Next, we applied z-score transformation to
the HFB power in each recording site. Z-scores
were computed across all items within the same
run, individually for each time point. Then, the
instantaneous power was binned in 50-ms time
bins (using 80% overlap). For the viewing data,
power values were averaged across the four
presentations of each picture, resulting in a
matrix of 28 items × 78 bipoles × 236 time bins.
For the recall data, power values were averaged
across all SWR events associated with the same
item, resulting in a matrix of 28 items × 78
bipoles × 106 time bins. For items that were not
recalled by a certain patient, or did not elicit
ripples, the corresponding (missing) entry in
the matrix was replaced by zero (i.e., the mean).
We next averaged the viewing data over the
entire stimulus duration (from 100 to 1,500 ms)
to construct a“template”feature matrix of visual
responses (28 items × 78 bipoles), and applied
PCA to reduce the dimensionality of the fea-
tures ( 84 ). To determine the number of PCs to
retain, we estimated the true dimensionality of
the data (i.e., intrinsic dimension) using a maxi-
mum likelihood estimation technique ( 85 ). This
led us to retain the first 11 PCs, which explained
83.8% of the variation in the data (see Fig. 6, A
and B).
To compute the similarity between cortical
patterns that emerged during viewing and peri-
ripple cortical patterns that emerged during re-
call, we brought all instantaneous patterns (binned
in 50-ms time bins) to the same linear space by
reapplying the same linear transformation that
was obtained from the PCA of the averaged
feature matrix described above (i.e., an out-of-
sample extension of the PCA). The same linear
mapping was applied to both viewing and free-
recall patterns.
Finally, we used Pearson correlation to quan-
tify the similarity between viewing and recall
patterns in each 50-ms time bin. This was done
to examine how the correlation changed rela-
tive to the SWR onset (during recall) and rel-
ative to the onset/offset of the picture (during
viewing) (Fig. 6D). To assess statistical significance,
we performed a nonparametric cluster-based per-
mutation test, shuffling item labels 2000 times,
recomputing the correlation values, and mea-
suring the maximal cluster size after applying a
threshold ofP< 0.01 on the correlation values.
Cross-classification analysis
To test whether we could decode the identity of
recalled items from the ripple-triggered cortical
HFB patterns, we trained ak-nearest neighbors
(k-NN) classifier on single-trial viewing patterns
(28 items, each presented four times,n=112
trials in total) and tested its classification per-
formance on the peri-ripple patterns that emerged
during the verbally reported recall events (i.e.,
cross-classification analysis; Fig. 6, E and F). For
this analysis, patterns elicited during viewing were
averaged over a time window of 100 to 500 ms
poststimulus [where visual responses are strongest
and most informative about stimulus identity
( 86 , 87 )]. Here again, we reduced the dimen-
sionality of the data using PCA, and applied the
same transformation also to the peri-ripple pat-
terns during recall, as described above for the
MVPA (i.e., an out-of-sample extension of the
same PCA that was applied on the viewing pat-
terns). We usedk= 9 nearest neighbors to de-
code image category andk= 1 nearest neighbors
to decode exemplar identity. To measure classi-
fication performance in the viewing condition,
we used a leave-one-out cross-validation technique.
For the cross-classification analysis, decoding
the recalled content, we computed classification
accuracy individually in each 50-ms time bin,
from–500 to 500 ms relative to the onset of the
hippocampal SWR event. Statistical significance
was assessed using a nonparametric cluster-based
permutation test, shuffling item labels 2000 times
and recomputing the cross-classification perform-
ance while measuring the size of the maximal
cluster in each iteration (using a cluster-defining
threshold ofP=0.05).FWE-correctedPvalues
were computed as the proportion of random clus-
ters larger than or equal to the clusters observed
in the actual data.Statistical analyses
For statistical testing, parametric methods were
used for normal data. Because HFB amplitude,
like other measures of population firing rate,
tends to follow a log-normal distribution, am-
plitude values were log-transformed into deci-
bel (10 × log 10 ) prior to any statistical testing.
