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\DOI{10.5802/crbiol.126}
\datereceived{2023-02-19}
\daterevised{2023-09-29}
\dateaccepted{2023-10-11}
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\begin{noXML}

\RubricEF{Research article}{Article de recherche}

\title{Stable coding of aversive associations in medial prefrontal
populations}

\alttitle{Codage stable d'associations aversives par les populations
pr\'{e}frontales m\'{e}diales}

\author{\firstname{Cyril} \lastname{Herry}\CDRorcid{0000-0003-2973-0843}\IsCorresp}
\address{INSERM, Neurocentre Magendie, U1215, 146 Rue L\'{e}o-Saignat,
33077~Bordeaux, France}
\address{Univ. Bordeaux, Neurocentre Magendie, U1215, 146 Rue
L\'{e}o-Saignat, 33077~Bordeaux, France}
\email[C. Herry]{cyril.herry@inserm.fr}

\author{\firstname{Daniel} \lastname{Jercog}\CDRorcid{0000-0003-3849-9196}\IsCorresp}
\addressSameAs{1}{INSERM, Neurocentre Magendie, U1215, 146 Rue
L\'{e}o-Saignat, 33077~Bordeaux, France}
\addressSameAs{2}{Univ. Bordeaux, Neurocentre Magendie, U1215, 146 Rue
L\'{e}o-Saignat, 33077~Bordeaux, France}
\email[D. Jercog]{daniel.jercog@gmail.com}

\keywords{\kwd{Associative learning}\kwd{Medial prefrontal
cortex}\kwd{Population coding}\kwd{Machine learning}}

\altkeywords{\kwd{Apprentissage associatif}\kwd{Cortex pr\'{e}frontal
m\'{e}dial}\kwd{Codage de population}\kwd{Apprentissage automatique}}

\def\thanksname{Note}
\thanks{Daniel Jercog received the Grandes Avanc\'{e}es en Biologie
2022 award.}

\thanks{\textbf{Funding.} French National Research Agency (Grant
agreement nos. ANR-DOPAFEAR, ANR-PRELONGIN, ANR-10-EQPX-08 OPTOPATH),
Conseil Regional d'Aquitaine, and Fondation pour la Recherche
M\'{e}dicale (Grant agreement no. FRM-Equipes FRM 2017)}

\begin{abstract} 
The medial prefrontal cortex (mPFC) is at the core of numerous
psychiatric conditions, including fear and anxiety-related disorders.
Whereas an abundance of evidence suggests a crucial role of the mPFC in
regulating fear behaviour, the precise role of the mPFC in this process
is not yet entirely clear. While studies at the single-cell level have
demonstrated the involvement of this area in various aspects of fear
processing, such as the encoding of threat-related cues and fear
expression, an increasingly prevalent idea in the systems neuroscience
field is that populations of neurons are, in fact, the essential unit
of computation in many integrative brain regions such as prefrontal
areas. What mPFC neuronal populations represent when we face threats?
To address this question, we performed electrophysiological single-unit
population recordings in the dorsal mPFC while mice faced
threat-predicting cues eliciting defensive behaviours, and performed
pharmacological and optogenetic inactivations of this area and the
amygdala. Our data indicated that the presence of threat-predicting
cues induces a stable coding dynamics of internally driven
representations in the dorsal mPFC, necessary to drive learned
defensive behaviours. Moreover, these neural population representations
primary reflect learned associations rather than specific defensive
behaviours, and the construct of such representations relies on the
functional integrity of the amygdala.
\end{abstract} 

\begin{altabstract} 
Le cortex pr\'{e}frontal m\'{e}dial (CPFm) est au c{\oe}ur de
nombreuses affections psychiatriques, notamment les troubles li\'{e}s
\`{a} la peur et \`{a} l'anxi\'{e}t\'{e}. Alors que de nombreuses
preuves sugg\`{e}rent un r\^{o}le crucial du CPFm dans la
r\'{e}gulation du comportement de peur, le r\^{o}le pr\'{e}cis du CPFm
dans ce processus n'est pas encore tout \`{a} fait clair. En effet, si
des \'{e}tudes au niveau de la cellule unique ont d\'{e}montr\'{e}
l'implication de cette zone dans divers aspects du traitement de la
peur, tels que l'encodage des indices li\'{e}s \`{a} la menace et
l'expression de la peur, l'id\'{e}e selon laquelle des populations de
neurones constituent en fait l'unit\'{e} de calcul essentielle dans de
nombreuses r\'{e}gions c\'{e}r\'{e}brales int\'{e}gratives, telles que
les zones pr\'{e}frontales, est de plus en plus r\'{e}pandue dans le
domaine des neurosciences syst\'{e}miques. Que repr\'{e}sentent les
populations de neurones du mPFC lorsque nous sommes confront\'{e}s
\`{a} des menaces~? Pour r\'{e}pondre \`{a} cette question, nous avons
effectu\'{e} des enregistrements \'{e}lectrophysiologiques de
populations d'unit\'{e}s uniques dans le CPFm dorsal pendant que des
souris \'{e}taient confront\'{e}es \`{a} des signaux de menace
suscitant des comportements d\'{e}fensifs, et nous avons
proc\'{e}d\'{e} \`{a} des inactivations pharmacologiques et
optog\'{e}n\'{e}tiques de cette zone et de l'amygdale. Nos donn\'{e}es
indiquent que la pr\'{e}sence de signaux de menace induit une dynamique
de codage stable des repr\'{e}sentations internes dans le CPFm dorsal,
n\'{e}cessaire \`{a} l'apprentissage de comportements d\'{e}fensifs. De
plus, ces repr\'{e}sentations neuronales refl\`{e}tent principalement
des associations apprises plut\^{o}t que des comportements
d\'{e}fensifs sp\'{e}cifiques, et la construction de ces
repr\'{e}sentations d\'{e}pend de l'int\'{e}grit\'{e} fonctionnelle de
l'amygdale.
\end{altabstract} 

