Connectivity In Major Depression Research Articles

A comparative study of interhemispheric functional connectivity in major depression and schizophrenia

BackgroundMajor depressive disorder (MDD) and schizophrenia (SZ) are serious psychiatric disorders that, despite exhibiting different diagnostic criteria, exhibit significant overlap regarding the biological and clinical features of affected patients. While prior evidence has shown that interhemispheric functional connectivity (FC) is abnormal in MDD and SZ, the particular similarities and differences that unify and characterize MDD and SZ regarding these interhemispheric FC patterns remain to be characterized. This study was thus designed to conduct an in-depth analysis of MDD- and SZ-related patterns of interhemispheric FC. MethodsThis study enrolled MDD patients, SZ patients, and normal control (NC) individuals (n = 36 each). Resting-state functional MRI (rs-fMRI) studies of these patients were conducted, after which voxel-mirrored homotopic connectivity (VMHC) was used to analyze the preprocesses rs-fMRI data. The VMHC values in these different values were then compared through one-way ANOVAs and post hoc analyses. ResultsSignificant differences were observed in both the striatum and middle frontal gyrus (MFG) when comparing these three groups. Through pairwise comparisons, MDD patients but not SZ patients exhibited reduced MFG VMHC values relative to the NC individuals. Conversely, striatum VMHC values significantly increased in SZ patients relative to NC individuals and MDD patients. ConclusionThese results support the interhemispheric functional disconnection hypothesis as a basis for the pathogenesis of MDD and SZ. The observed differences in interhemispheric FC in the MFG and striatum of MDD and SZ patients will offer a neuroimaging basis that can aid in the differential diagnosis of these debilitating conditions.

Resting-state functional connectivity graph-properties correlate with bipolar disorder-risk in young medication-free depressed subjects: Bipolar-risk Resting State Functional Connectivity in Major Depression

BackgroundMajor Depressive Disorder (MDD) is frequently associated with risk factors for the development of Bipolar Disorder (BD). Using graph theory, we investigated brain network properties associated with BD risk factors in young MDD subjects. MethodsResting-state fMRI was acquired from a large cohort (N= 104) of medication-free currently depressed participants (25 BD depression (BDD), 79 MDD). Lifetime mania symptom count (LMSC), current Young Mania Rating Scale (YMRS) score, and family history of mood disorders (FHMD) were examined as BD risk factors. Functional connectivity matrices from 280 regions of interests (ROIs) were first entered into the Network Based Statistic (NBS) toolbox to identify connections that varied with each risk factor. Next, within the correlated network for each risk factor, global and nodal graph properties for the top five linked nodes were calculated. Last, using identified graph properties, machine learning classification (MLC) between BDD, MDD with BD risk factors (MDD+), and without BD risk factors (MDD-) was conducted. ResultsLMSC positively correlated with left lateral orbitofrontal cortex (LOFC) Communication Efficiency and with left middle temporal Eigenvector Centrality. Current YMRS score positively correlated with right amygdala Communication Efficiency and Closeness Centrality. FHMD positively correlated with right insula Eigenvector Centrality. Acceptable MLC accuracy was seen between BDD and MDD- using middle temporal Eigenvector Centrality, whereas moderate accuracy was seen between MDD+ and MDD- using OFC Communication Efficiency. LimitationAlthough participants were medication-free, they were not medication-naïve. ConclusionFunctional connectome graph properties may serve as BD vulnerability biomarkers in young individuals with MDD.

Effects of Serial Ketamine Infusions on Corticolimbic Functional Connectivity in Major Depression

