White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

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.

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.

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.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

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.

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.

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.

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.

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.

Brain Structure and Function Show Distinct Relations With Genetic Predispositions to Mental Health and Cognition

BACKGROUND

Mental health and cognitive achievement are partly heritable, highly polygenic, and associated with brain variations in structure and function. However, the underlying neural mechanisms remain unclear.

METHODS

We investigated the association between genetic predispositions to various mental health and cognitive traits and a large set of structural and functional brain measures from the UK Biobank (N = 36,799). We also applied linkage disequilibrium score regression to estimate the genetic correlations between various traits and brain measures based on genome-wide data. To decompose the complex association patterns, we performed a multivariate partial least squares model of the genetic and imaging modalities.

RESULTS

The univariate analyses showed that certain traits were related to brain structure (significant genetic correlations with total cortical surface area from r_g = -0.101 for smoking initiation to r_g = 0.230 for cognitive ability), while other traits were related to brain function (significant genetic correlations with functional connectivity from r_g = -0.161 for educational attainment to r_g = 0.318 for schizophrenia). The multivariate analysis showed that genetic predispositions to attention-deficit/hyperactivity disorder, smoking initiation, and cognitive traits had stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders had stronger associations with brain function than with brain structure.

CONCLUSIONS

These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.

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.

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.

Associations of medication with subcortical morphology across the lifespan in OCD: Results from the international ENIGMA Consortium

BACKGROUND

Widely used psychotropic medications for obsessive-compulsive disorder (OCD) may change the volumes of subcortical brain structures, and differently in children vs. adults. We measured subcortical volumes cross-sectionally in patients finely stratified for age taking various common classes of OCD drugs.

METHODS

The ENIGMA-OCD consortium sample (1081 medicated/1159 unmedicated OCD patients and 2057 healthy controls aged 6-65) was divided into six successive 6-10-year age-groups. Individual structural MRIs were parcellated automatically using FreeSurfer into 8 regions-of-interest (ROIs). ROI volumes were compared between unmedicated and medicated patients and controls, and between patients taking serotonin reuptake inhibitors (SRIs), tricyclics (TCs), antipsychotics (APs), or benzodiazepines (BZs) and unmedicated patients.

RESULTS

Compared to unmedicated patients, volumes of accumbens, caudate, and/or putamen were lower in children aged 6-13 and adults aged 50-65 with OCD taking SRIs (Cohen's d = -0.24 to -0.74). Volumes of putamen, pallidum (d = 0.18-0.40), and ventricles (d = 0.31-0.66) were greater in patients aged 20-29 receiving APs. Hippocampal volumes were smaller in patients aged 20 and older taking TCs and/or BZs (d = -0.27 to -1.31).

CONCLUSIONS

Results suggest that TCs and BZs could potentially aggravate hippocampal atrophy of normal aging in older adults with OCD, whereas SRIs may reduce striatal volumes in young children and older adults. Similar to patients with psychotic disorders, OCD patients aged 20-29 may experience subcortical nuclear and ventricular hypertrophy in relation to APs. Although cross-sectional, present results suggest that commonly prescribed agents exert macroscopic effects on subcortical nuclei of unknown relation to therapeutic response.

Study of effect of nimodipine and acetaminophen on postictal symptoms in depressed patients after electroconvulsive therapy (SYNAPSE)

Background

Postictal phenomena as delirium, headache, nausea, myalgia, and anterograde and retrograde amnesia are common manifestations after seizures induced by electroconvulsive therapy (ECT). Comparable postictal phenomena also contribute to the burden of patients with epilepsy. The pathophysiology of postictal phenomena is poorly understood and effective treatments are not available. Recently, seizure-induced cyclooxygenase (COX)-mediated postictal vasoconstriction, accompanied by cerebral hypoperfusion and hypoxia, has been identified as a candidate mechanism in experimentally induced seizures in rats. Vasodilatory treatment with acetaminophen or calcium antagonists reduced postictal hypoxia and postictal symptoms. The aim of this clinical trial is to study the effects of acetaminophen and nimodipine on postictal phenomena after ECT-induced seizures in patients suffering major depressive disorder. We hypothesize that (1) acetaminophen and nimodipine will reduce postictal electroencephalographic (EEG) phenomena, (2) acetaminophen and nimodipine will reduce magnetic resonance imaging (MRI) measures of postictal cerebral hypoperfusion, (3) acetaminophen and nimodipine will reduce clinical postictal phenomena, and (4) postictal phenomena will correlate with measures of postictal hypoperfusion.

Methods

We propose a prospective, three-condition cross-over design trial with randomized condition allocation, open-label treatment, and blinded end-point evaluation (PROBE design). Thirty-three patients (age > 17 years) suffering from a depressive episode treated with ECT will be included. Randomly and alternately, single doses of nimodipine (60 mg), acetaminophen (1000 mg), or water will be given two hours prior to each ECT session with a maximum of twelve sessions per patient. The primary outcome measure is ‘postictal EEG recovery time’, expressed and quantified as an adapted version of the temporal brain symmetry index, yielding a time constant for the duration of the postictal state on EEG. Secondary outcome measures include postictal cerebral perfusion, measured by arterial spin labelling MRI, and the postictal clinical ‘time to orientation’.