\maketitle

\twocolumngrid

\end{noXML}

\section{Introduction}

Associative learning is the process by which relationships among
various stimuli, behaviours, and outcomes are acquired~\cite{1}. The
study of the neural basis of aversive associative learning has
played a major role in explaining the development and treatment of
fear-related disorders~\cite{2}. Indeed, such conditions are often
thought to result from deficits in associative learning
processes~\cite{3}. From a neuronal standpoint, forming aversive
associative memories is intrinsically linked to the representation of
\mbox{different} elements that constitute them, including
threat-predicting cues, aversive outcomes, behavioural responses as
well as the contextual environment, which combination can be conceived
as the occurrence of a specific threatening situation~\cite{4}.
Previous studies have provided invaluable insights about the brain
circuits and functions involved in defensive behaviour and fear
learning by studying single neuron responses in isolation or estimating
cell assemblies on specific defined timescales~\cite{5}. A number of
such studies investigated how medial prefrontal cortex neurons and
dedicated projections could regulate threat-related behaviour. While
prefrontal neurons have been shown to exhibit tone selective or
freezing selective responses during fear expression and extinction
learning~\cite{6,7}, prefrontal-amygdala projections have been shown to
exert control over the expression of threat-related
behaviours~\cite{8}. In addition, theta-related oscillations and
prefrontal-amygdala synchrony have been associated as a mechanism for
signalling safety or fearful states~\cite{9,10,11,12}. Beside the study
of the role of single neurons in threat-related behaviour, an idea that
it is becoming increasingly prevalent in systems neuroscience is that
neuronal populations are in fact the essential unit of computation in
many integrative brain regions~\cite{13,4}. A central idea behind
population coding is that a downstream area integrates the
heterogeneous activity from multiple neurons to determine some value
about the inputs (Figure~\ref{fig1}A). Such coding strategy has
numerous advantages including an increased information and sensitivity,
accurate representations, robustness to the effects of noise in
individual neuron representations, and the ability to represent complex
stimuli~\cite{14}. But what information is conveyed by prefrontal
populations during fear behaviour? To address this question, we
performed electrophysiological single-unit population recordings in the
dorsomedial prefrontal cortex (dmPFC) of mice during fear behaviours
during learning, extinction and reversal of instrumental aversive
conditioning. We found that threat-predicting cues, but not neutral
cues, induced sustained neuronal representations consistent with a
stable coding of information in dmPFC networks that primarily reflects
the aversive associations rather than specific defensive behaviours.
Pharmacological inactivation experiments showed that such dmPFC threat
representations require the functional integrity of the
{amygdala}, whereas temporal specific optogenetical inhibition of
the dmPFC showed the critical involvement of these representations in
the initiation of defensive actions.\looseness=-1

\begin{figure*}
\includegraphics{fig01}
\caption{\label{fig1}Aversive associative learning correlates in
dorsomedial prefrontal neuronal populations. (A)~Multiple neurons
encode heterogeneous information about the external world, and the
readout of this collective information from downstream neurons can be
used to establish some precise value about external inputs. We
monitored the activity of individual neurons from a certain region and
look at the collective activity pattern across recorded cells for a
given time point (so-called population vector, PV). We can assess the
presence of information on those population patterns about a certain
condition by comparing different patterns using neuronal decoders.
(B)~Top: Schematics of the cued differential avoidance task in mice and
auditory cues structure. Bottom: learning curves (ITI,
inter-trial-interval). Selective avoidance of CS$^{+}$ from the 5th
5-trial block on Day~1 ($^{**}P < 0.007$, two-way repeated-measures
ANOVA. $N=34$~mice). (C)~Single-unit $z$-scored activity for CS$^{+}$
(left) and CS$^{-}$ (right) ordered by magnitude of CS$^{+}$ responses
(day-3 and -4 data pooled: 34~mice, 68~sessions, 1261~units). (D)~dmPFC
population-activity-based decoding accuracies for CS$^{+}$ (top) or
CS$^{-}$ (bottom) from baseline activity across days (shuffled trial
label in grey; note the transient increases in accuracies during
sound-pips). (E)~Mean avoidance probability during extinction learning
($n=14$~mice). Although CS$^{+}$ avoidance behaviour (top) was
extinguished during the first extinction session ($^{**}P < 0.0110$
for 1st and 3rd block, two-way RM ANOVA), CS$^{+}$ from baseline
decoding accuracy (bottom) was reduced only on the subsequent
extinction session. (F)~Mean avoidance probability during reversal
learning ($n=11$~mice). Previous CS$^{+}$ tone became neutral and the
previous CS$^{-}$ tone was associated with the US. Avoidance behaviour
was progressively reversed during training (top) ($\mbox{}^{**}P < 0.01$\ 
$^{*}P < 0.05$, two-way RM ANOVA) and decoding accuracies changed
accordingly (bottom). Accuracy data are mean ${\pm}$1~s.d. Significant
decoding accuracy periods over shuffle accuracies were represented by
thick lines ($P < 0.05$, permutation test).}
\end{figure*}

