Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.

Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificity.

Brain network characteristics' potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

The epidemic spreading model and the direction of information flow in brain networks.

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.

How reliable are MEG resting-state connectivity metrics?

MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions.

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology.

[From querulous neurosis to querulous delusion: the psychopathological aspects of persistent dysfunctional complaining]. / Van querulantenneurose tot querulanten-waan: de psychopathologische aspecten van volhardend disfunctioneel klagen.

BACKGROUND: The clinical concept of querulous behaviour is used only sporadically in modern psychiatry, although the concept seems to be just as clinical relevant as it was many years ago. The extension of the right to complain has played a role in the acceptance of querulous behaviour. Judicial bodies and other government organisations are being kept busier than ever because people who feel they have been denied justice became entangled in interminable litigation. Gradually, querulous behaviour causes the initial sense of injustice to disappear and querulous patients are damaged economically, socially and personally and experience suffering and function less efficiently. AIM: To describe the history, clinical features, differential diagnosis, possible psychodynamic hypotheses and possible ways of treating querulous behaviour.
METHOD: We studied the literature using PubMed, PsychInfo, Google and Google Scholar. RESULTS: There seems to be a spectrum with fluent transitions from normal complaining behaviour, querulous behaviour which is based on a paranoid, narcissistic or obsessive-compulsive personality structure, to severe pathologies like a delusional disorder. There is little evidence that pharmacotherapy or psychotherapy is effective. Nevertheless, there are opportunities for administering psychiatric treatment in order to alleviate the loss and suffering for the patients and their next-of-kin. CONCLUSION: Querulous behaviour is an old clinical concept that still has relevance today for the individual patient, complaints officers and society in general. Our current diagnostic systems provide sufficient opportunities for diagnosing patients correctly. Patients may be best served by judicial mediation at an early stage, combined with psychiatric treatment aimed at controlling emotions and restricting harmful behaviour.

The relation between structural and functional connectivity patterns in complex brain networks.

OBJECTIVE: An important problem in systems neuroscience is the relation between complex structural and functional brain networks. Here we use simulations of a simple dynamic process based upon the susceptible-infected-susceptible (SIS) model of infection dynamics on an empirical structural brain network to investigate the extent to which the functional interactions between any two brain areas depend upon (i) the presence of a direct structural connection; and (ii) the degree product of the two areas in the structural network. METHODS: For the structural brain network, we used a 78×78 matrix representing known anatomical connections between brain regions at the level of the AAL atlas (Gong et al., 2009). On this structural network we simulated brain dynamics using a model derived from the study of epidemic processes on networks. Analogous to the SIS model, each vertex/brain region could be in one of two states (inactive/active) with two parameters ß and δ determining the transition probabilities. First, the phase transition between the fully inactive and partially active state was investigated as a function of ß and δ. Second, the statistical interdependencies between time series of node states were determined (close to and far away from the critical state) with two measures: (i) functional connectivity based upon the correlation coefficient of integrated activation time series; and (ii) effective connectivity based upon conditional co-activation at different time intervals. RESULTS: We find a phase transition between an inactive and a partially active state for a critical ratio τ=ß/δ of the transition rates in agreement with the theory of SIS models. Slightly above the critical threshold, node activity increases with degree, also in line with epidemic theory. The functional, but not the effective connectivity matrix closely resembled the underlying structural matrix. Both functional connectivity and, to a lesser extent, effective connectivity were higher for connected as compared to disconnected (i.e.: not directly connected) nodes. Effective connectivity scaled with the degree product. For functional connectivity, a weaker scaling relation was only observed for disconnected node pairs. For random networks with the same degree distribution as the original structural network, similar patterns were seen, but the scaling exponent was significantly decreased especially for effective connectivity. CONCLUSIONS: Even with a very simple dynamical model it can be shown that functional relations between nodes of a realistic anatomical network display clear patterns if the system is studied near the critical transition. The detailed nature of these patterns depends on the properties of the functional or effective connectivity measure that is used. While the strength of functional interactions between any two nodes clearly depends upon the presence or absence of a direct connection, this study has shown that the degree product of the nodes also plays a large role in explaining interaction strength, especially for disconnected nodes and in combination with an effective connectivity measure. The influence of degree product on node interaction strength probably reflects the presence of large numbers of indirect connections.

