Functional brain networks: linking thalamic atrophy to clinical disability in multiple sclerosis, a multimodal fMRI and MEG study

Subcortical grey matter structures in multiple sclerosis: what is their role in cognition?

The present study aimed to investigate altered grey matter (GM) and functional connectivity (FC) in deep subcortical areas, such as the thalamus and basal ganglia, and their relationship with cognitive impairment (CI) in multiple sclerosis (MS). Thirty-six patients were neuropsychologically assessed, classified as cognitive preserved (CP) and CI, and were compared with 18 healthy controls. GM atrophy and FC were observed in 10 predefined functional areas of the thalamus and in six of basal ganglia. GM atrophy was prominent in the basal ganglia in CI patients compared with CP MS patients. Increased FC was observed between the right caudate and the bilateral orbitofrontal cortex in CI versus CP patients. The discriminant and correlation analyses showed that the enhanced FC observed between the right caudate and the orbitofrontal cortex was closely associated with CI in MS patients. In conclusion, reduced GM volume and enhanced frontobasal ganglia connectivity are related to cognition in MS patients.

Disrupted Module Efficiency of Structural and Functional Brain Connectomes in Clinically Isolated Syndrome and Multiple Sclerosis

Recent studies have demonstrated disrupted topological organization of brain connectome in multiple sclerosis (MS). However, whether the communication efficiency between different functional systems is affected in the early stage of MS remained largely unknown. In this study, we constructed the structural connectivity (SC) and functional connectivity (FC) networks in 41 patients with clinically isolated syndrome (CIS), 32 MS patients and 35 healthy controls (HC) based on diffusion and resting-state functional MRI. To quantify the communication efficiency within and between different functional systems, we proposed two measures called intra- and inter-module efficiency. Based on the module parcellation of functional backbone network, the intra- and inter-module efficiency of SC and FC networks was calculated for each participant. For the SC network, CIS showed decreased inter-module efficiency between the sensory-motor network (SMN), the visual network (VN), the default-mode network (DMN) and the fronto-parietal network (FPN) compared with HC, while MS showed more widespread decreased module efficiency both within and between modules relative to HC and CIS. For the FC network, no differences were found between CIS and HC, and a decreased inter-module efficiency between SMN and FPN and between VN and FPN was identified in MS, compared with HC and CIS. Moreover, both intra- and inter-module efficiency of SC network were correlated with the disability and cognitive scores in MS. Therefore, our results demonstrated early SC changes between modules in CIS, and more widespread SC alterations and inter-module FC changes were observed in MS, which were further associated with cognitive impairment and physical disability.

Progressive brain rich-club network disruption from clinically isolated syndrome towards multiple sclerosis

Objective

To investigate the rich-club organization in clinically isolated syndrome (CIS) and multiple sclerosis (MS), and to characterize its relationships with physical disabilities and cognitive impairments.

Methods

We constructed high-resolution white matter (WM) structural networks in 41 CIS, 32 MS and 35 healthy controls (HCs) using diffusion MRI and deterministic tractography. Group differences in rich-club organization, global and local network metrics were investigated. The relationship between the altered network metrics, brain lesions and clinical variables including EDSS, MMSE, PASAT, disease duration were calculated. Additionally, reproducibility analysis was performed using different parcellation schemes.

Results

Compared with HCs, MS patients exhibited a decreased strength in all types of connections (rich-club: p < 0.0001; feeder: p = 0.0004; and local: p = 0.0026). CIS patients showed intermediate values between MS patients and HCs and exhibited a decreased strength in feeder and local connections (feeder: p = 0.019; and local: p = 0.031) but not in rich-club connections. Compared with CIS patients, MS patients showed significant reductions in rich-club connections (p = 0.0004). The reduced strength of rich-club and feeder connections was correlated with cognitive impairments in the MS group. These results were independent of lesion distribution and reproducible across different brain parcellation schemes.

Conclusion

The rich-club organization was disrupted in MS patients and relatively preserved in CIS. The disrupted rich-club connectivity was correlated with cognitive impairment in MS. These findings suggest that impaired rich-club connectivity is an essential feature of progressive structural network disruption, heralding the development of clinical disability in MS.

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The rich-club organization was disrupted in MS patients and preserved in CIS.