{\vspace*{-.2pc}}
\section{Results} \label{sec2}
{\vspace*{-.2pc}}
\subsection{Associated threats drive sustained{\hfill\break} population
representations in dmPFC{\hfill\break} networks} \label{sec2.1}
{\vspace*{-.2pc}}

To investigate what are medial prefrontal neuronal populations
collectively reflecting about defensive behaviours, mice were initially
implanted with electrode bundles targeting the dmPFC and submitted to a
differential active avoidance task~\cite{15} (Figure~\ref{fig1}B).
After a habituation session to the CS$^{+}$ and CS$^{-}$ sounds (50~ms
sound-pips at 1~Hz, see Section~\ref{sec4}), mice were trained in the
task during 4 consecutive days. In this task, mice learned to avoid a
conditioned stimulus (CS$^{+}$) that predicts the delivery of a mild
foot shock (US) by shuttling between two symmetric compartments
separated by a small hurdle. A second conditioned stimulus not
associated with the foot shock (CS$^{-}$) was used as an internal
control. After CS onset, shuttling from the current compartment within
a 7~s period was defined as an avoided CS response, leading to the
termination of the CS and preventing US delivery for CS$^{+}$ trials.
In contrast, remaining in the same compartment led to US delivery for
CS$^{+}$ trials after 7~s. After a habituation session where both CS
are presented but not reinforced, mice were trained in our task and
learned to selectively avoid CS$^{+}$ while CS$^{-}$ avoidance remained
low and similar to inter-trial shuttling levels.

CS$^{+}$ presentation induced a modulation of a large fraction of dmPFC
neurons compared to CS$^{-}$ when comparing their averaged activity
(Figure~\ref{fig1}C). To address how stimuli information is represented
in dmPFC populations, we used a decoding approach based on the
collective recorded dmPFC neuronal activity across
animals~\cite{15,16}. Here, the firing activity from the ensemble of
recorded cells at a given time point $t$ (so-called pseudo-population
vector) is compared for CS$^{+}$ and CS$^{-}$ trials versus the
spontaneous baseline activity preceding each stimuli (see
Section~\ref{sec4}). For each time point, we trained a set of linear
classifiers designed to maximally separate the dmPFC evoked population
patterns for both CS/baseline
conditions, while quantifying their
cross-validated decoding accuracy with the decoding accuracy obtained
from shuffling the identity of the trial conditions. While during the
habituation session to the auditory stimuli (before conditioning),
decoding stimuli information from dmPFC populations was not significant
from shuffle, training induced a progressive increase towards high and
sustained (in between sound-pips) decoding accuracy values for
CS$^{+}$, an observation in sharp contrast with CS$^{-}$ trials which
displayed modest accuracy levels throughout training
(Figure~\ref{fig1}D).

In our task CS$^{+}$ aversiveness is acquired through associative
learning, therefore we evaluated how avoidance extinction or reversal
of discriminative avoidance learning could influence CS decoding
accuracy. Extinction training in consecutive sessions progressively
decrease CS$^{+}$ decoding accuracy towards non-significant values
(Figure~\ref{fig1}E). Following avoidance extinction, we performed a
reversal experiment during which we paired the previous CS$^{-}$ with
the US while the previous CS$^{+}$ became not reinforced. While mice
progressively switched their avoidance responses towards the reversed
CS$^{-}$ and did not avoid the reversed CS$^{+}$ during subsequent
training sessions, this reversal was also observed in terms of decoding
accuracies (Figure~\ref{fig1}F).

Together, these results show that dmPFC neuronal populations reflect
associative processes during learned fear, and neural representations
during threat-predicting cues are sustained even in the absence of
sensory inputs.

\subsection{Sustained threat-related representations{\hfill\break} are
not explained by specific defensive{\hfill\break} behaviours}
\label{sec2.2}

We next tested if CS$^{+}$ decoding accuracy levels reflected certain
animals' defensive response at CS$^{+}$ onset. We first observed that
spontaneous freezing episodes outside of CSs presentation could be
decoded from CS$^{+}$ trials, indicating that high decoding accuracy
observed at CS$^{+}$ onset did not merely reflect freezing behaviour
(Figure~\ref{fig2}A). Indeed, while dmPFC populations carried
information as to whether mice were freezing or not during CS$^{+}$
presentations, the accuracy levels observed were somewhat limited
(Figure~\ref{fig2}B). Alternatively, to test that high accuracy at
CS$^{+}$ onset was not just related to an action preparation state,
after avoidance conditioning learning,{\break} mice were subjected to a
``Confined task'' where the placement of a wall between compartments
precluded the avoidance response and non-reinforced CS$^{+}$ and
CS$^{-}$ were presented, which induced a switch in behaviour to a
freezing strategy upon CS$^{+}$ presentations (Figure~\ref{fig2}C,
left). This condition also induced sustained CS$^{+}$ representations,
despite avoidance was prevented (Figure~\ref{fig2}C, right). Thus, to
compare both experimental conditions we trained decoders on the
Confined task and tested the performance of these decoders on the
Active Avoidance task data (and {vice versa}). In this condition
CS$^{+}$ evoked neuronal population patterns were still correctly
classified despite the change in active or passive defensive
strategies, indicating that accuracy observed at CS$^{+}$ onset in the
Active Avoidance task was not merely reflecting the avoidance action
preparation (Figure~\ref{fig2}D). Accordingly, the direct comparison by
decoding avoided from non-avoided CS$^{+}$ trials did not differ from
shuffle accuracies, reflecting that encoded information at CS$^{+}$
onset was not related to impending avoidance actions
(Figure~\ref{fig2}E). 