The minimum spanning tree: an unbiased method for brain network analysis.

The brain is increasingly studied with graph theoretical approaches, which can be used to characterize network topology. However, studies on brain networks have reported contradictory findings, and do not easily converge to a clear concept of the structural and functional network organization of the brain. It has recently been suggested that the minimum spanning tree (MST) may help to increase comparability between studies. The MST is an acyclic sub-network that connects all nodes and may solve several methodological limitations of previous work, such as sensitivity to alterations in connection strength (for weighted networks) or link density (for unweighted networks), which may occur concomitantly with alterations in network topology under empirical conditions. If analysis of MSTs avoids these methodological limitations, understanding the relationship between MST characteristics and conventional network measures is crucial for interpreting MST brain network studies. Here, we firstly demonstrated that the MST is insensitive to alterations in connection strength or link density. We then explored the behavior of MST and conventional network-characteristics for simulated regular and scale-free networks that were gradually rewired to random networks. Surprisingly, although most connections are discarded during construction of the MST, MST characteristics were equally sensitive to alterations in network topology as the conventional graph theoretical measures. The MST characteristics diameter and leaf fraction were very strongly related to changes in the characteristic path length when the network changed from a regular to a random configuration. Similarly, MST degree, diameter, and leaf fraction were very strongly related to the degree of scale-free networks that were rewired to random networks. Analysis of the MST is especially suitable for the comparison of brain networks, as it avoids methodological biases. Even though the MST does not utilize all the connections in the network, it still provides a, mathematically defined and unbiased, sub-network with characteristics that can provide similar information about network topology as conventional graph measures.

Bidirectional trans-synaptic axonal degeneration in the visual pathway in multiple sclerosis.

OBJECTIVE: To investigate the coexistence of anterograde and retrograde trans-synaptic axonal degeneration, and to explore the relationship between selective visual pathway damage and global brain involvement in longstanding multiple sclerosis (MS). METHODS: In this single-centre, cross-sectional study, patients with longstanding MS (N=222) and healthy controls (HC, N=62) were included. We analysed thickness of retinal layers (optical coherence tomography), damage within optic radiations (OR) (lesion volume and fractional anisotropy and mean diffusivity by diffusion tensor imaging) and atrophy of the visual cortex and that of grey and white matter of the whole-brain (structural MRI). Linear regression analyses were used to assess associations between the different components and for comparing patients with and without optic neuritis and HC. RESULTS: In patients with MS, an episode of optic neuritis (MSON) was significantly associated with decreased integrity of the ORs and thinning of the peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell complex (GCC). Lesion volume in the OR was negatively associated with pRNFL and GCC thickness in patients without optic neuritis (MSNON). The pRNFL and GCC showed associations with integrity of the OR, thickness of the primary visual cortex (only in patients with MSON), and also with global white and grey matter atrophy. In HCs, no such relationships were demonstrated. INTERPRETATION: This study provides evidence for presence of bidirectional (both anterograde and retrograde) trans-synaptic axonal degeneration in the visual pathway of patients with MS. Additionally, thinning of the retinal pRNFL and GCC are related to global white and grey matter atrophy in addition to pathology of the visual pathway.

Structural degree predicts functional network connectivity: a multimodal resting-state fMRI and MEG study.

Communication between neuronal populations in the human brain is characterized by complex functional interactions across time and space. Recent studies have demonstrated that these functional interactions depend on the underlying structural connections at an aggregate level. Multiple imaging modalities can be used to investigate the relation between the structural connections between brain regions and their functional interactions at multiple timescales. We investigated if consistent modality-independent functional interactions take place between brain regions, and whether these can be accounted for by underlying structural properties. We used functional MRI (fMRI) and magnetoencephalography (MEG) recordings from a population of healthy adults together with a previously described structural network. A high overlap in resting-state functional networks was found in fMRI and especially alpha band MEG recordings. This overlap was characterized by a strongly interconnected functional core network in temporo-posterior brain regions. Anatomically realistically coupled neural mass models revealed that this strongly interconnected functional network emerges near the threshold for global synchronization. Most importantly, this functional core network could be explained by a trade-off between the product of the degrees of structurally-connected regions and the Euclidean distance between them. For both fMRI and MEG, the product of the degrees of connected regions was the most important predictor for functional network connectivity. Therefore, irrespective of the modality, these results indicate that a functional core network in the human brain is especially shaped by communication between high degree nodes of the structural network.