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The disrupted rich-club connectivity correlated with cognitive impairment in MS.

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The rich-club results are reproducible across data analysis methods.

Neurovascular Coupling During Visual Stimulation in Multiple Sclerosis: A MEG-fMRI Study

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A reduced electrophysiological response to a visual stimulus in MS, characterized by reduced gamma power (30–80 Hz), with MEG.

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A reduced hemodynamic response to a visual stimulus in MS, characterized by reduced BOLD and CBF responses, with fMRI.

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The coupling between gamma power and BOLD/CBF was not significantly impaired in the MS group.

The process of neurovascular coupling ensures that increases in neuronal activity are fed by increases in cerebral blood flow. Evidence suggests that neurovascular coupling may be impaired in Multiple Sclerosis (MS) due to a combination of brain hypoperfusion, altered cerebrovascular reactivity and oxygen metabolism, and altered levels of vasoactive compounds.

Here, we tested the hypothesis that neurovascular coupling is impaired in MS. We characterized neurovascular coupling as the relationship between changes in neuronal oscillatory power within the gamma frequency band (30–80 Hz), as measured by magnetoencephalography (MEG), and associated hemodynamic changes (blood oxygenation level dependent, BOLD, and cerebral blood flow, CBF) as measured by functional MRI. We characterized these responses in the visual cortex in 13 MS patients and in 10 matched healthy controls using a reversing checkerboard stimulus at five visual contrasts.

There were no significant group differences in visual acuity, P100 latencies, occipital gray matter (GM) volumes and baseline CBF. However, in the MS patients we found a significant reduction in peak gamma power, BOLD and CBF responses. There were no significant differences in neurovascular coupling between groups, in the visual cortex.

Our results suggest that neuronal and vascular responses are altered in MS. Gamma power reduction could be an indicator of GM dysfunction, possibly mediated by GABAergic changes. Altered hemodynamic responses confirm previous reports of a vascular dysfunction in MS. Despite altered neuronal and vascular responses, neurovascular coupling appears to be preserved in MS, at least within the range of damage and disability studied here.

Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study

To understand the heterogeneity of functional connectivity results reported in the literature, we analyzed the separate effects of grey and white matter damage on functional connectivity and networks in multiple sclerosis. For this, we employed a biophysical thalamo‐cortical model consisting of interconnected cortical and thalamic neuronal populations, informed and amended by empirical diffusion MRI tractography data, to simulate functional data that mimic neurophysiological signals. Grey matter degeneration was simulated by decreasing within population connections and white matter degeneration by lowering between population connections, based on lesion predilection sites in multiple sclerosis. For all simulations, functional connectivity and functional network organization are quantified by phase synchronization and network integration, respectively. Modeling results showed that both cortical and thalamic grey matter damage induced a global increase in functional connectivity, whereas white matter damage induced an initially increased connectivity followed by a global decrease. Both white and especially grey matter damage, however, induced a decrease in network integration. These empirically informed simulations show that specific topology and timing of structural damage are nontrivial aspects in explaining functional abnormalities in MS. Insufficient attention to these aspects likely explains contradictory findings in multiple sclerosis functional imaging studies so far.

Correlation Between Resting-state Electroencephalographic Characteristics and Shooting Performance

According to the theories of neural plasticity and neural efficiency, professional skill training improves performance by strengthening the underlying neural mechanisms. Therefore, subjects trained professionally may exhibit changes in resting-state neurophysiological characteristics closely related to performance. To test this notion, the resting-state electroencephalogram (EEG) was measured from 35 rifle shooters after the same training regimen, and resting-state EEG characteristics were analyzed for correlations with shooting performance. The results showed a significant linear correlation between shooting performance and the coherence of electrode channels C3 and T3 in the beta1 band (r = 0.74, P < 4.2 × 10^-6). There was also a significant linear correlation between the characteristic path length of the resting-state theta band brain network and shooting performance (r = 0.56, P < 0.0005). This study identifies potential neural mechanisms underlying successful shooting and a new method for predicting and evaluating performance based on EEG characteristics.

Reduced rich-club connectivity is related to disability in primary progressive MS

OBJECTIVE

To investigate whether the structural connectivity of the brain's rich-club organization is altered in patients with primary progressive MS and whether such changes to this fundamental network feature are associated with disability measures.

