Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Medientyp: E-Artikel

Titel: Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Beteiligte: Dukic, Stefan [VerfasserIn]; McMackin, Roisin [VerfasserIn]; Costello, Emmet [VerfasserIn]; Metzger, Marjorie [VerfasserIn]; Buxo, Teresa [VerfasserIn]; Fasano, Antonio [VerfasserIn]; Chipika, Rangariroyashe [VerfasserIn]; Pinto-Grau, Marta [VerfasserIn]; Schuster, Christina [VerfasserIn]; Hammond, Michaela [VerfasserIn]; Heverin, Mark [VerfasserIn]; Coffey, Amina [VerfasserIn]; Broderick, Michael [VerfasserIn]; Iyer, Parameswaran M. [VerfasserIn]; Mohr, Kieran [VerfasserIn]; Gavin, Brighid [VerfasserIn]; McLaughlin, Russell [VerfasserIn]; Pender, Niall [VerfasserIn]; Bede, Peter [VerfasserIn]; Muthuraman, Muthuraman [VerfasserIn]; van den Berg, Leonard H. [VerfasserIn]; Hardiman, Orla [VerfasserIn]; Nasseroleslami, Bahman [VerfasserIn]

Erschienen: Augsburg University Publication Server (OPUS), 2022

Sprache: Englisch

DOI: https://doi.org/10.1093/brain/awab322

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Beschreibung: Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG ...

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