The trees and the forest: Characterization of complex brain networks with minimum spanning trees.

In recent years there has been a shift in focus from the study of local, mostly task-related activation to the exploration of the organization and functioning of large-scale structural and functional complex brain networks. Progress in the interdisciplinary field of modern network science has introduced many new concepts, analytical tools and models which allow a systematic interpretation of multivariate data obtained from structural and functional MRI, EEG and MEG. However, progress in this field has been hampered by the absence of a simple, unbiased method to represent the essential features of brain networks, and to compare these across different conditions, behavioural states and neuropsychiatric/neurological diseases. One promising solution to this problem is to represent brain networks by a minimum spanning tree (MST), a unique acyclic subgraph that connects all nodes and maximizes a property of interest such as synchronization between brain areas. We explain how the global and local properties of an MST can be characterized. We then review early and more recent applications of the MST to EEG and MEG in epilepsy, development, schizophrenia, brain tumours, multiple sclerosis and Parkinson's disease, and show how MST characterization performs compared to more conventional graph analysis. Finally, we illustrate how MST characterization allows representation of observed brain networks in a space of all possible tree configurations and discuss how this may simplify the construction of simple generative models of normal and abnormal brain network organization.

A dam for retrograde axonal degeneration in multiple sclerosis?

OBJECTIVE: Trans-synaptic axonal degeneration is a mechanism by which neurodegeneration can spread from a sick to a healthy neuron in the central nervous system. This study investigated to what extent trans-synaptic axonal degeneration takes place within the visual pathway in multiple sclerosis (MS). METHODS: A single-centre study, including patients with long-standing MS and healthy controls. Structural imaging of the brain (MRI) and retina (spectral-domain optical coherence tomography) were used to quantify the extent of atrophy of individual retinal layers and the primary and secondary visual cortex. Generalised estimation equations and multivariable regression analyses were used for comparisons. RESULTS: Following rigorous quality control (OSCAR-IB), data from 549 eyes of 293 subjects (230 MS, 63 healthy controls) were included. Compared with control data, there was a significant amount of atrophy of the inner retinal layers in MS following optic neuritis (ON) and also in absence of ON. For both scenarios, atrophy stopped at the level of the inner nuclear layer. In contrast, there was significant localised atrophy of the primary visual cortex and secondary visual cortex in MS following ON, but not in MS in absence of ON. INTERPRETATION: These data suggest that retrograde (trans-synaptic) axonal degeneration stops at the inner nuclear layer, a neuronal network capable of plasticity. In contrast, there seems to be no neuroplasticity of the primary visual cortex, rendering the structure vulnerable to anterograde (trans-synaptic) degeneration.

Disease course heterogeneity and OCT in multiple sclerosis.

BACKGROUND: The heterogeneity of the disease course in multiple sclerosis (MS) remains a challenge for patient management and clinical trials. OBJECTIVE: The objective of this paper is to investigate the relationship between disease course heterogeneity and retinal layer thicknesses in MS. METHODS: A total of 230 MS patients and 63 healthy control subjects were included. Spectral-domain OCT scanning of the peripapillary and macular regions was performed, followed by automated eight-layer segmentation. Generalised estimation equations were used for comparisons. Receiver operating characteristic (ROC) curves were calculated for distinguishing a benign from a typical disease course. RESULTS: Primary progressive patients showed relative preservation of inner retinal layers, compared to the relapsing onset MS types. Only in MS eyes without optic neuritis did patients with typical MS show more severe thinning of the inner retinal layers (RNFL to INL) compared to patients with a benign disease course, even after an average disease course of 20 years. CONCLUSION: The thicknesses, particularly of the innermost retinal layers (RNFL, GCC), were significantly related to the heterogeneous disease course in MS. The relative preservation of these layers in primary progressive and benign MS suggests rather limited susceptibility of the retina to neurodegeneration, which may be relevant for future neurodegenerative treatment trials employing OCT as a secondary outcome measure in primary progressive MS.

Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: an MEG source-space study.

