brain-like Intelligence Technology of China, the National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality of China, and the National Key Research and Development Program of China.

Changes in postictal cerebral perfusion are related to the duration of electroconvulsive therapy-induced seizures.

OBJECTIVE: Postictal symptoms may result from cerebral hypoperfusion, which is possibly a consequence of seizure-induced vasoconstriction. Longer seizures have previously been shown to cause more severe postictal hypoperfusion in rats and epilepsy patients. We studied cerebral perfusion after generalized seizures elicited by electroconvulsive therapy (ECT) and its relation to seizure duration. METHODS: Patients with a major depressive episode who underwent ECT were included. During treatment, 21-channel continuous electroencephalogram (EEG) was recorded. Arterial spin labeling magnetic resonance imaging scans were acquired before the ECT course (baseline) and approximately 1 h after an ECT-induced seizure (postictal) to quantify global and regional gray matter cerebral blood flow (CBF). Seizure duration was assessed from the period of epileptiform discharges on the EEG. Healthy controls were scanned twice to assess test-retest variability. We performed hypothesis-driven Bayesian analyses to study the relation between global and regional perfusion changes and seizure duration. RESULTS: Twenty-four patients and 27 healthy controls were included. Changes in postictal global and regional CBF were correlated with seizure duration. In patients with longer seizure durations, global decrease in CBF reached values up to 28 mL/100 g/min. Regional reductions in CBF were most prominent in the inferior frontal gyrus, cingulate gyrus, and insula (up to 35 mL/100 g/min). In patients with shorter seizures, global and regional perfusion increased (up to 20 mL/100 g/min). These perfusion changes were larger than changes observed in healthy controls, with a maximum median global CBF increase of 12 mL/100 g/min and a maximum median global CBF decrease of 20 mL/100 g/min. SIGNIFICANCE: Seizure duration is a key factor determining postictal perfusion changes. In future studies, seizure duration needs to be considered as a confounding factor due to its opposite effect on postictal perfusion.

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS: Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS: Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS: These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls.

OBJECTIVE: Electroconvulsive therapy (ECT) is effective for major depressive episodes. Understanding of underlying mechanisms has been increased by examining changes of brain connectivity but studies often do not correct for test-retest variability in healthy controls (HC). In this study, we investigated changes in resting-state networks after ECT in a multicenter study. METHODS: Functional resting-state magnetic resonance imaging data, acquired before start and within one week after ECT, from 90 depressed patients were analyzed, as well as longitudinal data of 24 HC. Group-information guided independent component analysis (GIG-ICA) was used to spatially restrict decomposition to twelve canonical resting-state networks. Selected networks of interest were the default mode network (DMN), salience network (SN), and left and right frontoparietal network (LFPN, and RFPN). Whole-brain voxel-wise analyses were used to assess group differences at baseline, group by time interactions, and correlations with treatment effectiveness. In addition, between-network connectivity and within-network strengths were computed. RESULTS: Within-network strength of the DMN was lower at baseline in ECT patients which increased after ECT compared to HC, after which no differences were detected. At baseline, ECT patients showed lower whole-brain voxel-wise DMN connectivity in the precuneus. Increase of within-network strength of the LFPN was correlated with treatment effectiveness. We did not find whole-brain voxel-wise or between-network changes. CONCLUSION: DMN within-network connectivity normalized after ECT. Within-network increase of the LFPN in ECT patients was correlated with higher treatment effectiveness. In contrast to earlier studies, we found no whole-brain voxel-wise changes, which highlights the necessity to account for test-retest effects.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this common causal network (CCN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis (Principal Component Analysis, PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CCN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CCN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes. This evidence further supports that treatment interventions converge on a CCN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy.

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. OBJECTIVE: We investigated whether there are consistent changes in effective resting-state connectivity. METHODS: This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. RESULTS: Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. CONCLUSIONS: A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.

Multimodal multilayer network centrality relates to executive functioning.

Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up.

BACKGROUND: The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level. METHODS: Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82). RESULTS: Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%). CONCLUSIONS: This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements.

Replicable brain-phenotype associations require large-scale neuroimaging data.

Numerous neuroimaging studies have investigated the neural basis of interindividual differences but the replicability of brain-phenotype associations remains largely unknown. We used the UK Biobank neuroimaging dataset (N = 37,447) to examine associations with six variables related to physical and mental health: age, body mass index, intelligence, memory, neuroticism and alcohol consumption, and assessed the improvement of replicability for brain-phenotype associations with increasing sampling sizes. Age may require only 300 individuals to provide highly replicable associations but other phenotypes required 1,500 to 3,900 individuals. The required sample size showed a negative power law relation with the estimated effect size. When only comparing the upper and lower quarters, the minimally required sample sizes for imaging decreased by 15-75%. Our findings demonstrate that large-scale neuroimaging data are required for replicable brain-phenotype associations, that this can be mitigated by preselection of individuals and that small-scale studies may have reported false positive findings.

