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J Neurodev Disord. 2017 May 8;9:17. doi: 10.1186/s11689-017-9195-8. eCollection 2017.

Delta rhythmicity is a reliable EEG biomarker in Angelman syndrome: a parallel mouse and human analysis.

Author information

1
Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599 USA.
2
Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599 USA.
3
Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA.
4
Department of Neurology, Massachusetts General Hospital, Boston, MA 02114 USA.
5
Harvard Medical School, Boston, MA 02215 USA.
6
Present Address: The Neurology Foundation, Rhode Island Hospital and Warren Alpert School of Medicine at Brown University, Providence, RI 02903 USA.
7
Department of Pediatrics, University of California, San Diego, CA USA.
8
Division of Dysmorphology/Genetics, Rady Children's Hospital, San Diego, CA USA.

Abstract

BACKGROUND:

Clinicians have qualitatively described rhythmic delta activity as a prominent EEG abnormality in individuals with Angelman syndrome, but this phenotype has yet to be rigorously quantified in the clinical population or validated in a preclinical model. Here, we sought to quantitatively measure delta rhythmicity and evaluate its fidelity as a biomarker.

METHODS:

We quantified delta oscillations in mouse and human using parallel spectral analysis methods and measured regional, state-specific, and developmental changes in delta rhythms in a patient population.

RESULTS:

Delta power was broadly increased and more dynamic in both the Angelman syndrome mouse model, relative to wild-type littermates, and in children with Angelman syndrome, relative to age-matched neurotypical controls. Enhanced delta oscillations in children with Angelman syndrome were present during wakefulness and sleep, were generalized across the neocortex, and were more pronounced at earlier ages.

CONCLUSIONS:

Delta rhythmicity phenotypes can serve as reliable biomarkers for Angelman syndrome in both preclinical and clinical settings.

KEYWORDS:

Angelman syndrome; Biomarker; Delta; EEG; Mouse model; Outcome measure; UBE3A

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