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.

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.

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.

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.

Hippocampal, thalamic, and amygdala subfield morphology in major depressive disorder: an ultra-high resolution MRI study at 7-Tesla.

Morphological changes in the hippocampal, thalamic, and amygdala subfields have been suggested to form part of the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented in-depth examinations at the subfield level, precluding a fine-grained understanding of these subfields and their involvement in MDD pathophysiology. We uniquely employed ultra-high field MRI at 7.0 Tesla to map hippocampal, thalamic, and amygdala subfields in MDD. Fifty-six MDD patients and 14 healthy controls (HCs) were enrolled in the final analysis. FreeSurfer protocols were used to segment hippocampal, thalamic, and amygdala subfields. Bayesian analysis was then implemented to assess differences between groups and relations with clinical features. While no effect was found for MDD diagnosis (i.e., case-control comparison), clinical characteristics of MDD patients were associated with subfield volumes of the hippocampus, thalamus, and amygdala. Specifically, the severity of depressive symptoms, insomnia, and childhood trauma in MDD patients related to lower thalamic subfield volumes. In addition, MDD patients with typical MDD versus those with atypical MDD showed lower hippocampal, thalamic, and amygdala subfield volumes. MDD patients with recurrent MDD versus those with first-episode MDD also showed lower thalamic subfield volumes. These findings allow uniquely fine-grained insights into hippocampal, thalamic, and amygdala subfield morphology in MDD, linking some of them to the clinical manifestation of MDD.

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.

Differences in Olivo-Cerebellar Circuit and Cerebellar Network Connectivity in Essential Tremor: a Resting State fMRI Study.

The olivo-cerebellar circuit is thought to play a crucial role in the pathophysiology of essential tremor (ET). Whether olivo-cerebellar circuit dysfunction is also present at rest, in the absence of clinical tremor and linked voluntary movement, remains unclear. Assessing this network in detail with fMRI is challenging, considering the brainstem is close to major arteries and pulsatile cerebrospinal fluid-filled spaces obscuring signals of interest. Here, we used methods tailored to the analysis of infratentorial structures. We hypothesize that the olivo-cerebellar circuit shows altered intra-network connectivity at rest and decreased functional coupling with other parts of the motor network in ET. In 17 ET patients and 19 healthy controls, we investigated using resting state fMRI intracerebellar functional and effective connectivity on a dedicated cerebellar atlas. With independent component analysis, we investigated data-driven cerebellar motor network activations during rest. Finally, whole-brain connectivity of cerebellar motor structures was investigated using identified components. In ET, olivo-cerebellar pathways show decreased functional connectivity compared with healthy controls. Effective connectivity analysis showed an increased inhibitory influence of the dentate nucleus towards the inferior olive. Cerebellar independent component analyses showed motor resting state networks are less strongly connected to the cerebral cortex compared to controls. Our results indicate the olivo-cerebellar circuit to be affected at rest. Also, the cerebellum is "disconnected" from the rest of the motor network. Aberrant activity, generated within the olivo-cerebellar circuit could, during action, spread towards other parts of the motor circuit and potentially underlie the characteristic tremor of this patient group.

Common and differential connectivity profiles of deep brain stimulation and capsulotomy in refractory obsessive-compulsive disorder.

Neurosurgical interventions including deep brain stimulation (DBS) and capsulotomy have been demonstrated effective for refractory obsessive-compulsive disorder (OCD), although treatment-shared/-specific network mechanisms remain largely unclear. We retrospectively analyzed resting-state fMRI data from three cohorts: a cross-sectional dataset of 186 subjects (104 OCD and 82 healthy controls), and two longitudinal datasets of refractory patients receiving ventral capsule/ventral striatum DBS (14 OCD) and anterior capsulotomy (27 OCD). We developed a machine learning model predictive of OCD symptoms (indexed by the Yale-Brown Obsessive Compulsive Scale, Y-BOCS) based on functional connectivity profiles and used graphic measures of network communication to characterize treatment-induced profile changes. We applied a linear model on 2 levels treatments (DBS or capsulotomy) and outcome to identify whether pre-surgical network communication was associated with differential treatment outcomes. We identified 54 functional connectivities within fronto-subcortical networks significantly predictive of Y-BOCS score in patients across 3 independent cohorts, and observed a coexisting pattern of downregulated cortico-subcortical and upregulated cortico-cortical network communication commonly shared by DBS and capsulotomy. Furthermore, increased cortico-cortical communication at ventrolateral and centrolateral prefrontal cortices induced by DBS and capsulotomy contributed to improvement of mood and anxiety symptoms, respectively (p < 0.05). Importantly, pretreatment communication of ventrolateral and centrolateral prefrontal cortices were differentially predictive of mood and anxiety improvements by DBS and capsulotomy (effect sizes = 0.45 and 0.41, respectively). These findings unravel treatment-shared and treatment-specific network characteristics induced by DBS and capsulotomy, which may facilitate the search of potential evidence-based markers for optimally selecting among treatment options for a patient.