BackgroundKetamine is a highly effective antidepressant for patients with treatment-resistant major depressive disorder (MDD). Resting-state functional magnetic resonance imaging studies show disruptions of functional connectivity (FC) between limbic regions and resting-state networks (RSNs) in MDD, including the default mode network, central executive network (CEN), and salience network (SN). Here, we investigated whether serial ketamine treatments change FC between limbic structures and RSNs. MethodsPatients with MDD (n = 44) were scanned at baseline (time 1 [T1]) and 24 hours after the first (T2) and fourth (T3) infusions of ketamine. Healthy control subjects (n = 50) were scanned at baseline, with a subgroup (n = 17) being rescanned at 2 weeks. Limbic regions included the amygdala and hippocampus, and RSNs included the default mode network, CEN, and SN. ResultsKetamine increased right amygdala FC to the right CEN (p = .05), decreased amygdala FC to the left CEN (p = .005) at T2 versus T1 (p = .015), which then increased at T3 versus T2 (p = .002), and decreased left amygdala FC to the SN (p = .016). Decreased left amygdala to SN FC at T2 predicted improvements in anxiety at T3 (p = .006). Ketamine increased right hippocampus FC to the left CEN (p = .001), and this change at T2 predicted decreased anhedonia at T3 (p = .005). ConclusionsKetamine modulates FC between limbic regions and RSNs implicated in MDD. Increases in FC between limbic regions and the CEN suggest that ketamine may be involved in restoring top-down control of emotion processing. FC decreases between the left amygdala and SN suggest that ketamine may ameliorate MDD-related dysconnectivity in these circuits. Early FC changes between limbic regions and RSNs may be predictive of clinical improvements.

Structural Connectivity in Currently Medicated Treatment-Resistant vs. Treatment-Responsive Depression: A Diffusion Tensor Imaging Study

Findings of changes of structural connectivity in major depression have been variable, and many prior studies excluded patients taking medications. The current study compared structural connectivity in medicated MDD patients with and without current, severe depression using tract-based spatial statistics (TBSS) and region-of-interest analyses.

P.651 Mood congruent helplessness associates with prefrontal Intrinsic connectivity in major depression

In this work we have proposed to use multifractal analysis to evaluate the effectiveness of the healing process in postresectal and postcystal bone loss using the guided bone regeneration (GBR).The material of the study consists of 19 radiographic images obtained from patients (13 females and 6 males), observed within 1-year-long period, who had undergone bone augmentation with xenogenic material. Using radiographic images (RVG) made with digital radiography set Kodak RVG 6100 on the day of the procedure (Group A) and 12 months later (Group B), the degree of reconstruction of intraosseous defects were compared. A visual subjective evaluation of the healing process based on RVG images may not be sufficient. For this purpose, modern computer image analysis methods can be used. Pre-processing of radiographic images consisted of separating areas of interest (ROI) and binarization. The resulting images were analyzed using multifractals. The properties of biological structures cannot be sufficiently described in the Euclidean space, but they can be characterized by a parameter called the fractal dimension.Significant increase (p < 0.0001) of multifractal features were found in patients from group B. Individual parameters may characterize bone structure, which had undergone bone augmentation with xenogenic material. Finally, we showed that the properly selected multifractal quantificators obtained for those images could be used to assign those images to the appropriate group.The proposed analysis based on RVG images could help to evaluate the healing process after bone loss. The analysis and interpretation of the healing process after bone loss using monofractal and multifractal analysis as a multi-steps process quantify a stable (good) clinical result.

Amygdala functional connectivity in major depression - disentangling markers of pathology, risk and resilience.

Limbic-cortical imbalance is an established model for the neurobiology of major depressive disorder (MDD), but imaging genetics studies have been contradicting regarding potential risk and resilience mechanisms. Here, we re-assessed previously reported limbic-cortical alterations between MDD relatives and controls in combination with a newly acquired sample of MDD patients and controls, to disentangle pathology, risk, and resilience. We analyzed functional magnetic resonance imaging data and negative affectivity (NA) of MDD patients (n = 48), unaffected first-degree relatives of MDD patients (n = 49) and controls (n = 109) who performed a faces matching task. Brain response and task-dependent amygdala functional connectivity (FC) were compared between groups and assessed for associations with NA. Groups did not differ in task-related brain activation but activation in the superior frontal gyrus (SFG) was inversely correlated with NA in patients and controls. Pathology was associated with task-independent decreases of amygdala FC with regions of the default mode network (DMN) and decreased amygdala FC with the medial frontal gyrus during faces matching, potentially reflecting a task-independent DMN predominance and a limbic-cortical disintegration during faces processing in MDD. Risk was associated with task-independent decreases of amygdala-FC with fronto-parietal regions and reduced faces-associated amygdala-fusiform gyrus FC. Resilience corresponded to task-independent increases in amygdala FC with the perigenual anterior cingulate cortex (pgACC) and increased FC between amygdala, pgACC, and SFG during faces matching. Our results encourage a refinement of the limbic-cortical imbalance model of depression. The validity of proposed risk and resilience markers needs to be tested in prospective studies. Further limitations are discussed.