\begin{figure*}
\includegraphics{fig02}
\bcaption{\label{fig2}Associated threat representations by dmPFC
neuronal population are not specific for defensive behaviors.
(A)~Decoding Spontaneous freezing events (outside CS) and CS$^{+}$
evoked activity. (B)~Decoding CS$^{+}$ trials when mice exhibit
freezing (CS$^{+}$ Fz) or non-freezing (CS$^{+}$ NFz) at CS onset.
Inset shows freezing probabilities for both trial types (error bars
display 95\%~CI), light red area indicate the period during which
animals showed pure freezing or non-freezing bouts ($n=13$~sessions).
(C)~After training, mice were confined to one of the compartments where
unreinforced conditioned stimuli were presented (confined task).
Freezing to CS$^{+}$ progressively increases during the first three
CS$^{+}$ trials (5th trial) and reaches stable levels from trial 8 to
12 ($^{***}P < 0.001$, one-way RM ANOVA; $n=21$~mice). Decoding
accuracy of CS$^{+}$ and CS$^{-}$ from baseline population activity
during the confined task (right).}{1}{(D)~Trained decoders to classify
CS$^{+}$ trials from baseline in the active avoidance task
($\text{CS}^{+}_{\mathrm{AA}}$) displayed high accuracy when decoding
CS$^{+}$ in the confined task ($\text{CS}^{+}_{\mathrm{CNF}}$) (left),
and {vice versa} (right). (E)~Decoding accuracy between CS$^{+}$
avoided and CS$^{+}$ non-avoided trials at CS-onset was not different
from shuffle values. (F)~Population trajectories at CS onset defined
via PCA on trial-averaged activity (50\% trials for each session and
trial condition; 100 repetitions). Inset showing variance explained and
dynamics of first 6 principal components. (G)~Cross-temporal decoding
of CS$^{+}$ from Baseline activity (activity patterns in 0.1~s bins),
consistent with stable dynamics of information coding. Accuracy data
are mean ${\pm}$1~s.d. Significant decoding accuracy periods over
shuffle accuracies were represented by thick lines ($P < 0.05$,
permutation test).\vspace*{-.4pc}}
\end{figure*}

We consistently observed sustained CS$^{+}$ population representations
even in the absence of sensory inputs (i.e. in between sound-pips),
despite the heterogeneous individual neuronal response dynamics
(Figure~\ref{fig1}C). Indeed, dmPFC populations during CS$^{+}$
described strong dynamics led by a few dominant features capturing a
large portion of the variance (Figure~\ref{fig2}F). In this scenario,
performing a cross-temporal decoding of CS$^{+}$ from baseline activity
showed that coding dynamics of CS$^{+}$ was largely consistent with a
stable coding of information at CS onset (Figure~\ref{fig2}G).

Altogether, these results suggest that high information observed by
neuronal population patterns in the dmPFC during CS$^{+}$ presentations
displayed dynamics of information coding consistent with a stable
coding, and such representations convey information about the learned
association rather than specific defensive behaviours.

\subsection{dmPFC threat representations depend on{\hfill\break}
amygdala and are necessary to drive{\hfill\break} defensive actions}
\label{sec2.3}

We hypothesised that information related to threat-predicting cues is
acquired in remote brain areas and integrated into dmPFC networks.
Because amygdala{\break} subnuclei are involved in active avoidance
behaviour \cite{17,18}, we next studied how amygdala CSs processing
impacts its representation in dmPFC networks. Consistent with previous
studies~\cite{18}, we observed that amygdala inactivation using
muscimol impaired avoidance behaviour (Figure~\ref{fig3}A).
Importantly, dmPFC decoding analyses during amygdala inactivation
revealed that threat-predicting cues were still encoded during
individual pips, but that the maintenance of information between pip
presentations was nearly absent (Figure~\ref{fig3}B). Altogether, these
results indicate that dmPFC populations at CS onset represent threat
information in a sustained manner and through associative processes, in
that these representations are amygdala-dependent.

\begin{figure*}
\includegraphics{fig03}
\caption{\label{fig3}Aversive association representations requires the
functional integrity of amygdala and are critical to drive defensive
actions. (A)~Muscimol inactivation of the amygdala impaired avoidance
behaviour. $^{***}P < 0.0001$, two-way repeated-measures ANOVA;
muscimol, $n=6$~mice; vehicle, $n=5$~mice; dots indicate outliers. Pre,
pre-infusion session; Infus., infusion session. BLA, basolateral
amygdala. (B)~Impact of amygdala inactivation on dmPFC CS decoding
accuracies comparing pre-infusion (light colours) with muscimol
infusion sessions (Mus, dark colours). (C)~The dmPFC was infected with
either ArchT or GFP under the CaMKII promoter, and continuous light was
delivered during CS presentations for half of the trials (ON/OFF laser
conditions). CS$^{+}$ avoidance was significantly reduced upon dmPFC
inhibition ($^{***}P < 0.0001$, repeated measures mixed-effects
model; GFP $n=9$~mice, ArchT $n=12$~mice). No significant effect was
observed on CS-evoked freezing during laser OFF and ON conditions ($P >
0.05$, repeated-measures mixed-effects model). (D)~Photostimulation
restricted at CS onset (Onset Perturbation; GFP $n=8$~mice, ArchT
$n=5$~mice) did not changed avoidance probability (unpaired $t$-test,
$P=0.76$) but significantly delayed avoidance responses (unpaired
$t$-test, $^{*}P=0.01$). Photostimulation restricted to after CS onset
periods (Offset Perturbation; GFP $n=12$~mice, ArchT $n=13$~mice)
reduced avoidance probability (unpaired $t$-test, $^{**}P=0.001$)
whereas avoidance was not delayed (unpaired $t$-test,
$P=0.86$).{\break} Thick lines indicate significant decoding accuracies
($P < 0.05$, permutation test). Accuracy data are mean~${\pm}$1~s.d.}
{\vspace*{-.05pc}}
\end{figure*}

