Genetic influences on composite neural activations supporting visual target identification

Abstract

Behavior genetic studies of brain activity associated with complex cognitive operations may further elucidate the genetic and physiological underpinnings of basic and complex neural processing. In the present project, monozygotic (N=51 pairs) and dizygotic (N=48 pairs) twins performed a visual oddball task with dense-array EEG. Using spatial PCA, two principal components each were retained for targets and standards; wavelets were used to obtain time-frequency maps of eigenvalue-weighted event-related oscillations for each individual. Distribution of inter-trial phase coherence (ITC) and single trial power (STP) over time indicated that the early principal component was primarily associated with ITC while the later component was associated with a mixture of ITC and STP. Spatial PCA on point-by-point broad sense heritability matrices revealed data-derived frequency bands similar to those well established in EEG literature. Biometric models of eigenvalue-weighted time-frequency data suggest a link between physiology of oscillatory brain activity and patterns of genetic influence.

Figure 1

Pictorial representation of data reduction methods utilized in this project, including matrix manipulation for spatial and heritability PCAs. Step 1 begins at the upper left corner (Butterfly plot) and proceeds in a serpentine, arrow-guided manner to the final step (Submit to genetic modeling).

Figure 2

Grand average butterfly plots, PCA component waveforms and component topographies for targets and standards. PCA component 1 for both conditions shows the largest deflection and has component topographies representative of late processing. PCA component 2 for both conditions has component topographies more representative of early stimulus registration. For orientation purposes, white dots on topographies indicate the position of sensor Pz.

Figure 3

A) Grand average ITC and STP time-frequency plots across all twins, conditions, and components indicate that early stimulus registration is primarily accompanied by an increase in ITC, while late processing primarily consists of a large low-frequency STP synchronization and corresponding beta desynchronization relative to baseline. B) Corresponding broad sense heritability plots, calculated point-by-point as twice the difference between MZ and DZ intraclass correlations. Note that peak heritability values do not necessarily correspond to peak ITC or STP amplitudes.

Figure 4

Grand average ITC (left column) and STP (right column) plots weighted by broad sense heritability PCA components, revealing a data-driven distribution of frequency bands that closely resemble traditional bands defined in the EEG literature. Scale is provided for each plot but denotes weighted values so is not directly comparable with raw ITC or STP values. Percent variance accounted for by each rotated component, ignoring other components, followed by percent variance uniquely accounted for by each component, is presented to the right of each set of plots. A). High gamma. B). Low gamma. C). High beta. D). Low beta. E). Alpha. F). Theta/Delta G). Scree plot of unrotated eigenvalues. The dot indicates where the final component retained is located.

Figure 5

Covariance matrix for heritability of full concatenated data set as input into the frequency PCA. Although there is a degree of overlap in the lower frequencies, frequency bands in the delta/theta, alpha, high and low beta, and high and low gamma can be seen based on relative covariance between and within bands. Similarities in power across time between evoked delta/theta and alpha frequency bands might be expected in this task, as they are primary contributors to the large ERPs such as P3 and slow wave (Basar et al., 1999). The similarities between the heritability patterns and those commonly seen in raw power values bolster the connection between genetic effects and the underlying biology of the distinctions between frequency bands.