METHODS

We recruited 37 patients with primary progressive MS and 21 healthy controls for an observational cohort study. Structural connectomes were reconstructed based on diffusion-weighted imaging data using probabilistic tractography and analyzed with graph theory.

RESULTS

We observed the same topological organization of brain networks in patients and controls. Consistent with the originally defined rich-club regions, we identified superior frontal, precuneus, superior parietal, and insular cortex in both hemispheres as rich-club nodes. Connectivity within the rich club was significantly reduced in patients with MS (p = 0.039). The extent of reduced rich-club connectivity correlated with clinical measurements of mobility (Kendall rank correlation coefficient τ = -0.20, p = 0.047), hand function (τ = -0.26, p = 0.014), and information processing speed (τ = -0.20, p = 0.049).

CONCLUSIONS

In patients with primary progressive MS, the fundamental organization of the structural connectome in rich-club and peripheral nodes was preserved and did not differ from healthy controls. The proportion of rich-club connections was altered and correlated with disability measures. Thus, the rich-club organization of the brain may be a promising network phenotype for understanding the patterns and mechanisms of neurodegeneration in MS.

Retinal atrophy in relation to visual functioning and vision-related quality of life in patients with multiple sclerosis

Background:

Inner retinal layer atrophy in patients with multiple sclerosis (MS) has been validated as a structural imaging biomarker for neurodegeneration.

Objective:

To determine how retinal layer thickness relates to high-contrast visual acuity (HCVA), low-contrast visual acuity (LCVA) and vision-related quality of life (QoL) and to investigate the effect of previous episodes on MS-associated optic neuritis (MSON).

Methods:

Spectral-domain optical coherence tomography (SD-OCT) was performed in 267 patients with MS. Images were segmented for the peripapillary retinal nerve fiber layer (pRNFL) and the macular ganglion cell inner plexiform layer (GCIPL). Ophthalmological evaluations included history of MSON, HCVA, LCVA, and vision-related QoL.

Results:

Independent of MSON, HCVA and LCVA were significantly associated with pRNFL and GCIPL thicknesses. Vision-related QoL was positively associated with pRNFL (β = 0.92, p = 0.06) and GCIPL (β = 0.93, p = 0.02) thicknesses. These associations were independent of MSON. Not only binocular but also monocular atrophy of the inner retinal layers was associated with lower vision-related QoL.

Conclusion:

This study showed that retinal atrophy has a significant impact on visual functioning in patients with MS. OCT may therefore provide useful insight to patients with visual dysfunction, and our findings support including OCT and vision-related QoL measures into optic neuritis treatment trials.

Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity

A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders.

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high fluid Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.

Dysregulation of energy metabolism in multiple sclerosis measured in vivo with diffusion-weighted spectroscopy

Objective:

We employed diffusion-weighted magnetic resonance spectroscopy (DW-MRS), which allows to measure in vivo the diffusion properties of metabolites, to explore the functional neuro-axonal damage and the ongoing energetic dysregulation in multiple sclerosis (MS).

Methods:

Twenty-five patients with MS and 18 healthy controls (HC) underwent conventional magnetic resonance imaging (MRI) and DW-MRS. The apparent diffusion coefficient (ADC) of total N-acetyl-aspartate (tNAA) and creatine–phosphocreatine (tCr) were measured in the parietal normal-appearing white matter (NAWM) and in the thalamic grey matter (TGM). Multiple regressions were used to compare metabolite ADCs between groups and to explore clinical correlations.

Results:

In patients compared with HCs, we found a reduction in ADC(tNAA) in the TGM, reflecting functional and structural neuro-axonal damage, and in ADC(tCr) in both NAWM and TGM, possibly reflecting a reduction in energy supply in neurons and glial cells. Metabolite ADCs did not correlate with tissue atrophy, lesional volume or metabolite concentrations, while in TGM metabolite ADCs correlated with clinical scores.

Conclusion:

DW-MRS showed a reduction in tCr diffusivity in the normal-appearing brain of patients with MS, which might reflect a state of ongoing energy dysregulation affecting neurons and/or glial cells. Reversing this energy dysregulation before neuro-axonal degeneration arises may become a key objective in future neuroprotective strategies.

