3,753 research outputs found

    How to build a functional connectomic biomarker for mild cognitive impairment from source reconstructed MEG resting-state activity: the combination of ROI representation and connectivity estimator matters

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    Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal and cingulo-opercular network. Our analysis supports the notion of analysing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings

    Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms

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    This study assesses the connectivity alterations caused by Alzheimer's disease (AD) and mild cognitive impairment (MCI) in magnetoencephalogram (MEG) background activity. Moreover, a novel methodology to adaptively extract brain rhythms from the MEG is introduced. This methodology relies on the ability of empirical mode decomposition to isolate local signal oscillations and constrained blind source separation to extract the activity that jointly represents a subset of channels. Inter-regional MEG connectivity was analysed for 36 AD, 18 MCI and 26 control subjects in δ, θ, α and β bands over left and right central, anterior, lateral and posterior regions with magnitude squared coherence—c(f). For the sake of comparison, c(f) was calculated from the original MEG channels and from the adaptively extracted rhythms. The results indicated that AD and MCI cause slight alterations in the MEG connectivity. Computed from the extracted rhythms, c(f) distinguished AD and MCI subjects from controls with 69.4% and 77.3% accuracies, respectively, in a full leave-one-out cross-validation evaluation. These values were higher than those obtained without the proposed extraction methodology

    Alterations of Effective Connectivity Patterns in Mild Cognitive Impairment: An MEG Study

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    Producción CientíficaNeuroimaging techniques have demonstrated over the years their ability to characterize the brain abnormalities associated with different neurodegenerative diseases. Among all these techniques, magnetoencephalography (MEG) stands out by its high temporal resolution and noninvasiveness. The aim of the present study is to explore the coupling patterns of resting-state MEG activity in subjects with mild cognitive impairment (MCI). To achieve this goal, five minutes of spontaneous MEG activity were acquired with a 148- channel whole-head magnetometer from 18 MCI patients and 26 healthy controls. Interchannel relationships were investigated by means of two complementary coupling measures: coherence and Granger causality. Coherence is a classical method of functional connectivity, while Granger causality quantifies effective (or causal) connectivity. Both measures were calculated in the five conventional frequency bands: delta (d, 1-4 Hz), theta (q, 4-8 Hz), alpha (a, 8-13 Hz), beta (b, 13-30 Hz), and gamma (g, 30-45Hz). Our results showed that connectivity values were lower for MCI patients than for controls in all frequency bands. However, only Granger causality revealed statistically significant differences between groups (p-values < 0.05, FDR corrected Mann-Whitney U-test), mainly in the beta band. Our results support the role of MCI as a disconnection syndrome, which elicits early alterations in effective connectivity patterns. These findings can be helpful to identify the neural substrates involved in prodromal stages of dementia.This research was supported by ‘Ministerio de Economía y Competitividad’ and ‘European Regional Development Fund’ under project TEC2014-53196-R, by ‘European Commission’ and ‘European Regional Development Fund’ under project ‘Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ (‘Cooperation Programme Interreg V-A Spain-Portugal POCTEP 2014-2020’), and by ‘Consejería de Educación de la Junta de Castilla y León’ under project VA037U16. Pablo Núñez was in receipt of a ‘Promoción de empleo joven e implantación de la Garantía Juvenil en I+D+i’ grant from ‘Ministerio de Economía y Competitividad’ and University of Valladolid

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

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    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

    Measures of Resting State EEG Rhythms for Clinical Trials in Alzheimer’s Disease:Recommendations of an Expert Panel

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    The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12Hz) and widespread delta (&lt;4Hz) and theta (4-8Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes

    Characterization of the spontaneous EEG activity in the Alzheimer's disease continuum: from local activation to network organization

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    La presente Tesis Doctoral se presenta como un compendio de cuatro publicaciones indexadas en el Journal Citation Reports. El objetivo de estas publicaciones es la caracterización de los cambios neuronales subyacentes en las diferentes etapas de la enfermedad de Alzheimer (EA) y su etapa prodrómica, el deterioro cognitivo leve (DCL), siguiendo tres niveles de análisis: activación local, interacción entre pares de sensores, y organización de red. Los principales cambios encontrados a medida que progresa la enfermedad son: (i) una lentificación, y una pérdida de complejidad e irregularidad de la actividad EEG espontánea; (ii) una disminución significativa de la conectividad en bandas altas de frecuencia y un aumento en las bandas bajas; y (iii) una pérdida en la integración y la segregación de las redes neuronales. Estos hallazgos han proporcionado información adicional sobre las alteraciones cerebrales de la EA en sus diferentes etapas, útiles para comprender mejor sus mecanismos fisiopatológicos.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione
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