Childhood trauma history is linked to abnormal brain connectivity in major depression

Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

Striatal dopamine deficits predict reductions in striatal functional connectivity in major depression: a concurrent 11C-raclopride positron emission tomography and functional magnetic resonance imaging investigation

Major depressive disorder (MDD) is characterized by the altered integration of reward histories and reduced responding of the striatum. We have posited that this reduced striatal activation in MDD is due to tonically decreased stimulation of striatal dopamine synapses which results in decremented propagation of information along the cortico-striatal-pallido-thalamic (CSPT) spiral. In the present investigation, we tested predictions of this formulation by conducting concurrent functional magnetic resonance imaging (fMRI) and 11C-raclopride positron emission tomography (PET) in depressed and control (CTL) participants. We scanned 16 depressed and 14 CTL participants with simultaneous fMRI and 11C-raclopride PET. We estimated raclopride binding potential (BPND), voxel-wise, and compared MDD and CTL samples with respect to BPND in the striatum. Using striatal regions that showed significant between-group BPND differences as seeds, we conducted whole-brain functional connectivity analysis using the fMRI data and identified brain regions in each group in which connectivity with striatal seed regions scaled linearly with BPND from these regions. We observed increased BPND in the ventral striatum, bilaterally, and in the right dorsal striatum in the depressed participants. Further, we found that as BPND increased in both the left ventral striatum and right dorsal striatum in MDD, connectivity with the cortical targets of these regions (default-mode network and salience network, respectively) decreased. Deficits in stimulation of striatal dopamine receptors in MDD could account in part for the failure of transfer of information up the CSPT circuit in the pathophysiology of this disorder.

The impact of age of onset on amygdala intrinsic connectivity in major depression.

BackgroundEarly-onset major depressive disorder (EO-MDD), beginning during childhood and adolescence, is associated with more illness burden and a worse prognosis than adult-onset MDD (AO-MDD), but little is known about the neural features distinguishing these subgroup phenotypes. Functional abnormalities of the amygdala are central to major depressive disorder (MDD) neurobiology; therefore, we examined whether amygdala intrinsic connectivity (IC) can differentiate EO-MDD from AO-MDD in a cohort of adult MDD patients.Subjects and methodsTwenty-one EO-MDD (age of onset ≤18 years), 31 AO-MDD patients (age of onset ≥19 years), and 19 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (7 minutes). Amygdala seed-based resting-state functional connectivity was compared between groups.ResultsAO-MDD patients showed loss of inverse left amygdala–left dorsolateral prefrontal cortex IC and increased inverse left amygdala–left inferior parietal IC, compared to both HCs and EO-MDD. EO-MDD showed a switch from inverse to positive IC with right dorsomedial prefrontal cortex, compared to HCs and AO-MDD. This effect was removed when we controlled for illness burden.ConclusionAlterations in amygdala IC with the default-mode network were specifically related to EO-MDD, whereas amygdala IC with executive cognitive control regions was preferentially disrupted in AO-MDD. Increased illness burden, an important clinical marker of EO-MDD, accounted for its specific effects on amygdala IC. Brain imaging has the potential for validation of clinical subtypes and can provide markers of prognostic value in MDD patients.

Instability of default mode network connectivity in major depression: a two-sample confirmation study

Major depression is associated with altered static functional connectivity in various brain networks, particularly the default mode network (DMN). Dynamic functional connectivity is a novel tool with little application in affective disorders to date, and holds the potential to unravel fluctuations in connectivity strength over time in major depression. We assessed stability of connectivity in major depression between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), key nodes in the DMN that are implicated in ruminative cognitions. Functional connectivity stability between the mPFC and PCC over the course of a resting-state functional magnetic resonance imaging (fMRI) scan was compared between medication-free patients with major depression and healthy controls matched for age, sex and handedness. We tested replicability of the results in an independent sample using multi-echo resting-state fMRI. The primary sample included 20 patients and 19 controls, while the validation sample included 19 patients and 19 controls. Greater connectivity variability was detected in major depression between mPFC and PCC. This was demonstrated in both samples indicating that the results were reliable and were not influenced by the fMRI acquisition approach used. Our results demonstrate that alterations within the DMN in major depression go beyond changes in connectivity strength and extend to reduced connectivity stability within key DMN regions. Findings were robustly replicated across two independent samples. Further research is necessary to better understand the nature of these fluctuations in connectivity and their relationship to the aetiology of major depression.