Discussion

With this clinical trial, we will systematically study postictal EEG, MRI and clinical phenomena after ECT-induced seizures and will test the effects of vasodilatory treatment intending to reduce postictal symptoms. If an effect is established, this will provide a novel treatment of postictal symptoms in ECT patients. Ultimately, these findings may be generalized to patients with epilepsy.

Trial registration

Inclusion in SYNAPSE started in December 2019. Prospective trial registration number is NCT04028596 on the international clinical trial register on July 22, 2019.

The thalamus and its subnuclei-a gateway to obsessive-compulsive disorder

Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T₁-weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered ( https://osf.io/73dvy ) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (<12 years) had larger lateral (d = 0.46), pulvinar (d = 0.33), ventral (d = 0.35) and whole thalamus (d = 0.40) volumes at unadjusted p-values <0.05. Adolescent patients showed no volumetric differences. Adult OCD patients compared with controls had smaller volumes across all subregions (anterior, lateral, pulvinar, medial, and ventral) and smaller whole thalamic volume (d = -0.15 to -0.07) after multiple comparisons correction, mostly driven by medicated patients and associated with symptom severity. The anterior thalamus was also significantly smaller in patients after adjusting for thalamus size. Our results suggest that OCD-related thalamic volume differences are global and not driven by particular subregions and that the direction of effects are driven by both age and medication status.

Individual prediction of trauma-focused psychotherapy response in youth with posttraumatic stress disorder using resting-state functional connectivity

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ML and rs-fMRI have shown promise in predicting treatment-response in adults with PTSD.

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Currently, no biomarkers for treatment-response are available in youth with PTSD.

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FC between the FPN and SMN was stronger in treatment non-responders on the group-level.

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A network within the bilateral STG predicted response for individual youth with 76% accuracy.

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Future studies should test generalizability of these findings and test if larger cohorts increase accuracy.

Randomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD). However, response varies considerably among individuals. Currently, no biomarkers are available to assist clinicians in identifying youth who are most likely to benefit from treatment. In this study, we investigated whether resting-state functional magnetic resonance imaging (rs-fMRI) could distinguish between responders and non-responders on the group- and individual patient level. Pre-treatment rs-fMRI was recorded in 40 youth (ages 8–17 years) with (partial) PTSD before trauma-focused psychotherapy. Change in symptom severity from pre- to post-treatment was assessed using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders (≥30% symptom reduction) and non-responders. Functional networks were identified using meta-independent component analysis. Group-differences within- and between-network connectivity between responders and non-responders were tested using permutation testing. Individual predictions were made using multivariate, cross-validated support vector machine classification. A network centered on the bilateral superior temporal gyrus predicted treatment response for individual patients with 76% accuracy (p_FWE = 0.02, 87% sensitivity, 65% specificity, area-under-receiver-operator-curve of 0.82). Functional connectivity between the frontoparietal and sensorimotor network was significantly stronger in non-responders (t = 5.35, p_FWE = 0.01) on the group-level. Within-network connectivity was not significantly different between groups. This study provides proof-of-concept evidence for the feasibility to predict trauma-focused psychotherapy response in youth with PTSD at an individual-level. Future studies are required to test if larger cohorts could increase accuracy and to test further generalizability of the prediction models.

Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach

BACKGROUND

Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.

METHODS

In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs).

RESULTS

At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features.

CONCLUSIONS

The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.

An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

The neurobiology of treatment-resistant depression: A systematic review of neuroimaging studies

Treatment-resistant depression (TRD) is a debilitating condition associated with higher medical costs, increased illness burden, and reduced quality of life compared to non-treatment-resistant major depressive disorder (MDD). The question arises whether TRD can be considered a distinct MDD sub-type based on neurobiological features. To answer this question we conducted a systematic review of neuroimaging studies investigating the neurobiological differences between TRD and non-TRD. Our main findings are that patients with TRD show 1) reduced functional connectivity (FC) within the default mode network (DMN), 2) reduced FC between components of the DMN and other brain areas, and 3) hyperactivity of DMN regions. In addition, aberrant activity and FC in the occipital lobe may play a role in TRD. The main limitations of most studies were related to inherent confounding factors for comparing TRD with non-TRD, such as differences in disease chronicity/severity and medication history. Future studies may use prospective longitudinal neuroimaging designs to delineate which effects are present in treatment-naive patients and which effects are the result of disease progression.

Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort

OBJECTIVE

Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.

DESIGN

Retrospective cohort study.

SETTING

A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.

PARTICIPANTS

SARS-CoV-2 positive patients (age ≥18) admitted to the hospital.

MAIN OUTCOME MEASURES

21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.

RESULTS

2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).

CONCLUSION

Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.

