Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging

Abstract

The individual alpha peak frequency (IAPF) of the human electroencephalography (EEG) typically experiences slowing with increasing age. Despite this hallmark change, studies that investigate modulations of conventional EEG alpha power and connectivity by aging and age-related neuropathology neglect to account for intergroup differences in IAPF. To investigate the relationship of age-related IAPF slowing with EEG power and connectivity, we recorded eyes-closed resting-state EEG in 37 young adults and 32 older adults. We replicated the finding of a slowed IAPF in older adults. IAPF values were significantly correlated with the frequency of maximum global connectivity and the means of their distributions did not differ, suggesting that connectivity was highest at the IAPF. Older adults expressed reduced global EEG power and connectivity at the conventional upper alpha band (10–12 Hz) compared with young adults. By contrast, groups had equivalent power and connectivity at the IAPF. The results suggest that conventional spectral boundaries may be biased against older adults or any group with a slowed IAPF. We conclude that investigations of alpha activity in aging and age-related neuropathology should be adapted to the IAPF of the individual and that previous findings should be interpreted with caution. EEG in the dominant alpha range may be unsuitable for examining cortico-cortical connectivity due to its subcortical origins.

Introduction

Aging is associated with a host of functional changes in the resting brain. Using the electroencephalography (EEG) neuroimaging method, some of the most commonly documented changes in older age are (1) reductions in oscillatory power, (2) a weakening of functional connectivity between electrode time series data, and (3) a slowing of the dominant alpha EEG frequency. The relationship between these hallmark changes has rarely been systematically investigated. Of particular interest is how the use of conventional EEG frequency bands, which do not account for age-related frequency shifts, may influence age-group differences in EEG power and connectivity. These relationships may have important implications for the interpretation of previous findings and for progression in the analysis of resting state EEG in aging research.

The EEG frequency spectrum ranges from 0 to ∼100 Hz. Most commonly, spectral properties of the EEG are analyzed within conventional frequency bands: delta (0–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (>30 Hz) bands. Research into the cognitive modulation of EEG frequency power emphasizes the interindividual variation across the spectral boundaries of these conventional frequency bands, and also the boundaries of subbands within the alpha range (for a review, see Klimesch, 1999). A suggested marker for determining the individualized frequency boundaries is the individual alpha peak frequency (IAPF). The IAPF is the average frequency of highest power between 6 and 13 Hz across the electrodes of the EEG montage (Angelakis et al., 2004). The IAPF is a stable and highly heritable physiological characteristic (Grandy et al., 2013, Posthuma et al., 2001, Smit et al., 2006) that typically increases during the first 20 years of development and commences slowing from age 40 years (Aurlien et al., 2004, Bazanova, 2008, Chiang et al., 2011). Reported averages range from 9.8–10.5 Hz in young adults (age 17–30 years), to 8.5–9.7 Hz in older adults aged 60 years and older (Dustman et al., 1993).

An abundance of research has demonstrated that there are clear age-related modulations of the IAPF. Despite this, very few studies have abandoned the use of conventional frequency bands when investigating oscillatory power and connectivity. Duffy et al. (1984) observed a weak negative correlation between chronological age and alpha (8–11.75 Hz) amplitude in 30- to 80-year-old males (r = –0.27). Barry and De Blasio (2017) reported reduced delta (0.5–3.5 Hz), theta (4–7.5 Hz), and alpha (8–13 Hz) power, and increased beta (13.5–24 Hz) power in older compared with young adults; age-related alpha reductions were mainly evident in the posterior right hemisphere, with a weaker effect than observed in the other frequency bands. Gaál et al. (2010) observed reductions of delta and alpha power in older adults. Babiloni et al. (2006) also reported reduced power for older adults in the lower and upper alpha bands (8–10.5 Hz, 10.5–13 Hz). However, reduced alpha power may not be specific to the latest decades of development, as Aurlien et al. (2004) described a steady decline of IAPF amplitude from birth to age 30 years, where amplitude stabilized for the rest of the lifespan.

