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Background: Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients. Objective: To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology. Methods: Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage. Results: A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years ( R adj 2 = 15 %), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band. Conclusions: The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS. [ABSTRACT FROM AUTHOR]

Abstract Background White matter hyperintensities (WMH) can lead to dementia but the underlying physiological mechanisms are unclear. We compared relative oscillatory power from electroencephalographic studies (EEGs) of 17 patients with subcortical ischemic vascular dementia, based on extensive white matter hyperintensities (SIVD-WMH) with 17 controls to investigate physiological changes underlying this diagnosis. Results Differences between the groups were large, with a decrease of relative power of fast activity in patients (alpha power 0.25 ± 0.12 versus 0.38 ± 0.13, p = 0.01; beta power 0.08 ± 0.04 versus 0.19 ± 0.07; p Conclusions This pattern of disturbance in oscillatory brain activity indicate loss of connections between neurons, providing a first step in the understanding of cognitive dysfunction in SIVD-WMH.

Abstract Background Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band) connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1), and six months later (t2). At these time points, magnetoencephalography (MEG) was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length. Results At the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology. Conclusions These results indicate that (pathologically) increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to seizures. Thus, functional connectivity and brain network architecture may prove to be important parameters of tumor-related epilepsy.

Abstract Background Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5–4 Hz, 4–8 Hz, 8–10 Hz, 10–13 Hz, 13–30 Hz and 30–45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks. Results In AD, the clustering coefficient decreased in the lower alpha and beta bands (p < 0.001), and the characteristic path length decreased in the lower alpha and gamma bands (p < 0.05) compared to controls. In FTLD no significant differences with controls were found in these measures. The degree correlation decreased in both alpha bands in AD compared to controls (p < 0.05), but increased in the FTLD lower alpha band compared with controls (p < 0.01). Conclusion With decreasing local and global connectivity parameters, the large-scale functional brain network organization in AD deviates from the optimal 'small-world' network structure towards a more 'random' type. This is associated with less efficient information exchange between brain areas, supporting the disconnection hypothesis of AD. Surprisingly, FTLD patients show changes in the opposite direction, towards a (perhaps excessively) more 'ordered' network structure, possibly reflecting a different underlying pathophysiological process.

Background: Recent fMRI studies have provided evidence that tau proteins may spread through the brain in stereotypical patterns following functional connections. However, fMRI is an indirect measure of neuronal activity, and has limited temporal resolution, contrary to magnetoencephalography(MEG). We aimed to investigate whether changes in neuronal activity, functional connectivity and network organization mirror regional tau retention in (preclinical) Alzheimer's disease(AD), using MEG and in‐vivo 18F‐flortaucipir (tau) PET. Methods: Twenty participants with AD pathology according to the ATN‐criteria (CSF‐Aβ42 and/or amyloid‐PET) were recruited from the Amsterdam Dementia Cohort, classed as subjective cognitive decline (SCD; n=10) and probable AD dementia (n=10), Table‐1. Participants underwent dynamic 130‐minutes 18F‐flortaucipir PET, and 10‐minutes eyes‐closed, task‐free 306‐channel MEG recordings. Neuronal activity and 18F‐flortaucipir non‐displaceable binding potential(BPND) were reconstructed for 52 regions‐of‐interest(ROIs) in an adjusted version of the probabilistic Hammers atlas. Associations between 18F‐flortaucipir BPND and MEG spectral measures (peak frequency, relative delta(0.5‐4Hz); theta(4‐8Hz); alpha(8‐13Hz); beta(13‐30Hz); gamma(30‐48Hz) power); connectivity (volume conduction‐corrected amplitude envelope correlation(AEC‐c) in delta, alpha and beta band); and network measures (minimum spanning tree betweenness centrality(BC)) were investigated using generalized estimating equation(GEE) models, for SCD and AD dementia combined. To investigate possible group effect, the GEE was repeated using only the AD dementia‐group. Result: Increased regional 18F‐flortaucipir binding correlated strongly with oscillatory slowing in both the total sample and AD‐group, reflected by negative correlations with peak frequency, relative alpha and beta power, and positive correlations with relative delta and theta power, most pronounced in parietal and occipital regions (Fig‐1A+B). Regional 18F‐flortaucipir binding correlated with loss of connectivity for both groups mainly in the occipital regions (average β=‐0.677±0.16, β=‐0.660±0.23 resp.) for the beta band, Fig‐2A+B. The delta band showed an opposite effect in frontal and occipital regions. Higher 18F‐flortaucipir binding was associated with lower centrality in temporal (delta) and subcortical regions (alpha, beta), and higher centrality in the parietal region (alpha), Fig‐2A+B. Conclusion: We provide putative evidence that increased regional 18F‐flortaucipir binding associates to reduced neuronal activity, as well as loss of connectivity in higher frequency bands, which could be replicated in a separate AD analysis. Furthermore, tau deposition seems to be related to damage to highly connected hub nodes. [ABSTRACT FROM AUTHOR]