Tractography-based versus anatomical landmark-based targeting in vALIC deep brain stimulation for refractory obsessive-compulsive disorder.

Deep brain stimulation (DBS) of the ventral anterior limb of the internal capsule (vALIC) is effective for refractory obsessive-compulsive disorder (OCD). Retrospective evaluation showed that stimulation closer to the supero-lateral branch of the medial forebrain bundle (slMFB), within the vALIC, was associated with better response to DBS. The present study is the first to compare outcomes of DBS targeted at the vALIC using anatomical landmarks and DBS with connectomic tractography-based targeting of the slMFB. We included 20 OCD-patients with anatomical landmark-based DBS of the vALIC that were propensity score matched to 20 patients with tractography-based targeting of electrodes in the slMFB. After one year, we compared severity of OCD, anxiety and depression symptoms, response rates, time to response, number of parameter adjustments, average current, medication usage and stimulation-related adverse effects. There was no difference in Y-BOCS decrease between patients with anatomical landmark-based and tractography-based DBS. Nine (45%) patients with anatomical landmark-based DBS and 13 (65%) patients with tractography-based DBS were responders (BF10 = 1.24). The course of depression and anxiety symptoms, time to response, number of stimulation adjustments or medication usage did not differ between groups. Patients with tractography-based DBS experienced fewer stimulation-related adverse effects than patients with anatomical landmark-based DBS (38 vs 58 transient and 1 vs. 17 lasting adverse effects; BF10 = 14.968). OCD symptoms in patients with anatomical landmark-based DBS of the vALIC and tractography-based DBS of the slMFB decrease equally, but patients with tractography-based DBS experience less adverse effects.

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.

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.

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.

Deep brain stimulation modulates directional limbic connectivity in obsessive-compulsive disorder.

Deep brain stimulation is effective for patients with treatment-refractory obsessive-compulsive disorder. Deep brain stimulation of the ventral anterior limb of the internal capsule rapidly improves mood and anxiety with optimal stimulation parameters. To understand these rapid effects, we studied functional interactions within the affective amygdala circuit. We compared resting state functional MRI data during chronic stimulation versus 1 week of stimulation discontinuation in patients, and obtained two resting state scans from matched healthy volunteers to account for test-retest effects. Imaging data were analysed using functional connectivity analysis and dynamic causal modelling. Improvement in mood and anxiety following deep brain stimulation was associated with reduced amygdala-insula functional connectivity. Directional connectivity analysis revealed that deep brain stimulation increased the impact of the ventromedial prefrontal cortex on the amygdala, and decreased the impact of the amygdala on the insula. These results highlight the importance of the amygdala circuit in the pathophysiology of obsessive-compulsive disorder, and suggest a neural systems model through which negative mood and anxiety are modulated by stimulation of the ventral anterior limb of the internal capsule for obsessive-compulsive disorder and possibly other psychiatric disorders.

Protocol Across study: longitudinal transdiagnostic cognitive functioning, psychiatric symptoms, and biological parameters in patients with a psychiatric disorder.

BACKGROUND: Patients with psychiatric disorders, such as major depressive disorder, schizophrenia or obsessive-compulsive disorder, often suffer from cognitive dysfunction. The nature of these dysfunctions and their relation with clinical symptoms and biological parameters is not yet clear. Traditionally, cognitive dysfunction is studied in patients with specific psychiatric disorders, disregarding the fact that cognitive deficits are shared across disorders. The Across study aims to investigate cognitive functioning and its relation with psychiatric symptoms and biological parameters transdiagnostically and longitudinally. METHODS: The study recruits patients diagnosed with a variety of psychiatric disorders and has a longitudinal cohort design with an assessment at baseline and at one-year follow-up. The primary outcome measure is cognitive functioning. The secondary outcome measures include clinical symptoms, electroencephalographic, genetic and blood markers (e.g., fatty acids), and hair cortisol concentration levels. DISCUSSION: The Across study provides an opportunity for a transdiagnostic, bottom-up, data-driven approach of investigating cognition in relation to symptoms and biological parameters longitudinally in patients with psychiatric disorders. The study may help to find new clusters of symptoms, biological markers, and cognitive dysfunctions that have better prognostic value than the current diagnostic categories. Furthermore, increased insight into the relationship among cognitive deficits, biological parameters, and psychiatric symptoms can lead to new treatment possibilities. TRIAL REGISTRATION: Netherlands Trial Register (NTR): NL8170.

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

BACKGROUND: 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.

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.

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.