2,282 research outputs found
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
The Role of Corpus Callosum Development in Functional Connectivity and Cognitive Processing
The corpus callosum is hypothesized to play a fundamental role in integrating information and mediating complex behaviors. Here, we demonstrate that lack of normal callosal development can lead to deficits in functional connectivity that are related to impairments in specific cognitive domains. We examined resting-state functional connectivity in individuals with agenesis of the corpus callosum (AgCC) and matched controls using magnetoencephalographic imaging (MEG-I) of coherence in the alpha (8–12 Hz), beta (12–30 Hz) and gamma (30–55 Hz) bands. Global connectivity (GC) was defined as synchronization between a region and the rest of the brain. In AgCC individuals, alpha band GC was significantly reduced in the dorsolateral pre-frontal (DLPFC), posterior parietal (PPC) and parieto-occipital cortices (PO). No significant differences in GC were seen in either the beta or gamma bands. We also explored the hypothesis that, in AgCC, this regional reduction in functional connectivity is explained primarily by a specific reduction in interhemispheric connectivity. However, our data suggest that reduced connectivity in these regions is driven by faulty coupling in both inter- and intrahemispheric connectivity. We also assessed whether the degree of connectivity correlated with behavioral performance, focusing on cognitive measures known to be impaired in AgCC individuals. Neuropsychological measures of verbal processing speed were significantly correlated with resting-state functional connectivity of the left medial and superior temporal lobe in AgCC participants. Connectivity of DLPFC correlated strongly with performance on the Tower of London in the AgCC cohort. These findings indicate that the abnormal callosal development produces salient but selective (alpha band only) resting-state functional connectivity disruptions that correlate with cognitive impairment. Understanding the relationship between impoverished functional connectivity and cognition is a key step in identifying the neural mechanisms of language and executive dysfunction in common neurodevelopmental and psychiatric disorders where disruptions of callosal development are consistently identified
Network perspectives on epilepsy using EEG/MEG source connectivity
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience
Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients
OBJECTIVE: Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network.; METHODS: Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n=42) and without (n=41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated.; RESULTS: Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p<0.05). General cognitive performance for both patient groups was worse compared with healthy controls. Lower DMN alpha band functional connectivity correlated with poorer general cognitive ability in patients with microvascular complications.; DISCUSSION: Altered RSN functional connectivity was found in T1DM patients depending on clinical status. Lower DMN functional connectivity was related to poorer cognitive functioning. These results indicate that functional connectivity may play a key role in T1DM-related cognitive dysfunction
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals
Neural Connectivity with Hidden Gaussian Graphical State-Model
The noninvasive procedures for neural connectivity are under questioning.
Theoretical models sustain that the electromagnetic field registered at
external sensors is elicited by currents at neural space. Nevertheless, what we
observe at the sensor space is a superposition of projected fields, from the
whole gray-matter. This is the reason for a major pitfall of noninvasive
Electrophysiology methods: distorted reconstruction of neural activity and its
connectivity or leakage. It has been proven that current methods produce
incorrect connectomes. Somewhat related to the incorrect connectivity
modelling, they disregard either Systems Theory and Bayesian Information
Theory. We introduce a new formalism that attains for it, Hidden Gaussian
Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden
by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS
is equivalent to a frequency domain Linear State Space Model (LSSM) but with
sparse connectivity prior. The mathematical contribution here is the theory for
high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS
can attenuate the leakage effect in the most critical case: the distortion EEG
signal due to head volume conduction heterogeneities. Its application in EEG is
illustrated with retrieved connectivity patterns from human Steady State Visual
Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence
for noninvasive procedures of neural connectivity: concurrent EEG and
Electrocorticography (ECoG) recordings on monkey. Open source packages are
freely available online, to reproduce the results presented in this paper and
to analyze external MEEG databases
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