8,020 research outputs found
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
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
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
Magnetoencephalography as a tool in psychiatric research: current status and perspective
The application of neuroimaging to provide mechanistic insights into circuit dysfunctions in major psychiatric conditions and the development of biomarkers are core challenges in current psychiatric research. In this review, we propose that recent technological and analytic advances in Magnetoencephalography (MEG), a technique which allows the measurement of neuronal events directly and non-invasively with millisecond resolution, provides novel opportunities to address these fundamental questions. Because of its potential in delineating normal and abnormal brain dynamics, we propose that MEG provides a crucial tool to advance our understanding of pathophysiological mechanisms of major neuropsychiatric conditions, such as Schizophrenia, Autism Spectrum Disorders, and the dementias. In our paper, we summarize the mechanisms underlying the generation of MEG signals and the tools available to reconstruct generators and underlying networks using advanced source-reconstruction techniques. We then survey recent studies that have utilized MEG to examine aberrant rhythmic activity in neuropsychiatric disorders. This is followed by links with preclinical research, which have highlighted possible neurobiological mechanisms, such as disturbances in excitation/inhibition parameters, which could account for measured changes in neural oscillations. In the final section of the paper, challenges as well as novel methodological developments are discussed which could pave the way for a widespread application of MEG in translational research with the aim of developing biomarkers for early detection and diagnosis
Magnetoencephalography in Stroke Recovery and Rehabilitation
Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility
Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications
Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic
resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of
Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity
underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the
use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to
cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers
have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic,
and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity
across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power
and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.
Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral
reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains
in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
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