For non-normal data or small sample sizes, we
used Wilcoxon signed-rank/rank sum tests. All
statistical tests were two-sided unless stated
otherwise. A Greenhouse-Geisser correction for
sphericity was applied to repeated-measures
analyses of variance when necessary. Multiple-
comparisons correction was performed either
through the Benjamini-Hochberg method ( 76 )
for FDR adjustment or by using nonparametric
cluster-based permutation tests developed by
others ( 88 , 89 ), in which the family-wise error
rate(FWE)isinherentlycontrolled( 90 ). No
statistical methods were used to predetermine
sample sizes; however, sample sizes were sim-
ilar to those generally used in the field. Data col-
lection and analysis were not performed blind to
the conditions of the experiments.REFERENCES AND NOTES- G. Buzsáki, L.-W. S. Leung, C. H. Vanderwolf, Cellular bases of
hippocampal EEG in the behaving rat.Brain Res. Rev. 287 ,
139 – 171 (1983). doi:10.1016/0165-0173(83)90037-1;
pmid: 6357356 - G. Buzsáki, Z. Horváth, R. Urioste, J. Hetke, K. Wise,
High-frequency network oscillation in the hippocampus.
Science 256 , 1025–1027 (1992). doi:10.1126/science.1589772;
pmid: 1589772 - A. Ylinenet al., Sharp wave-associated high-frequency
oscillation (200 Hz) in the intact hippocampus: Network
and intracellular mechanisms.J. Neurosci. 15 ,30– 46
(1995). doi:10.1523/JNEUROSCI.15-01-00030.1995;
pmid: 7823136 - D. Sullivanet al., Relationships between hippocampal sharp
waves, ripples, and fast gamma oscillation: Influence of
dentate and entorhinal cortical activity.J. Neurosci. 31 ,
8605 – 8616 (2011). doi:10.1523/JNEUROSCI.0294-11.2011;
pmid: 21653864 - B. K. Hulse, L. C. Moreaux, E. V. Lubenov, A. G. Siapas,
Membrane Potential Dynamics of CA1 Pyramidal Neurons
during Hippocampal Ripples in Awake Mice.Neuron 89 ,
800 – 813 (2016). doi:10.1016/j.neuron.2016.01.014;
pmid: 26889811- G. Buzsákiet al., Hippocampal network patterns of activity
in the mouse.Neuroscience 116 , 201–211 (2003).
doi:10.1016/S0306-4522(02)00669-3; pmid: 12535953 - A. Bragin, J. Engel Jr., C. L. Wilson, I. Fried, G. Buzsáki, High-
frequency oscillations in human brain.Hippocampus 9 ,
137 – 142 (1999). doi:10.1002/(SICI)1098-1063(1999)9:2<137::
AID-HIPO5>3.0.CO;2-0; pmid: 10226774 - M. Le Van Quyenet al., Cell Type-Specific Firing during
Ripple Oscillations in the Hippocampal Formation of Humans.
J. Neurosci. 28 , 6104–6110 (2008). doi:10.1523/
JNEUROSCI.0437-08.2008 - B. P. Staresinaet al., Hierarchical nesting of slow oscillations,
spindles and ripples in the human hippocampus during sleep.
Nat. Neurosci. 18 , 1679–1686 (2015). doi:10.1038/nn.4119;
pmid: 26389842 - N. K. Logothetiset al., Hippocampal-cortical interaction during
periods of subcortical silence.Nature 491 , 547–553 (2012).
doi:10.1038/nature11618; pmid: 23172213 - Y. Niret al., Regional slow waves and spindles in human sleep.