To test the causal role of such threat representation in dmPFC
networks, we used an optogenetic inactivation approach on dmPFC
excitatory neurons (Figure~\ref{fig3}C, left). Whereas photo-inhibition
of dmPFC excitatory neurons during CS$^{-}$ presentations had no effect
compared to GFP controls or non-stimulated trials, it strongly impaired
avoidance behaviour during CS$^{+}$ trials (Figure~\ref{fig3}C, right).
In addition, avoidance impairment was not explained by a switch in
defensive behaviour, as freezing levels during CS$^{+}$ presentations
were unaltered (Figure~\ref{fig3}D). To further evaluate whether
representation of threat-predicting cues was necessary to drive the
selection of active avoidance behaviour, we perform temporally specific
optogenetic inhibition of the dmPFC specifically around CS onset
(Figure~\ref{fig3}E). Importantly, we observed that inhibiting dmPFC
activity at CS onset delayed the initiation of avoidance behaviour
(Figure~\ref{fig3}E, left). Conversely, dmPFC inhibition after CS onset
impaired active avoidance behaviour (Figure~\ref{fig3}E, right).
Altogether, these circuit manipulations indicate that generation of
CS-induced threat information was amygdala-dependent, but importantly
this information was integrated within the dmPFC network to generate
sustained threat representations. In contrast, avoidance action
initiation critically relied on dmPFC activity after CS onset,
indicating that dmPFC links threat information with actions to
ultimately drive active defensive responses.

\section{Discussion} \label{sec3}

Using a combination of behavioural, pharmacological and optogenetic
approaches along with single-unit recordings and neuronal decoding
techniques, we showed that threat representations in dmPFC populations
mainly represent learned associations rather than specific defensive
behaviours. Moreover, these representations exhibiting a stable coding
dynamics are necessary to bridge threat-predicting stimuli to defensive
actions. Our data also indicate that the dmPFC threat representations
depend on amygdala functional integrity. Moreover, the dmPFC
optogenetic inhibition during CS presentation selectively impaired
active avoidance behaviour without impacting animals' threat assessment
or altering the selection of defensive behaviour. Finally, our data
indicate that whereas dmPFC inhibition restricted to CS onset delayed
avoidance behaviour, the same manipulation performed after CS onset
reduced avoidance probability.

We observed that discrimination of threatening and non-threatening CSs
is not impaired during inhibition of the dmPFC, and that the decoding
accuracy of CS$^{+}$ pips is preserved in the dmPFC upon amygdala
inactivation. These data first suggest that threat-related information
is acquired in remote brain areas and subsequently represented in dmPFC
networks. This idea is consistent with previous results indicating that
the formation of fear-related CS-US associations occurs in subcortical
networks~\cite{19,20,21,22} and is integrated by the mPFC to drive
defensive-related behaviours~\cite{23,24,25}. In addition, previous
studies demonstrate that amygdala inactivations~\cite{8} or
lesions~\cite{26} alter dmPFC single-cell responses to cue-predicting
threats. Our data indicate rather than CS$^{+}$ sensory information
encoding in the dmPFC is largely independent of the amygdala, but that
amygdala processing of threatening cues is critical for the maintenance
of sustained threat representations in the dmPFC. Second, the fact that
dmPFC inhibition reduces freezing upon threat presentations during tone
fear conditioning~\cite{27,28} but remains unaffected during avoidance
tasks despite the \mbox{reduction} in avoidance behaviour~\cite{18},
highlights the flexibility of the dmPFC in the control of passive or
active defensive responses depending on contextual inputs. Third,
although a current view postulates that the basolateral amygdala (BLA)
mediate avoidance behaviour~\cite{17,29,30}, our data demonstrate that
the amygdala allows sustained threat representations in the dmPFC, that
ultimately links threats with the initiation of active avoidance
responses.

Despite the heterogeneous dynamics in individual cells' activity, we
observed a relatively stable \mbox{dynamics} of information coding by
dmPFC neuronal populations during an ongoing associated threat,
consistent to what is observed during the delay period of working
memory tasks~\cite{31}. The resemblance in how mnemonic and threat
information is encoded by neuronal populations suggests that stable
coding is a fundamental computational principle in the prefrontal
cortex for connecting associated stimuli to goal-directed actions.

We observed that sustained threat representations are largely not
reflecting sensory inputs or specific defensive behaviours. What these
threat representations actually represent? Overall, our data is
consistent with the general view that the dmPFC guide actions
specifically based on internal outcome expectations~\cite{32}. However,
a precise demonstration if our observed threat representations convey
information about the expected aversive outcomes require complex
behavioural paradigms. Indeed, a recent study in mice performing an
approach-avoidance task suggests that neuronal population
representations are liked with the value of expected aversive
outcome~\cite{33}.