Deep brain stimulation normalizes amygdala responsivity in treatment-resistant depression.

Deep brain stimulation (DBS) of the ventral anterior limb of the internal capsule (vALIC) is a promising intervention for treatment-resistant depression (TRD). However, the working mechanisms of vALIC DBS in TRD remain largely unexplored. As major depressive disorder has been associated with aberrant amygdala functioning, we investigated whether vALIC DBS affects amygdala responsivity and functional connectivity. To investigate the long-term effects of DBS, eleven patients with TRD performed an implicit emotional face-viewing paradigm during functional magnetic resonance imaging (fMRI) before DBS surgery and after DBS parameter optimization. Sixteen matched healthy controls performed the fMRI paradigm at two-time points to control for test-retest effects. To investigate the short-term effects of DBS de-activation after parameter optimization, thirteen patients additionally performed the fMRI paradigm after double-blind periods of active and sham stimulation. Results showed that TRD patients had decreased right amygdala responsivity compared to healthy controls at baseline. Long-term vALIC DBS normalized right amygdala responsivity, which was associated with faster reaction times. This effect was not dependent on emotional valence. Furthermore, active compared to sham DBS increased amygdala connectivity with sensorimotor and cingulate cortices, which was not significantly different between responders and non-responders. These results suggest that vALIC DBS restores amygdala responsivity and behavioral vigilance in TRD, which may contribute to the DBS-induced antidepressant effect.

Severe Postictal Confusion After Electroconvulsive Therapy: A Retrospective Study.

OBJECTIVES: Severe postictal confusion (sPIC) is an important but poorly investigated adverse effect of electroconvulsive therapy (ECT). In this retrospective study, prevalence of sPIC and potential risk factors were explored. METHODS: Medical charts of 295 ECT patients (mean ± SD age, 57 ± 15 years; male, 36%) were scrutinized for occurrence of sPIC, as well as demographic, clinical, and treatment characteristics. Patients showing sPIC were compared with patients who did not, using univariate statistics. Multivariate analyses with a split-sample validation procedure were used to assess whether predictive models could be developed using independent data sets. RESULTS: O 295 patients, 74 (25.1%) showed sPIC. All patients showing sPIC needed extra medication, 9% (n = 7) required physically restraints, and 5% (n = 4) had to be secluded. Univariate analyses showed several trends: patients with sPIC were more often males (P = 0.05), had more often history of cerebrovascular incident (P = 0.02), did not use concomitant selective serotonin reuptake inhibitors (P = 0.01), received higher median dosage of succinylcholine (P = 0.02), and received pretreatment with flumazenil more often (P = 0.07), but these associations did not remain significant after correction for multiple comparisons. Multiple logistic regression analysis did not result in a model that could predict sPIC in the holdout data set. CONCLUSIONS: In this retrospective naturalistic study in 295 ECT patients, the prevalence of sPIC appeared to be 25%. Patients showing sPIC were characterized by male sex, history of cerebrovascular incident, use of higher-dose succinylcholine, and pretreatment with flumazenil. However, multivariate analysis revealed no significant model to predict sPIC in independent data.

Patient specific intracranial neural signatures of obsessions and compulsions in the ventral striatum.

Objective. Deep brain stimulation is a treatment option for patients with refractory obsessive-compulsive disorder. A new generation of stimulators hold promise for closed loop stimulation, with adaptive stimulation in response to biologic signals. Here we aimed to discover a suitable biomarker in the ventral striatum in patients with obsessive compulsive disorder using local field potentials.Approach.We induced obsessions and compulsions in 11 patients undergoing deep brain stimulation treatment using a symptom provocation task. Then we trained machine learning models to predict symptoms using the recorded intracranial signal from the deep brain stimulation electrodes.Main results.Average areas under the receiver operating characteristics curve were 62.1% for obsessions and 78.2% for compulsions for patient specific models. For obsessions it reached over 85% in one patient, whereas performance was near chance level when the model was trained across patients. Optimal performances for obsessions and compulsions was obtained at different recording sites.Significance. The results from this study suggest that closed loop stimulation may be a viable option for obsessive-compulsive disorder, but that intracranial biomarkers are patient and not disorder specific.Clinical Trial:Netherlands trial registry NL7486.

