Electrooculography

PSG on the experimental night was recorded using a Grass Technologies
Comet XL system (Astro-Med, inc., West Warwick, RI), including 19-channel
electroencephalography (EEG) placed using the 10–20 system,
electrooculography (EOG) recorded at the right and left outer canthi (right
superior; left inferior), and electromyography (EMG). Reference electrodes were
recorded at both the left and right mastoid (A1, A2). Data were digitized at
400Hz, and stored unfiltered (recovered frequency range of 0.1–100 Hz),
except for a 60-Hz notch filter. Sleep was scored using standard
criteria⁶⁸ . Sleep
monitoring on the screening night was recorded using a Grass Technologies AURA
PSG Ambulatory system (Astro-Med, inc., West Warwick, RI), and additionally
included nasal/oral airflow, abdominal and chest belts, and pulse oximetry.
EEG data from the experimental night were imported into EEGLAB
(http://sccn.ucsd.edu/eeglab/) and epoched into 5 s bins. Epochs
containing artifacts were manually rejected by a trained scorer (author B.A.M.),
and the remaining epochs were filtered between 0.4–50Hz (645±80
epochs per participant with 4.6%±2.1% of epochs
rejected). A fast Fourier transform (FFT) was then applied to the filtered EEG
signal at 5-second intervals with 50% overlap and employing hanning
windowing. Analyses in the current report focused, a priori, on
slow wave activity (SWA), defined as relative spectral power between
0.6–4.6Hz during slow wave sleep (NREM stages 3&4)^{10 (link), 11 (link)}. Spectral power was subdivided into two bins for
analysis (0.6–1Hz/1–4Hz), to examine the impact of
β-amyloid on SWA frequencies particularly relevant to memory
functions^{14 (link), 23 (link)}. A single summary proportional measure
was also derived by dividing the spectral power between 0.6–1Hz by the
sum of spectral power between 0.6–4Hz, to determine the relative
dominance of memory-relevant slow waves. Furthermore, due to our a
priori focus on mPFC, SWA measures at FZ and CZ derivations were
averaged and used as a measure of mPFC SWA (Fig.
1b). To ascertain topographic specificity of effects, SWA measures at
F3, F4, F7, and F8 derivations were averaged and used as a measure of dlPFC SWA,
SWA measures at P3, P4, and PZ derivations were averaged and used as a measure
of Parietal SWA, SWA measures at T3, T4, T5, and T6 derivations were averaged
and used as a measure of Temporal SWA, and SWA measures at O1 and O2 derivations
were averaged and used as a measure of Occipital SWA.
Slow wave detection and source analysis were performed to (1) calculate
the impact of mPFC Aβ on slow wave density, and (2) determine whether
memory-relevant FZ and CZ measured slow waves (0.6–1Hz) have an mPFC
source (Fig. 1b and Supplementary Fig. 1). EEG data
were filtered between 0.5–4Hz, and individual slow waves were detected
using a validated algorithm^{25 (link)}.
Standardized low resolution brain electromagnetic tomography (sLORETA) was
employed^{26 (link)} as
previously described^{69 (link), 70 (link)}. In short, this method
calculates current density sources using a discrete, three-dimensionally
distributed, linear minimum norm solution to the forward problem. Computations
are made using a head model based on the MNI152 template^{71 (link)}. Prior to sLORETA analysis, EEG
preprocessing was conducted in MATLAB using the EEGLAB toolbox. For each
participant, filtered (0.5Hz–4Hz), artifact-rejected EEG was
event-marked separately for detected slow wave (0.6–1Hz) midpoints in
the FZ and CZ derivations. EEG was then epoched around each detected slow wave
midpoint (±100 ms). Slow wave epochs were then averaged and exported
separately for CZ and FZ detected slow waves. sLORETA analyses of slow wave
epochs were carried out using the freeware sLORETA utilities (http://www.uzh.ch/keyinst/loreta.htm), consistent with previous
source analysis examinations^{69 (link),
70 (link)}. Prior to current
density source calculation, all electrode derivations were registered and
transformed into 3D MNI space, yielding a spatial transformation matrix. Current
density source maps were then derived for each participant separately for CZ and
FZ time-locked EEG averages. CZ and FZ source maps were then averaged within
each participant, with CZ-FZ averaged source maps then averaged across
participants to generate a grand mean average source image for memory-relevant
CZ and FZ slow waves (Supplementary Fig. 1).