Finally, our results highlight a dynamic process of encoding
sensory-threat and goal-directed avoidance states by the concerted
activity of populations of cells in the dmPFC. Such changes in
prefrontal representations have been described during contextual
changes in appetitive-based sensory-decision representations in
non-human primates~\cite{34}. In our study, we observed that dmPFC
neuronal population dynamics bridge a transition from representing an
aversive state to representing an active defensive response. This
sustained, multi-representational, transfer of environmental
information by the dmPFC is required for accurate threat encoding and
the computations of action-outcome, which ultimately control the
selection of active defensive responses strategies.

\section{Methods} \label{sec4}

All procedures were performed in accordance with standard ethical
guidelines (European Communities Directive 86/60-EEC) and were approved
by the committee on Animal Health and Care of Institut National de la
Sant\'{e} et de la Recherche M\'{e}dicale and French Ministry of
Agriculture and Forestry (agreement \#A3312001). Male C57BL6/J mice
(Janvier) aged 8--9 weeks were individually housed \mbox{under} a
12~h{\break} light--dark cycle, and provided with food and water ad
libitum. Active Avoidance task was performed in a shuttlebox comprised
of a plexiglass box ($40 \times 10 \times 30$~cm) with a floor grid
connected to a shocker, where a small plastic hurdle (1~cm height)
divided the arena into two equal compartments while infrared beams
detection automatically monitored the mice shuttling between
compartments (Imetronic). Shuttlebox was enclosed inside an acoustic
foam isolated box where two speakers mounted on top of each compartment
delivered the auditory conditioned stimulus (CS) consisting in either
7~kHz or white-noise 50~ms pips at 1~Hz (maximum 13~pips, i.e. CS
maximum duration of 12~s), 2~ms rise and fall, 80~dB sound pressure
level. Scrambled foot shocks of 50~ms (${\sim}$7~Hz) at an intensity of
0.7~mA and maximum duration 5 s applied through the grid floor
(Imetronic) served as an unconditioned stimulus (US). LEDs mounted on
top of each compartment provided house light. During the Confined task,
an additional opaque white wall was placed in the middle of the maze
preventing shuttling between \mbox{compartments}. \looseness=-1
\vspace*{-.2pc}

\subsection{Behavioural paradigm} \label{sec4.1}
\subsubsection{Active avoidance task} \label{sec4.1.1}

Mice ($n=34$, implanted) were first habituated to the context and tones
(Day~0). During the habituation, alternating 20~CS$^{+}$ and
20~CS$^{-}$ were presented without any US. Training days (Day~${>}$~0)
consisted of randomly intermingled 30~CS$^{+}$ and 30~CS$^{-}$, where
CS$^{+}$ trials were paired with the US if mice did not shuttle
compartment before 7~s after CS$^{+}$ onset. CSs started independently
from the animal location in the shuttle box after an inter-trial
interval of \mbox{25--40}~s, and shuttling between compartments stopped
any ongoing CS or US. The first CS$^{+}$ trial of the first training
session was always paired with the US disregarding the behaviour of the
animal. All sessions started with 2~min acclimation periods before the
first CS presentation. In a subset of mice ($n=9$, non-implanted), the
tones used for CS$^{-}$ and CS$^{+}$ were switched.

\subsubsection{Confined task} \label{sec4.1.2}

On Day~5, a fraction of the mice ($n=21$, implanted) were trained in
the Confined task. The session started as an active avoidance training
session of intermingled 15~CS$^{+}$ and 15~CS$^{-}$ trials. Next, a
plastic wall was introduced at the hurdle position, preventing mice
from shuttling between compartments. In this condition, a sequence of
4~CS$^{-}$ followed by non-reinforced 8~CS$^{+}$, followed by
4~CS$^{-}$ was presented. CSs were unavoidable/unescapable, therefore
lasted for 12~s (inter-trial interval 25--40~s). Two minutes after the
last trial, the plastic wall was removed and the active avoidance
training session was resumed with intermingled 15~CS$^{+}$ and
15~CS$^{-}$ trials. Mice were never disconnected during the entire
session.

\subsubsection{Extinction training} \label{sec4.1.3}

On Day~5 and 6, mice ($n=14$, implanted) were submitted into an
extinction training procedure consisting of non-reinforced 50~CS$^{+}$
and 50~CS$^{-}$ trial presentations. The first 5~CS$^{+}$ of the first
extinction session were still conditionally paired with the US to
induce high aversion towards CS$^{+}$ over a longer timescale.

\subsubsection{Reversal training} \label{sec4.1.4}

After 2~days of extinction training, mice ($n=14$, implanted) were
submitted to 3 consecutive reversal training sessions consisting of
sessions analogous to the active avoidance training sessions but where
tones associated with the CSs during the active avoidance training
sessions were exchanged, using the same structure as the aforementioned
Active Avoidance task. Therefore, CS$^{-}$ was paired with the US
(rCS$^{+}$) while CS$^{+}$ was not reinforced (rCS$^{-}$). Only mice
displaying rCS$^{+}$ avoidance probability greater than 0.3 after
3~days of reversal training were included in the analyses (11/14 mice).