Cognitive dysfunction in Multiple Sclerosis (MS) is closely related to altered functional brain network topology. Conventional network analyses to compare groups are hampered by differences in network size, density and suffer from normalization problems. We therefore computed the Minimum Spanning Tree (MST), a sub-graph of the original network, to counter these problems. We hypothesize that functional network changes analysed with MSTs are important for understanding cognitive changes in MS and that changes in MST topology also represent changes in the critical backbone of the original brain networks. Here, resting-state magnetoencephalography (MEG) recordings from 21 early MS patients and 17 age-, gender-, and education-matched controls were projected onto atlas-based regions-of-interest (ROIs) using beamforming. The phase lag index was applied to compute functional connectivity between regions, from which a graph and subsequently the MST was constructed. Results showed lower global integration in the alpha2 (10-13Hz) and beta (13-30Hz) bands in MS patients, whereas higher global integration was found in the theta band. Changes were most pronounced in the alpha2 band where a loss of hierarchical structure was observed, which was associated with poorer cognitive performance. Finally, the MST in MS patients as well as in healthy controls may represent the critical backbone of the original network. Together, these findings indicate that MST network analyses are able to detect network changes in MS patients, which may correspond to changes in the core of functional brain networks. Moreover, these changes, such as a loss of hierarchical structure, are related to cognitive performance in MS.

Cognition in MS correlates with resting-state oscillatory brain activity: An explorative MEG source-space study.

Clinical and cognitive dysfunction in Multiple Sclerosis (MS) is insufficiently explained by structural damage as identified by traditional magnetic resonance imaging (MRI) of the brain, indicating the need for reliable functional measures in MS. We investigated whether altered resting-state oscillatory power could be related to clinical and cognitive dysfunction in MS. MEG recordings were acquired using a 151-channel whole-head MEG system from 21 relapsing remitting MS patients and 17 healthy age-, gender-, and education-matched controls, using an eyes-closed no-task condition. Relative spectral power was estimated for 78 regions of interest, using an atlas-based beamforming approach, for classical frequency bands; delta, theta, alpha1, alpha2, beta and gamma. These cortical power estimates were compared between groups by means of permutation analysis and correlated with clinical disability (Expanded Disability Status Scale: EDSS), cognitive performance and MRI measures of atrophy and lesion load. Patients showed increased power in the alpha1 band and decreased power in the alpha2 band, compared to controls, mainly in occipital, parietal and temporal areas, confirmed by a lower alpha peak-frequency. Increased power in the alpha1 band was associated with worse overall cognition and especially with information processing speed. Our quantitative relative power analysis of MEG recordings showed abnormalities in oscillatory brain dynamics in MS patients in the alpha band. By applying source-space analyses, this study provides a detailed topographical view of abnormal brain activity in MS patients, especially localized to occipital areas. Interestingly, poor cognitive performance was related to high resting-state alpha1 power indicating that changes in oscillatory activity might be of value as an objective measure of disease burden in MS patients.

Cerebrospinal fluid anti-whole myelin antibodies are not correlated to magnetic resonance imaging activity in multiple sclerosis.

BACKGROUND: The role of antimyelin antibodies as biomarker in multiple sclerosis is subject of debate. Here antimyelin antibody reactivity against native myelin is studied in CSF and serum. OBJECTIVE: To compare antimyelin antibody reactivity between patients with multiple sclerosis (MS) and patients with other neurological diseases in CSF and serum. In addition, MRI measures were studied in relation to antimyelin antibody reactivity. METHODS: 77 MS patients (13 primary progressive, 27 secondary progressive and 37 relapsing remitting), 26 patients with other non-inflammatory neurological diseases and 9 patients with inflammatory neurological diseases other than MS were included. A myelin flow cytometry assay was used to detect anti-myelin antibody levels which were expressed as mean fluorescence intensity (myelin-MFI). MRI outcome measures were new or persistent T2 lesions, gadolinium enhancing T1 lesions and brain atrophy which were assessed by normalized brain volumes. RESULTS: There was no significant difference between myelin-MFI values in serum and CSF between MS patients and controls (Mann-Whitney test p=0.19 and p=0.51). Myelin-MFI values in CSF were not correlated with number of T2 lesions (Spearman r=-0.023, p=0.85), number of gadolinium enhancing T1 lesions (Spearman r=-0.066, p=0.588) or normalized brain volume (Spearman r=-0.065, p=0.594). CONCLUSIONS: These results do not confirm an association between anti-myelin antibody reactivity and the presence of MS or MRI measures of disease activity.