Abnormal task driven neural oscillations in multiple sclerosis: A visuomotor MEG study

Multiple sclerosis (MS) is a debilitating disease commonly attributed to degradation of white matter myelin. Symptoms include fatigue, as well as problems associated with vision and movement. Although areas of demyelination in white matter are observed routinely in patients undergoing MRI scans, such measures are often a poor predictor of disease severity. For this reason, it is instructive to measure associated changes in brain function. Widespread white‐matter demyelination may lead to delays of propagation of neuronal activity, and with its excellent temporal resolution, magnetoencephalography can be used to probe such delays in controlled conditions (e.g., during a task). In healthy subjects, responses to visuomotor tasks are well documented: in motor cortex, movement elicits a localised decrease in the power of beta band oscillations (event‐related beta desynchronisation) followed by an increase above baseline on movement cessation (post‐movement beta rebound (PMBR)). In visual cortex, visual stimulation generates increased gamma oscillations. In this study, we use a visuomotor paradigm to measure these responses in MS patients and compare them to age‐ and gender‐matched healthy controls. We show a significant increase in the time‐to‐peak of the PMBR in patients which correlates significantly with the symbol digit modalities test: a measure of information processing speed. A significant decrease in the amplitude of visual gamma oscillations in patients is also seen. These findings highlight the potential value of electrophysiological imaging in generating a new understanding of visual disturbances and abnormal motor control in MS patients. Hum Brain Mapp 38:2441–2453, 2017. © 2017 Wiley Periodicals, Inc.

Delineation of cortical pathology in multiple sclerosis using multi-surface magnetization transfer ratio imaging

The purpose of our study was to evaluate the utility of measurements of cortical surface magnetization transfer ratio (csMTR) on the inner, mid and outer cortical boundaries as clinically accessible biomarkers of cortical gray matter pathology in multiple sclerosis (MS). Twenty-five MS patients and 12 matched controls were recruited from the MS Clinic of the Montreal Neurological Institute. Anatomical and magnetization transfer ratio (MTR) images were acquired using 3 Tesla MRI at baseline and two-year time-points. MTR maps were smoothed along meshes representing the inner, mid and outer neocortical boundaries. To evaluate csMTR reductions suggestive of sub-pial demyelination in MS patients, a mixed model analysis was carried out at both the individual vertex level and in anatomically parcellated brain regions. Our results demonstrate that focal areas of csMTR reduction are most prevalent along the outer cortical surface in the superior temporal and posterior cingulate cortices, as well as in the cuneus and precentral gyrus. Additionally, age regression analysis identified that reductions of csMTR in MS patients increase with age but appear to hit a plateau in the outer caudal anterior cingulate, as well as in the precentral and postcentral cortex. After correction for the naturally occurring gradient in cortical MTR, the difference in csMTR between the inner and outer cortex in focal areas in the brains of MS patients correlated with clinical disability. Overall, our findings support multi-surface analysis of csMTR as a sensitive marker of cortical sub-pial abnormality indicative of demyelination in MS patients.

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Novel cortical MTR analysis identifies areas of sub-pial abnormality in MS patients.

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A greater area of sub-pial abnormality in MS exists on the outer cortical layer.

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Cortical regions most affected were involved in executive function and processing speed.

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Normalized differences between outer and inner cortex MTR correlate with EDSS in MS.

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This technique can be applied for clinical trials at the MRI field strength of 3 T.

Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach

Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis

The brain connectome of multiple sclerosis (MS) has been investigated by several previous studies; however, it is still unknown how the network changes in clinically isolated syndrome (CIS), the earliest stage of MS, and how network alterations on a functional level relate to the structural level in MS disease. Here, we investigated the topological alterations of both the structural and functional connectomes in 41 CIS and 32 MS patients, compared to 35 healthy controls, by combining diffusion tensor imaging and resting-state functional MRI with graph analysis approaches. We found that the structural connectome showed a deviation from the optimal pattern as early as the CIS stage, while the functional connectome only showed local changes in MS patients, not in CIS. When comparing two patient groups, the changes appear more severe in MS. Importantly, the disruptions of structural and functional connectomes in patients occurred in the same direction and locally correlated in sensorimotor component. Finally, the extent of structural network changes was correlated with several clinical variables in MS patients. Together, the results suggested early disruption of the structural brain connectome in CIS patients and provided a new perspective for investigating the relationship of the structural and functional alterations in MS.