Neuron 70 , 153–169 (2011). doi:10.1016/j.neuron.2011.02.043;
pmid: 21482364 - J. J. Chrobak, G. Buzsáki, High-frequency oscillations in the
output networks of the hippocampal-entorhinal axis of the
freely behaving rat.J. Neurosci. 16 , 3056–3066 (1996).
doi:10.1523/JNEUROSCI.16-09-03056.1996; pmid: 8622135 - K. Mizuseki, G. Buzsáki, Preconfigured, skewed distribution of
firing rates in the hippocampus and entorhinal cortex.
Cell Rep. 4 , 1010–1021 (2013). doi:10.1016/
j.celrep.2013.07.039; pmid: 23994479 - H. R. Joo, L. M. Frank, The hippocampal sharp wave-ripple in
memory retrieval for immediate use and consolidation.
Nat. Rev. Neurosci. 19 , 744–757 (2018). doi:10.1038/s41583-
018-0077-1; pmid: 30356103 - G. Buzsáki, Hippocampal sharp waves: Their origin
and significance.Brain Res. 398 , 242–252 (1986).
doi:10.1016/0006-8993(86)91483-6; pmid: 3026567 - G. Buzsáki, Hippocampal sharp wave-ripple: A cognitive
biomarker for episodic memory and planning.Hippocampus
25 , 1073–1188 (2015). doi:10.1002/hipo.22488;
pmid: 26135716 - T. K. Leonard, K. L. Hoffman, Sharp-Wave Ripples in Primates
Are Enhanced near Remembered Visual Objects.Curr. Biol. 27 ,
257 – 262 (2017). doi:10.1016/j.cub.2016.11.027;
pmid: 28041797 - T. K. Leonardet al., Sharp Wave Ripples during Visual
Exploration in the Primate Hippocampus.J. Neurosci. 35 ,
14771 – 14782 (2015). doi:10.1523/JNEUROSCI.0864-15.2015;
pmid: 26538648 - D. Khodagholy, J. N. Gelinas, G. Buzsáki, Learning-enhanced
coupling between ripple oscillations in association cortices
and hippocampus.Science 358 , 369–372 (2017).
doi:10.1126/science.aan6203; pmid: 29051381 - N. Maingret, G. Girardeau, R. Todorova, M. Goutierre,
M. Zugaro, Hippocampo-cortical coupling mediates memory
consolidation during sleep.Nat. Neurosci. 19 , 959–964 (2016).
doi:10.1038/nn.4304; pmid: 27182818 - A. G. Siapas, M. A. Wilson, Coordinated interactions between
hippocampal ripples and cortical spindles during slow-wave
sleep.Neuron 21 , 1123–1128 (1998). doi:10.1016/S0896-6273
(00)80629-7; pmid: 9856467 - Z. Clemenset al., Temporal coupling of parahippocampal
ripples, sleep spindles and slow oscillations in humans.
Brain 130 , 2868–2878 (2007). doi:10.1093/brain/awm146;
pmid: 17615093 - A. Peyrache, M. Khamassi, K. Benchenane, S. I. Wiener,
F. P. Battaglia, Replay of rule-learning related neural
patterns in the prefrontal cortex during sleep.
Nat. Neurosci. 12 ,919–926 (2009). doi:10.1038/nn.2337;
pmid: 19483687 - D. Ji, M. A. Wilson, Coordinated memory replay in the
visual cortex and hippocampus during sleep.
Nat. Neurosci. 10 ,100–107 (2007). doi:10.1038/nn1825;
pmid: 17173043 - G. Girardeau, I. Inema, G. Buzsáki, Reactivations of emotional
memory in the hippocampus-amygdala system during sleep.
Nat. Neurosci. 20 , 1634–1642 (2017). doi:10.1038/nn.4637;
pmid: 28892057 - S. P. Jadhav, G. Rothschild, D. K. Roumis, L. M. Frank,
Coordinated Excitationand Inhibition of Prefrontal Ensembles
during Awake Hippocampal Sharp-Wave Ripple Events.Neuron
Normanet al.,Science 365 , eaax1030 (2019) 16 August 2019 12 of 14
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