[Artificial intelligence in psychiatry: predictive value of characteristics on MR imaging of the brain]

The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological assessment of brain scans usually does not explain the psychological symptoms. However, that does not mean that psychological symptoms have no neurobiological basis. Hope has therefore been placed on functional MRI, which measures the activity of the brain. However, this has not yet resulted in clinical applications. A multivariate approach using machine learning analysis now appears to be changing this. Recent studies show that machine learning analysis of functional as well as structural MRI images can also provide diagnostic, prognostic and predictive biomarkers for psychiatry. Larger studies are needed to develop clinical applications, such as clinical decision support systems to support personalized treatment choices.

Electric field strength induced by electroconvulsive therapy is associated with clinical outcome

The clinical effect of electroconvulsive therapy (ECT) is mediated by eliciting a generalized seizure, which is achieved by applying electrical current to the head via scalp electrodes. The anatomy of the head influences the distribution of current flow in each brain region. Here, we investigated whether individual differences in simulated local electrical field strength are associated with ECT efficacy. We modeled the electric field of 67 depressed patients receiving ECT. Patient's T1 magnetic resonance images were segmented, conductivities were assigned to each tissue and the finite element method was used to solve for the electric field induced by the electrodes. We investigated the correlation between modelled electric field and ECT outcome using voxel-wise general linear models. The difference between bilateral (BL) and right unilateral (RUL) electrode placement was striking. Even within electrode configuration, there was substantial variability between patients. For the modeled BL placement, stronger electric field strengths appeared in the left hemisphere and part of the right temporal lobe. Importantly, a stronger electric field in the temporal lobes was associated with less optimal ECT response in patients treated with BL-ECT. No significant differences in electric field distributions were found between responders and non-responders to RUL-ECT. These results suggest that overstimulation of the temporal lobes during BL stimulation has negative consequences on treatment outcome. If replicated, individualized pre-ECT computer-modelled electric field distributions may inform the development of patient-specific ECT protocols.

[Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave]

OBJECTIVE

To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands.

DESIGN

Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported.

METHODS

Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission.

RESULTS

Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels.

CONCLUSION

Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.

Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

IMPORTANCE

Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood.

OBJECTIVE

To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia.

DESIGN, SETTING, AND PARTICIPANTS

Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244.

MAIN OUTCOMES AND MEASURES

Interregional profiles of group difference in cortical thickness between cases and controls.

RESULTS

A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders.

CONCLUSIONS AND RELEVANCE

In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups

OBJECTIVE

Attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. The authors sought to directly compare these disorders using structural brain imaging data from ENIGMA consortium data.

METHODS

Structural T1-weighted whole-brain MRI data from healthy control subjects (N=5,827) and from patients with ADHD (N=2,271), ASD (N=1,777), and OCD (N=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. The authors examined subcortical volume, cortical thickness, and cortical surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults, using linear mixed-effects models adjusting for age, sex, and site (and intracranial volume for subcortical and surface area measures).

RESULTS

No shared differences were found among all three disorders, and shared differences between any two disorders did not survive correction for multiple comparisons. Children with ADHD compared with those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller intracranial volume than control subjects and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared with adult control subjects and other clinical groups. No OCD-specific differences were observed across different age groups and surface area differences among all disorders in childhood and adulthood.

CONCLUSIONS

The study findings suggest robust but subtle differences across different age groups among ADHD, ASD, and OCD. ADHD-specific intracranial volume and hippocampal differences in children and adolescents, and ASD-specific cortical thickness differences in the frontal cortex in adults, support previous work emphasizing structural brain differences in these disorders.

Effectiveness of Emotional Memory Reactivation vs Control Memory Reactivation Before Electroconvulsive Therapy in Adult Patients With Depressive Disorder: A Randomized Clinical Trial

IMPORTANCE

Although electroconvulsive therapy (ECT) is often effective, approximately half of patients with depression undergoing ECT do not benefit sufficiently, and relapse rates are high. ECT sessions have been shown to weaken reactivated memories. The effect of emotional memory retrieval on cognitive schemas remains unknown.

OBJECTIVE

To assess whether emotional memory retrieval just before patients receive ECT sessions weakens underlying cognitive schemas, improves ECT effectiveness, increases ECT response, and reduces relapse rates.

DESIGN, SETTING, AND PARTICIPANTS

In this multicenter randomized clinical trial conducted from 2014 to 2018 in the departments of psychiatry in 3 hospitals in the Netherlands, 72 participants were randomized 1:1 to 2 parallel groups to receive either emotional memory reactivation (EMR-ECT) or control memory reactivation (CMR-ECT) interventions before ECT sessions. The Hamilton Depression Rating Scale (HDRS [total score range: 0-52, with 0-7 indicating no depression and ≥24 indicating severe depression]) was used to measure symptoms of depression during and after ECT, with a 6-month follow-up period. Participants were between ages 18 and 70 years with a primary diagnosis of unipolar major depressive disorder (MDD) according to the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision) and in whom ECT was indicated. Data analysis was performed from July to November 2019.

