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. 2019 Feb:74:112-120.
doi: 10.1016/j.neurobiolaging.2018.10.016. Epub 2018 Oct 19.

Brain age from the electroencephalogram of sleep

Haoqi Sun  1 , Luis Paixao  1 , Jefferson T Oliva  2 , Balaji Goparaju  1 , Diego Z Carvalho  1 , Kicky G van Leeuwen  3 , Oluwaseun Akeju  4 , Robert J Thomas  5 , Sydney S Cash  1 , Matt T Bianchi  1 , M Brandon Westover  6
Affiliations

Brain age from the electroencephalogram of sleep

Haoqi Sun et al. Neurobiol Aging. 2019 Feb.

Abstract

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age (BA)," which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18-80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40-80). The model obtains a mean absolute deviation of 7.6 years between BA and chronological age (CA) in healthy participants in the MGH data set. As validation, a subset of SHHS containing longitudinal EEGs 5.2 years apart shows an average of 5.4 years increase in BA. Participants with significant neurological or psychiatric disease exhibit a mean excess BA, or "brain age index" (BAI = BA-CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess BA of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging.

Keywords: Brain age; EEG; Machine learning; Sleep.

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Conflict of interest statement

Disclosure

The authors declare that they have no conflict of interest.

Figures

Figure 1.
Figure 1.
Scatter plot of predicted BA vs. CA using (A) sleep EEG macro-structure features (described in Table S5) and (B) sleep micro-structure features (described in Table S4). The red dashed diagonal line is the identity line where BA is equal to CA. Both plots are obtained from the same testing participants, including both healthy and with significant neurological or psychiatric disease.
Figure 2.
Figure 2.
Cases in which BA matches (diagonal) or is younger or older than CA (off diagonal). Each panel consists of a hypnogram and the corresponding spectrogram. Spectrograms are calculated as the average across the 6 EEG channels. The horizontal axis is time in hours. The case indicated by * is described in the main text.
Figure 3.
Figure 3.
(A, B) Chronological age (CA) vs. Brain age (BA) from the 470 testing EEGs in SHHS when trained on the other part of SHHS. The red dash line indicates identity. (D, E) CA vs. BA from the 1,974 testing EEGs in SHHS when trained on the healthy participants in MGH dataset. (C, F) Histogram of BA differences between two SHHS visits, showing tracking of CA at the population level. The mean difference in predicted BA was 5.4 years in (C); and 4.3 years in (F). The dashed red line is the Gaussian fit to the histogram.
Figure 4.
Figure 4.
(A) Histograms of BAI s for participants with (red) and without (blue) significant neurological or psychiatric disease defined in Table S1. The dashed lines show the Gaussian fit to these distributions. The difference of group means is 4 years. (B) Histograms of BAIs for participants with (red) and without (blue) hypertension and diabetes. The difference of group means is 3.5 years.
Figure 5.
Figure 5.
(A) The hypnogram and EEG spectrogram from frontal (F), central (C) and occipital (O) channels. (B) The left two bars show the chronological age and brain age for this participant. The right parts show the top five features that contribute most positively to the older brain age. The blue bar is the feature value based on the model-based age norm at age 29, with the error bar indicating the standard deviation. The red bar is the feature value for this participant. The number at the top/bottom indicates the model weight associated with this feature.
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