Ketamine Treatment and Global Brain Connectivity in Major Depression.

Capitalizing on recent advances in resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) and the distinctive paradigm of rapid mood normalization following ketamine treatment, the current study investigated intrinsic brain networks in major depressive disorder (MDD) during a depressive episode and following treatment with ketamine. Medication-free patients with MDD and healthy control subjects (HC) completed baseline rs-fcMRI. MDD patients received a single infusion of ketamine and underwent repeated rs-fcMRI at 24 h posttreatment. Global brain connectivity with global signal regression (GBCr) values were computed as the average of correlations of each voxel with all other gray matter voxels in the brain. MDD group showed reduced GBCr in the prefrontal cortex (PFC) but increased GBCr in the posterior cingulate, precuneus, lingual gyrus, and cerebellum. Ketamine significantly increased GBCr in the PFC and reduced GBCr in the cerebellum. At baseline, 2174 voxels of altered GBCr were identified, but only 310 voxels significantly differed relative to controls following treatment (corrected α<0.05). Responders to ketamine showed increased GBCr in the lateral PFC, caudate, and insula. Follow-up seed-based analyses illustrated a pattern of dysconnectivity between the PFC/subcortex and the rest of the brain in MDD, which appeared to normalize postketamine. The extent of the functional dysconnectivity identified in MDD and the swift and robust normalization following treatment suggest that GBCr may serve as a treatment response biomarker for the development of rapid acting antidepressants. The data also identified unique prefrontal and striatal circuitry as a putative marker of successful treatment and a target for antidepressants' development.

Dynamic Resting-State Functional Connectivity in Major Depression

Dynamic Resting-State Functional Connectivity in Major Depression

Dynamic Resting-State Functional Connectivity in Major Depression.

Major depressive disorder (MDD) is characterized by abnormal resting-state functional connectivity (RSFC), especially in medial prefrontal cortical (MPFC) regions of the default network. However, prior research in MDD has not examined dynamic changes in functional connectivity as networks form, interact, and dissolve over time. We compared unmedicated individuals with MDD (n=100) to control participants (n=109) on dynamic RSFC (operationalized as SD in RSFC over a series of sliding windows) of an MPFC seed region during a resting-state functional magnetic resonance imaging scan. Among participants with MDD, we also investigated the relationship between symptom severity and RSFC. Secondary analyses probed the association between dynamic RSFC and rumination. Results showed that individuals with MDD were characterized by decreased dynamic (less variable) RSFC between MPFC and regions of parahippocampal gyrus within the default network, a pattern related to sustained positive connectivity between these regions across sliding windows. In contrast, the MDD group exhibited increased dynamic (more variable) RSFC between MPFC and regions of insula, and higher severity of depression was related to increased dynamic RSFC between MPFC and dorsolateral prefrontal cortex. These patterns of highly variable RSFC were related to greater frequency of strong positive and negative correlations in activity across sliding windows. Secondary analyses indicated that increased dynamic RSFC between MPFC and insula was related to higher levels of recent rumination. These findings provide initial evidence that depression, and ruminative thinking in depression, are related to abnormal patterns of fluctuating communication among brain systems involved in regulating attention and self-referential thinking.

P.2.b.041 Alterations of molecular connectivity in major depression before and during treatment with selective serotonin reuptake inhibitors

P.2.b.041 Alterations of molecular connectivity in major depression before and during treatment with selective serotonin reuptake inhibitors

Functional connectivity in major depression: Increased phase synchronization between frontal cortical EEG-source estimates