INTERVENTIONS

EMR-ECT or CMR-ECT interventions prior to ECT sessions.

MAIN OUTCOMES AND MEASURES

Depression scores and relapse rates within 6 months were assessed with the HDRS and analyzed using logistic and linear multiple regression analyses.

RESULTS

A total of 66 patients (mean [SD] age, 49.3 [12.3] years; 39 [59.1%] women) were randomized to the EMR-ECT group (n = 32) or the CMR-ECT group (n = 34). Regardless of the memory intervention, 42.4% (28 of 66) of patients responded (≥50% decrease of symptom severity on the HDRS). Of patients who responded, 39.3% (11 of 28) relapsed within 6 months. Remission rates (CMR-ECT group, 29.4% [10 of 34] vs EMR-ECT group, 25.0% [8 of 32]; P = .58), mean (SD) HDRS scores after the ECT course (CMR-ECT group, 14.6 [8.6] vs EMR-ECT group, 14.9 [8.8]; P = .88), total mean (SD) number of required ECT sessions for response (CMR-ECT group, 14.9 [7.9] vs EMR-ECT group, 15.6 [7.3]; P = .39), and relapse rates (CMR-ECT group, 46.7% [7 of 15] vs EMR-ECT group, 30.8% [4 of 13]; P = .33) were not significantly altered by the intervention.

CONCLUSIONS AND RELEVANCE

Study findings suggest that the EMR-ECT intervention just before patient receipt of ECT for depression did not improve effectiveness, increase speed of response, or reduce relapse rates after the ECT course compared with patients receiving CMR-ECT.

TRIAL REGISTRATION

Trialregister.nl Identifier: NL4289.

Distance to white matter trajectories is associated with treatment response to internal capsule deep brain stimulation in treatment-refractory depression

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Stimulation closer to tracts was associated with better outcome in DBS for depression.

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Lead placement was consistent across (non)responders w.r.t. anatomical landmarks.

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Tractography-guided surgery needed to ensure tracts lie within activated tissue.

Background

Deep brain stimulation (DBS) is an innovative treatment for treatment-refractory depression. DBS is usually targeted at specific anatomical landmarks, with patients responding to DBS in approximately 50% of cases. Attention has recently shifted to white matter tracts to explain DBS response, with initial open-label trials targeting white matter tracts yielding much higher response rates (>70%).

Objective/Hypothesis

Our aim was to associate distance to individual white matter tracts around the stimulation target in the ventral anterior limb of the internal capsule to treatment response.

Methods

We performed diffusion magnetic resonance tractography of the superolateral branch of the medial forebrain bundle and the anterior thalamic radiation in fourteen patients that participated in our randomized clinical trial. We combined the tract reconstructions with the postoperative images to identify the DBS leads and estimated the distance between tracts and leads, which we subsequently associated with treatment response.

Results

Stimulation closer to both tracts was significantly correlated to a larger symptom decrease (r = 0.61, p = 0.02), suggesting that stimulation more proximal to the tracts was beneficial. Biophysical modelling indicated that 37.5% of tracts were even outside the volume of activated tissue. There was no difference in lead placement with respect to anatomical landmarks, which could mean that differences in treatment response were driven by individual differences in white matter anatomy.

Conclusions

Our results suggest that deep brain stimulation of the ventral anterior limb of the internal capsule could benefit from targeting white matter bundles. We recommend acquiring diffusion magnetic resonance data for each individual patient.

Mapping Cortical and Subcortical Asymmetry in Obsessive-Compulsive Disorder: Findings From the ENIGMA Consortium

BACKGROUND

Lateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.

METHODS

We studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.

RESULTS

In the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -0.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.

CONCLUSIONS

The results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.

Effects of Recreational GHB Use and Multiple GHB-Induced Comas on Brain Structure and Impulsivity

BACKGROUND AND AIMS

The regular use of gamma-hydroxybutyrate acid (GHB) can induce GHB-induced comas. Other substance use disorders are associated with alterations in brain structure and impulsivity. Here we aim to investigate if these are also modulated by either regular GHB use or GHB-induced comas.

METHODS

In a sample of human males, structural and diffusion neuroimaging data were collected for 27 GHB users with ≥4 GHB-induced comas (GHB-Coma), 27 GHB users without GHB-induced comas (GHB-NoComa), and 27 polydrug users who never used GHB (No-GHB). The structural brain parameters were analyzed macroscopically using voxel-based morphometry and microscopically using tract-based spatial statistics (TBSS) and tractography. Impulsivity was assessed with the Barrat Impulsivity Scale.

RESULTS

In comparison to the other two groups, the GHB-Coma group showed a higher fractional anisotropy in the body of the corpus callosum and a lower mean diffusivity in the forceps minor (i.e., whole-brain TBSS analysis). No macrostructural differences nor microstructural differences, as assessed with tractography, were observed. The GHB-Coma group also reported higher impulsivity, which was more strongly associated with white matter volume and fractional anisotropy in tracts involved in impulse control (post-hoc analysis). GHB use per se was associated neither with differences in brain structure nor with impulsivity.