Quantifying the Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity

The coordinated activity of the resting-state brain can be evaluated with magnetoencephalography (MEG) for distinct brain rhythms by performing source reconstruction to estimate the activities of target brain regions and employing one of the many existent functional connectivity (FC) algorithms. Although this procedure has been applied in a great amount of studies both with healthy and pathological populations, the reliability of such FC estimates is unknown, and this impairs the use of resting-state MEG FC at the individual level. In this study, the test-retest reliability of MEG resting FC was evaluated by exploring both within- and between-subject variability in FC in 16 healthy subjects who underwent three resting-state MEG scans. FC was computed after beamforming source reconstruction with four popular FC metrics: phase-locking value (PLV), phase lag index (PLI), direct envelope correlation (d-ecor), and envelope correlation with leakage correction (lc-ecor). Then, test-restest reliability and within- and between-subject agreement were evaluated with the intraclass correlation coefficient (ICC) and Kendall's W, respectively. Reliability was found to depend on the FC metric, the frequency band, and the specific link. As a general trend, greater test-retest reliability was found for PLV in theta to gamma, and for lc-ecor and d-ecor in beta. Further inspection of the ICC distribution revealed that volume conduction effects could be contributing to high ICC in PLV and d-ecor. In addition, stronger links were found to be more reliable. Overall, this encourages the further use of resting-state MEG FC for individual-level studies, especially with PLV or envelope correlation metrics.

Direction of information flow in large-scale resting-state networks is frequency-dependent

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

A Mapping Between Structural and Functional Brain Networks

The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

Causes, effects and connectivity changes in MS-related cognitive decline

Cognitive decline is a frequent but undervalued aspect of multiple sclerosis (MS). Currently, it remains unclear what the strongest determinants of cognitive dysfunction are, with grey matter damage most directly related to cognitive impairment. Multi-parametric studies seem to indicate that individual factors of MS-pathology are highly interdependent causes of grey matter atrophy and permanent brain damage. They are associated with intermediate functional effects (e.g. in functional MRI) representing a balance between disconnection and (mal) adaptive connectivity changes. Therefore, a more comprehensive MRI approach is warranted, aiming to link structural changes with functional brain organization. To better understand the disconnection syndromes and cognitive decline in MS, this paper reviews the associations between MRI metrics and cognitive performance, by discussing the interactions between multiple facets of MS pathology as determinants of brain damage and how they affect network efficiency.

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.

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We introduce a mathematical model to predict fMRI-based RSNs using MEG.

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Our model is based on a multi-variate Taylor series expansion.

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The electrophysiological basis of RSNs goes beyond frequency-band specific analysis.

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RSNs result 1) from multiple frequency bands and cross-frequency coupling.

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RSNs result 2) from direct and shared electrophysiological connectivity.

The Union of Shortest Path Trees of Functional Brain Networks

Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major role in the communication within the brain. So far, for the functional brain network, only the average length of the shortest paths has been analyzed. In this article, we propose to construct the union of shortest path trees (USPT) as a new topology for the functional brain network. The minimum spanning tree, which has been successful in a lot of recent studies to comprise important features of the functional brain network, is always included in the USPT. After interpreting the link weights of the functional brain network as communication probabilities, the USPT of this network can be uniquely defined. Using data from magnetoencephalography, we applied the USPT as a method to find differences in the network topology of multiple sclerosis patients and healthy controls. The new concept of the USPT of the functional brain network also allows interesting interpretations and may represent the highways of the brain.

Networks: On the relation of bi- and multivariate measures

A reliable inference of networks from observations of the nodes’ dynamics is a major challenge in physics. Interdependence measures such as a the correlation coefficient or more advanced methods based on, e.g., analytic phases of signals are employed. For several of these interdependence measures, multivariate counterparts exist that promise to enable distinguishing direct and indirect connections. Here, we demonstrate analytically how bivariate measures relate to the respective multivariate ones; this knowledge will in turn be used to demonstrate the implications of thresholded bivariate measures for network inference. Particularly, we show, that random networks are falsely identified as small-world networks if observations thereof are treated by bivariate methods. We will employ the correlation coefficient as an example for such an interdependence measure. The results can be readily transferred to all interdependence measures partializing for information of thirds in their multivariate counterparts.