Structural and metabolic alterations in prefrontal brain areas, including the subgenual (SGPFC), medial (MPFC) and dorsolateral prefrontal cortex (DLPFC), have been shown in major depressive disorder (MDD). Still it remains largely unknown how brain connectivity within these regions is altered at the level of neuronal oscillations. Therefore, the goal was to analyze prefrontal electroencephalographic phase synchronization in MDD and its changes after antidepressant treatment. In 60 unmedicated patients and 60 healthy controls (HC), a 15-min resting electroencephalogram (EEG) was recorded in subjects at baseline and in a subgroup of patients after 2 weeks of antidepressant medication. EEG functional connectivity between the SGPFC and the MPFC/DLPFC was assessed with eLORETA (low resolution brain electromagnetic tomography) by means of lagged phase synchronization. At baseline, patients revealed increased prefrontal connectivity at the alpha frequency between the SGPFC and the left DLPFC/MPFC. After treatment, an increased connectivity between the SGPFC and the right DLPFC/MPFC at the beta frequency was found for MDD. A positive correlation was found for baseline beta connectivity and reduction in scores on the Hamilton depression rating scale. MDD is characterized by increased EEG functional connectivity within frontal brain areas. These EEG markers of disturbed neuronal communication might have potential value as biomarkers.

Functional connectivity in major depression: Increased phase synchronization between frontal cortical EEG-source estimates

Structural and metabolic alterations in prefrontal brain areas, including the subgenual (SGPFC), medial (MPFC) and dorsolateral prefrontal cortex (DLPFC), have been shown in major depressive disorder (MDD). Still it remains largely unknown how brain connectivity within these regions is altered at the level of neuronal oscillations. Therefore, the goal was to analyze prefrontal electroencephalographic phase synchronization in MDD and its changes after antidepressant treatment. In 60 unmedicated patients and 60 healthy controls (HC), a 15-min resting electroencephalogram (EEG) was recorded in subjects at baseline and in a subgroup of patients after 2 weeks of antidepressant medication. EEG functional connectivity between the SGPFC and the MPFC/DLPFC was assessed with eLORETA (low resolution brain electromagnetic tomography) by means of lagged phase synchronization. At baseline, patients revealed increased prefrontal connectivity at the alpha frequency between the SGPFC and the left DLPFC/MPFC. After treatment, an increased connectivity between the SGPFC and the right DLPFC/MPFC at the beta frequency was found for MDD. A positive correlation was found for baseline beta connectivity and reduction in scores on the Hamilton depression rating scale. MDD is characterized by increased EEG functional connectivity within frontal brain areas. These EEG markers of disturbed neuronal communication might have potential value as biomarkers.

Differences in functional connectivity in major depression versus bipolar II depression

BackgroundObjective methods of differentiating unipolar versus bipolar depression would enhance our ability to treat these disorders by providing more accurate diagnoses. One first step towards developing diagnostic methodology is determining whether brain function as assessed by functional MRI (fMRI) and functional connectivity analyses might differentiate the two disorders. MethodsFourteen subjects with bipolar II depression and 26 subjects with recurrent unipolar depression were studied using fMRI and functional connectivity analyses. ResultsThe first key finding of this study was that functional connectivity of the right posterior cingulate cortex differentiates bipolar II and unipolar depression. Additionally, results suggest that functional connectivity of this region is associated with suicidal ideation and depression severity in unipolar but not bipolar II depression. LimitationsThe primary limitation is the relatively small sample size, particularly for the correlational analyses. ConclusionsThe functional connectivity of right posterior cingulate cortex may differential unipolar from bipolar II depression. Further, connectivity of this region may be associated with depression severity and suicide risk in unipolar but not bipolar depression.

Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.

Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients = 86.4%, controls = 96.2%; permutation test, p<0.0001) of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.

Whole brain resting-state analysis reveals decreased functional connectivity in major depression

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

Reduced Functional Connectivity in Major Depression: a Whole Brain Study of Multiple Resting-State Networks

I. M. Veer, C. F. Beckmann, E. Baerends, M. J. van Tol, L. Ferrarini, J. R. Milles, D. J. Veltman, A. Aleman, M. A. van Buchem, N. J. van der Wee, and S. A. Rombouts Leiden Institute for Brain and Cognition (LIBC), Leiden, Netherlands, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, Netherlands, FMRIB, University of Oxford, Oxford, United Kingdom, Department of Clinical Neuroscience, Imperial College London, London, United Kingdom, Department of Psychiatry, Leiden University Medical Center (LUMC), Leiden, Netherlands, Department of Radiology, Division of Image Processing, Leiden University Medical Center (LUMC), Leiden, Netherlands, Department of Psychiatry, Free University Medical Center (VUMC), Amsterdam, Netherlands, BCN Neuroimaging Center, University of Groningen, Groningen, Netherlands