CONCLUSIONS

The results suggest that multiple GHB-induced comas, but not GHB use per se, are associated with microstructural alterations in white matter and with higher self-reported impulsivity, which in turn was associated with white matter tracts involved in impulse control.

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.

Demographic and Clinical Characteristics of Regular GHB-Users with and without GHB-Induced Comas

Gamma hydroxybutyric acid (GHB) has been used recreationally for nearly three decades and its chronic use is frequently associated with serious adverse events including GHB-intoxication with GHB-induced comas. Moreover, despite its low prevalence, the number of individuals with GHB-use disorders is steadily increasing. However, the risk-factors associated with chronic GHB-use or the development of a GHB-use disorders remain poorly understood. Purpose: This study aims to profile two types of GHB-users, those with and those without GHB-induced comas. Methods: We included 27 GHB users with ≥4 GHB-induced comas (GHB-Coma), 27 GHB users without a coma (GHB-NoComa), and 27 polydrug users who never used GHB (No-GHB). Participants completed self-reported questionnaires in order to assess their demographic and clinical features, and their use profile of GHB and other drugs. Results: The typical GHB user in our sample was young, single, living alone, well-educated, and a student. The GHB-Coma group had lower self-control and reported higher negative affect than the GHB-NoComa group. GHB-Coma participants were heavier GHB users and mostly used GHB alone at home, whereas the GHB-NoComa group mostly used GHB with friends and in nightclubs. Remarkably, the majority of participants were not concerned about potential neurocognitive impairments induced by GHB-intoxication and/or GHB-induced comas. Conclusion: In this assessment, different profiles for recreational users with and without GHB-induced comas were well expressed. Their description contributes to a better understanding of the risk factors associated with recreational GHB-use, GHB-induced coma, and the development of GHB-use disorders.

The Link Between Structural and Functional Brain Abnormalities in Depression: A Systematic Review of Multimodal Neuroimaging Studies

BACKGROUND

Adequate and timely identification of depression is essential to improve patient care. A potential method to achieve this is by using neuroimaging. Many neuroimaging studies have revealed widespread abnormalities in brain structure and function in patients with depression, but in most studies only single neuroimaging modalities were used. Links between abnormalities in brain structure and function need to be therefore further explored in order to define diagnostic and therapeutic applications.

METHODS

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was conducted.

RESULTS

Out of 2,516 articles, only 14 studies were eligible to be included. These studies combined structural and functional neuroimaging methods in depressed patients compared to controls. Four studies reported a negative relationship between brain structure and function within the default mode network: reduced gray or white matter integrity in depressed patients compared to healthy controls was associated with enhanced neural activity or connectivity. The other studies reported positive relationships (two studies), mixed relationships (two studies), or no link (six studies) between structural and functional brain abnormalities.

CONCLUSION

This systematic literature review revealed no robust relationship between abnormalities in brain structure and function in patients with depression. Remarkably, only 14 studies could be included and four of these suggested enhanced default mode network connectivity associated with reduced structural brain integrity. In the ongoing development of the diagnostic and treatment applications of neuroimaging, large-scale studies that combine structural with functional neuroimaging are required to determine the relationship between structural and functional abnormalities in depression.

Spatial versus angular resolution for tractography-assisted planning of deep brain stimulation

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Deep brain stimulation (DBS) benefits from precise targeting of white matter tracts.

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Better to increase spatial vs. angular resolution for separating parallel tracts.

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Scanning time trade-off between angular & spatial resolution depends on local anatomy.

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We recommend increased spatial resolution dMRI for tract-guided internal capsule DBS.

Given the restricted total scanning time for clinical neuroimaging, it is unclear whether clinical diffusion MRI protocols would benefit more from higher spatial resolution or higher angular resolution. In this work, we investigated the relative benefit of improving spatial or angular resolution in diffusion MRI to separate two parallel running white matter tracts that are targets for deep brain stimulation: the anterior thalamic radiation and the supero-lateral branch of the medial forebrain bundle. Both these tracts are situated in the ventral anterior limb of the internal capsule, and recent studies suggest that targeting a specific tract could improve treatment efficacy. Therefore, we scanned 19 healthy volunteers at 3T and 7T according to three diffusion MRI protocols with respectively standard clinical settings, increased spatial resolution of 1.4 mm, and increased angular resolution (64 additional gradient directions at b = 2200s/mm²). We performed probabilistic tractography for all protocols and quantified the separability of both tracts. The higher spatial resolution protocol improved separability by 41% with respect to the clinical standard, presumably due to decreased partial voluming. The higher angular resolution protocol resulted in increased apparent tract volumes and overlap, which is disadvantageous for application in precise treatment planning. We thus recommend to increase the spatial resolution for deep brain stimulation planning to 1.4 mm while maintaining angular resolution. This recommendation complements the general advice to aim for high angular resolution to resolve crossing fibers, confirming that the specific application and anatomical considerations are leading in clinical diffusion MRI protocol optimization.

