Date of Award

12-2014

Level of Access

Campus-Only Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Functional Genomics/Interdisciplinary

Advisor

Wayne Frankel

Second Committee Member

Andre Khalil

Third Committee Member

Greg Carter

Abstract

Spike-wave discharges (SWD) found in neuroelectrical recordings are pathognomonic to absence epilepsy. The characteristic spike-wave morphology of the spike-wave complex (SWC) constituents of SWDs can be mathematically described by a subset of possible spectral power and phase values thus potentially disposing them to quantitative analysis. Morlet wavelet transform (MWT) generates time-frequency representations well-suited to identifying this SWC-associated subset. MWT decompositions of SWDs reveal spectral power concentrated at harmonic frequencies. The phase relationships underlying SWC morphology were identified by calculating the differences between phase angles at SWD fundamental frequency and the 2nd, 3 rd and 4th harmonics. The three phase differences were then used as coordinates to generate a density distribution in a {360°x360°x360°} phase difference space. Strain-specific density distributions were generated from SWDs of mice carrying the Gria4, Gabrg2 or Scn8a mutations to determine whether SWC morphological variants reliably mapped to the same regions of the distribution, and if distribution values could be used to detect SWD. To the best of our knowledge, this algorithm is the first to employ spectral phase to quantify SWC morphology, making it possible to computationally phenotype SWC subtypes and SWD fundamental frequency, and automate SWD detection. Proof-of-concept testing of the SWDreader algorithm shows: (1) a major pattern of variation in SWC morphology maps to one axis of the phase difference distribution, (2) variability between the strain-specific distributions reflects differences in the proportion of SWC subtypes generated during SWD, (3) regularities in the spectral power and phase profiles of SWCs can be used to detect waveforms possessing SWC-like morphology, and (4) that it can accurately identify strain-specific differences in SWD fundamental frequencies.

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