Background: Although numerous electroencephalogram (EEG) studies have described differences in functional connectivity in Alzheimer's disease (AD) compared to healthy subjects, there is no general consensus on the methodology of estimating functional connectivity in AD. Inconsistent results are reported due to multiple methodological factors such as diagnostic criteria, small sample sizes and the use of functional connectivity measures sensitive to volume conduction. We aimed to investigate the reproducibility of the disease-associated effects described by commonly used functional connectivity measures with respect to the amyloid, tau and neurodegeneration (A/T/N) criteria. Methods: Eyes-closed task-free 21-channel EEG was used from patients with probable AD and subjective cognitive decline (SCD), to form two cohorts. Artefact-free epochs were visually selected and several functional connectivity measures (AEC(-c), coherence, imaginary coherence, PLV, PLI, wPLI) were estimated in five frequency bands. Functional connectivity was compared between diagnoses using AN(C)OVA models correcting for sex, age and, additionally, relative power of the frequency band. Another model predicted the Mini-Mental State Exam (MMSE) score of AD patients by functional connectivity estimates. The analysis was repeated in a subpopulation fulfilling the A/T/N criteria, after correction for influencing factors. The analyses were repeated in the second cohort. Results: Two large cohorts were formed (SCD/AD; n = 197/214 and n = 202/196). Reproducible effects were found for the AEC-c in the alpha and beta frequency bands (p = 6.20 × 10−7, Cohen's d = − 0.53; p = 5.78 × 10−4, d = − 0.37) and PLI and wPLI in the theta band (p = 3.81 × 10−8, d = 0.59; p = 1.62 × 10−8, d = 0.60, respectively). Only effects of the AEC-c remained significant after statistical correction for the relative power of the selected bandwidth. In addition, alpha band AEC-c correlated with disease severity represented by MMSE score. Conclusion: The choice of functional connectivity measure and frequency band can have a large impact on the outcome of EEG studies in AD. Our results indicate that in the alpha and beta frequency bands, the effects measured by the AEC-c are reproducible and the most valid in terms of influencing factors, correlation with disease severity and preferable properties such as correction for volume conduction. Phase-based measures with correction for volume conduction, such as the PLI, showed reproducible effects in the theta frequency band. [ABSTRACT FROM AUTHOR]

Background: Neurophysiological measures of brain function, such as magnetoencephalography (MEG), are widely used in clinical neurology and have strong relations with cognitive impairment and dementia but are still underdeveloped in multiple sclerosis (MS). Objectives: To demonstrate the value of clinically applicable MEG-measures in evaluating cognitive impairment in MS. Methods: In eyes-closed resting-state, MEG data of 83 MS patients and 34 healthy controls (HCs) peak frequencies and relative power of six canonical frequency bands for 78 cortical and 10 deep gray matter (DGM) areas were calculated. Linear regression models, correcting for age, gender, and education, assessed the relation between cognitive performance and MEG biomarkers. Results: Increased alpha1 and theta power was strongly associated with impaired cognition in patients, which differed between cognitively impaired (CI) patients and HCs in bilateral parietotemporal cortices. CI patients had a lower peak frequency than HCs. Oscillatory slowing was also widespread in the DGM, most pronounced in the thalamus. Conclusion: There is a clinically relevant slowing of neuronal activity in MS patients in parietotemporal cortical areas and the thalamus, strongly related to cognitive impairment. These measures hold promise for the application of resting-state MEG as a biomarker for cognitive disturbances in MS in a clinical setting. [ABSTRACT FROM AUTHOR]