Understanding the development of psychopathology has recently focused on the importance of endophenotypes. Particularly in instances where the genetic and neurological etiology of the disorder is not well characterized, endophenotypes serve as ‘intermediate phenotypes,’ which form a bridge between the biological and the psychological aspects of neuropsychiatric phenomena 
. Endophenotypes are often biological markers associated with a given disorder and provide insight to its origins. One characteristic of endophenotypes is that they are often present in the first-degree relatives of affected individuals. Endophenotypes have been identified in family members of individuals with a variety of neuropsychiatric disorder such as depression 
, schizophrenia 
, bipolar disorder 
, and ADHD 
The study of endophenotypes is particularly helpful in understanding developmental disorders, such as Autism Spectrum Disorder (ASD), that are defined behaviorally, but are neurobiological in origin. In order to study endophenotypes of ASD and their relation to developmental processes, recent studies have focused on the infant siblings of children with ASD 
. These infants are considered to be at high-risk for developing ASD given the high heritability and sibling recurrence rates of ASD 
. Despite the elevated incidence of ASD in this population (i.e. 1
, the majority (4:5) will likely not develop ASD, which makes them a key group to examine the developmental nature of endophenotypes. Some ASD endophenotypes that have been identified in high-risk infants are differences in patterns of head growth in the first year of life 
, ERP differences related to face processing in 10-month olds 
, and differences in hemispheric asymmetry in alpha band EEG activity as early as 6 months of age 
. Given that ASD is a heterogeneous disorder at both ends of the causal chain–behavioral and genetic–it is likely that there are many pathways in between that can lead to the singular ASD diagnosis and endophenotypes may help chart that intervening territory 
Several other candidate endophenotypes that may be functionally relevant to the etiology of ASD are related to the integration of neural networks throughout the brain. A prominent idea in the neurobiology literature is that ASD is a disorder of neural synchrony, which has its origins in the functional connections within and among regions of the brain 
. Studies using power spectra, a measure of oscillatory amplitude that contributes to neural synchrony, have documented differences associated with ASD. Adults with ASD have higher frontal and posterior theta and posterior beta power while they also have lower frontal and posterior alpha power 
. Children with ASD have been found to have less delta activity in frontal, central, and posterior regions and less beta activity in frontal and posterior regions 
. Additionally, studies of event-related gamma activity have demonstrated differences in adults and children with ASD, although there are some inconsistencies in scalp location and in the direction of the differences 
. Together these studies support the idea that neural oscillations are disrupted in ASD and EEG power captures some of the dynamics associated with this disruption.
Importantly, differences in power of resting EEG, particularly in frontal regions, have been functionally linked to cognitive functions that may be relevant to ASD. For example, variation in low alpha activity is related to individual differences in temperament 
. Additionally, gamma power has been shown to be negatively associated with language skills and general intellectual abilities 
, while delta and theta power are inversely related to default mode network activation 
. Differences in each of these functions have also been documented in individuals with ASD 
. Furthermore, in the case of temperament, variation therein is associated with resting EEG power in children with ASD 
. While this appears in a broad range of cognitive and neural outcomes that are seemingly unrelated in nature, there is evidence that each one is affected in individuals with ASD 
. Each metric of cognitive function may be the result of a more general set of neural process, encoded in the time-frequency domain of neural communication, for which there is evidence of disruption in ASD.
It is unclear to what extent the differences in EEG power associated with ASD are present specifically in individuals with the disorder or whether they are also present among their first-degree relatives. There is a substantial body of evidence showing that EEG power is an endophenotype of other psychopathologies such as schizophrenia 
, alcoholism 
, and depression 
, but the evidence for ASD is limited. Studies from our project on infant siblings of children with ASD have shown that properties of resting EEG activity differentiate high- and low-risk infants 
, while EEG asymmetry in alpha power is lower in high-risk infants 
. Additionally, Elsabbagh and colleagues 
documented higher baseline and lower induced gamma power in high-risk infants while Rojas and colleagues 
documented no differences in baseline gamma power but higher induced gamma power of MEG activity in parents of children of ASD. Thus, despite the fact that these studies document differences in first-degree relatives of individuals with ASD, systematic study of spectral power has not been done.
Furthermore, despite the fact that changes in spectral power are evident in individuals with ASD, there are some discrepancies in the nature of the differences as they relate to the age of the study participants. For example, despite reporting similar trends in power of 3–6 Hz resting EEG, Coben et al. 
reported lower levels of power in the 1.5–3.5 Hz range in children with ASD, while Murias et al. 
did not report differences in this frequency range. Given that power of resting EEG changes over developmental time 
, age differences of the participants may explain some discrepancies in how power varies in relation to ASD.
In the current study we examined developmental trajectories in spectral power of resting EEG in infants at high-risk for ASD. Specifically, we examined EEG activity in frontal regions of the brain, as there is structural and functional evidence that these areas are dysfunctional in ASD 
and because, as described above, frontal EEG power is associated with cognitive traits that are disrupted in individuals with ASD. We hypothesized that having an older sibling with ASD would confer risk-related differences in the levels of spectral power as well as on the rate at which they change within the first two years of life. Examining longitudinal trajectories of change, as opposed to differences at any given time point, may provide an additional metric upon which to evaluate the nature of EEG activity as an ASD endophenotype. Furthermore, given that an infant at high-risk has a range of potential outcomes 
, understanding the development of their neural activity may provide insight into why an individual follows one developmental path and not another.