Overall sleep spindle density and duration were compared across
nuclei performing repeated-measures ANOVA with the factors spindle type
(slow vs. fast) × nucleus (ANT vs. MD), where missing values were
handled as missing data. Comparison of SP(ripple/pure) density and
duration were performed by repeated-measure ANOVA with the factors
spindle type (slow vs. fast) × association (ripple vs. pure) × nucleus
(ANT vs. MD). Post-hoc tests were conducted by Tukey’s HSD.
Time-frequency wavelet analysis was performed on 4600 ms long epochs
extracted in the -2300 to 2300 ms latency range around sleep spindle
onsets (separately for the slow and fast SP(ripple/pure)) on all ANT and
MD derivations. Furthermore, time-frequency analysis was also performed
on frontal and parietal scalp records using the same time intervals as
for the ANT and MD sleep spindles (scalp EEG activity around thalamic
sleep spindles). Data from the F3 and F4 electrodes were analyzed for
the frontal region, except for patient #15, where Fp1 was used instead
of F3 due to the low signal quality. Parietal electrodes were P3 and P4,
except for patient #2, where Pz was used instead of P3 and P4 as these
electrodes were missing for this patient. Data were averaged across the
frontal and parietal electrodes, separately. Time-frequency analysis was
performed by Morlet wavelet transformation with linearly increasing
cycle numbers from 4 cycles to 20 cycles for the 1–450 Hz frequency
range using 0.5 Hz frequency resolution in the 1–40 Hz range, 1 Hz
frequency resolution in the 41–80 Hz range and 5 Hz frequency
resolution in the 81–450 Hz range. The baseline interval was set at
-2000 ms to 0 ms before sleep spindle onset. Time-frequency power
spectra were averaged across all derivations within the same nucleus,
separately for the fast and slow SP(ripple/pure). Statistical comparison
of SP(ripple) vs. SP(pure) was performed by nonparametric randomization
test separately for the slow and fast spindles and for the ANT, MD and
scalp (frontal and parietal), applying the Monte-Carlo estimate of
significance probabilities (1000 permutations), using the Fieldtrip
toolbox (Oostenveld et al. 2011), applying FDR correction.
For assessing phase-amplitude coupling between sleep spindle phases and
ripple amplitudes in the thalamus, the modulation index (MI) was
calculated based on the method by Tort et al. (2010), separately for
each thalamic derivation. First, 4600 ms long epochs were extracted
around individual sleep spindles with co-occurring ripples (SP(ripple)),
in the -2300 to 2300 ms latency range where 0 ms corresponds to the
onset of sleep spindles. For all detected SP(ripple) data were band-pass
filtered in the frequency bands of interest, 80–200 Hz for extracting
the ripple amplitude and individual slow and fast spindle bands,
detected by the IAM of sleep spindle analysis (see above) for extracting
instantaneous phase of sleep spindles (FIR filter). Bandpass-filtered
data were than Hilbert transformed using the 0–2000 ms time range for
avoiding edge effects. MI were then calculated for each electrode using
18 phase bins from –π to +π, pooling all phase and amplitude values
extracted from the epoched data. The observed MI were then subjected to
permutation testing in order to quantify the difference between the
observed MI and the distribution of shuffled coupling values. Shuffled
MI distribution were calculated by measuring the MI between the original
phase time series and permuted amplitude time series where amplitude
data points were randomly shuffled, using 1000 iterations for each
electrode. The observed MI values were then z-standardized to the
shuffled phase-amplitude coupling distribution, where normalized
z-values directly reflect p-values, MI(z) equal to 1.645 corresponds to
the 5% p-value. Thus, MI(z) values larger than 1.645 reflects a
significant spindle-ripple coupling in the thalamus.
We aimed to estimate the time dynamics of spindle co-occurrence between
thalamic and cortical channels. Spindle co-occurrence was
defined in the following way: when the initiation of a sleep spindle was
detected on any channel (cortical or thalamic), all subsequent spindles
initiating on any other channel before the end of the original spindle
were considered to co-occur, comprising a single spindle event involving
multiple channels with a time lag on each channel, defined as the time
difference of spindle initiation relative to the first spindle. For
thalamocortical co-occurrence analysis, we selected all instances when
sleep spindles co-occurred on both 1) a selected scalp channel (F3 or F4
for frontal spindles, P3 or P4 for parietal spindles, for patient #2
Fp2-Fpz and Pz-Oz instead) and 2) on a channel localized in a specific
thalamic nucleus (ANT or MD). That is, the analysis included spindles
which could originate elsewhere, but were later detected on both
specific scalp channels and in the thalamus. We defined thalamocortical
spindle lags as the time lag (relative to the first spindle) of the
scalp channel minus the time lag of the thalamic channel. We modeled
spindle lags with a linear mixed model implemented in the MATLAB 2017a
fitlme() function using lag as the dependent variable, spindle type,
thalamic nucleus and scalp channel as fixed effects with random
intercepts by patient (Ujma et al. 2022).
For assessing the potential correlates and functions of thalamic ripples
and spindles Pearson correlations coefficient were calculated between
sleep spindle density (scalp-detected parietal fast sleep spindle
density, overall thalamic spindle density, thalamic SP(ripple) and
SP(pure) density), clinical epilepsy characteristics (years since
epilepsy onset and seizures/month (before DBS)), and general
intelligence (Table 3 shows the WAIS IQ scores ); separately for
the ANT and MD, and for the slow and fast spindles. Relationships
between scalp-detected, parietal fast sleep spindle density were
measured based on the average recording locations P3 and P4, except
Patient #2, where Pz was used.