Loss of brain inter-frequency hubs in Alzheimer's disease

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.Comment: 27 pages, 6 figures, 3 tables, 3 supplementary figure

Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity

BACKGROUND: Local network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data. METHODOLOGY/PRINCIPAL FINDINGS: 18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions. CONCLUSIONS/SIGNIFICANCE: We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease

Insulin Modulation of Magnetoencephalographic Resting State Dynamics in Lean and Obese Subjects

Lean and obese subjects can exhibit differences in neuronal activity during resting state and tasks. Changes in hormonal status and their action related to increased body weight may be the determining factor for these differences. One prime candidate is insulin, which until recently was mainly related to its metabolic function for the transport and regulation of glucose in the periphery. However insulin also acts as an anorexic signal in the central nervous system contributing to the termination of food intake in the postprandial state. In our study, we examined with whole-head magnetoencephalography the effect of intranasal insulin on the dynamics of the resting state network in a placebo controlled study. Weighted clustering coefficient C, which describes local interconnectedness, and weighted path length L, a measure of global interconnectedness, were computed. These parameters showed high intraindividual reliability. However, no difference for the network dynamics was found between lean and obese subjects in the basal state. The application of insulin led to subject specific changes and we found a statistically significant positive correlation between the insulin induced change in path length in the theta band (4–8 Hz) and body mass index. The change in pathway length after insulin administration indicates a strong insulin modulation on global communication efficiency, which is probably related to the signaling between different regions involved in satiation and homeostatic control

Topological changes of brain network during mindfulness meditation: an exploratory source level magnetoencephalographic study

We have previously evidenced that Mindfulness Meditation (MM) in experienced meditators (EMs) is associated with long-lasting topological changes in resting state condition. However, what occurs during the meditative phase is still debated. Utilizing magnetoencephalography (MEG), the present study is aimed at comparing the topological features of the brain network in a group of EMs (n = 26) during the meditative phase with those of individuals who had no previous experience of any type of meditation (NM group, n = 29). A wide range of topological changes in the EM group as compared to the NM group has been shown. Specifically, in EMs, we have observed increased betweenness centrality in delta, alpha, and beta bands in both cortical (left medial orbital cortex, left postcentral area, and right visual primary cortex) and subcortical (left caudate nucleus and thalamus) areas. Furthermore, the degree of beta band in parietal and occipital areas of EMs was increased too. Our exploratory study suggests that the MM can change the functional brain network and provides an explanatory hypothesis on the brain circuits characterizing the meditative process

Frequency-based brain networks: From a multiplex framework to a full multilayer description

We explore how to study dynamical interactions between brain regions using functional multilayer networks whose layers represent the different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as a multilayer network in which all brain regions can interact with each other at different frequency bands, instead of as a multiplex network, in which interactions between different frequency bands are only allowed within each brain region and not between them. We study the second smallest eigenvalue of the combinatorial supra-Laplacian matrix of the multilayer network in detail, and we thereby show that the heterogeneity of interlayer edges and, especially, the fraction of missing edges crucially modify the spectral properties of the multilayer network. We illustrate our results with both synthetic network models and real data sets obtained from resting state magnetoencephalography. Our work demonstrates an important issue in the construction of frequency-based multilayer brain networks.Comment: 13 pages, 8 figure

Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury.

Cognitive processes require a functional interaction between specialized multiple, local and remote brain regions. Although these interactions can be strongly altered by an acquired brain injury, brain plasticity allows network reorganization to be principally responsible for recovery. The present work evaluates the impact of brain injury on functional connectivity patterns. Networks were calculated from resting-state magnetoencephalographic recordings from 15 brain injured patients and 14 healthy controls by means of wavelet coherence in standard frequency bands. We compared the parameters defining the network, such as number and strength of interactions as well as their topology, in controls and patients for two conditions: following a traumatic brain injury and after a rehabilitation treatment. A loss of delta- and theta-based connectivity and conversely an increase in alpha- and beta-band-based connectivity were found. Furthermore, connectivity parameters approached controls in all frequency bands, especially in slow-wave bands. A correlation between network reorganization and cognitive recovery was found: the reduction of delta-band-based connections and the increment of those based on alpha band correlated with Verbal Fluency scores, as well as Perceptual Organization and Working Memory Indexes, respectively. Additionally, changes in connectivity values based on theta and beta bands correlated with the Patient Competency Rating Scale. The current study provides new evidence of the neurophysiological mechanisms underlying neuronal plasticity processes after brain injury, and suggests that these changes are related with observed changes at the behavioural leve

Connectivity in MEG resting-state networks increases after resective surgery for low-grade glioma and correlates with improved cognitive performance☆

Purpose Low-grade glioma (LGG) patients often have cognitive deficits. Several disease- and treatment related factors affect cognitive processing. Cognitive outcome of resective surgery is unpredictable, both for improvement and deterioration, especially for complex domains such as attention and executive functioning. MEG analysis of resting-state networks (RSNs) is a good candidate for presurgical prediction of cognitive outcome. In this study, we explore the relation between alterations in connectivity of RSNs and changes in cognitive processing after resective surgery, as a stepping stone to ultimately predict postsurgical cognitive outcome. Methods: Ten patients with LGG were included, who had no adjuvant therapy. MEG recording and neuropsychological assessment were obtained before and after resective surgery. MEG data were recorded during a no-task eyes-closed condition, and projected to the anatomical space of the AAL atlas. Alterations in functional connectivity, as characterized by the phase lag index (PLI), within the default mode network (DMN), executive control network (ECN), and left- and right-sided frontoparietal networks (FPN) were compared to cognitive changes. Results: Lower alpha band DMN connectivity was increased after surgery, and this increase was related to improved verbal memory functioning. Similarly, right FPN connectivity was increased after resection in the upper alpha band, which correlated with improved attention, working memory and executive functioning. Discussion Increased alpha band RSN functional connectivity in MEG recordings correlates with improved cognitive outcome after resective surgery. The mechanisms resulting in functional connectivity alterations after resection remain to be elucidated. Importantly, our findings indicate that connectivity of MEG RSNs may be used for presurgical prediction of cognitive outcome in future studies