Functional connectivity in EEG generally refers to the temporal correlations between time series data from 2 or more independent EEG channels or sources. Connectivity strength between regions of interest may be extracted from the data, or the entire data set may be summarized in terms of its network properties (Rubinov and Sporns, 2010). Connectivity metrics, which attempt to characterize the strength of connectivity in the presence of noise, are plentiful. Many early investigations of EEG functional connectivity have relied on the EEG coupling metric known as spectral coherence. Coherence is an estimate of the linear correlation between a pair of signals in the time-frequency domain (Bowyer, 2016, French and Beaumont, 1984) and is usually computed within a frequency band of interest. Coherence between EEG channels is prone to overestimation due to volume conduction, whereby neighboring electrodes may record the signal from the same underlying neural generator, resulting in inflated estimates of connectivity between them. Novel connectivity metrics such as the phase-lag index (PLI) (Stam et al., 2007) and weighted phase-lag index (WPLI) (Vinck et al., 2011) aim to attenuate the effects of volume conduction by disregarding the zero-phase relationships between a pair of EEG signals. In many cases, EEG functional connectivity has been shown to reflect the underlying structural properties of the brain. For example, coherence between electrodes placed on each hemisphere is weakened in individuals with surgically sectioned or underdeveloped commissural white matter fibers (Koeda et al., 1995, Montplaisir et al., 1990, Nagase et al., 1994, Nielsen et al., 1993). Furthermore, studies have noted positive correlations between functional EEG coherence and white matter tract integrity in patients with Alzheimer's disease pathology (Pogarell et al., 2005, Teipel et al., 2009, Vecchio et al., 2015).

While many studies have investigated EEG connectivity in age-related diseases, relatively few have characterized the connectivity changes that occur with typical aging. Duffy et al. (1996) examined EEG coherence in a large sample (N = 350) of adults aged 20–79 years and reported age-related reductions in theta, alpha, and beta frequencies. However, they exclusively considered interhemispheric pairs of electrodes and could not differentiate between eyes-open and eyes-closed conditions in their factor-analytic approach, which are known to generate distinct functional connectivity networks (Miraglia et al., 2016). Kikuchi et al. (2000) also found reductions in delta, theta, upper alpha (11–12.5 Hz), and beta interhemispheric EEG coherence during eyes-closed recordings. In a large sample of 17,722 individuals, an age-related decrease of global theta and alpha (8–12.5 Hz) coherence, along with an increase in beta coherence was reported (Vysata et al., 2014). Few studies have used connectivity measures that aim to reduce volume conduction. In a sample of 1500 individuals between age 5 and 71 years, alpha PLI (6–13 Hz) derived from minimum spanning tree graphs declined in late adulthood from about age 50 years (Smit et al., 2016). Vecchio et al. (2014) described a reduction of lagged linear coherence in the upper alpha frequencies (10.5–13 Hz) in early (50–70 years) and later old aged (>70 years) adults, which were accompanied by age-related increases in delta and theta connectivity.

A critical issue for the study of EEG power and connectivity in the conventional alpha frequency range is the role of interindividual IAPF variability. In 1999, Klimesch argued that because the spectral boundaries of the alpha frequency band and sub-bands vary between individuals, the alpha frequency boundaries should be adapted to the IAPF of the individual under study (Klimesch, 1999). Older adults would therefore be at an artificial disadvantage when compared with young adults within conventional frequency bands, such as the upper alpha (10–12 Hz) frequencies, due to the age-related slowing of the IAPF. While this is certain for EEG power, the relationship between IAPF variability and connectivity is largely unknown. We are unaware of any studies that have investigated age-related changes of EEG connectivity while also considering age-related IAPF slowing. By considering IAPF, we can uncover real age-related modulations of EEG activity and connectivity to inform future studies of aging and age-related neuropathology.

The present study aimed to examine whether the IAPF is associated with EEG power and connectivity in the context of age-related physiological differences. We first replicated the finding of a slowed IAPF in older adults, to demonstrate that our data are coherent with previous findings. We then determined whether global connectivity was modulated by the IAPF, that is, whether the frequency at which connectivity was strongest was correlated with, and different to the IAPF across and within participant groups. We then compared younger and older adults on global power and connectivity at both the IAPF and the conventional upper alpha frequency band. We hypothesized that age differences would be observed in the conventional upper alpha band, as reported in previous studies (Gaál et al., 2010, Kikuchi et al., 2000, Vecchio et al., 2014), while age differences may be absent at the IAPF, outlining the redundancy of conventional frequency boundaries where IAPF is slowed in 1 group.