Recreational use of GHB is associated with alterations of resting state functional connectivity of the central executive and default mode networks

Gamma-hydroxybutyrate acid (GHB) is a recreational drug with a high addictive potential. Severe side effects such as GHB-induced coma are common and linked to increased emergency room attendances. Task-based functional-imaging studies have revealed an association between the regular use of GHB and multiple GHB-induced comas, and altered neurocognitive function. However the effects of multiple GHB-induced comas and regular GHB-use on intrinsic brain connectivity during rest remain unknown. The study population consisted of 23 GHB-users with ≥4 GHB-induced comas (GHB-Coma), 22 GHB-users who never experienced a GHB-induced coma (GHB-NoComa) and 24 polydrug users who never used GHB (No-GHB). Resting-state scans were collected to assess resting-state functional-connectivity within and between the default mode network (DMN), the bilateral central executive network (CEN) and the salience network (SN). The GHB-NoComa group showed decreased rsFC of the right CEN with a region in the anterior cingulate cortex (pFWE  = 0.048) and decreased rsFC between the right CEN and the DMN (pFWE  = 0.048) when compared with the No-GHB group. These results suggest that regular GHB-use is associated with decreased rsFC within the right CEN and between the right CEN and the DMN. The presence of multiple GHB-induced comas is not associated with (additional) alterations in rsFC.

Individual Prediction of Behavioral Variant Frontotemporal Dementia Development Using Multivariate Pattern Analysis of Magnetic Resonance Imaging Data

BACKGROUND

Patients with behavioral variant of frontotemporal dementia (bvFTD) initially may only show behavioral and/or cognitive symptoms that overlap with other neurological and psychiatric disorders. The diagnostic accuracy is dependent on progressive symptoms worsening and frontotemporal abnormalities on neuroimaging findings. Predictive biomarkers could facilitate the early detection of bvFTD.

OBJECTIVE

To determine the prognostic accuracy of clinical and structural MRI data using a support vector machine (SVM) classification to predict the 2-year clinical follow-up diagnosis in a group of patients presenting late-onset behavioral changes.

METHODS

Data from 73 patients were included and divided into probable/definite bvFTD (n = 18), neurological (n = 28), and psychiatric (n = 27) groups based on 2-year follow-up diagnosis. Grey-matter volumes were extracted from baseline structural MRI scans. SVM classifiers were used to perform three binary classifications: bvFTD versus neurological and psychiatric, bvFTD versus neurological, and bvFTD versus psychiatric group(s), and one multi-class classification. Classification performance was determined for clinical and neuroimaging data separately and their combination using 5-fold cross-validation.

RESULTS

Accuracy of the binary classification tasks ranged from 72-82% (p < 0.001) with adequate sensitivity (67-79%), specificity (77-88%), and area-under-the-receiver-operator-curve (0.80-0.9). Multi-class accuracy ranged between 55-59% (p < 0.001). The combination of clinical and voxel-wise whole brain data showed the best performance overall.

CONCLUSION

These results show the potential for automated early confirmation of diagnosis for bvFTD using machine learning analysis of clinical and neuroimaging data in a diverse and clinically relevant sample of patients.

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.

Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.

Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.

Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

The Longitudinal Effects of Electroconvulsive Therapy on Ictal Interhemispheric Coherence and Its Associations With Treatment Outcome: A Naturalistic Cohort Study

OBJECTIVES

Electroconvulsive therapy (ECT) is an effective treatment for severe depression. Electroencephalogram (EEG) measures between ECT sessions seem to be related to the antidepressant efficacy of ECT. In this naturalistic cohort study, we examine longitudinal effects of ECT on interhemispheric EEG coherence measures during seizure activity and its relation to the antidepressant efficacy.

METHODS

This study included 65 patients diagnosed with severe depressive disorder. Depressive symptoms were rated according to the Montgomery-Åsberg Depression Rating Scale before and after the course of ECT. Frequency-specific ictal interhemispheric (fp1-fp2) EEG coherence measures were established during the first and each consecutive sixth treatment session. Linear mixed-effect models were used to determine longitudinal changes in ictal coherence measures during the course of ECT and its relation to treatment efficacy.

RESULTS

Ictal interhemispheric coherence in the theta and alpha frequency bands increased over the course of treatment, whereas no significant change was found for the delta and beta frequency bands. A main effect of treatment efficacy on the interhemispheric coherence in the delta and theta band was revealed. However, the longitudinal effects of ECT were not associated with treatment efficacy.

CONCLUSION

The current study suggests that interhemispheric coherence during ECT-induced seizures increases over the course of treatment. Furthermore, these longitudinal effects seem to be unrelated to the antidepressant efficacy of ECT. These findings contribute to the understanding of the mechanism of action of ECT.

Influence of Gamma-Hydroxybutyric Acid-Use and Gamma-Hydroxybutyric Acid-Induced Coma on Affect and the Affective Network

BACKGROUND

Gamma-hydroxybutyric acid (GHB) is a drug of abuse associated with increased emergency room attendances, due to GHB-induced comas. Withdrawal from GHB often increases social anxiety and is linked to alterations in emotion processing. However, little is known about the effects of GHB-use and GHB-induced comas on affect regulation in humans.