Increased subcortical brain activity in anxious but not depressed individuals.

BACKGROUND: Anxiety and depressive symptoms usually co-occur. Neuroimaging abnormalities in patients with depression and anxiety disorders are therefore related to a combination of symptoms. Here, we used a large population study to select individuals with anxiety, depressive, or both anxiety and depressive symptoms to identify whether neuroimaging differences are unique or shared between anxiety and depressive symptoms. METHODS: We selected four groups of 200 individuals (anxiety, depression, anxiety and depression, controls) from the UK Biobank, matched for age, sex, intelligence, and educational attainment (total N = 800). We extracted the amplitude of low frequency fluctuations (ALFF) from resting-state functional magnetic resonance imaging data, which indexes spontaneous neuronal activity. Group differences were assessed using permutation testing to correct for multiple comparisons, with age, sex, IQ, and head motion as covariates. RESULTS: Compared to controls, anxious individuals had higher ALFF values in many subcortical brain regions including the striatum, thalamus, medial temporal lobe, midbrain, pons, as well as the cerebellum. Anxious individuals also showed higher ALFF in the hippocampus, parahippocampal gyrus, cerebellum, and pons compared to individuals with depressive symptoms. No significant differences were found for the depression and combined anxiety/depression groups. Post-hoc tests with largest possible samples showed comparable results in the anxiety group and in the combined group, but still no significant differences for the depression group. CONCLUSIONS: Anxiety but not depressive symptoms were associated with increased subcortical activity during rest. This suggest that anxiety symptoms may have the largest contribution to the neuroimaging differences in individuals with depression and anxiety disorders.

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.

Gene Expression has Distinct Associations with Brain Structure and Function in Major Depressive Disorder.

Major depressive disorder (MDD) is associated with structural and functional brain abnormalities. MDD as well as brain anatomy and function are influenced by genetic factors, but the role of gene expression remains unclear. Here, this work investigates how cortical gene expression contributes to structural and functional brain abnormalities in MDD. This work compares the gray matter volume and resting-state functional measures in a Chinese sample of 848 MDD patients and 749 healthy controls, and these case-control differences are then associated with cortical variation of gene expression. While whole gene expression is positively associated with structural abnormalities, it is negatively associated with functional abnormalities. This work observes the relationships of expression levels with brain abnormalities for individual genes, and found that transcriptional correlates of brain structure and function show opposite relations with gene dysregulation in postmortem cortical tissue from MDD patients. This work further identifies genes that are positively or negatively related to structural abnormalities as well as functional abnormalities. The MDD-related genes are enriched for brain tissue, cortical cells, and biological pathways. These findings suggest that distinct genetic mechanisms underlie structural and functional brain abnormalities in MDD, and highlight the importance of cortical gene expression for the development of cortical abnormalities.

The relationship between cognitive functioning and psychopathology in patients with psychiatric disorders: a transdiagnostic network analysis.

BACKGROUND: Patients with psychiatric disorders often experience cognitive dysfunction, but the precise relationship between cognitive deficits and psychopathology remains unclear. We investigated the relationships between domains of cognitive functioning and psychopathology in a transdiagnostic sample using a data-driven approach. METHODS: Cross-sectional network analyses were conducted to investigate the relationships between domains of psychopathology and cognitive functioning and detect clusters in the network. This naturalistic transdiagnostic sample consists of 1016 psychiatric patients who have a variety of psychiatric diagnoses, such as depressive disorders, anxiety disorders, obsessive-compulsive and related disorders, and schizophrenia spectrum and other psychotic disorders. Psychopathology symptoms were assessed using various questionnaires. Core cognitive domains were assessed with a battery of automated tests. RESULTS: Network analysis detected three clusters that we labelled: general psychopathology, substance use, and cognition. Depressive and anxiety symptoms, verbal memory, and visual attention were the most central nodes in the network. Most associations between cognitive functioning and symptoms were negative, i.e. increased symptom severity was associated with worse cognitive functioning. Cannabis use, (subclinical) psychotic experiences, and anhedonia had the strongest total negative relationships with cognitive variables. CONCLUSIONS: Cognitive functioning and psychopathology are independent but related dimensions, which interact in a transdiagnostic manner. Depression, anxiety, verbal memory, and visual attention are especially relevant in this network and can be considered independent transdiagnostic targets for research and treatment in psychiatry. Moreover, future research on cognitive functioning in psychopathology should take a transdiagnostic approach, focusing on symptom-specific interactions with cognitive domains rather than investigating cognitive functioning within diagnostic categories.