\subsection{Electrode implantation and{\hfill\break}
electrophysiological recordings} \label{sec4.2}

Mice (10~weeks old) were anaesthetised with isoflurane (induction 3\%,
maintenance 1.5\%) in O$_{2}$. Body temperature was maintained at
37~{\textdegree}C with a temperature controller system (FHC), and eyes
were hydrated with Lacrigel (Europhta Laboratories). Mice were placed
in a stereotaxic frame (Kopf Instruments), and 3 stainless steel screws
were attached to the skull. Following craniotomy, mice were bilaterally
implanted in the dmPFC with electrode array targeting the following
coordinates relative to bregma: ${+}$2.0--2.1~mm~AP;
${\pm}$0.55--0.7~mm~ML; and 1.20--1.30~mm~DV from dura with an angle of
14{\textdegree}. Each electrode \mbox{bundle} consisted of 16
{individually} insulated nichrome wires (13~mm diameter, impedance
60--100~KU; Kanthal) fixed to an electrode guide. Each electrode bundle
was attached to one 18-pin connector (Omnetics). Connectors were
referenced and grounded via a silver wires ($127~\upmu$m diameter,
A-M~Systems) placed above the cerebellum. All implants were secured
using Super-Bond cement (Sun Medical). During surgery, long- and
short-lasting analgesic agents were injected (Metacam, Boehringer;
Luroca\"{i}ne, Vetoquinol). After surgery, mice were allowed to recover
for at least 10~days. Electrodes were connected to a headstage (Plexon)
containing sixteen unity-gain operational amplifiers. Each headstage
was connected to a 16-channel PBX preamplifier where the signal was
replicated and bandpass-filtered at 300~Hz and 8~kHz and at 0.5~Hz and
200~Hz for local field potential recordings. Spiking activity was
digitised at 40~kHz and isolated by time-amplitude window
discrimination and template matching using an Omniplex system (Plexon).
Single-unit spike sorting was performed using Off-Line Spike Sorter
(OFSS, Plexon), where Pairwise-P (multivariate ANOVA) statistics was
used to assess unit isolation quality ($P < 0.05$). At the end of the
experiment, electrolytic lesions were administered before transcardial
perfusion to verify electrode tip location using standard histological
techniques.

\subsection{Muscimol inactivations} \label{sec4.3}

Mice were bilaterally implanted with stainless steel cannula guide (26
gauge; Plastics One) aimed at the PFC ($n=12$~mice) or BLA
($n=22$~mice). To target the dmPFC, guides were implanted following
coordinates relative to bregma: $+$2.1~mm~AP; ${\pm}$0.55~mm~ML; and
0.7~mm~DV; with a 14{\textdegree} angle. To target the BLA, guides were
implanted following coordinates relative to bregma: $-$1.4~mm~AP;
${\pm}$3.7~mm~ML; 3.5~mm~DV from dura; with a 4{\textdegree} angle.
Cannula guides were secured using Super-Bond cement (Sun Medical).
Dummy cannulas were used to fill the guide and removed only during the
injection period. For a subset of mice implanted with cannula guides in
the BLA ($n=11$~mice), were also implanted with recording electrodes in
the dmPFC as described in the ``Electrode implantation and
electrophysiological recordings'' methodological section. On the
injection day, muscimol (Sigma; $0.25~{\upmu}\text{g}/{\upmu}$L mM in
PBS 0.1~M, pH~$=$~7.2--7.3; based on~\cite{19}) or vehicle (PBS0.1~M,
pH~$=$~7.2--7.3) was infused at a rate of $0.1~{\upmu}$L/min{\break}
(total volume of $0.15~{\upmu}$L for dmPFC, 0.$2~{\upmu}$L for BLA)
with 0.5~mm protruding injector cannulas. After infusion, injecting
cannulas were left in place for 5~min to allow drug diffusion. Muscimol
was infused 40~min before the behavioural test.

\subsection{Virus injections and optogenetics} \label{sec4.4}

For optical silencing of dmPFC CaMKIIa-expressing neurons,
0.15--$0.2~{\upmu}$L of either GFP (AAV5-CaMKIIa-GFP, titer $1.5 \times
10^{12}$, UNC Vector Core Facility) or ArchT (AAV5-CaMKIIa-ArchT-GFP,
titer $5.3 \times 10^{12}$, Addgene) were bilaterally injected into the
dmPFC of 8/9~weeks old wild-type mice from glass pipettes (tip diameter
20--$30~{\upmu}$m) at the following coordinates relative to bregma:
$+$2.1~mm~AP; ${\pm}$0.55--0.65~mm~ML; 1.15--1.3~mm~DV from dura; with
a 14{\textdegree} angle. At \mbox{2--3} weeks after the injection, mice
were implanted bilaterally with custom-built optic fibers (diameter:
200~mm; numerical aperture: 0.39; Thorlabs) above the dmPFC at the
following coordinates relative to bregma: $+$2.1~mm~AP;
${\pm}$0.65~mm~ML; $-$0.65~mm~DV from dura; with a 14{\textdegree}
angle. For optical silencing BLA to dmPFC projecting cells, we injected
0.15--$0.2~{\upmu}$L retro-ArchT (ssAAV-retro/2-mCaMKIIalpha-eArchT3.0,
titer $8.8 \times 10^{12}$, Zurich Viral Vector Facility) into the
dmPFC using the same coordinates and procedure. At 4~weeks after
injection, mice were bilaterally implanted with commercial optic fibers
(diameter: 400~mm; numerical aperture: 0.66; Doric) targeting the upper
part of the BLA, guides were implanted following coordinates relative
to bregma: ${-}$1.4~mm~AP; ${\pm}$3.7~mm~ML; 3.5~mm~DV from dura; with a
4{\textdegree} angle. Only mice with correct placement of optic fibers
or virus expression restricted to dmPFC were included in the analyses.