OBJECTIVES

We aimed to assess the effect of GHB-use and GHB-induced comas on the affective network.

METHOD

We recruited 27 GHB users with ≥4 GHB-induced comas (GHB-Coma), 27 GHB users without a GHB-induced coma (GHB-NoComa), and 27 polydrug users who never used GHB (No-GHB). Participants completed self-report questionnaires assessing negative affect (depression, anxiety and stress) and performed an emotional face matching task during functional magnetic resonance imaging to probe activity of the amygdala and the hippocampus.

RESULTS

The GHB-Coma group reported higher levels of depression, anxiety, and stress; showed decreased activity of the hippocampus; and increased functional connectivity of the left hippocampus with the left fusiform gyrus and a cluster on the left temporal-parietal-occipital junction, when compared with the 2 other groups. The GHB-NoComa group showed decreased functional connectivity of the left hippocampus with the amygdala in comparison with the No-GHB group.

CONCLUSIONS

GHB-use but in particular GHB-induced comas, are associated with altered emotion identification and hippocampal functioning. Awareness campaigns are required to raise consciousness about the adverse effects of GHB-induced comas on affect regulation, despite the absence of subjective side effects.

Effect of GHB-use and GHB-induced comas on dorsolateral prefrontal cortex functioning in humans

BACKGROUND

Gamma-hydroxybutyric acid (GHB) is a recreational drug associated with increasing numbers of GHB-dependent patients and emergency attendances often related to GHB-induced comas. Working memory (WM) deficits have been reported in association with GHB use, and animal studies have shown that GHB induces oxidative stress in vulnerable WM-related brain areas such as the dorsolateral prefrontal cortex (DLPFC). However, the effects of chronic GHB use and multiple GHB-induced comas on WM-related brain function in humans remains unknown.

METHODS

We recruited 27 GHB users with ≥4 GHB-induced comas (GHB-Coma), 27 GHB users who never experienced GHB-induced coma (GHB-NoComa), and 27 polydrug users who never used GHB (No-GHB). Participants performed an n-back WM task during functional magnetic resonance imaging (fMRI) to probe DLPFC functioning.

RESULTS

The GHB-Coma group had lower premorbid IQ (p = .006) than the GHB-NoComa group despite comparable age and education level. There were also group differences in the use of other drugs than GHB. Therefore, all group comparisons were adjusted for IQ and drug use other than GHB. Compared with the GHB-NoComa and the No-GHB groups, the GHB-Coma group showed increased activity in the right DLPFC (pSVC = 0.028) and increased functional connectivity of the right DLPFC with a cluster comprising the left anterior cingulate and medial frontal gyrus (pFWE = 0.003). No significant fMRI differences were observed between the GHB-NoComa and No-GHB groups. Due to technical problems, no behavioural data were collected.

DISCUSSION

These results suggest that multiple GHB-induced comas, but not GHB-use per se, are associated with alterations in WM-related brain function. Public awareness campaigns are required to minimize the potential adverse effects induced by GHB recreational use, and especially GHB-induced comas, even if no immediate side effects are experienced.

Adverse effects of GHB-induced coma on long-term memory and related brain function

BACKGROUND

Gamma-Hydroxybutyric acid (GHB) is a drug of abuse associated with increasing numbers of GHB-dependent patients and emergency attendances often related to GHB-induced coma. Animal studies suggest that GHB induces oxidative stress in the hippocampus, resulting in memory impairments. However, the consequences of chronic GHB use and GHB-induced coma on human brain function and cognition are unknown.

METHODS

We recruited 27 GHB users with ≥4 GHB-induced comas (GHB-Coma), 27 GHB users without a coma (GHB-NoComa), and 27 polydrug users who never used GHB (No-GHB). Participants completed verbal and spatial memory tests and an associative memory encoding task during functional magnetic resonance imaging (fMRI) to probe hippocampus functioning.

RESULTS

The GHB-Coma group showed a lower premorbid IQ (p = 0.006) and performed worse on the verbal memory test (p = 0.017) compared to the GHB-NoComa group, despite exhibiting similar levels of education. Compared with the other two groups, the GHB-Coma group showed lower left hippocampus (p_SVC = 0.044) and left lingual gyrus (p_FWE = 0.017) activity, and a trend for lower hippocampal functional connectivity with the left superior temporal cortex during performance of the associative memory encoding task (p_FWE = 0.063). No significant differences were observed between the GHB-NoComa group and the No-GHB group.

CONCLUSIONS

These results suggest that multiple GHB-induced comas, but not the use of GHB per se, are associated with alterations of memory performance and memory-related brain, although no causal link can be inferred from this cross-sectional study. The results highlight the need for public awareness to minimize the negative health consequences of recreational GHB use, in particular when related with GHB-induced comas.

