Supplementary Information
Supplementary methods
Spike-LFP phase locking analysis: The phase vector of the LFP was computed using the
Hilbert transform for each network rhythm (theta and gamma). Filtering in the specified
frequency bands was performed in the forward and reverse direction to eliminate phase
distortions, and the first and last second in the trace was removed. The instantaneous phase of
each spike was identified and the mean resultant length of this distribution was calculated.
Two hundred surrogate calculations were performed on the data by shifting the phase signal
by randomly selected increments and recalculating the phase locking on each phase-shifted
data set. This provided a distribution of surrogate mean resultant length estimates. To be
significantly phase locked to the LFP in a given frequency band, the spike was required to
have a mean resultant length greater than 2 S.D. above the mean surrogate distribution. The
Circstat toolbox was used for circular statistics (Berens, 2009). For all plots, 0° indicates the
oscillation peak and spikes were binned into 36 bins of 10° each.
Figure S1: Determination of vigilance states
A two-dimensional (2-D) state space was defined by two spectral amplitude ratios calculated
by dividing integrated spectral amplitudes at selected frequency bands from the dHPC field
recordings. Using this technique, all major global brain states can be identified as distinct
spectral clusters. (Gervasoni et al, 2004)
Figure S2: oscillatory activity within the LHb.
To control for volume conduction, we first analyzed the presence of oscillations within the
LHb by performing locally reference recordings using the two tungsten micro wires spaced
~300µm in the antero-posterior axis within the LHb. One was used as the reference and the
other as the active recording site. This experimental paradigm was recently shown to produce
massive attenuation of far-field contamination (Kajikawa and Schroeder, 2011). It can be seen
on Figure S2 (left) that genuine theta oscillations were still detected within the LHb. In
addition, we performed LHb LFP recordings in 2 freely-moving rats with bilateral -induced
excitotoxic lesions of the dHPC (ibotenic acid, dissolved in 0.1M phosphate buffer saline;
1mg/mL, 9 injections per side, 0.06µL per injection; adapted from (Jarrard, 1989)). As
expected, local theta (theta power during REM: 7.97±2.9 x10-3 mV²) and gamma (gamma
power during REM 1.26±0.3 x10-3 mV²) oscillations were still present within the LHb 10
days following the lesion procedure (Figure S2, right).
Figure S3: LHb inactivation during the spatial recognition task: control for possible
bias, including objects characteristics, motivation and behavioral activity.
To ensure that the deficits induced by LHb inactivation were non-biased memory deficits, we
performed several analyses. Object characteristics: The deficits are unlikely to reflect bias
due to object characteristics as a two-way ANOVA of total sniffing-times collected during
sessions 4-6, with Object as the repeated measure showed no significant effect of Group (F1,10
= 2.60; p > 0.1) or Object (F2,20 = 0.73; p > 0.1) and no significant interaction (F2,20 = 0.63; p
> 0.1) (data not shown). Motivation: To complete any behavioral task rodents need enough
motivation. This concern is particularly relevant here considering that the LHb, through its
link with the dopaminergic system, has been implicated in motivational processes (Hikosaka,
2010). a possible lack of motivation towards the objects can be ruled out as a t-test analysis
of the total amount of sniffing time during session 7 showed no difference between group (t10
= 1.41; p > 0.1, paired t-test; Figure S3a), indicating an identical overall level of objects
investigation and therefore an identical level of motivation for the task. Behavioral activity:
The deficits cannot be accounted for a possible alteration of exploratory behavior as there was
no statistically significant difference between the two groups in the amount of locomotor
activity, although Musc-treated rats tended to be more active (paired t-test: t10 = -1.95, p =
0.079; Figure S3b), while they spent less time at the center of the OF (paired t-test: t10 = 2.43;
p < 0.05; Figure S3c) and made more crossings at the OF center (paired t-test: t10 = -2.31; p <
0.05; Figure S3d). Those data confirm that LHb inactivation induces a general increased
behavioral activity, such as previously shown (Lecourtier et al, 2008). Overall, those analyses
confirm that the observed spatial recognition deficits induced by LHb inactivation were not
biased either by the characteristics of the objects or by a Musc-induced reduced motivation or
altered behavioral activity.
Figure S4: Identification of two subgroups according to performance and LHb-dHPC
theta coherence
A closer look at the performance reveals that even if all rats were above chance level, they do
not perform equally on the test. Indeed, K-means clustering reveals that 2 classes of rats can
be individualized based on performance (Figure S4a), with the class 1 rat performing
significantly better than the class 2 (t(4) = 5.288, p = 0.0061, Figure S4a). We therefore
compute the coherence between the Lhb and the dHPC across the different session of the test
for the two classes of animals. We showed that class 1 rats exhibit a consistent and
significantly higher coherence between the LHb and the dHPC than the class 2 animals (group
x test session interaction: F(2,6) = 2.897, p=0.0287). Post-Hoc analyses showed that during all
the sessions class 1 rats exhibited higher LHb-dHPC coherence that the class 2 ones (p <
0.001 for all the sessions, Figure S4b). To determine whether this increased theta coherences
was related to an increased dHPC and/or LHb theta power in the class 1 rats, we computed
theta power in both structures during the test. No difference were found between the two
groups for both parameters (F(2,6) = 0.6187, p=0.7134 for dHPC theta and F(2,6) = 0.9956,
p= 0.4507 for LHb theta; Figure S4c).
Altogether, our data clearly indicate that LHb-dHPC coherence might play a key role in
hippocampal dependent spatial memory processing.
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