Three different protocols for optogenetic stimulation at a continuous
green light (532~nm, ${\sim}$6--8~mW at fiber tip) were used: (i)~from
1~s before CS onset until 1~s after CS offset, for global CS
perturbation ($n=16$~mice); (ii)~from 1~s before CS onset until 2.5~s
after CS onset, for the onset perturbation experiments ($n=13$~mice)
and (iii)~from 1.5~s after CS onset until 1~s after CS offset, for the
offset perturbation experiments ($n=25$~mice). The optogenetics
protocol structure for different conditions was the first half of the
trials within a session were non-stimulated, followed by the second
half of the trials being {stimulated}, providing an internal
control of light effect for each condition (except retro-ArchT
experiments, where also all trials were stimulated on Day~4).

\section*{Data analysis} \label{sec5}

To get an estimate of the neuronal responses for different trial types
or conditions, we included recording sessions from Day~3 and~4
(stabilized performance). Due to the low number and behavioural
variability observed in CS$^{-}$ avoided trials, we consider in the
analysis CS$^{-}$ as the CS$^{-}$ non-avoided trials. CS$^{+}$ avoided
trials considered for CS onset aligned related analyses were those
having avoidance shuttle latency above 4~s.

To address how informative the firing rates of the dmPFC cells
ensembles were about stimulus identity, threat cues, avoidance action
and behavioural states, we used linear-kernel Support Vector Machine
(SVM) classifiers. Individual trial pseudo-population firing rate
vectors were constructed for each 250~ms time bin using units and
trials from different animals, therefore removing decoding
contributions due to noise correlations. In every case, the number of
trials was equalised in both classes by randomly subsampling the
overrepresented class, and randomly sampling the minimum amount of
available trials across animals. For each time bin, an independent set
of SVM classifiers were used to perform our decoding accuracies
estimations and statistical tests. The decoding accuracy of a
classifier (5-fold cross validation) was defined as the proportion of
correctly classified trials in the cross-validation procedure. To get
an estimate of the accuracy variability, we performed a bootstrap
200~times on randomly selecting trials for pseudo-population trial
construction. To evaluate the statistical significance of decoding
accuracy, pseudo-population surrogate trial construction was obtained
by randomly shuffling the label of the classes over 1000~bootstrap
runs. The shuffled labels decoding was used to get a null distribution
of the decoding accuracies that would occur by chance. For each time
bin, we performed a Permutation test by computing $P$-values as the
proportion of shuffle repetitions that exceed the real mean decoding
accuracies~\cite{15},
and significant mean decoding accuracies were defined as those time
bins where $P < 0.05$. Since decoding accuracy levels depend on the
number of included units, in order to compare the stimulus identity and
decoding accuracy across days/conditions, we randomly sampled the same
number of units (250~units) for each day/condition and bootstrap run.
For spontaneous freezing decoding analyses we selected spontaneous
freezing and non-freezing episodes longer than 4~s taking place outside
of the CS. For freezing versus non-freezing behaviour decoding at
CS$^{+}$ onset, we considered CS$^{+}$ AV and CS$^{+}$ NAV trials of at
least 2.5~s, for which first freezing intervals started within 0.5~s
after CS$^{+}$ onset (CS$^{+}$ FzOns) and trials for which freezing
started at least 0.75~s after CS onset (CS$^{+}$ NFzOns), ($n=13$ out
of 68~sessions, total 269~units). During the time window from 0.45~s
till 0.75~s, mice exhibited freezing for CS$^{+}$ FzOns trials and
non-freezing for CS$^{+}$ NFzOns trials with probability~1.
Cross-decoding CS$^{+}$ versus baseline in Active Avoidance and
Confined tasks, was performed by doing the described decoding procedure
at CS onset aligned condition to train the SVM models in one task, and
testing the decoding performance in the other one. Principal component
analysis (PCA) was performed on trial-averaged data by randomly
selecting 50\% of trials for each recording session and neuron, and
repeated 100~times. All the analyses were performed using built-in
implementation together with in-house codes in Matlab (The MathWorks,
Inc.,\break Natick, MA, USA). 

\section*{Data availability}

Data supporting the findings of this study are available from the
corresponding author on request. 

\section*{Code availability}

Custom-written codes used to analyse data from this study are available
from the corresponding author on request. 

\section*{Declaration of interests}

The authors do not work for, advise, own shares in, or receive funds
from any organization that could benefit from this article, and have
declared no affiliations other than their research organizations.

\section*{Acknowledgements}

We thank N. Winke and other members of the Herry laboratory for helpful
discussions. In addition, we thank S.~Laumond, J.~Tessaire and the
technical staff of the housing and experimental animal facility of the
Neurocentre Magendie. Microscopy was performed in the Bordeaux Imaging
Center of the CNRS-INSERM and Bordeaux University, a member of France
BioImaging. This work was supported by grants from the French National
Research Agency (ANR-DOPAFEAR, ANR-PRELONGIN, ANR-10-EQPX-08 OPTOPATH),
the Conseil Regional d'Aquitaine, and the Fondation pour la Recherche
M\'{e}dicale (FRM-Equipes FRM 2017).

\CDRGrant[ANR]{ANR-DOPAFEAR}
\CDRGrant[ANR]{ANR-PRELONGIN}
\CDRGrant[ANR]{ANR-10-EQPX-08 OPTOPATH}
\CDRGrant[FRM]{FRM-Equipes FRM 2017}

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