Cortical Abnormalities Associated With Pediatric and Adult Obsessive-Compulsive Disorder: Findings From the ENIGMA Obsessive-Compulsive Disorder Working Group

OBJECTIVE

Brain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken.

METHOD

T₁-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume.

RESULTS

In adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortex and a thinner inferior parietal cortex. Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showed significant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from -0.10 to -0.33.

CONCLUSIONS

The parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication.

Enhanced Amygdala-Striatal Functional Connectivity during the Processing of Cocaine Cues in Male Cocaine Users with a History of Childhood Trauma

BACKGROUND AND AIMS

Childhood trauma is associated with increased levels of anxiety later in life, an increased risk for the development of substance use disorders, and neurodevelopmental abnormalities in the amygdala and frontostriatal circuitry. The aim of this study was to investigate the (neurobiological) link among childhood trauma, state anxiety, and amygdala-frontostriatal activity in response to cocaine cues in regular cocaine users.

METHODS

In this study, we included 59 non-treatment seeking regular cocaine users and 58 non-drug using controls. Blood oxygenation level-dependent responses were measured using functional magnetic resonance imaging while subjects performed a cue reactivity paradigm with cocaine and neutral cues. Psychophysiological interaction analyses were applied to assess functional connectivity between the amygdala and other regions in the brain. Self-report questionnaires were used to measure childhood trauma, state anxiety, drug use, drug use severity, and craving.

RESULTS

Neural activation was increased during the presentation of cocaine cues, in a widespread network including the frontostriatal circuit and amygdala in cocaine users but not in controls. Functional coupling between the amygdala and medial prefrontal cortex was reduced in response to cocaine cues, in both cocaine users and controls, which was further diminished with increasing state anxiety. Importantly, amygdala-striatal connectivity was positively associated with childhood trauma in regular cocaine users, while there was a negative association in controls. At the behavioral level, state anxiety was positively associated with cocaine use severity and craving related to negative reinforcement.

CONCLUSION

Childhood trauma is associated with enhanced amygdala-striatal connectivity during cocaine cue reactivity in regular cocaine users, which may contribute to increased habit behavior and poorer cognitive control. While we cannot draw conclusions on causality, this study provides novel information on how childhood trauma may contribute to the development and persistence of cocaine use disorder.

Prefrontal Glx and GABA concentrations and impulsivity in cigarette smokers and smoking polysubstance users

Glutamate and GABA play an important role in substance dependence. However, it remains unclear whether this holds true for different substance use disorders and how this is related to risk-related traits such as impulsivity. We, therefore, compared Glx (as a proxy measure for glutamate) and GABA concentrations in the dorsal anterior cingulate cortex (dACC) of 48 male cigarette smokers, 61 male smoking polysubstance users, and 90 male healthy controls, and investigated the relationship with self-reported impulsivity and substance use. Glx and GABA concentrations were measured using proton Magnetic Resonance Spectroscopy. Impulsivity, smoking, alcohol and cocaine use severity and cannabis use were measured using self-report instruments. Results indicate a trend towards group differences in Glx. Post-hoc analyses showed a difference between smokers and healthy controls (p=0.04) and a trend towards higher concentrations in smoking polysubstance users and healthy controls (p=0.09), but no differences between smokers and smoking polysubstance users. dACC GABA concentrations were not significantly different between groups. Smoking polysubstance users were more impulsive than smokers, and both groups were more impulsive than controls. No significant associations were observed between dACC neurotransmitter concentrations and impulsivity and level and severity of smoking, alcohol or cocaine use or the presence of cannabis use. The results indicate that differences in dACC Glx are unrelated to type and level of substance use. No final conclusion can be drawn on the lack of GABA differences due to assessment difficulties. The relationship between dACC neurotransmitter concentrations and cognitive impairments other than self-reported impulsivity should be further investigated.

White matter alterations in cocaine users are negatively related to the number of additionally (ab)used substances

Diffusion tensor imaging studies have provided evidence for white matter (WM) alterations in cocaine users. While polysubstance use is a widespread phenomenon among cocaine users, its role in WM alterations in cocaine users is currently unknown. This study examined the relation between the number of substances that are used(cocaine, alcohol and marijuana) and WM alterations in 67 male non-drug users and 67 male regular cocaine users, who were classified into five groups based on the number of used substances. Diffusion-weighted images were acquired on a 3.0 T magnetic resonance imaging scanner. Using tract-based spatial statistics we demonstrated that there was a negative relation between the number of used substances and fractional anisotropy, a global measure of WM integrity. Also, we demonstrated a positive relation between the number of used substance and radial diffusivity within the prefrontal lobe, suggesting an increase in demyelination with the number of used substances. We did not find a dose-effect between the level of substance use and WM alterations. The results of the current study may reflect the presence of a pre-existing vulnerability to polysubstance use resulting from prefrontal WM abnormalities and related impaired cognitive control although WM alterations because of polysubstance use cannot be fully excluded. This study is an important first step in understanding the problems related to polysubstance use among cocaine users.