Brain electrophysiological endophenotypes for externalizing psychopathology: a multivariate approach

Abstract

Abnormalities in electrophysiological measures of stimulus-evoked brain activity (including the P3 event-related potential (ERP) and its associated delta and theta time-frequency (TF) components), and intrinsic, resting state brain activity (including EEG in the beta frequency band) have each been associated with biological vulnerability to a variety of externalizing (EXT) spectrum disorders, such as substance use disorders, conduct disorder, and antisocial behavior. While each of these individual measures has shown promise as an endophenotype for one or more aspects of EXT, we proposed that the power to identify EXT-related genes may be enhanced by using these measures collectively. Thus, we sought to explore a multivariate approach to identifying electrophysiological endophenotypes related to EXT, using measures identified in the literature as promising individual endophenotypes for EXT. Using data from our large twin sample (634 MZ and 335 DZ, male and female same-sex pairs), and fitting multivariate biometric Cholesky models, we found that these measures (1) were heritable, (2) showed significant phenotypic and genetic correlation with a general vulnerability to EXT (which is itself highly heritable), (3) showed modest phenotypic and genetic correlation with each other, and (4) were sensitive to genetic effects that differed as a function of gender. These relationships suggest that these endophenotypes are likely tapping into neurophysiological processes and genes that are both common across them and unique to each-all of which are relevant to a biological vulnerability to EXT psychopathology.

Figure 1

Path diagram for the AE Cholesky model. Standardized path coefficients are shown for both males and females (males / females); significant paths (p≤.01) are printed in bold. All paths should be squared to estimate the proportion of variance accounted for by the respective path. Summing all the squared coefficients pointing to each phenotype equals (within rounding error) the phenotypic variance which is standardized at 1. A1 and E1 represent the additive genetic and non-shared environmental variance, respectively, that is shared by all three endophenotypes and EXT. A2 and E2 are the variances shared by TF-PC1, Beta, and EXT, independent of that shared with P3. A3 and E3 are the variances shared by Beta and EXT, independent of that shared with P3 and TF-PC1. A4 and E4 are the variances unique to EXT. Note: Because E includes measurement error as well as other sources of nonshared influence, the paths from each E to its corresponding phenotype (E1 on P3, E2 on TF-PC1, E3 on beta, and E4 on EXT) could not be constrained to 0 for significance testing.

Figure 2

Results of TF-PCA analyses. Grand-averaged ERP and TF (Avg) plots are presented at the top. ERP (from 0 ms – 1000 ms) is from electrode Pz, grand averaged over all participants; P3 is the peak in the waveform occurring around 450 ms. The five time-frequency components (PCs 1–5) retained from the principal components analysis decomposition of the averaged TF surface are presented below the grand averages. For all time-frequency plots, data has been downsampled to 32 Hz; the x-axis is time from stimulus onset (0 ms) to 1000 ms, and y-axes range from 0 – 7.5 Hz. Components are numbered (1–5: highest to lowest) based on the amount of variance for which they accounted in the varimax-rotated solution. PC1 (with peak energy centered around 2.5 Hz and 330 ms) is the TF component that loaded most strongly with EXT in a PCA analysis (see Methods and Results for details), thus, it is the TF-PCA component included in subsequent biometric analyses.